Implementation and evaluation of FD-MIMO beamforming schemes for highway scenarios

Size: px
Start display at page:

Download "Implementation and evaluation of FD-MIMO beamforming schemes for highway scenarios"

Transcription

1 TECHNISCHE UNIVERSITÄT WIEN DIPLOMA THESIS Implementation and evaluation of FD-MIMO beamforming schemes for highway scenarios Author: Félix Pablo CANO PAÍNO Supervisor: Fjolla ADEMAJ Martin K. MÜLLER Stefan SCHWARZ Markus RUPP A thesis submitted in fulfillment of the requirements of the Telecommunications Master programme August 29, 2017

2 ii Technische Universität Wien Abstract Institute of Telecommunications Mobile Communications Department Telecommunications Master programme Implementation and evaluation of FD-MIMO beamforming schemes for highway scenarios by Félix Pablo CANO PAÍNO With the widespread growth of urban environments and the appearance of vehicleto-x access communications, new techniques have emerged to overcome this densification matter. One promising technology is the Full-Dimension MIMO (FD-MIMO), which by using 2-dimensional planar antenna arrays, can model the beams and steer them not only horizontally, but also in the vertical domain. This is called 3Dbeamforming. As a generalization of beamforming in multi-antenna wireless communications, three different non-codebook based precoders have been studied and compared in this thesis. These are, the Maximum Ratio Transmission (MRT), the so-called Geometry Based and the Exhaustive Search over azimuth precoders. Using the Vienna LTE-A Downlink System Level Simulator, several simulations have been performed to evaluate the performance of the aforementioned scenarios. These are the comparison between Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) propagation conditions and the inclusion of an uncertainty area around the users, accounting for moving users. All these contrasting scenarios will help the reader to understand the pros and cons of the three aforementioned precoding methods.

3 iii Acknowledgements Special thanks to my Principal Supervisors, Prof. Markus Rupp and Dr. Stefan Schwarz for giving me the opportunity to work along with the Mobile Communications Department. I would also like to express my sincere appreciation to Martin Müller and especially to my supervisor Fjolla Ademaj for all the support she has shown to me during my whole Master Thesis project, without whom this work would have not been possible. Finally, I am truly grateful to my friends Patricia, Sergio, Victoria and Álvaro and to my family for their encouragement throughout this whole year.

4 iv Contents Abstract Acknowledgements ii iii 1 Introduction Motivation Vehicular Communications Challenges Full-Dimension MIMO Theoretical framework Simulation setup The Vienna LTE-A Downlink System Level Simulator Link Level Simulations Link Quality Model Link Performance Model Link-to-System (L2S) Model Validation The Spatial Channel Model The 3GPP 3D channel model Propagation conditions Line-of-sight propagation Non-line-of-sight propagation D Beamforming Antenna modeling Array steering First Null Beamwidth (FNBW) and Half Power Beamwidth (HPBW) Methodology Codebook based precoding Maximum Ratio Transmission precoder Geometry based precoder Exhaustive search over azimuth Position uncertainty Simulations Scenario definition Fixed user location Precoders comparison for fixed UE location Uncertainty versus Non-uncertainty comparison LOS versus NLOS comparison Random user location Precoders comparison for random UE location

5 v LOS versus NLOS comparison (Random case) LOS versus NLOS comparison for all precoders (Random case) 33 5 Conclusion 34

6 vi List of Figures 1.1 V2X access technologies FD-mimo and beamforming Link quality and link performance model Link quality and link performance model Comparison between link and system level simulation time Zenith angle of Departure (ZoD) and Zenith angle of Arrival (ZoA) in outdoor LOS conditions [6] Clusters and multipath Definition of d2d and d3d for outdoor UEs [18] Definition of d2d and d3d for indoor UEs [18] LOS versus NLOS Beamforming comparison between 2D case and 3D case dimensional planar antenna arrays Broadside direction Steering Antenna radiation pattern Exhaustive search over azimuth Azimuth angles per RB Uncertainty area Scenario definition Fixed UE locations Precoders comparison Uncertainty versus Non-uncertainty comparison LOS versus NLOS comparison Random UE locations Precoders comparison (Random case) LOS versus NLOS comparison (Random case) LOS versus NLOS comparison for all precoders (Random case)

7 vii List of Tables 2.1 FNBW and HPBW for different number of beams. Broadside (Θ=π/2) LTE codebook for CLSM mode and two transmit antennas for each of the possible number of layers (v) Maximum deviation for UEs for given speeds of 30 km/h and 120 km/h Simulation parameters Antenna parameters Scenario parameters

8 viii List of Abbreviations 3GPP 5G AAS BS BLER CLSM CoMP CRS CSI CSIT enodeb FDD FD-MIMO FNBW HPBW ITS L2S LNA LOS LTE MRT NLOS OLSM PA PDP PMI QoS RB SINR SCM SM TTI UE ULA V2I V2P V2V V2X 3rd Generation Partnership Project Fifth Generation Active Antenna Systems Base Station Block Error Rate Close Loop Spatial Multiplexing Coordinated Multi Point Common ReferenceSignal Channel State Information Channel State Information at Transmitter evolved Node B Frequency Division Duplex Full Dimension Multiple Input Multiple Output First Null Beam Width Half Power Beam Width Intelligent Transport System Link To System Low Noise Amplifier Line of Sight Long Term Evolution Maximum Ratio Transmission Non Line of Sight Open Loop Spatial Multiplexing Power Amplifier Power Delay Profile Precoding Matrix Index Quality of Service Resource Block Signal to Interference plus Noise Ratio Spatial Channel Model Spatial Multiplexing Transmit Time Interval User Equipment Uniform Linear Array Vehicle to Infraestructure Vehicle to Pedestrian Vehicle to Vehicle Vehicle to Everything

9 ix List of Symbols A broadside pattern C k,j covariance matrix between the user k and the enodeb j H channel matrix k wave number m 1 N number of antenna array elements N tx number of transmitting antennas N rx number of receiving antennas p power allocation ˆr spherical unit vector ( o ) t time between measurements s m v user speed s W precoding matrix distance between antenna array elements m λ wavelength m ϕ azimuth departure angle array ( o ) ϕ 0 azimuth departure angle from direct link ( o ) µ mean of the Gaussian distribution for an uncertainty area m σ 2 variance of the Gaussian distribution for an uncertainty area m σk 2 W noise power Hz θ elevation departure angle ( o )

10 1 Chapter 1 Introduction 1.1 Motivation Day by day, society is experiencing a huge development in technological fields such as vehicular communications. These improvements, unfortunately, come along with some challenges. One of them, is the necessity of supporting crowded scenarios of quasi-static users, while at the same time, giving service to moving users. Another example, related to highly mobile users, is that yet, the Long-Term Evolution (LTE) standard cannot support both efficient and reliable wireless communication at such high mobility users. In this Master Thesis, we first introduce relevant concepts such as Vehicular-toeverything (V2X) and the Full Dimension Multiple Input Multiple Output (FD-MIMO). Next we give a theoretical framework which presents the system model utilized and the beamforming schemes. In chapter 3, different precoding techniques are analyzed. Besides, several scenarios with moving users placed on a highway are explained and the methodology used to tackle these challenging set-ups introduced before. A final discuss along with the results will give detailed insights in the findings of this thesis. 1.2 Vehicular Communications The main goal of vehicular communications is to improve the safety on roads. By enabling bilateral data conversation between vehicles as well as improving the efficiency of transportation through smart traffic management, road fatality reduction of up to 50% in the near future is an ambitious but feasible goal. This objective is achievable by so-called Intelligent Transport Systems (ITSs). Furthermore, a minimum traffic environmental impact will be needed due to the reutilization of available road infrastructure by adaptive traffic management. In addition to traffic efficiency and safety-related issues, entertainment systems that support on-demand video streaming and online Internet access for passengers are growing gradually in interest. V2X communications includes different connectivity types. Vehicle-to-vehicle (V2V), Vehicle-to-pedestrian (V2P) if communication between people in proximity and the cars is carried and Vehicle-to-infrastructure (V2I). In this Thesis we focus on the V2I case. V2I communication is realized by employing Base Stations (BSs) as transmission hubs. Figure 1.1 shows the different access technologies. [5]

11 2 Chapter 1. Introduction FIGURE 1.1: Vehicular communication scenario of an urban area with different access technologies for moving users [5] 1.3 Challenges Regarding some challenges to overcome in vehicular comunications, we distinct two groups of users: Large groups of (mostly) indoor quasi-static best effort users which need almost unlimited bandwidth wireless communications. Moving users who demand diverse Quality of Service (QoS). Even though LTE provides point-to-point connectivity to moving users up to 500 km/h, such big data throughput (up to Gbps) with latency below 10 ms can only be achieved by few static users. Moreover, the increasing demand of higher static network capacity, makes many Five Generation (5G) proposals to focus only on the first group at expenses of the second group that can even experence a degradation in its performance.

12 1.4. Full-Dimension MIMO Full-Dimension MIMO FD-MIMO has been identified as one of the main technologies for the fifth generation of mobile cellular networks, 5G. By employing hundreds of antennas (often referred to as massive MIMO systems) at the BS as a two dimensional array, the spectral efficiency gain improves various orders of magnitude. Another interesting feature of the FD-MIMO system is the introduction of active antennas with Two-dimensional (2D) planar arrays. In the Active Antenna-based Systems (AAS), gain and phase are controlled by active components, such as Power Amplifier (PA) and Low Noise Amplifier (LNA), attached to each antenna element. In the 2D structured antenna array, one can control the radiation beam pattern to provide more degrees of freedom in supporting users on both vertical (elevation) and horizontal (azimuth) direction so that the control of the transmit beam in 3D space is possible. This technique is called Three-dimensional (3D) Beamforming. 3D Beamforming is a signal processing technique which, combining elements in an antenna array, allows that signals at particular angles experience constructive interference while others experience destructive interference. The 3D Beamforming technique is explained in more detail in the next chapter [8]. Figure 1.2 gives an example of FD-MIMO supporting 3D-beamforming. FIGURE 1.2: Example of FD-MIMO antenna supporting both azimuth and elevation beamforming [6] Another important benefit of 2D AAS is that it can accommodate a large number of antennas without increasing the deployment space. For instance, large linear antenna arrays deployed in a horizontal direction require surfaces up to some meters. Due to the limited space on a rooftop or mast, this space would be excessive for most of the cell sites. In contrast, when the antennas are arranged in a square array, the space required is drastically reduced.

