Impact of Antenna Geometry on Adaptive Switching in MIMO Channels
|
|
- Osborn Whitehead
- 5 years ago
- Views:
Transcription
1 Impact of Antenna Geometry on Adaptive Switching in MIMO Channels Ramya Bhagavatula, Antonio Forenza, Robert W. Heath Jr. he University of exas at Austin University Station, C0803, Austin, exas, {bhagavat, Rearden LLC 355 Bryant Street, Suite 0, San Francisco, CA 9407 Abstract Adaptive switching between multiple-input multiple-output MIMO transmission schemes like diversity and spatial multiplexing yields significantly higher capacity gains over fixed transmission schemes. Previous work has shown the impact of channel correlation and SNR on the switching between different schemes like beamforming and spatial multiplexing. his paper explores the influence of antenna geometry on the switching points between these transmission schemes. Closed-form expressions for the switching points for a two element uniform linear array ULA as a function of antenna geometry and channel statistics are derived. he impact of the channel parameters and antenna geometry on the switching points is also shown by simulations. he paper is concluded with a discussion on the impact of varying antenna array configurations on the switching points between beamforming and spatial multiplexing. I. INRODUCION Multiple-input multiple-output MIMO systems can offer significant capacity gains over single-input single-output SISO systems through the use of multiple transmit and multiple receive antennas. he additional spatial dimension offered by MIMO technology can be exploited by adaptively switching between techniques like diversity or spatial multiplexing to obtain higher link robustness or capacity gains [], [], [3], [4]. he adaptive switching can occur using either instantaneous channel state information [] or spatial correlation [3]. As the spatial correlation of a channel plays a dominant role in determining the optimal transmit strategy and varies more slowly than the instantaneous channel, adaptation based on the spatial correlation matrix provides a balance between performance and reduced feedback requirements. Previous work on correlation-based switching considered adaptive switching between diversity modes like statistical beamforming or space-time block coding, hybrid multiplexing double space time transmit diversity, and spatial multiplexing [3]. Switching points that indicate when to switch between these three modes of operation were derived using capacity as a switching criterion in [3]. hese switching points are a function of the transmit and receive correlations in the channel, which in turn are a function of the channel and antenna parameters [5], [6]. Antenna geometry in particular has a significant impact on the achievable capacity gains [6]. Unfortunately, previous work did not address the impact of antenna geometry on adaptive switching between transmission schemes. In this paper, we explore the impact of antenna geometry and channel parameters on the switching points between statistical beamforming BF and spatial multiplexing SM. For analytical tractability, we focus on the uniform linear array ULA with two active antenna elements, at the transmitter side. he antenna geometry is varied by varying the inter-element spacing between the antennas in the ULA. his is equivalent to selecting a subset of two transmit antennas. We derive closed form expressions for the switching points of the ULA between BF and SM, using bounds on the ergodic capacity. his gives us an insight into how the antenna geometries might be reconfigured adaptively in response to the channel parameters to improve performance. In particular, BF benefits from using antennas that are close together as the higher correlation between the antenna elements allows the channel to have a stronger single spatial direction. SM works better with antennas that are further apart. his causes the multiple streams to have lower correlation between them, which in turn is beneficial for SM. Using numerical simulations we study the switching between BF and SM for other array configurations, like the uniform circular array UCA and star configurations, to show the impact of antenna configurations on adaptive switching between BF and SM. his motivates the development of reconfigurable MIMO antenna arrays. II. CHANNEL MODEL Consider a wireless link with M transmit antennas and M R receive antennas. he channel impulse response for the open loop MIMO system is represented by an M R M matrix, H. he channel is also assumed to be frequency flat. he received signal, y is given by Es y = Hx + n M where y C MR, E s is the average transmit power per symbol, x C M is the input signal vector, subject to the power constraint E x = M, n is
2 Fig.. Array geometries considered: a ULA with an interelement spacing of λ, and b ULA with an inter-element spacing of 5λ. he active elements are shown in black, and the inactive element is in grey. the zero-mean additive white Gaussian noise vector with covariance matrix given by Enn = N o I MR, where N o is the noise power. γ o = E s /N o represents the signal-tonoise ratio SNR. he statistical correlation between the entries of the channel matrix, H can be expressed as R tot = EvecH vech. Assuming a Kronecker model for the correlation [5], R tot can be written as R tot = R R R 3 where R and R R stand for the transmit and receive covariance matrices respectively and stands for the kronecker product. o simplify our analysis, we consider a single-cluster channel with single-sided correlation and set R R = I MR. hus, we are essentially studying the impact of using different array configurations at the transmitter side. III. ANALYSIS For our analysis, we use the ULA configuration consisting of two antenna elements. Fig. shows the three-element configuration that uses two active antenna elements at a time for a given configuration. he two array geometries - ULA and ULA have inter-element spacings of λ and 5λ, respectively as shown in Fig.. We analyze the performance of each of these arrays in terms of capacity by considering two MIMO transmit strategies - beamforming BF and spatial multiplexing SM. In [3], an adaptive algorithm to switch between BF and SM in spatially correlated MIMO channels was proposed based on channel correlation and SNR. In this paper, we demonstrate that the SNR switching thresholds between BF and SM are a function of array geometries i.e the inter-element spacing and the channel parameters. For small values of the angle-of-departure AoD and angular spread, the correlation coefficients of a ULA with a Laplacian distributed power azimuth spectrum, are approximated as [7] e kodm lsinφc r l,m 4 + σ φ [k odm lcosφ c ] where k o = π/λ is the wavenumber λ is the wavelength at which the antenna operates, σ φ is the standard E stands for expectation,. stands for transposition and conjugation, I R stands for a R R identity