13 4 Chapter 1. Introduction Finally, it is important to note some undesirable effects that can be problematic in high-speed scenarios. For instance, the multi-user interference and the degradation of the Signal-to-interference-plus-noise Ratio (SINR). In order to avoid multi-user interference (especially noticeable due to the high spatial resolution that is achievable) accurate Channel State Information (CSI) is required. The CSI describes how the signal propagates from the transmitter to the receiver and represents the combined effect of, for example, scattering, fading, and power decay with distance.

14 5 Chapter 2 Theoretical framework 2.1 Simulation setup As mobile communications evolve, new challenges must be overcome. Such development, incurs in a growing complexity and the effort of simulations becomes enormous. A common solution is to divide the simulations into two stages, the linklevel and the system-level, so that computational costs and complexity in systemlevel simulations are reduced while keeping accuracy. For that purpose, we use the Vienna LTE-A Downlink System Level Simulator [19][20][21] The Vienna LTE-A Downlink System Level Simulator System level simulations aims to evaluate the performance of a whole network with multiple User Equipments (UEs) and Evolved Node B (enodeb) stations. Moreover, the propagation effects are modeled in terms of large- and small-scale fading, considering both desired and destructive interferences. The large-scale parameters comprise the geometric positions of the enodeb sectors and the UEs, they are used to parameterize the statistics of the small scale parameters. The latter, also known as channel models, are challenging when describing wireless communications. In order to evaluate the system level performance of a wireless network, complex simulations comprising a high number of network elements and its interconnecting links are employed. A simple approach to system level performance evaluation would be to perform all of the Physical (PHY) and Medium Access Control (MAC) layer procedures. But due to the high computational complexity of the channel coding/decoding procedures and the MIMO receiver, this approach does not scale well and results in impractical simulation times. The increasing interest in wireless cellular systems and a method for predicting it, makes the system-level simulations a fundamental tool. However, exact link-level modeling is unfeasible due to its huge complexity. Therefore, mathematical abstraction is recommended in order to obtain equivalent results reducing the complexity. A widely accepted solution is the application of link abstraction models that specify the interaction between link- and system level simulators.

15 6 Chapter 2. Theoretical framework Link Level Simulations Regarding link level simulations, by upscaling the number of simulated links and network elements, it is possible to affirm that link level improvements do also improve the network performance. Moreover it is also possible to test and evaluate the algorithms controlling the PHY and MAC layers. Link level simulations normally calculates a range of Signal to Noise Ratios (SNRs) for which link performance is evaluated in terms of throughput. The simulation run time varies depending on the LTE system bandwidth chosen. For instance, a bandwidth of 1.4 MHz, results in a simulation run time in the order of hours. If we increment the bandwidth up to 20 MHz and we generate a simple interference-limited scenario, such a typical LTE system level simulation would require a simulation time in the order of months, which is clearly not practical. In order to simplify this problem, it can be divided in two parts, which jointly model the performance of the link: a link quality model and a link performance model. The link quality model, quantifies the quality of the received signal after reception and equalization, the channel quality output measured by the link quality model serves as input to the link performance model. Figure 2.1 illustrates the separation of the link into a link quality and a link performance model, as well as the inputs necessary to perform each step. FIGURE 2.1: Separation of the LTE link into link quality and link performance model [24]. Link Quality Model The link quality model models the measurements used for link adaptation and resource allocation. It is a measure of the quality of the signal received. A straightforward solution to do so, is the post-equalization SINR. Moreover, the complexity of the link quality model can be reduced by considering only a subset of the total post-equalization SINRs. Link Performance Model As said in section 2.1.2, the link quality model measures the channel quality output which serves as input to the link performance model. For the RB in which the UE is scheduled (if scheduled), the link performance model combines the output of the link quality model with the applied modulation order and code rate and predicts the BLER of the received Transport Block (TB). Figure 2.2 describes the aforementioned inputs to the link quality and link performance models.

16 2.1. Simulation setup 7 FIGURE 2.2: Full scheme of the Link to System model [24] Link-to-System (L2S) Model Validation The objective of the link quality and link performance models is to provide an accurate link throughput prediction. Furthermore, with a negligible loss of accuracy, the computationally-intensive MIMO precoder feedback is additionally performed off-line, speeding-up simulation run-time. Link abstraction models for system level simulations, such as a capacity-based model suggested in the LTE standard are employed as a much simpler solution, since accurate link abstraction models are laborious to design and implement. Regarding the complexity evaluation, the run-time complexity of system level simulations compared to link level simulations, shows a significant reduction in simulation run-time when employing the L2S model. As from the values, listed in Table 2.3, link level simulation run times scale linearly with the number of RBs. The link level simulation time for the 20MHz case has been extrapolated from the existing values. Hence, Table 2.3 validates the statement, that a link abstraction model is required for significantly faster simulation times compared to detailed link level simulations.

17 8 Chapter 2. Theoretical framework FIGURE 2.3: Simulation run time comparison in seconds. Bold face: system level simulation times. Normal type: link level simulation time [24]. The reduced and almost constant simulation run time of the system level simulator can be explained by the removal of the most computationally-intensive task, which is the channel trace generation. Thus, the complexity increase of the bandwidthdependent part of the L2S model is almost negligible in comparison to the overall run time. Such a computational complexity reduction enables performing multi-user simulations with high channel bandwidths, necessary to evaluate complex scheduling scenarios or multi-user gain with more practical simulation time durations The Spatial Channel Model Channel models can be divided into two categories, deterministic and stochastic. Deterministic models describe the channel for a specific propagation environment between enodeb sector and UE. While in stochastic models, the channel characteristics are gathered into a statistical description such as the Power Delay Profile (PDP). In order to model such different approaches, the 3rd Generation Partnership Project (3GPP) introduced the Spatial Channel Model (SCM). The SCM is a geometric stochastic model that difference large-scale parameters such as shadow fading, delay spread and angular spread from small-scale parameters (such as delays, arrival and departure angles and cluster powers). SCM includes several different scenarios such as the urban, rural or WINNER models, each of them representing a unique environment. Moreover, a 3D SCM that describes channel characteristics in three dimensions has been introduced in 3GPP TR This extension to the third spatial dimension, the elevation, allows to use large antenna arrays at both transmitter and receiver.

18 2.1. Simulation setup The 3GPP 3D channel model In this section, deeper insight about the 3D channel model will be given. As previously explained, multi-antenna techniques are capable of exploiting the elevation dimension. Since the existing 2D channel models do not capture the elevation channel characteristics, the channel models are not handling the expected growth in mobile traffic communications. Therefore, new techniques are necessary that require new models. In order to evaluate techniques such as UE specific elevation beamforming and FD-MIMO, where the transmission is adapted efficiently in both elevation and azimuth to a particular UE, a 3D channel model is necessary. Thus, the 3GPP has recently developed a 3D channel model. One key aspect of this model is the ability to design channels for users located on different floors of a building (at different heights). This is achieved by capturing a user height dependency in modeling some channel characteristics including pathloss and LOS probability. Regarding the application environments, Urban Macro (3D-UMa) and Urban Micro (3D-UMi) with enodebs located outdoors are considered. The 3D-UMa and the 3D-UMi scenarios follow the conventional 2D-UMa and the 2D-UMi scenarios as determined in ITU-R. Both scenarios are considered to be densely and homogeneously populated by buildings. A detailed description of antenna modeling, LOS probability and pathloss is given in subsections 2.3 and 2.4. Hence, fast fading model is explained in this section. The fast fading channel coefficients model the time-varying fluctuations of wireless channels that are caused by the combination of multipath component and UE movement. Cellular downlink is assumed for describing the fast fading model, so the departure angles are defined at the enodeb side and the arrival angles are defined at the UE side. The channel coefficients of a link between a transmitter and a receiver are determined by the composite channel impulse responses of the Multi-path Components (MPCs) [11]. Each MPC is characterized by a path delay, a path power and random phases introduced during the propagation as well as the incident path angles, azimuth and elevation angles of departure and arrival (see Figure 2.4). After these angles are calculated, the spherical unit vector with azimuth departure angle φ and elevation departure angle θ is generated: sinθ cosφ ˆr tx = sinθ sinφ (2.1) cosθ

19 10 Chapter 2. Theoretical framework FIGURE 2.4: Zenith angle of Departure (ZoD) and Zenith angle of Arrival (ZoA) in outdoor LOS conditions [6]. These angles will be used to compute the channel matrix H with dimensions N Rx x N T x for each sampling point on the time-frequency grid. The term N T x represents the number of transmitting antenna ports and N Rx the number of receiving antenna ports. These channel realizations are generated per Resource Block (RB) and Transmission Time Interval (TTI). The channel coefficients are calculated at runtime and depend on the position of the UE location [2]. Finally, some clusters (Figure 2.5) are placed around the scenario in order to simulate obstacles, buildings and any objects that may produce multipath propagation. FIGURE 2.5: Scattering concept in the 3D model. φ and θ represent the azimuth and elevation departure angles [2].

20 2.2. Propagation conditions Propagation conditions Two propagation conditions are described in the technical report TR of the 3D channel model, LOS and NLOS. The probability of being a LOS propagation is described in equation 2.2 and 2.3 [18]. For Urban Micro (3D-UMi): P r LOSUMi = { 18 d 2D out + exp ( 1 ) ( ) d 2D out 18m 1 18 (2.2) d 2D out 18m < d 2D out d 2D out 36 and for the Urban Macro case (3D-UMa): P r LOSUMa = ( ( 18 d 2D out + exp 1 ) ( ) d 2D out 18m d 2D out ) 3 ( ) ) exp 18m < d 2D out d 2D out 1 + C (h UT ) 5 4 ( d2d out 100 d 2D out 150 (2.3) where h UT is the antenna height of the UE, d 2D out the horizontal distance between the BS and the UE (Figure 2.6) and C (h UT ) is described as: C (h UT ) = { ( hut h UT 13m ) m < h UT 23m (2.4) FIGURE 2.6: Definition of d2d and d3d for outdoor UEs [18]

21 12 Chapter 2. Theoretical framework In case that we have to distinguish between indoor and outdoor users, we consider two different 2D distances (d 2D out and d 2D in ) and 3D distances (d 3D out and d 3D in ) as showed in Figure 2.7. Note that FIGURE 2.7: Definition of d2d and d3d for indoor UEs [18] d 3D out + d 3D in = (d 2D out + d 2D out ) 2 + (h BS + h UT ) 2 (2.5) where the term d 2D out is the distance d 2D for the outdoor case, and the term d 2D in the distance from the external wall to the UE Line-of-sight propagation In this section, we explain the model we consider for LOS environment. Depending on how far the UEs are from the base station, two different equations are used: For distances between 10 meters and the breakpoint For higher distances (up to 5000 meters) ( ) f d BP = 4h BS h c UE c P L = 22.0log 10 d log 10 f c (2.6) P L = 22.0log 10 d log 10 f c 9 log 10 (d 2 + (h BS h UE ) 2 ) (2.7)

22 2.2. Propagation conditions Non-line-of-sight propagation We also distinguish two cases for the NLOS case: For Urban Macro (UMa) ( ( h P L = log 10 W log 10 h h BS ) 2 ) log 10 d log 10 f c ( 3.2(log 10 (11.75h UT )) )) ) 0.6 (h UT 1.5) (2.8) For Urban Micro (UMi) P L = 36.7log 10 d log 10 f c 0.3 (h UE 1.5) (2.9) where h BS and h UE are the heights of the enodeb and the UEs respectively, d the distance between them and f c is the carrier frequency. Figure 2.8 shows an example of these two propagation effects. FIGURE 2.8: LOS and NLOS propagation [12]

23 14 Chapter 2. Theoretical framework 2.3 3D Beamforming As an improvement for horizontal beamforming techniques, 3D beamforming allows an enhancement in the signal strength at the UE combining the vertical dimension with the horizontal dimension using a 2D active array. Figure 2.9 shows the beam pattern comparison between 2D beamforming and 3D beamforming. FIGURE 2.9: Beamforming comparison between 2D case and 3D case [3] The relative displacements of the antenna elements with respect to each other introduce relative phase shifts in the radiation vectors, which can then add constructively in some directions or destructively in others. This is a direct consequence of the translational phase-shift property of Fourier transforms: a translation in space or time becomes a phase shift in the Fourier domain. To steer the beam directionality when transmitting, the beamforming coordinator controls the phase and relative amplitude of the signal at each transmitter. And therefore create a pattern of constructive and destructive interference in the wavefront [13].

24 2.3. 3D Beamforming Antenna modeling In order to apply 3D beamforming, 2-dimensional antenna arrays are necessary. The structure of an antenna array comprises of antenna elements arranged in both horizontal and vertical directions as depicted in Figure FIGURE 2.10: Antenna elements scheme for a 2D array [3] Since each antenna port contains V vertical antenna elements, the channel coefficient H (k) for the k-th UE is composed of S by T antenna ports (S is the number of antenna ports on BS and T is the number of antenna ports on UE). This can be written as: where H (k) = ĥ (k) ĥ (k) 1,1 ĥ (k) 1, ĥ (k) 1,T s,t = V i=1 ĥ (k) 2,1... ĥ (k) S,1 ĥ (k) 2,2... ĥ (k) S,2 ĥ (k) 2,T... ĥ (k) S,T (2.10) ŵ (k) s,i.h(k) i,s,t (2.11) where ĥ(k) s,t is the channel coefficient from the s-th antenna port of to the t-th antenna port of the k-th UE (we assume that each antenna port of UE has only one antenna element).

25 16 Chapter 2. Theoretical framework As said before, the beamforming is a signal processing technique which applies the beamforming weights to adjust the phase and the amplitude of signals to form the beam pattern toward the desired direction. The beamforming weights are applied on each antenna elements as shown in Figure The term w s,i represents the beamforming weight of the i-th element on the s-th antenna port which affects the antenna pattern in the vertical direction [3] Array steering An array is typically designed to have maximum directive gain at broadside, that is, at φ = 90 o (for an array along the x-axis.) The maximum of the array factor A(Ψ) corresponds to Ψ= kd cosφ= 0, so that A max = A(0). Where k is the wavenumber, d the array spacing and φ the direction the beam is pointing at. In order to steer "electronically" the antenna array to a different angle φ 0 without changing the physical orientation of the antenna, it can be achieved by wavenumber translation in Ψ-space, that is, replacing the broadside pattern A(Ψ) by the translated pattern A(Ψ - Ψ 0 ). The translated wave number is: Ψ 0 = kd cos(φ 0 ) (2.12) Ψ = Ψ Ψ 0 = kd(cos(φ) cos(φ 0 )) (2.13) Therefore, the maximum of A (Ψ) will coincide with the maximum of A(Ψ ), which occurs at Ψ = 0, or equivalently at Ψ = Ψ 0, or at angle φ = φ 0. Figure 2.11 explains the scheme for steering a beam in the broadside direction. Figure 2.12, an example of a steered beam in azimuth. FIGURE 2.11: Horizontal array scheme to steer the beam in the broadside direction [14]

26 2.3. 3D Beamforming 17 FIGURE 2.12: 30 o steering angle in azimuth [22] First Null Beamwidth (FNBW) and Half Power Beamwidth (HPBW) In order to set a maximum number of beams, we calculate the FNBW and HPBW for different number of beams N. The FNBW is the angular span between the first pattern nulls adjacent to the main lobe and is represented as G F NBW = 2[(π/2) cos 1 (λ/nd)] (2.14) where N is the number of beams and d denotes the spacing between antenna elements. The HPBW is the angular separation, in which the magnitude of the radiation pattern decreases by 50% or -3dB from the peak of the main beam. It can be represented as G HP BW = 2[(π/2) cos 1 (1.39λ/πNd)] (2.15) Note that these equations only hold for regularly spaced Uniform Linear Arrays (ULAs). Figure 2.13 illustrates both FNBW and HPBW radiation patterns. In table 2.1 are shown the corresponding values for FNBW and HPBW, considering a broadside array and a spacing of half-wavelength between antenna elements, for 2 to 32 antenna elements. We see that with 32 beams we achieve a high beam resolution, hence in this thesis we consider the maximum beam resolution to be equal to 32 beams.

27 18 Chapter 2. Theoretical framework FIGURE 2.13: Antenna radiation pattern [14] TABLE 2.1: FNBW and HPBW for different number of beams. Broadside (Θ=π/2) Number of beams FNBW ( o ) HPBW ( o )

28 19 Chapter 3 Methodology 3.1 Codebook based precoding The codebook based precoding is a promising technology adopted by LTE, which fixes a common codebook comprising a set of vectors and matrices at both the transmitter and the receiver. To design this precoding technique, high precoding gain, lower feedback overhead and flexibility are mandatory to support various antenna configurations and different numbers of data streams. In order to increase diversity, data rate, or both, the supported multi-antenna transmit modes employ either a Transmit Diversity (TxD) or Spatial Multiplexing (SM) transmission scheme. SM can be operated in two modes: Open Loop Spatial Multiplexing (OLSM) and Closed Loop Spatial Multiplexing (CLSM). The main difference between them, is that in the latter, the optimum precoding matrix information is additionally fed back to the enodeb by the UE. Based on whether a finite number of precoding matrices is used, the close-loop MIMO can be categorized as codebook and non-codebook based precoding.[15] The procedure of codebook-based precoding technology performs as follows: The UE gets the CSI from the Common Reference Signal (CRS) sent by the enodeb and feeds back a Precoding Matrix Index (PMI). Then the enodeb applies the spatial domain precoding on the transmitted signal taking into account the PMI so that the transmitted signal matches with the channel experienced by the UE. The PMI may be changed by the enodeb according to the instantaneous state and then will be sent back to UE. After the precoding operation, the UE receives the information from the enodeb on what precoding matrix is used, which is utilized by the UE for demodulating the data. Table 3.1 lists the available precoders for the two-transmit-antenna case. For the four-antenna case, the codebook size increases to sixteen precoders, supporting up to four layers (v). TABLE 3.1: LTE codebook for CLSM mode and two transmit antennas for each of the possible number of layers (v) Layers (v) Precoder codebook [ ] [ ] [ ] [ ] , , , 1 1 i 2 [ ] [ ] i , i i

29 20 Chapter 3. Methodology Nevertheless, codebook based precoders major drawback is it limitation up to 8- by-8 MIMO antennas. Since the number of ports assumed in this thesis exceeds that quantity (such as 16, 32 or higher), this thesis will take into account non-codebook based precoders. Those are the Maximum Ratio Transmission and the Geometry Based precoders Maximum Ratio Transmission precoder The maximum ratio transmission resembles a matched filter where the gain of each unit-norm beamforming direction, w is the strength of the corresponding channel coefficient h and the phase makes the signal contribution from each channel coefficient add up constructively. Generally, the term w is a 3-dimensional matrix defined per RB of dimensions the number of transmitting antennas (ntx) by the number of receiving antennas (nrx) by the assigned RB. h is again a 3-dimension matrix (dimensions: nrx x ntx x RB). For this thesis though, the nrx will be 1, reducing the matrices to vectors. Therefore, the vector w is calculated dividing the channel coefficient by its Euclidean norm as showed in 3.1 w j,k = h j,k h j,k 2 (3.1) where j represents each of the BSs (in this Thesis we consider only one) and k represents each of the users. The inner product between the precoding vector and the channel coefficient is therefore maximized, which protects the useful signal against channel fading and gives a close-to-optimal solution. Thus, the MRT maximizes the SNR at the mobile station in multi-antenna transmissions, providing the optimal beamforming directions in a low-snr regime, independently of which point in the performance region we are interested in. The exact operating point is determined by the power allocation. After the MRT selects the beamforming directions w, the power allocation p determines the operating point in the performance region that is achieved by the heuristic transmit strategy[16]. Therefore, the SINR is computed as: SINR k = p j,k ρ k,k σ 2 k + i k p j,i ρ i,k (3.2) where SINR is the signal-to-interference-plus-noise ratio, σk 2 the noise power, and ρ is fixed and equal to: ρ i,k = h H j,k C j,k w j,i 2 (3.3) where C j,k is the covariance matrix of each base station j and user k. The subindex i represents each of the interfering co-users[17].