matrix and. is the transpose. deviation of the power azimuth spectrum and φ c is the mean AoD of the single cluster and d is the interelement spacing of the antenna elements in the ULA. he eigenvalues for a ULA [8] for low values of angular spread and around the broadside direction of AoD φ c = 0 degrees can be derived from 4 as λ ULA, = ± 5 + σ φ k odcosφ c where λ ULA, are the two eigenvalues of the transmit correlation matrix. It is to be noted that as we are considering a single-sided correlated channel model, we let λ ULA R, =, where λ ULA R, are the eigenvalues of the receive correlation matrix. A. Capacity of the MIMO System Using Beamforming he closed-form expression of the capacity of a MIMO system using BF in [3] is not valid for a single-sided correlated channel. he general capacity of a MIMO channel that employs beamforming using a maximum ratio combining receiver is given in [3] as C BF γ o = E [log + γ ] oλ tmax η 6 where where λ tmax is the maximum eigenvalue of the transmit covariance matrix, R. η = M R i= ε i, where ε i s are i.i.d. exponentially-distributed random variables. For a MIMO system, η = ε + ε. he p.d.f. of η is given as fη = 4 ηe η/, for η > 0. 7 he capacity of the MIMO system using beamforming can then be calculated as C BF γ o = log + γ oλ tmax η fηdη.8 0 his can in turn be expressed in closed-form as 4Λ o + [ Λ o ] e Λo 0, Λ o Γ C BF Λ o = 9 4Λ o ln where Λ o = γ o λ tmax / and Γ.,. is the incomplete Gamma function. For a two-element ULA, as per 5, for small values of AoD and angular spread, λ tmax can be written as λ ULA = σ φ k odcosφ c BF needs a single strong spatial direction, which in turn implies a large maximum eigenvalue at the transmit side. From 0, it is evident that as the inter-element spacing, increases. his in turn increases the value of Λ o, which will increase capacity. herefore, it can be said that for a constant SNR, BF will perform best for smaller values of d. Intuitively, this can be explained by recognizing that the correlation between antenna elements is maximum for smaller d, leading to better performance using BF. d reduces, the value of λ ULA
3 B. Capacity of the MIMO System Using Spatial Multiplexing he capacity of a MIMO channel employing spatial multiplexing and using a zero-forcing ZF receiver, is given by the following closed form expression [3] C SM γ o = exp k= R kk R γ o ln Γ 0, Rkk R γ o where R kk corresponds to R with the k th row and k th column removed. In the case of the two-element ULA, R and R =. can be obtained from [8] and [7] as R = R Using R ULA = λ ULA λ ULA, we obtain R ULA = + σ φ k odcosφ c. For a MIMO system, using ULA at the transmit side, can be rewritten as CSM ULA γ o = ln exp R ULA Γ 0, γ o R ULA γ o 3 where R ULA is given by. It is to be noted here that and hence, 3 are approximations that are valid only for small values of AoD and angular spread. From, it is seen that as d increases at constant SNR, the value of R ULA increases, thereby increasing the value of the capacity of the MIMO system. his can also be explained by considering that larger interelement spacing will cause the multiple data streams from the different antenna elements to have lower correlation among them, which is desired for SM. C. Switching Points between BF and SM From the previous sections, we saw that a ULA with closely spaced antenna elements will produce larger capacity than a ULA with widely spaced antenna elements using BF. We also saw that the converse is true for SM. Hence, we consider in our analysis, switching between the ULA -BF combination to ULA -SM combination as a means to obtain maximum capacity gains. he theoretical crossing points between BF and SM are derived in [3] for both the transmission schemes using the same antenna geometry. We extend this analysis to obtain the switching point for the case when antenna geometry is varied along with the transmission scheme, i.e. from ULA -BF to ULA -SM. We define Mode to be the ULA -BF combination and Mode to be the ULA -SM combination. Let R,ULA and R,ULA be the transmit covariance matrices of the two configurations shown in Fig.. he switching point for a MIMO system that switches Fig.. Variation of the switching point with respect to the AoD and angular spread for an inter-element spacing at ULA and ULA of 5λ. between the transmission schemes with the same antenna geometry is given in [3] as γ CP = 4 4 R λ tmin where λ tmin is the minimum eigenvalue for the transmit covariance matrix, R. he switching point for a MIMO system that switches from Mode ULA - BF to Mode ULA -SM is γ CP = 4 λ ULA R,ULA R,ULA. 5 It is to be noted that these switching points have been derived from the upper limit of the capacities of BF and SM, as given in [3]. IV. RESULS In this section, we show the impact of channel parameters and antenna geometry on the switching points between Mode and Mode. We finally show the impact of varying antenna configurations on the switching points, by means of simulations. A. Impact of Channel Parameters on Switching Points In this section, we show that the influence of channel parameters AoD and the angular spread on the switching points between BF and SM. 5 shows the impact of σ φ and φ c on the eigenvalues of the transmit correlation matrix of the ULA and hence on the switching point, as shown in 5. In this section, we explore this effect by using the ULA configuration with an inter-element spacing of d = 5λ. he impact of AoD and angular spread on the switching point is shown in Fig.. he plot shows the variation
4 of the switching point only for AoD and angular spread up to 30 degrees, because 4 is valid only for small values of AoD and angular spread. It is seen from Fig. that the switching point decreases with increasing angular spread. As the angular spread increases for a given AoD, in 5,which determines the SM-ZF performance, gets larger. Hence, from 4, the crossing point reduces with an increase in angular spread. It is also seen from Fig. that for a constant angular spread, the crossing point increases slightly with an increase in AoD. his can be explained from 5 by noting that as the value of λ ULA t min and in turn R,ULA increases. his causes the crossing point to reduce, as per 4. AoD increases for constant angular spread, λ ULA t min B. Impact of Antenna Geometry on Switching Points he impact of the antenna geometry employed at the transmit side is evident from 5. his has been illustrated in this section. We considered the two ULA configurations shown in Fig.. For the purpose of analysis we consider a single cluster channel with AoD = 0 degrees and angular spread = 5 degrees. We compute the switching points between BF and SM for the two configurations in Fig., and then compare the results with those obtained by means of the closed-form expressions derived. Mode performs best in the case of low SNR, which is expected, as Mode corresponds to beamforming and closely