30 3.1. Codebook based precoding Geometry based precoder In order to reduce the feedback overhead at the UEs, a precoder which does not need full knowledge of the channel is desirable. Thus, the so-called Geometry based precoder has the advantage over the MRT precoder, that only the location of the user is needed. In this regard, azimuth and elevation angles from the direct link between enodeb and UE is calculated. Complex weights are calculated and applied for each antenna port as number of antenna elements in the vertical direction w n = 1 N e i(n 1) d sin(ϕ 0) sin(θ) 2π λ (3.4) where N is the number of ports set on the antenna configuration, n denotes the weight for the n-th antenna element horizontally (antenna port), ϕ 0 is the azimuth angle (from 0 to 2π) and θ, the elevation angle (from 0 to π ). In case we have LOS propagation, the direct link is strong enough to capture most of the channel energy. On the other hand, if there is NLOS between the enodeb and the UEs, we lack this direct link, and thus, the precoding matrix (vector in case that the nrx is 1) calculated for this link may not be optimal due to destructive interference and multipath components. This undesirable effect results in a degradation of the service experimented by the users. In order to alleviate this effect, not only the direct link but also all the azimuth angles in a 2π circumference, must be taken into account. Thus, the link who gives the best performance will be selected, optimizing the resulting precoding matrix. This improved method is called Exhaustive search over azimuth Exhaustive search over azimuth In order to improve the chosen precoder, especially in the NLOS environment, we propose to perform exhaustive search over azimuth. This search explores the optimal precoding matrix throughout the whole azimuth angles (from 0 to 2π) ϕ = ϕ 0 + ϕ (3.5) where ϕ 0 is the azimuth departure angle, corresponding to the direct link between enodeb and the UE, ϕ, the whole azimuth angles vector (evenly-spaced from 0 to 2π) with dimension, the number of antenna ports selected. Finally ϕ is an array with every azimuth angles that search for the maximum channel energy. Figure 3.1 displays these concepts. We calculate for each azimuth angle the precoding matrix w. It is done with the following equation: w n = 1 N e i(n 1) d sin(ϕ) sin(θ) 2π λ (3.6) which is similar to Equation 3.4, but now, instead of having just one azimuth departure angle ϕ 0, we have an array with all the azimuth angles between 0 and 2π.

31 22 Chapter 3. Methodology FIGURE 3.1: Example of the exhaustive search over azimuth for the 4 antenna ports case. Direct link azimuth angle ϕ 0 in black, rest of the angle sweep in light blue The exhaustive search over azimuth, needs full knowledge of the channel h. Hence, for the k-th RB we multiply the channel vector h with the precoding vector w and then choose the angle which maximizes the norm, max m RB ean( h H k w k ) (3.7) In Figure 3.2 a scheme of how the exhaustive search chooses the best angle per RB is depicted. The first column corresponds to the azimuth angle (ϕ) of the direct link between the enodeb and the UE. The following columns (as many as antenna ports) complete the circumference from ϕ to ϕ + 2π. Thus, the more ports the antenna uses, the more angles can be searched and more accurate will be the examination of the optimal precoding matrix.

32 3.2. Position uncertainty 23 FIGURE 3.2: Azimuth angle that captures most of the channel energy per RB. 3.2 Position uncertainty In order to be more realistic, we assume that the CSI is delayed. Since the user is moving, this can lead to an outdated CSI. In our work we account for an uncertainty region around the user position. This implies that the angles calculated with respect to the location of the user differs from its actual position. Therefore, this offset reduces the performance of the precoders. An example of an uncertainty area is represented in figure 3.3. FIGURE 3.3: Scheme of uncertainty areas around the UEs [23] The offset x from the actual user position is modeled as Gaussian distributions as follows: x N (µ, σ 2 ) (3.8)

33 24 Chapter 3. Methodology TABLE 3.2: Maximum deviation for UEs for given speeds of 30 km/h and 120 km/h Velocity Max. deviation 30 Km/h 8.3 meters 120 Km/h 33.3 meters Where µ is the actual location of the user and σ 2 is the variance that depends on the velocity of the user and the time between measurements, σ 2 = (v t) 2 (3.9) where v is the UEs speed (in m s ), and t the time between measurements (we consider the time between two measurements to be 1s or 1000 TTIs). Table 3.2 summarizes the maximum uncertainty areas σ max that can be experimented for the enodeb when the users move at 30 km/h and 120 km/h. The term σ max is calculated as the maximum deviation between the actual location of the user and the location the enodeb expects the UE to be. This value is obtained as an average over 100 realizations with a single enodeb and a single UE always in the same position.

34 25 Chapter 4 Simulations In this chapter, we describe the results of the simulations that we have performed in order to compare the three different precoders (MRT, Geometry based and Exhaustive search). Firstly, we define the scenario utilized on the simulations. After the scenario definition, we describe two different set-ups regarding the UE locations. The first one considers fixed UE locations, while the second part assumes random UEs positions throughout the highway. 4.1 Scenario definition The scenario setup for the simulations consists of a single enodeb that covers 120 o and encompasses a highway of 25 meters width by 200 meters length. The enodeb antenna consists of 10 elements in each antenna port, and up to 32 ports. The carrier frequency is 2 GHz with 10 MHz of bandwidth. A co-polarized multiple-antenna array is assumed with a spacing between antenna elements of λ 2 (corresponding to 7.49 cm). Finally, an electrical downtilt of o is assumed (90 0 represents the horizontal direction perpendicular to the enodeb ). The distance between the enodeb and the highway is 58 meters as seen in Figure 4.1 and it has been selected in order to cover a distance of 200 meters of highway with the enodeb. Throughout the highway, there are placed 11 users, each of them with a single omni-directional antenna. To reduce the simulations complexity, especially when we deal with a very high number of antenna elements, (see [2]), we consider no interfering base stations. In order to account for interference, we increase the noise level. To define a realistic interference level, we perform simulations with an hexagonal arrangement of enodebs with 6 interfering nodes and a 500 m inter site distance. Tables 4.1, 4.2 and 4.3 gathers the main parameters used in the simulations.

35 26 Chapter 4. Simulations TABLE 4.1: Simulation parameters Parameter Value(s) Carrier frequency 2 GHz LTE Bandwidth 10 MHz Wavelength (λ) cm TX power 40 db TTI 10 Feedback channel delay 3 TTI Macro-site deployment Hexagonal grid Scenarios 3D-UMa, 3D-UMi Propagation conditions LOS, NLOS OTOI Only outdoor Parameter TABLE 4.2: Antenna parameters Value(s) Antenna ports N=2,4,8,16,32 Antenna elements per port M=10 UE antenna elements 1 Horizontal spacing between elements 0.5λ Vertical spacing between elements 0.5λ Antenna polarization Co-pol Slant angle 0 o Mechanical downtilt 0 o Electrical downtilt o Sector antenna height 25 meters UE antenna height 1.5 meters Transmission mode TM7 (Non-codebook based precoding) UE antenna pattern Omni directional Parameter TABLE 4.3: Scenario parameters Value(s) Number of enodeb 1 Number of enodeb sectors 1 Number of UEs 11 UE distribution Uniform along road Highway length 200 meters Highway width 25 meters Distance enodeb-highway 58 meters UE speed 30 km/h, 120 km/h Maximum uncertainty area σ max = 11 meters, 48 meters

36 4.2. Fixed user location 27 FIGURE 4.1: Scenario scheme of a enodeb covering a 120 degrees area. 4.2 Fixed user location Different settings have been studied regarding the propagation conditions (LOS versus NLOS) and the position uncertainty of the users for the three precoding techniques. The UEs are located in fixed positions in the middle of the highway and are placed all 20 meters. UEs are assumed to be at 1.5 meters height. This results in eleven user for a highway length of 200 meters. The following image shows the described setup: FIGURE 4.2: Fixed UE locations throughout the highway.

37 28 Chapter 4. Simulations Precoders comparison for fixed UE location Firstly we compare the three precoders for each number of beams from 2 to 32. The continuous line is the MRT precoder, the dashed line is the Geometry based precoder and the dotted line is the Exhaustive Search precoder. The x-axis represents the position of each user every 20 meters with respect to the throughput (in Mbps) in the y-axis. The UEs are placed with respect to the horizontal position of the base station (x=0), falling in the negative part of the y-axis if they are located on the left part of the highway and in the positive part of the x-axis otherwise. Throughput (Mbps) Comparison between precoders for LOS propagation 2 beams MRT 4 beams MRT 8 beams MRT 16 beams MRT 32 beams MRT 2 beams Geom. 4 beams Geom. 8 beams Geom. 16 beams Geom. 32 beams Geom. 2 beams Geom. Ex. 4 beams Geom. Ex. 8 beams Geom. Ex. 16 beams Geom. Ex. 32 beams Geom. Ex x position (m) FIGURE 4.3: Comparison between precoders for LOS propagation. As shown in Figure 4.3, the Geometry based and the Exhaustive search precoders get similar performance and slightly higher throughput than the MRT precoder. This is reasonable since in the LOS case the Exhaustive search catches most of the channel energy on the angle established with the direct link between the base station and the UE, which is exactly the angle chosen by the Geometry Based precoder. On the other side, the MRT performs acceptably good, but a bit worse due to the lack of knowledge of the exact position of the UE. Another important fact is that the performance is slightly better in the center of the highway than at the edges. This is because the base station boresight direction is physically pointing to the middle of the highway.

38 4.2. Fixed user location Uncertainty versus Non-uncertainty comparison In order to characterize the UE location uncertainty experimented by the base station due to moving users, Figure 4.4 shows the average throughput for each user when the location is known (denoted by a continuous line) and when the UE location at maximum speed is unknown (denoted by a dashed line) for the Exhaustive Search precoder. We assume a velocity of 120 km/h and a maximum deviation uncertainty of 48 meters. Throughput (Mbps) Comparison between Uncertainty and No Uncertainty for LOS propagation 2 beams Geom. Ex. 4 beams Geom. Ex. 8 beams Geom. Ex. 16 beams Geom. Ex. 32 beams Geom. Ex. 2 beams Geom. Ex. Uncertainty 4 beams Geom. Ex. Uncertainty 8 beams Geom. Ex. Uncertainty 16 beams Geom. Ex. Uncertainty 32 beams Geom. Ex. Uncertainty x position (m) FIGURE 4.4: Uncertainty versus No Uncertainty for LOS propagation. The simulations consider from 2 to 32 beams. As we increment the beam resolution, the gap between the uncertainty and non-uncertainty performances (regarding same beam resolution curves) grows. This is because, as we increase the number of beams, the beamwidth lowers. In case of full knowledge of the user location, this provokes an improvement of the performance as the directivity increases. However, if the user location is uncertain, since the departure angles (azimuth and elevation) calculated for the direct link between the enodeb and the UEs are not exactly pointing to the user location, the chances of hitting the user diminishes as the beams get sharper.

39 30 Chapter 4. Simulations LOS versus NLOS comparison Regarding the propagation conditions, we perform simulations considering both LOS and NLOS. Figure 4.5 compares both propagation conditions for the Geometry based and the Exhaustive Search precoders for 16 and 32 beams. The blue and orange lines correspond to LOS propagation and the green and purple to the NLOS propagation. Throughput (Mbps) Comparison between LOS and NLOS propagation for Geom. and Exhaustive precoders 16 beams Geom. NLOS 32 beams Geom. NLOS 16 beams Geom. Ex. NLOS 32 beams Geom. Ex. NLOS 16 beams Geom. LOS 32 beams Geom. LOS 16 beams Geom. Ex. LOS 32 beams Geom. Ex. LOS x position (m) FIGURE 4.5: Comparison between LOS and NLOS propagation for Exhaustive Search precoder having 16 and 32 antenna ports. Note that in the NLOS propagation case, the response throughout the position of the users is flatter than in the LOS propagation case. This is fundamentally because no direct link component between the base station and the UEs is available. For LOS case, both precoders perform similar as explained in section In NLOS case, however, contrasting performances appear between the precoders. While the Exhaustive search improves its performance as the number of beams increases, no visible enhancement can be appreciated for the Geometry Based precoder. Furthermore, no improvement is achieved even if we increase the number of beams. The lack of a direct link component, the multipath effect, as well as the Geometry Based precoder that wrongly chooses the physical direct link as the one who captures most of the channel energy, incurs in no further improvement, even if the beams directivity increases as we increment the number of beams.

40 4.3. Random user location Random user location In the second part of this chapter, the setup chosen, assumes uniformly random positions of the UEs throughout the highway. Figure 4.6 shows an example of how the users could be placed. The simulations averaged the uniformly random UE locations over 400 realizations. FIGURE 4.6: Example of one realization of random UE locations throughout the highway Precoders comparison for random UE location As before for fixed UE locations, now we study the performance for the Geometry Based and the Exhaustive Search precoders for the random situation. The dashed lines represent the Geometry based approach and the dotted lines, the Exhaustive Search precoder. Comparison between precoders for random UE location. ecdf beams Geom. 4 beams Geom. 8 beams Geom. 16 beams Geom. 32 beams Geom. 2 beams Geom. Ex. 4 beams Geom. Ex. 8 beams Geom. Ex. 16 beams Geom. Ex. 32 beams Geom. Ex throughput (Mbps) FIGURE 4.7: Comparison between precoders for LOS propagation for random location UEs.

Potential Throughput Improvement of FD MIMO in Practical Systems

Potential Throughput Improvement of FD MIMO in Practical Systems 2014 UKSim-AMSS 8th European Modelling Symposium Potential Throughput Improvement of FD MIMO in Practical Systems Fangze Tu, Yuan Zhu, Hongwen Yang Mobile and Communications Group, Intel Corporation Beijing

More information

System Performance of Cooperative Massive MIMO Downlink 5G Cellular Systems

System Performance of Cooperative Massive MIMO Downlink 5G Cellular Systems IEEE WAMICON 2016 April 11-13, 2016 Clearwater Beach, FL System Performance of Massive MIMO Downlink 5G Cellular Systems Chao He and Richard D. Gitlin Department of Electrical Engineering University of

More information

Analysis of RF requirements for Active Antenna System

Analysis of RF requirements for Active Antenna System 212 7th International ICST Conference on Communications and Networking in China (CHINACOM) Analysis of RF requirements for Active Antenna System Rong Zhou Department of Wireless Research Huawei Technology

More information

Beamforming for 4.9G/5G Networks

Beamforming for 4.9G/5G Networks Beamforming for 4.9G/5G Networks Exploiting Massive MIMO and Active Antenna Technologies White Paper Contents 1. Executive summary 3 2. Introduction 3 3. Beamforming benefits below 6 GHz 5 4. Field performance

More information

Multiple Input Multiple Output (MIMO) Operation Principles

Multiple Input Multiple Output (MIMO) Operation Principles Afriyie Abraham Kwabena Multiple Input Multiple Output (MIMO) Operation Principles Helsinki Metropolia University of Applied Sciences Bachlor of Engineering Information Technology Thesis June 0 Abstract

More information

Multiple Antenna Techniques

Multiple Antenna Techniques Multiple Antenna Techniques In LTE, BS and mobile could both use multiple antennas for radio transmission and reception! In LTE, three main multiple antenna techniques! Diversity processing! The transmitter,

More information

An Adaptive Algorithm for MU-MIMO using Spatial Channel Model

An Adaptive Algorithm for MU-MIMO using Spatial Channel Model An Adaptive Algorithm for MU-MIMO using Spatial Channel Model SW Haider Shah, Shahzad Amin, Khalid Iqbal College of Electrical and Mechanical Engineering, National University of Science and Technology,

More information

Chapter 4 DOA Estimation Using Adaptive Array Antenna in the 2-GHz Band

Chapter 4 DOA Estimation Using Adaptive Array Antenna in the 2-GHz Band Chapter 4 DOA Estimation Using Adaptive Array Antenna in the 2-GHz Band 4.1. Introduction The demands for wireless mobile communication are increasing rapidly, and they have become an indispensable part

More information

ECE 476/ECE 501C/CS Wireless Communication Systems Winter Lecture 6: Fading

ECE 476/ECE 501C/CS Wireless Communication Systems Winter Lecture 6: Fading ECE 476/ECE 501C/CS 513 - Wireless Communication Systems Winter 2003 Lecture 6: Fading Last lecture: Large scale propagation properties of wireless systems - slowly varying properties that depend primarily

More information

Full-Dimension MIMO Arrays with Large Spacings Between Elements. Xavier Artiga Researcher Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)

Full-Dimension MIMO Arrays with Large Spacings Between Elements. Xavier Artiga Researcher Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) Full-Dimension MIMO Arrays with Large Spacings Between Elements Xavier Artiga Researcher Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) APS/URSI 2015, 22/07/2015 1 Outline Introduction to Massive

More information

Millimeter Wave Mobile Communication for 5G Cellular

Millimeter Wave Mobile Communication for 5G Cellular Millimeter Wave Mobile Communication for 5G Cellular Lujain Dabouba and Ali Ganoun University of Tripoli Faculty of Engineering - Electrical and Electronic Engineering Department 1. Introduction During

More information

A Novel 3D Beamforming Scheme for LTE-Advanced System

A Novel 3D Beamforming Scheme for LTE-Advanced System A Novel 3D Beamforming Scheme for LTE-Advanced System Yu-Shin Cheng 1, Chih-Hsuan Chen 2 Wireless Communications Lab, Chunghwa Telecom Co, Ltd No 99, Dianyan Rd, Yangmei City, Taoyuan County 32601, Taiwan

More information

MIMO Systems and Applications

MIMO Systems and Applications MIMO Systems and Applications Mário Marques da Silva marques.silva@ieee.org 1 Outline Introduction System Characterization for MIMO types Space-Time Block Coding (open loop) Selective Transmit Diversity

More information

Simulation Analysis of the Long Term Evolution

Simulation Analysis of the Long Term Evolution POSTER 2011, PRAGUE MAY 12 1 Simulation Analysis of the Long Term Evolution Ádám KNAPP 1 1 Dept. of Telecommunications, Budapest University of Technology and Economics, BUTE I Building, Magyar tudósok

More information

Analysis of Massive MIMO With Hardware Impairments and Different Channel Models

Analysis of Massive MIMO With Hardware Impairments and Different Channel Models Analysis of Massive MIMO With Hardware Impairments and Different Channel Models Fredrik Athley, Giuseppe Durisi 2, Ulf Gustavsson Ericsson Research, Ericsson AB, Gothenburg, Sweden 2 Dept. of Signals and

More information

Test strategy towards Massive MIMO

Test strategy towards Massive MIMO Test strategy towards Massive MIMO Using LTE-Advanced Pro efd-mimo Shatrughan Singh, Technical Leader Subramaniam H, Senior Technical Leader Jaison John Puliyathu Mathew, Senior Engg. Project Manager Abstract

More information

Beamforming with Finite Rate Feedback for LOS MIMO Downlink Channels

Beamforming with Finite Rate Feedback for LOS MIMO Downlink Channels Beamforming with Finite Rate Feedback for LOS IO Downlink Channels Niranjay Ravindran University of innesota inneapolis, N, 55455 USA Nihar Jindal University of innesota inneapolis, N, 55455 USA Howard

More information

Long Term Evolution (LTE) and 5th Generation Mobile Networks (5G) CS-539 Mobile Networks and Computing

Long Term Evolution (LTE) and 5th Generation Mobile Networks (5G) CS-539 Mobile Networks and Computing Long Term Evolution (LTE) and 5th Generation Mobile Networks (5G) Long Term Evolution (LTE) What is LTE? LTE is the next generation of Mobile broadband technology Data Rates up to 100Mbps Next level of

More information

Massive MIMO a overview. Chandrasekaran CEWiT

Massive MIMO a overview. Chandrasekaran CEWiT Massive MIMO a overview Chandrasekaran CEWiT Outline Introduction Ways to Achieve higher spectral efficiency Massive MIMO basics Challenges and expectations from Massive MIMO Network MIMO features Summary

More information

Advanced Channel Measurements and Channel Modeling for Millimeter-Wave Mobile Communication. Wilhelm Keusgen

Advanced Channel Measurements and Channel Modeling for Millimeter-Wave Mobile Communication. Wilhelm Keusgen Advanced Channel Measurements and Channel Modeling for Millimeter-Wave Mobile Communication Wilhelm Keusgen International Workshop on Emerging Technologies for 5G Wireless Cellular Networks December 8

More information

Performance Evaluation of Limited Feedback Schemes for 3D Beamforming in LTE-Advanced System

Performance Evaluation of Limited Feedback Schemes for 3D Beamforming in LTE-Advanced System Performance Evaluation of Limited Feedback Scemes for 3D Beamforming in LTE-Advanced System Sang-Lim Ju, Young-Jae Kim, and Won-Ho Jeong Department of Radio and Communication Engineering Cungbuk National