spaced antennas are preferred for beamforming. Mode, which is a widely spaced antenna array configuration performs best at the higher SNRs. Hence, the best performance in terms of capacity is obtained by switching between Mode to Mode. Fig. 3 shows the crossing point between the two modes. It is to be noted here that the upper bounds of the capacities for Modes and have been plotted here to show that the crossing point obtained here is 8.5 db, which is in close correspondence with that obtained by means of the closed- form expression in 5, which is 8.8 db. We also show the impact of the spacing between the antenna elements in Fig. 4. It is seen from here that as the distance between the antenna elements increases, the switching point reduces. his is because with all the other parameters constant, the as d increases, the value of R,ULA reduces. his causes the crossing point to reduce, as shown in 5. V. IMPAC OF ANENNA CONFIGURAION ON SWICHING POINS We show by simulation results, the impact of different antenna configurations on the switching points between BF and SM. For this purpose, we consider three commonly used array configurations - ULA, UCA and star configurations. o illustrate the concept of reconfigurable MIMO antenna arrays, we consider an antenna configuration with nine antenna elements shown in Fig. 5. At any time, only four of the antenna elements are active shown in black. Fig. 3. Capacities of the two transmission modes: Mode - ULA and BF, and Mode - ULA and SM, as a function of the SNR. he crossing point between the two modes is also shown. Fig. 4. Variation of the switching point with respect to the interelement spacing in wavelengths at ULA and ULA for AoD = 0 degrees and angular spread = 5 degrees. he diameter of the circular access point is taken as 3λ for the purpose of analysis. Using the array response vectors described in [6], the capacity of each of the three antenna array configurations was obtained for the two transmission schemes - beamforming and spatial multiplexing. A single-sided correlated clustered channel model was considered here with the AoD = 30 degrees, and an angular spread = 00 degrees. he performance of beamforming and spatial multiplexing for each of the three different antenna types is reported in Fig. 6. It is seen that for BF, the ULA performs the best among all the three configurations. his is because the
5 inter-element spacing for the ULA is the small enough that no grating lobes are present. he inter-element spacing in other configurations is comparatively larger leading to the presence of grating lobes that degrade performance. For the case of SM, the scenario is reversed. his is again explained by means of the inter-element spacing, which is least for the ULA. his causes the performance of the ULA to be the worst of all the three configurations. he other two, having relatively larger inter-element spacing, have better performance, as seen in Fig. 6. It is also seen that the different array configurations give a significant performance difference for SM, as against BF, where the performance does not vary too much. It is seen from [3] that for BF, capacity is a function of just the maximum eigenvalue of the transmit covariance matrix. For SM with the ZF receiver, capacity is a function of all the eigenvalues of the transmit covariance matrix. his makes SM with the ZF receiver more sensitive to changes in the array configurations as compared to BF. Finally, Fig. 6 reveals that the SNR switching thresholds are a function of the array configurations. In the case of a single MIMO antenna array system, the switching threshold is only a function of the channel parameters. In reconfigurable antenna array systems, capacity can be maximized by switching between different antenna arrays for different schemes based on the channel parameters. For the current channel model, an ideal adaptive algorithm for reconfigurable MIMO arrays would select BF with ULA for SNRs lower than 7dB and SM with UCA configuration for SNRs greater than 7dB. VI. CONCLUSIONS In this contribution, we showed the impact of the antenna geometry and channel parameters on the switching points between BF and SM. Considering a MIMO system and a single-sided correlated clustered channel model that uses ULA configuration at the transmit side, we derived closed form expressions for the switching points between the transmission schemes as a function of both the channel parameters and array geometries. We also showed that antenna array configurations also impact the switching points obtained between BF and SM. Future work involves Fig. 5. Array configurations considered: a ULA, b UCA, and c Star configurations. he reference system for the angle-ofdeparture AoD has been shown. he active elements for each configuration are shown in black, and the inactive elements, in grey. Fig. 6. Comparison of the performance of beamforming and spatial multiplexing for three different antenna array configurations. analyzing the impact of varying antenna configurations on the switching points between BF and SM. Future work in this area is aimed towards developing an adaptive algorithm that will give the best performance with respect to capacity. VII. ACKNOWLEDGEMENS his material is based upon work supported by the National Science Foundation under Grant No. CCF-5494, the Office of Naval Research under grant number N REFERENCES [] D. Gesbert, M. Shafi, D. Shiu, P. J. Smith, and A. Naguib, From heory to Practice: An Overview of MIMO Spaceime Coded Wireless Systems, IEEE Jrnl. on Select. Areas of Comm., vol., no. 3, pp. 8-30, Apr [] R. W. Heath, Jr. and A. J. Paulraj, Switching Between Diversity and Multiplexing in MIMO Systems, IEEE rans. on Comm., vol. 53, no. 6, pp , June 005. [3] A. Forenza, M. R. McKay, A. Pandharipande, R. W. Heath Jr., and I. B. Collings, Adaptive MIMO ransmission for Exploiting the Capacity of Spatially Correlated Channels, accepted for publication in IEEE rans. on Veh. ech., Apr [4] S. Catreux, V. Erceg, D. Gesbert and R. W. Heath Jr., Adaptive Modulation and MIMO Coding for Broadband Wireless Data Networks, IEEE Comm. Mag., vol. 40, no. 6, pp. 08-5, June 00. [5] D. S. Shiu, G. J. Foschini, M. J. Gans, and J. M. Kahn, Fading Correlation and its Effect on the Capacity of Multielement Antenna Systems, IEEE rans. on Comm., vol. 48, no. 3, pp , Mar [6] A. Forenza and R. W. Heath Jr., Impact of Antenna Geometry on MIMO Communication in Indoor Clustered Channels, Proc. of AP-S Intern. Symp., vol., pp , June 004. [7] A. Forenza, D. J. Love, and R. W. Heath Jr., A Low Complexity Algorithm to Simulate the Spatial Covariance Matrix for Clustered MIMO Channels, Proc. of the IEEE Veh. ech. Conf., vol., pp , May 004. [8] A. Forenza and R. W. Heath, Jr., Benefit of Pattern Diversity Via -element Array of Circular Patch Antennas in Indoor Clustered MIMO Channels, IEEE rans. on Comm., vol. 54, no. 5, pp , May 006.