More information

ECE 476/ECE 501C/CS Wireless Communication Systems Winter Lecture 6: Fading

ECE 476/ECE 501C/CS Wireless Communication Systems Winter Lecture 6: Fading ECE 476/ECE 501C/CS 513 - Wireless Communication Systems Winter 2004 Lecture 6: Fading Last lecture: Large scale propagation properties of wireless systems - slowly varying properties that depend primarily

More information

ECE 476/ECE 501C/CS Wireless Communication Systems Winter Lecture 6: Fading

ECE 476/ECE 501C/CS Wireless Communication Systems Winter Lecture 6: Fading ECE 476/ECE 501C/CS 513 - Wireless Communication Systems Winter 2005 Lecture 6: Fading Last lecture: Large scale propagation properties of wireless systems - slowly varying properties that depend primarily

More information

STATISTICAL DISTRIBUTION OF INCIDENT WAVES TO MOBILE ANTENNA IN MICROCELLULAR ENVIRONMENT AT 2.15 GHz

STATISTICAL DISTRIBUTION OF INCIDENT WAVES TO MOBILE ANTENNA IN MICROCELLULAR ENVIRONMENT AT 2.15 GHz EUROPEAN COOPERATION IN COST259 TD(99) 45 THE FIELD OF SCIENTIFIC AND Wien, April 22 23, 1999 TECHNICAL RESEARCH EURO-COST STATISTICAL DISTRIBUTION OF INCIDENT WAVES TO MOBILE ANTENNA IN MICROCELLULAR

More information

Technical Aspects of LTE Part I: OFDM

Technical Aspects of LTE Part I: OFDM Technical Aspects of LTE Part I: OFDM By Mohammad Movahhedian, Ph.D., MIET, MIEEE m.movahhedian@mci.ir ITU regional workshop on Long-Term Evolution 9-11 Dec. 2013 Outline Motivation for LTE LTE Network

More information

Adaptive Transmission Scheme for Vehicle Communication System

Adaptive Transmission Scheme for Vehicle Communication System Sangmi Moon, Sara Bae, Myeonghun Chu, Jihye Lee, Soonho Kwon and Intae Hwang Dept. of Electronics and Computer Engineering, Chonnam National University, 300 Yongbongdong Bukgu Gwangju, 500-757, Republic

More information

Application Note. StarMIMO. RX Diversity and MIMO OTA Test Range

Application Note. StarMIMO. RX Diversity and MIMO OTA Test Range Application Note StarMIMO RX Diversity and MIMO OTA Test Range Contents Introduction P. 03 StarMIMO setup P. 04 1/ Multi-probe technology P. 05 Cluster vs Multiple Cluster setups Volume vs Number of probes

More information

Multiple Antenna Processing for WiMAX

Multiple Antenna Processing for WiMAX Multiple Antenna Processing for WiMAX Overview Wireless operators face a myriad of obstacles, but fundamental to the performance of any system are the propagation characteristics that restrict delivery

More information

Uplink Closed Loop Transmit Diversity for HSPA Yibo Jiang, Haitong Sun, Sharad Sambhwani, Jilei Hou Qualcomm Inc

Uplink Closed Loop Transmit Diversity for HSPA Yibo Jiang, Haitong Sun, Sharad Sambhwani, Jilei Hou Qualcomm Inc Uplink Closed Loop Transmit Diversity for HSPA Yibo Jiang, Haitong Sun, Sharad Sambhwani, Jilei Hou Qualcomm Inc Abstract The closed loop transmit diversity scheme is a promising technique to improve the

More information

Performance Evaluation of Adaptive MIMO Switching in Long Term Evolution

Performance Evaluation of Adaptive MIMO Switching in Long Term Evolution Performance Evaluation of Adaptive MIMO Switching in Long Term Evolution Muhammad Usman Sheikh, Rafał Jagusz,2, Jukka Lempiäinen Department of Communication Engineering, Tampere University of Technology,

More information

The Impact of Carrier Frequency at 800 MHz and 3.5 GHz in Urban and Rural Environments Using Large Antenna Arrays

The Impact of Carrier Frequency at 800 MHz and 3.5 GHz in Urban and Rural Environments Using Large Antenna Arrays The Impact of Carrier Frequency at 8 MHz and 3.5 GHz in Urban and Rural Environments Using Large Antenna Arrays Blanca Ramos Elbal, Fjolla Ademaj, Stefan Schwarz and Markus Rupp Christian Doppler Laboratory

More information

MIMO Wireless Communications

MIMO Wireless Communications MIMO Wireless Communications Speaker: Sau-Hsuan Wu Date: 2008 / 07 / 15 Department of Communication Engineering, NCTU Outline 2 2 MIMO wireless channels MIMO transceiver MIMO precoder Outline 3 3 MIMO

More information

Advances in Radio Science

Advances in Radio Science Advances in Radio Science (23) 1: 149 153 c Copernicus GmbH 23 Advances in Radio Science Downlink beamforming concepts in UTRA FDD M. Schacht 1, A. Dekorsy 1, and P. Jung 2 1 Lucent Technologies, Thurn-und-Taxis-Strasse

More information

Analysis and Improvements of Linear Multi-user user MIMO Precoding Techniques

Analysis and Improvements of Linear Multi-user user MIMO Precoding Techniques 1 Analysis and Improvements of Linear Multi-user user MIMO Precoding Techniques Bin Song and Martin Haardt Outline 2 Multi-user user MIMO System (main topic in phase I and phase II) critical problem Downlink

More information

K.NARSING RAO(08R31A0425) DEPT OF ELECTRONICS & COMMUNICATION ENGINEERING (NOVH).

K.NARSING RAO(08R31A0425) DEPT OF ELECTRONICS & COMMUNICATION ENGINEERING (NOVH). Smart Antenna K.NARSING RAO(08R31A0425) DEPT OF ELECTRONICS & COMMUNICATION ENGINEERING (NOVH). ABSTRACT:- One of the most rapidly developing areas of communications is Smart Antenna systems. This paper

More information

Modeling Mutual Coupling and OFDM System with Computational Electromagnetics

Modeling Mutual Coupling and OFDM System with Computational Electromagnetics Modeling Mutual Coupling and OFDM System with Computational Electromagnetics Nicholas J. Kirsch Drexel University Wireless Systems Laboratory Telecommunication Seminar October 15, 004 Introduction MIMO

More information

Investigation on Multiple Antenna Transmission Techniques in Evolved UTRA. OFDM-Based Radio Access in Downlink. Features of Evolved UTRA and UTRAN

Investigation on Multiple Antenna Transmission Techniques in Evolved UTRA. OFDM-Based Radio Access in Downlink. Features of Evolved UTRA and UTRAN Evolved UTRA and UTRAN Investigation on Multiple Antenna Transmission Techniques in Evolved UTRA Evolved UTRA (E-UTRA) and UTRAN represent long-term evolution (LTE) of technology to maintain continuous

More information

Boosting Microwave Capacity Using Line-of-Sight MIMO

Boosting Microwave Capacity Using Line-of-Sight MIMO Boosting Microwave Capacity Using Line-of-Sight MIMO Introduction Demand for network capacity continues to escalate as mobile subscribers get accustomed to using more data-rich and video-oriented services

More information

Providing Extreme Mobile Broadband Using Higher Frequency Bands, Beamforming, and Carrier Aggregation

Providing Extreme Mobile Broadband Using Higher Frequency Bands, Beamforming, and Carrier Aggregation Providing Extreme Mobile Broadband Using Higher Frequency Bands, Beamforming, and Carrier Aggregation Fredrik Athley, Sibel Tombaz, Eliane Semaan, Claes Tidestav, and Anders Furuskär Ericsson Research,

More information

TOWARDS A GENERALIZED METHODOLOGY FOR SMART ANTENNA MEASUREMENTS

TOWARDS A GENERALIZED METHODOLOGY FOR SMART ANTENNA MEASUREMENTS TOWARDS A GENERALIZED METHODOLOGY FOR SMART ANTENNA MEASUREMENTS A. Alexandridis 1, F. Lazarakis 1, T. Zervos 1, K. Dangakis 1, M. Sierra Castaner 2 1 Inst. of Informatics & Telecommunications, National

More information

Millimeter-Wave Communication and Mobile Relaying in 5G Cellular Networks

Millimeter-Wave Communication and Mobile Relaying in 5G Cellular Networks Lectio praecursoria Millimeter-Wave Communication and Mobile Relaying in 5G Cellular Networks Author: Junquan Deng Supervisor: Prof. Olav Tirkkonen Department of Communications and Networking Opponent:

More information

NR Physical Layer Design: NR MIMO

NR Physical Layer Design: NR MIMO NR Physical Layer Design: NR MIMO Younsun Kim 3GPP TSG RAN WG1 Vice-Chairman (Samsung) 3GPP 2018 1 Considerations for NR-MIMO Specification Design NR-MIMO Specification Features 3GPP 2018 2 Key Features

More information

Cross-correlation Characteristics of Multi-link Channel based on Channel Measurements at 3.7GHz

Cross-correlation Characteristics of Multi-link Channel based on Channel Measurements at 3.7GHz Cross-correlation Characteristics of Multi-link Channel based on Channel Measurements at 3.7GHz Myung-Don Kim*, Jae Joon Park*, Hyun Kyu Chung* and Xuefeng Yin** *Wireless Telecommunications Research Department,

More information

Channel Models for IEEE MBWA System Simulations Rev 03

Channel Models for IEEE MBWA System Simulations Rev 03 IEEE C802.20-03/92 IEEE P 802.20 /PD/V Date: Draft 802.20 Permanent Document Channel Models for IEEE 802.20 MBWA System Simulations Rev 03 This document is a Draft

More information

WiMAX Summit Testing Requirements for Successful WiMAX Deployments. Fanny Mlinarsky. 28-Feb-07

WiMAX Summit Testing Requirements for Successful WiMAX Deployments. Fanny Mlinarsky. 28-Feb-07 WiMAX Summit 2007 Testing Requirements for Successful WiMAX Deployments Fanny Mlinarsky 28-Feb-07 Municipal Multipath Environment www.octoscope.com 2 WiMAX IP-Based Architecture * * Commercial off-the-shelf

More information

Wireless Physical Layer Concepts: Part III

Wireless Physical Layer Concepts: Part III Wireless Physical Layer Concepts: Part III Raj Jain Professor of CSE Washington University in Saint Louis Saint Louis, MO 63130 Jain@cse.wustl.edu These slides are available on-line at: http://www.cse.wustl.edu/~jain/cse574-08/