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 informationSpatial Correlation Effects on Channel Estimation of UCA-MIMO Receivers
11 International Conference on Communication Engineering and Networks IPCSIT vol.19 (11) (11) IACSIT Press, Singapore Spatial Correlation Effects on Channel Estimation of UCA-MIMO Receivers M. A. Mangoud
More informationOptimization of Coded MIMO-Transmission with Antenna Selection
Optimization of Coded MIMO-Transmission with Antenna Selection Biljana Badic, Paul Fuxjäger, Hans Weinrichter Institute of Communications and Radio Frequency Engineering Vienna University of Technology
More informationA 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 informationEffects of Antenna Mutual Coupling on the Performance of MIMO Systems
9th Symposium on Information Theory in the Benelux, May 8 Effects of Antenna Mutual Coupling on the Performance of MIMO Systems Yan Wu Eindhoven University of Technology y.w.wu@tue.nl J.W.M. Bergmans Eindhoven
More informationMultiple Antennas in Wireless Communications
Multiple Antennas in Wireless Communications Luca Sanguinetti Department of Information Engineering Pisa University lucasanguinetti@ietunipiit April, 2009 Luca Sanguinetti (IET) MIMO April, 2009 1 / 46
More informationCorrelation and Calibration Effects on MIMO Capacity Performance
Correlation and Calibration Effects on MIMO Capacity Performance D. ZARBOUTI, G. TSOULOS, D. I. KAKLAMANI Departement of Electrical and Computer Engineering National Technical University of Athens 9, Iroon
More information[P7] c 2006 IEEE. Reprinted with permission from:
[P7 c 006 IEEE. Reprinted with permission from: Abdulla A. Abouda, H.M. El-Sallabi and S.G. Häggman, Effect of Mutual Coupling on BER Performance of Alamouti Scheme," in Proc. of IEEE International Symposium
More informationUNEQUAL POWER ALLOCATION FOR JPEG TRANSMISSION OVER MIMO SYSTEMS. Muhammad F. Sabir, Robert W. Heath Jr. and Alan C. Bovik
UNEQUAL POWER ALLOCATION FOR JPEG TRANSMISSION OVER MIMO SYSTEMS Muhammad F. Sabir, Robert W. Heath Jr. and Alan C. Bovik Department of Electrical and Computer Engineering, The University of Texas at Austin,
More informationInternational Conference on Emerging Trends in Computer and Electronics Engineering (ICETCEE'2012) March 24-25, 2012 Dubai. Correlation. M. A.
Effect of Fading Correlation on the VBLAST Detection for UCA-MIMO systems M. A. Mangoud Abstract In this paper the performance of the Vertical Bell Laboratories Space-Time (V-BLAST) detection that is used
More informationKeyhole Effects in MIMO Wireless Channels - Measurements and Theory
MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Keyhole Effects in MIMO Wireless Channels - Measurements and Theory Almers, P.; Tufvesson, F. TR23-36 December 23 Abstract It has been predicted
More informationMIMO Receiver Design in Impulsive Noise
COPYRIGHT c 007. ALL RIGHTS RESERVED. 1 MIMO Receiver Design in Impulsive Noise Aditya Chopra and Kapil Gulati Final Project Report Advanced Space Time Communications Prof. Robert Heath December 7 th,
More informationMIMO Capacity and Antenna Array Design
1 MIMO Capacity and Antenna Array Design Hervé Ndoumbè Mbonjo Mbonjo 1, Jan Hansen 2, and Volkert Hansen 1 1 Chair of Electromagnetic Theory, University Wuppertal, Fax: +49-202-439-1045, Email: {mbonjo,hansen}@uni-wuppertal.de
More informationAnalysis 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 informationSpatial Multiplexing in Correlated Fading via the Virtual Channel Representation
856 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 21, NO. 5, JUNE 2003 Spatial Multiplexing in Correlated Fading via the Virtual Channel Representation Zhihong Hong, Member, IEEE, Ke Liu, Student
More informationAchievable Unified Performance Analysis of Orthogonal Space-Time Block Codes with Antenna Selection over Correlated Rayleigh Fading Channels
Achievable Unified Performance Analysis of Orthogonal Space-Time Block Codes with Antenna Selection over Correlated Rayleigh Fading Channels SUDAKAR SINGH CHAUHAN Electronics and Communication Department
More informationMeasured Capacities at 5.8 GHz of Indoor MIMO Systems with MIMO Interference
Measured Capacities at.8 GHz of Indoor MIMO Systems with MIMO Interference Jeng-Shiann Jiang, M. Fatih Demirkol, and Mary Ann Ingram School of Electrical and Computer Engineering Georgia Institute of Technology,
More informationMIMO Channel Capacity in Co-Channel Interference
MIMO Channel Capacity in Co-Channel Interference Yi Song and Steven D. Blostein Department of Electrical and Computer Engineering Queen s University Kingston, Ontario, Canada, K7L 3N6 E-mail: {songy, sdb}@ee.queensu.ca
More informationMIMO 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 informationELEC 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 informationPROGRESSIVE CHANNEL ESTIMATION FOR ULTRA LOW LATENCY MILLIMETER WAVE COMMUNICATIONS
PROGRESSIVECHANNELESTIMATIONFOR ULTRA LOWLATENCYMILLIMETER WAVECOMMUNICATIONS Hung YiCheng,Ching ChunLiao,andAn Yeu(Andy)Wu,Fellow,IEEE Graduate Institute of Electronics Engineering, National Taiwan University
More informationEfficient Signaling Schemes for mmwave LOS MIMO Communication Using Uniform Linear and Circular Arrays
Efficient Signaling Schemes for mmwave LOS MIMO Communication Using Uniform Linear and Circular Arrays G. D. Surabhi and A. Chockalingam Department of ECE, Indian Institute of Science, Bangalore 562 Abstract
More informationCHAPTER 8 MIMO. Xijun Wang
CHAPTER 8 MIMO Xijun Wang WEEKLY READING 1. Goldsmith, Wireless Communications, Chapters 10 2. Tse, Fundamentals of Wireless Communication, Chapter 7-10 2 MIMO 3 BENEFITS OF MIMO n Array gain The increase
More informationThis is an author produced version of Capacity bounds and estimates for the finite scatterers MIMO wireless channel.
This is an author produced version of Capacity bounds and estimates for the finite scatterers MIMO wireless channel. White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/653/ Article:
More informationMultiple Antennas. Mats Bengtsson, Björn Ottersten. Basic Transmission Schemes 1 September 8, Presentation Outline
Multiple Antennas Capacity and Basic Transmission Schemes Mats Bengtsson, Björn Ottersten Basic Transmission Schemes 1 September 8, 2005 Presentation Outline Channel capacity Some fine details and misconceptions
More informationMeasurement of Keyholes and Capacities in Multiple-Input Multiple-Output (MIMO) Channels
MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Measurement of Keyholes and Capacities in Multiple-Input Multiple-Output (MIMO) Channels Almers, P.; Tufvesson, F. TR23-4 August 23 Abstract
More information[2005] IEEE. Reprinted, with permission, from [Tang Zhongwei; Sanagavarapu Ananda, Experimental Investigation of Indoor MIMO Ricean Channel Capacity,
[2005] IEEE. Reprinted, with permission, from [Tang Zhongwei; Sanagavarapu Ananda, Experimental Investigation of Indoor MIMO Ricean Channel Capacity, IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, VOL.
More informationChannel Capacity Estimation in MIMO Systems Based on Water-Filling Algorithm
Channel Capacity Estimation in MIMO Systems Based on Water-Filling Algorithm 1 Ch.Srikanth, 2 B.Rajanna 1 PG SCHOLAR, 2 Assistant Professor Vaagdevi college of engineering. (warangal) ABSTRACT power than
More informationAntennas 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 informationEFFECT OF MUTUAL COUPLING ON CAPACITY OF MIMO WIRELESS CHANNELS IN HIGH SNR SCENARIO
Progress In Electromagnetics Research, PIER 65, 27 40, 2006 EFFECT OF MUTUAL COUPLING ON CAPACITY OF MIMO WIRELESS CHANNELS IN HIGH SNR SCENARIO A A Abouda and S G Häggman Helsinki University of Technology
More informationA New Approach to Layered Space-Time Code Design
A New Approach to Layered Space-Time Code Design Monika Agrawal Assistant Professor CARE, IIT Delhi maggarwal@care.iitd.ernet.in Tarun Pangti Software Engineer Samsung, Bangalore tarunpangti@yahoo.com
More informationThe Impact of Correlation on Multi-Antenna System Performance: Correlation Matrix Approach
he Impact of Correlation on Multi-Antenna System Performance: Correlation Matrix Approach S. Loya, A. Koui Department of Electrical Engineering, Ecole de echnologie Superieure 00, Notre-Dame St. West,
More informationJoint Transmit and Receive Multi-user MIMO Decomposition Approach for the Downlink of Multi-user MIMO Systems
Joint ransmit and Receive ulti-user IO Decomposition Approach for the Downlin of ulti-user IO Systems Ruly Lai-U Choi, ichel. Ivrlač, Ross D. urch, and Josef A. Nosse Department of Electrical and Electronic
More informationPerformance Study of MIMO-OFDM System in Rayleigh Fading Channel with QO-STB Coding Technique
e-issn 2455 1392 Volume 2 Issue 6, June 2016 pp. 190 197 Scientific Journal Impact Factor : 3.468 http://www.ijcter.com Performance Study of MIMO-OFDM System in Rayleigh Fading Channel with QO-STB Coding
More informationREALISTIC SPATIO-TEMPORAL CHANNEL MODEL FOR BROADBAND MIMO WLAN SYSTEMS EMPLOYING UNIFORM CIRCUILAR ANTENNA ARRAYS
REALISTIC SPATIO-TEMPORAL CHANNEL MODEL FOR BROADBAND MIMO WLAN SYSTEMS EMPLOYING UNIFORM CIRCUILAR ANTENNA ARRAYS M. A. Mangoud and Z. Mahdi Department of Electrical and Electronics Engineering, University
More informationSPLIT MLSE ADAPTIVE EQUALIZATION IN SEVERELY FADED RAYLEIGH MIMO CHANNELS
SPLIT MLSE ADAPTIVE EQUALIZATION IN SEVERELY FADED RAYLEIGH MIMO CHANNELS RASHMI SABNUAM GUPTA 1 & KANDARPA KUMAR SARMA 2 1 Department of Electronics and Communication Engineering, Tezpur University-784028,
More informationBER PERFORMANCE AND OPTIMUM TRAINING STRATEGY FOR UNCODED SIMO AND ALAMOUTI SPACE-TIME BLOCK CODES WITH MMSE CHANNEL ESTIMATION
BER PERFORMANCE AND OPTIMUM TRAINING STRATEGY FOR UNCODED SIMO AND ALAMOUTI SPACE-TIME BLOC CODES WITH MMSE CHANNEL ESTIMATION Lennert Jacobs, Frederik Van Cauter, Frederik Simoens and Marc Moeneclaey
More informationChannel 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 informationChannel 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 informationAnalysis 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 informationLecture 5: Antenna Diversity and MIMO Capacity Theoretical Foundations of Wireless Communications 1
Antenna, Antenna : Antenna and Theoretical Foundations of Wireless Communications 1 Friday, April 27, 2018 9:30-12:00, Kansliet plan 3 1 Textbook: D. Tse and P. Viswanath, Fundamentals of Wireless Communication
More informationPerformance of Closely Spaced Multiple Antennas for Terminal Applications
Performance of Closely Spaced Multiple Antennas for Terminal Applications Anders Derneryd, Jonas Fridén, Patrik Persson, Anders Stjernman Ericsson AB, Ericsson Research SE-417 56 Göteborg, Sweden {anders.derneryd,
More informationCompact Antenna Spacing in mmwave MIMO Systems Using Random Phase Precoding
Compact Antenna Spacing in mmwave MIMO Systems Using Random Phase Precoding G D Surabhi and A Chockalingam Department of ECE, Indian Institute of Science, Bangalore 56002 Abstract Presence of strong line
More informationHybrid ARQ Scheme with Antenna Permutation for MIMO Systems in Slow Fading Channels
Hybrid ARQ Scheme with Antenna Permutation for MIMO Systems in Slow Fading Channels Jianfeng Wang, Meizhen Tu, Kan Zheng, and Wenbo Wang School of Telecommunication Engineering, Beijing University of Posts
More informationModeling 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 informationAWGN Channel Performance Analysis of QO-STB Coded MIMO- OFDM System
AWGN Channel Performance Analysis of QO-STB Coded MIMO- OFDM System Pranil Mengane 1, Ajitsinh Jadhav 2 12 Department of Electronics & Telecommunication Engg, D.Y. Patil College of Engg & Tech, Kolhapur