More information

5G Antenna Design & Network Planning

5G Antenna Design & Network Planning 5G Antenna Design & Network Planning Challenges for 5G 5G Service and Scenario Requirements Massive growth in mobile data demand (1000x capacity) Higher data rates per user (10x) Massive growth of connected

More information

Channel Modelling for Beamforming in Cellular Systems

Channel Modelling for Beamforming in Cellular Systems Channel Modelling for Beamforming in Cellular Systems Salman Durrani Department of Engineering, The Australian National University, Canberra. Email: salman.durrani@anu.edu.au DERF June 26 Outline Introduction

More information

AN ADAPTIVE MOBILE ANTENNA SYSTEM FOR WIRELESS APPLICATIONS

AN ADAPTIVE MOBILE ANTENNA SYSTEM FOR WIRELESS APPLICATIONS AN ADAPTIVE MOBILE ANTENNA SYSTEM FOR WIRELESS APPLICATIONS G. DOLMANS Philips Research Laboratories Prof. Holstlaan 4 (WAY51) 5656 AA Eindhoven The Netherlands E-mail: dolmans@natlab.research.philips.com

More information

Next Generation Mobile Networks NGMN Liaison Statement to 5GAA

Next Generation Mobile Networks NGMN Liaison Statement to 5GAA Simulation assumptions and simulation results of LLS and SLS 1 THE LINK LEVEL SIMULATION 1.1 Simulation assumptions The link level simulation assumptions are applied as follows: For fast fading model in

More information

Use of Multiple-Antenna Technology in Modern Wireless Communication Systems

Use of Multiple-Antenna Technology in Modern Wireless Communication Systems Use of in Modern Wireless Communication Systems Presenter: Engr. Dr. Noor M. Khan Professor Department of Electrical Engineering, Muhammad Ali Jinnah University, Islamabad Campus, Islamabad, PAKISTAN Ph:

More information

SNS COLLEGE OF ENGINEERING COIMBATORE DEPARTMENT OF INFORMATION TECHNOLOGY QUESTION BANK

SNS COLLEGE OF ENGINEERING COIMBATORE DEPARTMENT OF INFORMATION TECHNOLOGY QUESTION BANK SNS COLLEGE OF ENGINEERING COIMBATORE 641107 DEPARTMENT OF INFORMATION TECHNOLOGY QUESTION BANK EC6801 WIRELESS COMMUNICATION UNIT-I WIRELESS CHANNELS PART-A 1. What is propagation model? 2. What are the

More information

LTE System Level Performance in the Presence of CQI Feedback Uplink Delay and Mobility

LTE System Level Performance in the Presence of CQI Feedback Uplink Delay and Mobility LTE System Level Performance in the Presence of CQI Feedback Uplink Delay and Mobility Kamran Arshad Mobile and Wireless Communications Research Laboratory Department of Engineering Systems University

More information

LTE-Advanced research in 3GPP

LTE-Advanced research in 3GPP LTE-Advanced research in 3GPP GIGA seminar 8 4.12.28 Tommi Koivisto tommi.koivisto@nokia.com Outline Background and LTE-Advanced schedule LTE-Advanced requirements set by 3GPP Technologies under investigation

More information

System-Level Performance of Downlink Non-orthogonal Multiple Access (NOMA) Under Various Environments

System-Level Performance of Downlink Non-orthogonal Multiple Access (NOMA) Under Various Environments System-Level Permance of Downlink n-orthogonal Multiple Access (N) Under Various Environments Yuya Saito, Anass Benjebbour, Yoshihisa Kishiyama, and Takehiro Nakamura 5G Radio Access Network Research Group,

More information

Experimental mmwave 5G Cellular System

Experimental mmwave 5G Cellular System Experimental mmwave 5G Cellular System Mark Cudak Principal Research Specialist Tokyo Bay Summit, 23 rd of July 2015 1 Nokia Solutions and Networks 2015 Tokyo Bay Summit 2015 Mark Cudak Collaboration partnership

More information

Smart antenna technology

Smart antenna technology Smart antenna technology In mobile communication systems, capacity and performance are usually limited by two major impairments. They are multipath and co-channel interference [5]. Multipath is a condition

More information

Written Exam Channel Modeling for Wireless Communications - ETIN10

Written Exam Channel Modeling for Wireless Communications - ETIN10 Written Exam Channel Modeling for Wireless Communications - ETIN10 Department of Electrical and Information Technology Lund University 2017-03-13 2.00 PM - 7.00 PM A minimum of 30 out of 60 points are

More information

The Radio Channel. COS 463: Wireless Networks Lecture 14 Kyle Jamieson. [Parts adapted from I. Darwazeh, A. Goldsmith, T. Rappaport, P.

The Radio Channel. COS 463: Wireless Networks Lecture 14 Kyle Jamieson. [Parts adapted from I. Darwazeh, A. Goldsmith, T. Rappaport, P. The Radio Channel COS 463: Wireless Networks Lecture 14 Kyle Jamieson [Parts adapted from I. Darwazeh, A. Goldsmith, T. Rappaport, P. Steenkiste] Motivation The radio channel is what limits most radio

More information

VOL. 3, NO.11 Nov, 2012 ISSN Journal of Emerging Trends in Computing and Information Sciences CIS Journal. All rights reserved.

VOL. 3, NO.11 Nov, 2012 ISSN Journal of Emerging Trends in Computing and Information Sciences CIS Journal. All rights reserved. Effect of Fading Correlation on the Performance of Spatial Multiplexed MIMO systems with circular antennas M. A. Mangoud Department of Electrical and Electronics Engineering, University of Bahrain P. O.

More information

Millimeter Wave Small-Scale Spatial Statistics in an Urban Microcell Scenario

Millimeter Wave Small-Scale Spatial Statistics in an Urban Microcell Scenario Millimeter Wave Small-Scale Spatial Statistics in an Urban Microcell Scenario Shu Sun, Hangsong Yan, George R. MacCartney, Jr., and Theodore S. Rappaport {ss7152,hy942,gmac,tsr}@nyu.edu IEEE International

More information

All Beamforming Solutions Are Not Equal

All Beamforming Solutions Are Not Equal White Paper All Beamforming Solutions Are Not Equal Executive Summary This white paper compares and contrasts the two major implementations of beamforming found in the market today: Switched array beamforming

More information

Antennas Multiple antenna systems

Antennas Multiple antenna systems Channel Modelling ETIM10 Lecture no: 8 Antennas Multiple antenna systems Fredrik Tufvesson Department of Electrical and Information Technology Lund University, Sweden Fredrik.Tufvesson@eit.lth.se 2012-02-13

More information

Channel Modelling ETIM10. Channel models

Channel Modelling ETIM10. Channel models Channel Modelling ETIM10 Lecture no: 6 Channel models Fredrik Tufvesson Department of Electrical and Information Technology Lund University, Sweden Fredrik.Tufvesson@eit.lth.se 2012-02-03 Fredrik Tufvesson

More information

REALISTIC ANTENNA ELEMENTS AND DIFFERENT ARRAY TOPOLOGIES IN THE DOWNLINK OF UMTS-FDD NETWORKS

REALISTIC ANTENNA ELEMENTS AND DIFFERENT ARRAY TOPOLOGIES IN THE DOWNLINK OF UMTS-FDD NETWORKS REALISTIC ANTENNA ELEMENTS AND DIFFERENT ARRAY TOPOLOGIES IN THE DOWNLINK OF UMTS-FDD NETWORKS S. Bieder, L. Häring, A. Czylwik, P. Paunov Department of Communication Systems University of Duisburg-Essen

More information

Interference Scenarios and Capacity Performances for Femtocell Networks

Interference Scenarios and Capacity Performances for Femtocell Networks Interference Scenarios and Capacity Performances for Femtocell Networks Esra Aycan, Berna Özbek Electrical and Electronics Engineering Department zmir Institute of Technology, zmir, Turkey esraaycan@iyte.edu.tr,

More information

Channel Modelling ETI 085. Antennas Multiple antenna systems. Antennas in real channels. Lecture no: Important antenna parameters

Channel Modelling ETI 085. Antennas Multiple antenna systems. Antennas in real channels. Lecture no: Important antenna parameters Channel Modelling ETI 085 Lecture no: 8 Antennas Multiple antenna systems Antennas in real channels One important aspect is how the channel and antenna interact The antenna pattern determines what the

More information

Abstract. Marío A. Bedoya-Martinez. He joined Fujitsu Europe Telecom R&D Centre (UK), where he has been working on R&D of Second-and

Abstract. Marío A. Bedoya-Martinez. He joined Fujitsu Europe Telecom R&D Centre (UK), where he has been working on R&D of Second-and Abstract The adaptive antenna array is one of the advanced techniques which could be implemented in the IMT-2 mobile telecommunications systems to achieve high system capacity. In this paper, an integrated

More information

Planning of LTE Radio Networks in WinProp

Planning of LTE Radio Networks in WinProp Planning of LTE Radio Networks in WinProp AWE Communications GmbH Otto-Lilienthal-Str. 36 D-71034 Böblingen mail@awe-communications.com Issue Date Changes V1.0 Nov. 2010 First version of document V2.0

More information

Closed-loop MIMO performance with 8 Tx antennas

Closed-loop MIMO performance with 8 Tx antennas Closed-loop MIMO performance with 8 Tx antennas Document Number: IEEE C802.16m-08/623 Date Submitted: 2008-07-14 Source: Jerry Pi, Jay Tsai Voice: +1-972-761-7944, +1-972-761-7424 Samsung Telecommunications

More information

Self-Management for Unified Heterogeneous Radio Access Networks. Symposium on Wireless Communication Systems. Brussels, Belgium August 25, 2015

Self-Management for Unified Heterogeneous Radio Access Networks. Symposium on Wireless Communication Systems. Brussels, Belgium August 25, 2015 Self-Management for Unified Heterogeneous Radio Access Networks Twelfth ISWCS International 2015 Symposium on Wireless Communication Systems Brussels, Belgium August 25, 2015 AAS Evolution: SON solutions

More information

Coordinated Joint Transmission in WWAN

Coordinated Joint Transmission in WWAN Coordinated Joint Transmission in WWAN Sreekanth Annapureddy, Alan Barbieri, Stefan Geirhofer, Sid Mallik and Alex Gorokhov May 2 Qualcomm Proprietary Multi-cell system model Think of entire deployment

More information

Downlink Scheduling in Long Term Evolution

Downlink Scheduling in Long Term Evolution From the SelectedWorks of Innovative Research Publications IRP India Summer June 1, 2015 Downlink Scheduling in Long Term Evolution Innovative Research Publications, IRP India, Innovative Research Publications

More information

Ten Things You Should Know About MIMO

Ten Things You Should Know About MIMO Ten Things You Should Know About MIMO 4G World 2009 presented by: David L. Barner www/agilent.com/find/4gworld Copyright 2009 Agilent Technologies, Inc. The Full Agenda Intro System Operation 1: Cellular

More information

Channel Modeling ETI 085

Channel Modeling ETI 085 Channel Modeling ETI 085 Overview Lecture no: 9 What is Ultra-Wideband (UWB)? Why do we need UWB channel models? UWB Channel Modeling UWB channel modeling Standardized UWB channel models Fredrik Tufvesson

More information

3D Beamforming for Capacity Boosting in LTE-Advanced System

3D Beamforming for Capacity Boosting in LTE-Advanced System 3D Beamforming for Capacity Boosting in LTE-Advanced System Hyoungju Ji, Byungju Lee and Byonghyo Shim Seoul National University, Seoul, Korea Email: {hyoungjuji, bjlee}@islabsnuackr, bshim@snuackr Young-Han

More information

Antennas and Propagation. Chapter 6b: Path Models Rayleigh, Rician Fading, MIMO

Antennas and Propagation. Chapter 6b: Path Models Rayleigh, Rician Fading, MIMO Antennas and Propagation b: Path Models Rayleigh, Rician Fading, MIMO Introduction From last lecture How do we model H p? Discrete path model (physical, plane waves) Random matrix models (forget H p and

More information

CHAPTER 10 CONCLUSIONS AND FUTURE WORK 10.1 Conclusions

CHAPTER 10 CONCLUSIONS AND FUTURE WORK 10.1 Conclusions CHAPTER 10 CONCLUSIONS AND FUTURE WORK 10.1 Conclusions This dissertation reported results of an investigation into the performance of antenna arrays that can be mounted on handheld radios. Handheld arrays

More information

Performance Evaluation of Uplink Closed Loop Power Control for LTE System

Performance Evaluation of Uplink Closed Loop Power Control for LTE System Performance Evaluation of Uplink Closed Loop Power Control for LTE System Bilal Muhammad and Abbas Mohammed Department of Signal Processing, School of Engineering Blekinge Institute of Technology, Ronneby,

More information

CHAPTER 2 WIRELESS CHANNEL

CHAPTER 2 WIRELESS CHANNEL CHAPTER 2 WIRELESS CHANNEL 2.1 INTRODUCTION In mobile radio channel there is certain fundamental limitation on the performance of wireless communication system. There are many obstructions between transmitter

More information

American Journal of Engineering Research (AJER) 2015

American Journal of Engineering Research (AJER) 2015 American Journal of Engineering Research (AJER) 215 Research Paper American Journal of Engineering Research (AJER) e-issn : 232-847 p-issn : 232-936 Volume-4, Issue-1, pp-175-18 www.ajer.org Open Access

More information

UWB Channel Modeling

UWB Channel Modeling Channel Modeling ETIN10 Lecture no: 9 UWB Channel Modeling Fredrik Tufvesson & Johan Kåredal, Department of Electrical and Information Technology fredrik.tufvesson@eit.lth.se 2011-02-21 Fredrik Tufvesson

More information

Channel Modelling ETIN10. Directional channel models and Channel sounding

Channel Modelling ETIN10. Directional channel models and Channel sounding Channel Modelling ETIN10 Lecture no: 7 Directional channel models and Channel sounding Ghassan Dahman / Fredrik Tufvesson Department of Electrical and Information Technology Lund University, Sweden 2014-02-17

More information

A Complete MIMO System Built on a Single RF Communication Ends

A Complete MIMO System Built on a Single RF Communication Ends PIERS ONLINE, VOL. 6, NO. 6, 2010 559 A Complete MIMO System Built on a Single RF Communication Ends Vlasis Barousis, Athanasios G. Kanatas, and George Efthymoglou University of Piraeus, Greece Abstract

More information

Simulation of Outdoor Radio Channel

Simulation of Outdoor Radio Channel Simulation of Outdoor Radio Channel Peter Brída, Ján Dúha Department of Telecommunication, University of Žilina Univerzitná 815/1, 010 6 Žilina Email: brida@fel.utc.sk, duha@fel.utc.sk Abstract Wireless

More information

Extension of ITU IMT-A Channel Models for Elevation Domains and Line-of-Sight Scenarios

Extension of ITU IMT-A Channel Models for Elevation Domains and Line-of-Sight Scenarios Extension of ITU IMT-A Channel Models for Elevation Domains and Line-of-Sight Scenarios Zhimeng Zhong 1, Xuefeng Yin 2, Xin Li 1 and Xue Li 1 1 Huawei Technology Company, Xi an, China 2 School of Electronics

More information

6 Uplink is from the mobile to the base station.

6 Uplink is from the mobile to the base station. It is well known that by using the directional properties of adaptive arrays, the interference from multiple users operating on the same channel as the desired user in a time division multiple access (TDMA)

More information

Analytical Evaluation of the Cell Spectral Efficiency of a Beamforming Enhanced IEEE m System

Analytical Evaluation of the Cell Spectral Efficiency of a Beamforming Enhanced IEEE m System Analytical Evaluation of the Cell Spectral Efficiency of a Beamforming Enhanced IEEE 802.16m System Benedikt Wolz, Afroditi Kyrligkitsi Communication Networks (ComNets) Research Group Prof. Dr.-Ing. Bernhard

More information

Canadian Evaluation Group

Canadian Evaluation Group IEEE L802.16-10/0061 Canadian Evaluation Group Raouia Nasri, Shiguang Guo, Ven Sampath Canadian Evaluation Group (CEG) www.imt-advanced.ca Overview What the CEG evaluated Compliance tables Services Spectrum

More information

ELEC E7210: Communication Theory. Lecture 11: MIMO Systems and Space-time Communications

ELEC E7210: Communication Theory. Lecture 11: MIMO Systems and Space-time Communications ELEC E7210: Communication Theory Lecture 11: MIMO Systems and Space-time Communications Overview of the last lecture MIMO systems -parallel decomposition; - beamforming; - MIMO channel capacity MIMO Key

More information

Performance Studies on LTE Advanced in the Easy-C Project Andreas Weber, Alcatel Lucent Bell Labs

Performance Studies on LTE Advanced in the Easy-C Project Andreas Weber, Alcatel Lucent Bell Labs Performance Studies on LTE Advanced in the Easy-C Project 19.06.2008 Andreas Weber, Alcatel Lucent Bell Labs All Rights Reserved Alcatel-Lucent 2007 Agenda 1. Introduction 2. EASY C 3. LTE System Simulator

More information

EITN85, FREDRIK TUFVESSON ELECTRICAL AND INFORMATION TECHNOLOGY

EITN85, FREDRIK TUFVESSON ELECTRICAL AND INFORMATION TECHNOLOGY Wireless Communication Channels Lecture 6: Channel Models EITN85, FREDRIK TUFVESSON ELECTRICAL AND INFORMATION TECHNOLOGY Content Modelling methods Okumura-Hata path loss model COST 231 model Indoor models

More information

University of Bristol - Explore Bristol Research. Link to published version (if available): /VTCF

University of Bristol - Explore Bristol Research. Link to published version (if available): /VTCF Bian, Y. Q., & Nix, A. R. (2006). Throughput and coverage analysis of a multi-element broadband fixed wireless access (BFWA) system in the presence of co-channel interference. In IEEE 64th Vehicular Technology

More information

Noncoherent Communications with Large Antenna Arrays

Noncoherent Communications with Large Antenna Arrays Noncoherent Communications with Large Antenna Arrays Mainak Chowdhury Joint work with: Alexandros Manolakos, Andrea Goldsmith, Felipe Gomez-Cuba and Elza Erkip Stanford University September 29, 2016 Wireless

More information

Detection of Multipath Propagation Effects in SAR-Tomography with MIMO Modes

Detection of Multipath Propagation Effects in SAR-Tomography with MIMO Modes Detection of Multipath Propagation Effects in SAR-Tomography with MIMO Modes Tobias Rommel, German Aerospace Centre (DLR), tobias.rommel@dlr.de, Germany Gerhard Krieger, German Aerospace Centre (DLR),

More information

Performance Analysis of LTE Downlink System with High Velocity Users

Performance Analysis of LTE Downlink System with High Velocity Users Journal of Computational Information Systems 10: 9 (2014) 3645 3652 Available at http://www.jofcis.com Performance Analysis of LTE Downlink System with High Velocity Users Xiaoyue WANG, Di HE Department

More information

Diversity. Spring 2017 ELE 492 FUNDAMENTALS OF WIRELESS COMMUNICATIONS 1

Diversity. Spring 2017 ELE 492 FUNDAMENTALS OF WIRELESS COMMUNICATIONS 1 Diversity Spring 2017 ELE 492 FUNDAMENTALS OF WIRELESS COMMUNICATIONS 1 Diversity A fading channel with an average SNR has worse BER performance as compared to that of an AWGN channel with the same SNR!.

More information

System Performance Challenges of IMT-Advanced Test Environments

System Performance Challenges of IMT-Advanced Test Environments 156919956 1 System Performance Challenges of IMT-Advanced Test Environments Per Burström, Anders Furuskär, Stefan Wänstedt, Sara Landström, Per Skillermark, Aram Antó Ericsson Research [per.burstrom, anders.furuskar,

More information

LTE Transmission Modes and Beamforming White Paper

LTE Transmission Modes and Beamforming White Paper LTE Transmission Modes and Beamforming White Paper Multiple input multiple output (MIMO) technology is an integral part of 3GPP E-UTRA long term evolution (LTE). As part of MIMO, beamforming is also used

More information

Multiple Antenna Systems in WiMAX

Multiple Antenna Systems in WiMAX WHITEPAPER An Introduction to MIMO, SAS and Diversity supported by Airspan s WiMAX Product Line We Make WiMAX Easy Multiple Antenna Systems in WiMAX An Introduction to MIMO, SAS and Diversity supported

More information