More informationSIGNAL MODEL AND PARAMETER ESTIMATION FOR COLOCATED MIMO RADAR
SIGNAL MODEL AND PARAMETER ESTIMATION FOR COLOCATED MIMO RADAR Moein Ahmadi*, Kamal Mohamed-pour K.N. Toosi University of Technology, Iran.*moein@ee.kntu.ac.ir, kmpour@kntu.ac.ir Keywords: Multiple-input
More informationAdaptive MIMO Transmission Techniques for Broadband Wireless Communication Systems
TOPICS IN WIRELESS COMMUNICATIONS Adaptive MIMO Transmission Techniques for Broadband Wireless Communication Systems Chan-Byoung Chae, Bell Laboratories, Alcatel-Lucent Antonio Forenza, Rearden, LLC Robert
More informationIEEE Antennas and Wireless Propagation Letters 13 (2014) pp
This document is published in: IEEE Antennas and Wireless Propagation Letters 13 (2014) pp. 1309-1312 DOI: 10.1109/LAWP.2014.2336174 2014 IEEE. Personal use of this material is permitted. Permission from
More informationBeamforming with Imperfect CSI
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the WCNC 007 proceedings Beamforming with Imperfect CSI Ye (Geoffrey) Li
More informationSpace Time Coding over Correlated Fading Channels with Antenna Selection
Space Time Coding over Correlated Fading Channels with Antenna Selection İsrafil Bahçeci,Yücel Altunbaşak and Tolga M. Duman School of Electrical and Computer Engineering Department of Electrical Engineering
More informationNumber of Multipath Clusters in. Indoor MIMO Propagation Environments
Number of Multipath Clusters in Indoor MIMO Propagation Environments Nicolai Czink, Markus Herdin, Hüseyin Özcelik, Ernst Bonek Abstract: An essential parameter of physical, propagation based MIMO channel
More informationIN RECENT years, wireless multiple-input multiple-output
1936 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 3, NO. 6, NOVEMBER 2004 On Strategies of Multiuser MIMO Transmit Signal Processing Ruly Lai-U Choi, Michel T. Ivrlač, Ross D. Murch, and Wolfgang
More informationPerformance Analysis of Multiuser MIMO Systems with Scheduling and Antenna Selection
Performance Analysis of Multiuser MIMO Systems with Scheduling and Antenna Selection Mohammad Torabi Wessam Ajib David Haccoun Dept. of Electrical Engineering Dept. of Computer Science Dept. of Electrical
More informationOn limits of Wireless Communications in a Fading Environment: a General Parameterization Quantifying Performance in Fading Channel
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol. 2, No. 3, September 2014, pp. 125~131 ISSN: 2089-3272 125 On limits of Wireless Communications in a Fading Environment: a General
More informationProportional Fair Scheduling for Wireless Communication with Multiple Transmit and Receive Antennas 1
Proportional Fair Scheduling for Wireless Communication with Multiple Transmit and Receive Antennas Taewon Park, Oh-Soon Shin, and Kwang Bok (Ed) Lee School of Electrical Engineering and Computer Science
More informationHybrid Transceivers for Massive MIMO - Some Recent Results
IEEE Globecom, Dec. 2015 for Massive MIMO - Some Recent Results Andreas F. Molisch Wireless Devices and Systems (WiDeS) Group Communication Sciences Institute University of Southern California (USC) 1
More informationCapacity of Multi-Antenna Array Systems for HVAC ducts
Capacity of Multi-Antenna Array Systems for HVAC ducts A.G. Cepni, D.D. Stancil, A.E. Xhafa, B. Henty, P.V. Nikitin, O.K. Tonguz, and D. Brodtkorb Carnegie Mellon University, Department of Electrical and
More informationMIMO Channel Modeling and Capacity Analysis for 5G Millimeter-Wave Wireless Systems
M. K. Samimi, S. Sun, T. S. Rappaport, MIMO Channel Modeling and Capacity Analysis for 5G Millimeter-Wave Wireless Systems, in the 0 th European Conference on Antennas and Propagation (EuCAP 206), April
More informationAntennas 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 informationAn HARQ scheme with antenna switching for V-BLAST system
An HARQ scheme with antenna switching for V-BLAST system Bonghoe Kim* and Donghee Shim* *Standardization & System Research Gr., Mobile Communication Technology Research LAB., LG Electronics Inc., 533,
More informationMultiple 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 informationAdaptive selection of antenna grouping and beamforming for MIMO systems
RESEARCH Open Access Adaptive selection of antenna grouping and beamforming for MIMO systems Kyungchul Kim, Kyungjun Ko and Jungwoo Lee * Abstract Antenna grouping algorithms are hybrids of transmit beamforming
More informationStudy of Performance Evaluation of Quasi Orthogonal Space Time Block Code MIMO-OFDM System in Rician Channel for Different Modulation Schemes
Volume 4, Issue 6, June (016) Study of Performance Evaluation of Quasi Orthogonal Space Time Block Code MIMO-OFDM System in Rician Channel for Different Modulation Schemes Pranil S Mengane D. Y. Patil
More information"Communications in wireless MIMO channels: Channel models, baseband algorithms, and system design"
Postgraduate course on "Communications in wireless MIMO channels: Channel models, baseband algorithms, and system design" Lectures given by Prof. Markku Juntti, University of Oulu Prof. Tadashi Matsumoto,
More informationComparative Channel Capacity Analysis of a MIMO Rayleigh Fading Channel with Different Antenna Spacing and Number of Nodes
Comparative Channel Capacity Analysis of a MIMO Rayleigh Fading Channel with Different Antenna Spacing and Number of Nodes Anand Jain 1, Kapil Kumawat, Harish Maheshwari 3 1 Scholar, M. Tech., Digital
More informationPerformance Analysis of Maximum Likelihood Detection in a MIMO Antenna System
IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 50, NO. 2, FEBRUARY 2002 187 Performance Analysis of Maximum Likelihood Detection in a MIMO Antenna System Xu Zhu Ross D. Murch, Senior Member, IEEE Abstract In
More informationUniversity of Bristol - Explore Bristol Research. Peer reviewed version. Link to published version (if available): /ICCE.2012.
Zhu, X., Doufexi, A., & Koçak, T. (2012). A performance enhancement for 60 GHz wireless indoor applications. In ICCE 2012, Las Vegas Institute of Electrical and Electronics Engineers (IEEE). DOI: 10.1109/ICCE.2012.6161865
More informationarray (URA) and uniform cubic array (UCuA), with eight elements at both ends and fixed inter-element spacing. The
Impact of Antenna Array Geometry on MIMO Channel Bigenvalues A.A. Abouda, H.M. El-Sallabi and S.G. Haggman Helsinki University of Technology P.O.Box 3000, FIN-02015 HUT, Finland {abouda, hsallabi, sgh}@cc.hut.fi
More informationINVESTIGATION OF CAPACITY GAINS IN MIMO CORRELATED RICIAN FADING CHANNELS SYSTEMS
INVESTIGATION OF CAPACITY GAINS IN MIMO CORRELATED RICIAN FADING CHANNELS SYSTEMS NIRAV D PATEL 1, VIJAY K. PATEL 2 & DHARMESH SHAH 3 1&2 UVPCE, Ganpat University, 3 LCIT,Bhandu E-mail: Nirav12_02_1988@yahoo.com
More informationMinimum BER Transmit Optimization for Two-Input Multiple-Output Spatial Multiplexing
Minimum BER Transmit Optimization for Two-Input Multiple-Output Spatial Multiplexing Neng Wang and Steven D. Blostein Department of Electrical and Computer Engineering Queen s University, Kingston, Ontario,
More informationRandom Beamforming in Correlated MISO Channels for Multiuser Systems
Random Beamforming in Correlated MISO Channels for Multiuser Systems Andreas Senst, Peter Schulz-Rittich, Ulrich Krause, Gerd Ascheid, and Heinrich Meyr Institute for Integrated Signal Processing Systems
More informationTransmit Antenna Selection in Linear Receivers: a Geometrical Approach
Transmit Antenna Selection in Linear Receivers: a Geometrical Approach I. Berenguer, X. Wang and I.J. Wassell Abstract: We consider transmit antenna subset selection in spatial multiplexing systems. In
More informationDiversity Techniques
Diversity Techniques Vasileios Papoutsis Wireless Telecommunication Laboratory Department of Electrical and Computer Engineering University of Patras Patras, Greece No.1 Outline Introduction Diversity
More informationEigenvalues and Eigenvectors in Array Antennas. Optimization of Array Antennas for High Performance. Self-introduction
Short Course @ISAP2010 in MACAO Eigenvalues and Eigenvectors in Array Antennas Optimization of Array Antennas for High Performance Nobuyoshi Kikuma Nagoya Institute of Technology, Japan 1 Self-introduction
More informationAdvances 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 informationInternational Journal of Advance Engineering and Research Development. Channel Estimation for MIMO based-polar Codes
Scientific Journal of Impact Factor (SJIF): 4.72 International Journal of Advance Engineering and Research Development Volume 5, Issue 01, January -2018 Channel Estimation for MIMO based-polar Codes 1
More informationAnalysis of massive MIMO networks using stochastic geometry
Analysis of massive MIMO networks using stochastic geometry Tianyang Bai and Robert W. Heath Jr. Wireless Networking and Communications Group Department of Electrical and Computer Engineering The University
More informationPerformance Comparison of Downlink User Multiplexing Schemes in IEEE ac: Multi-User MIMO vs. Frame Aggregation
2012 IEEE Wireless Communications and Networking Conference: MAC and Cross-Layer Design Performance Comparison of Downlink User Multiplexing Schemes in IEEE 80211ac: Multi-User MIMO vs Frame Aggregation
More informationIMPROVED QR AIDED DETECTION UNDER CHANNEL ESTIMATION ERROR CONDITION
IMPROVED QR AIDED DETECTION UNDER CHANNEL ESTIMATION ERROR CONDITION Jigyasha Shrivastava, Sanjay Khadagade, and Sumit Gupta Department of Electronics and Communications Engineering, Oriental College of
More informationDetecting the Number of Transmit Antennas with Unauthorized or Cognitive Receivers in MIMO Systems
Detecting the Number of Transmit Antennas with Unauthorized or Cognitive Receivers in MIMO Systems Oren Somekh, Osvaldo Simeone, Yeheskel Bar-Ness,andWeiSu CWCSPR, Department of Electrical and Computer
More informationPerformance of wireless Communication Systems with imperfect CSI
Pedagogy lecture Performance of wireless Communication Systems with imperfect CSI Yogesh Trivedi Associate Prof. Department of Electronics and Communication Engineering Institute of Technology Nirma University
More informationCapacity Evaluation of an Indoor Wireless Channel at 60 GHz Utilizing Uniform Rectangular Arrays
Capacity Evaluation of an Indoor Wireless Channel at 60 GHz Utilizing Uniform Rectangular Arrays NEKTARIOS MORAITIS 1, DIMITRIOS DRES 1, ODYSSEAS PYROVOLAKIS 2 1 National Technical University of Athens,
More informationOn Limits of Multi-Antenna. Wireless Communications in. Spatially Selective Channels
On Limits of Multi-Antenna Wireless Communications in Spatially Selective Channels Tony Steven Pollock B.E.(Hons 1) (Canterbury) B.Sc. (Otago) July 2003 A thesis submitted for the degree of Doctor of Philosophy
More informationLecture 4 Diversity and MIMO Communications
MIMO Communication Systems Lecture 4 Diversity and MIMO Communications Prof. Chun-Hung Liu Dept. of Electrical and Computer Engineering National Chiao Tung University Spring 2017 1 Outline Diversity Techniques
More informationAnalysis of Space-Time Block Coded Spatial Modulation in Correlated Rayleigh and Rician Fading Channels
Analysis of Space-Time Block Coded Spatial Modulation in Correlated Rayleigh and Rician Fading Channels B Kumbhani, V K Mohandas, R P Singh, S Kabra and R S Kshetrimayum Department of Electronics and Electrical
More informationOn the Value of Coherent and Coordinated Multi-point Transmission
On the Value of Coherent and Coordinated Multi-point Transmission Antti Tölli, Harri Pennanen and Petri Komulainen atolli@ee.oulu.fi Centre for Wireless Communications University of Oulu December 4, 2008
More informationDFT-Based Hybrid Antenna Selection Schemes for Spatially Correlated MIMO Channels
MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com DFT-Based Hybrid Antenna Selection Schemes for Spatially Correlated MIMO Channels Zhang, X.; Kung, S.Y. TR23-7 October 23 Abstract We address
More informationExam 3 is two weeks from today. Today s is the final lecture that will be included on the exam.
ECE 5325/6325: Wireless Communication Systems Lecture Notes, Spring 2010 Lecture 19 Today: (1) Diversity Exam 3 is two weeks from today. Today s is the final lecture that will be included on the exam.
More informationUniversity of Bristol - Explore Bristol Research. Link to published version (if available): /VTCF
Han, C., Armour, S. M. D., Doufexi, A., Ng, K. H., & McGeehan, J. P. (26). Link adaptation performance evaluation for a MIMO-OFDM physical layer in a realistic outdoor environment. In IEEE 64th Vehicular
More informationRANDOM SAMPLE ANTENNA SELECTION WITH ANTENNA SWAPPING
RANDOM SAMPLE ANTENNA SELECTION WITH ANTENNA SWAPPING by Edmund Chun Yue Tam A thesis submitted to the Department of Electrical and Computer Engineering in conformity with the requirements for the degree
More informationChannel Capacity of TDD OFDM MIMO for Multiple Access Points in a Wireless Single Frequency Network
hannel apacity of T OFM MIMO for Multiple ccess Points in a Wireless Single Frequency Network Y. Takatori NTT Network Innovation Laboratories, (yt@kom.aau.dk) F. Fitzek enter for TeleInFrastructure (TIF),
More informationInterpolation Based Transmit Beamforming. for MIMO-OFDM with Partial Feedback
Interpolation Based Transmit Beamforming for MIMO-OFDM with Partial Feedback Jihoon Choi and Robert W. Heath, Jr. The University of Texas at Austin Department of Electrical and Computer Engineering Wireless
More informationUniversity of Bristol - Explore Bristol Research. Peer reviewed version. Link to published version (if available): /VETECS.2004.
Webb, MW, Beach, MA, & Nix, AR (24) Capacity limits of MIMO channels with co-channel interference IEEE 9th Vehicular Technology Conference, 24 (VTC 24-Spring), 2, 73-77 DOI: 19/VETECS241388919 Peer reviewed
More informationAntenna Selection with RF Pre-Processing: Robustness to RF and Selection Non-Idealities
MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Antenna Selection with RF Pre-Processing: Robustness to RF and Selection Non-Idealities Pallav Sudarshan, Neelesh B. Mehta, Andreas F. Molisch
More informationBy choosing to view this document, you agree to all provisions of the copyright laws protecting it.
This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of elsinki University of Technology's products or services. Internal
More informationAmplitude and Phase Distortions in MIMO and Diversity Systems
Amplitude and Phase Distortions in MIMO and Diversity Systems Christiane Kuhnert, Gerd Saala, Christian Waldschmidt, Werner Wiesbeck Institut für Höchstfrequenztechnik und Elektronik (IHE) Universität
More informationARQ strategies for MIMO eigenmode transmission with adaptive modulation and coding
ARQ strategies for MIMO eigenmode transmission with adaptive modulation and coding Elisabeth de Carvalho and Petar Popovski Aalborg University, Niels Jernes Vej 2 9220 Aalborg, Denmark email: {edc,petarp}@es.aau.dk
More informationSpatial Limits to MIMO Capacity in General Scattering Environments
Spatial Limits to MIMO Capacity in General Scattering Environments Tony S. Pollock, Thushara D. Abhayapala and Rodney A. Kennedy National ICT Australia Locked Bag 81 Canberra ACT 261, Australia tony.pollock@nicta.com.au
More informationMIMO Capacity in a Pedestrian Passageway Tunnel Excited by an Outside Antenna
MIMO Capacity in a Pedestrian Passageway Tunnel Excited by an Outside Antenna J. M. MOLINA-GARCIA-PARDO*, M. LIENARD**, P. DEGAUQUE**, L. JUAN-LLACER* * Dept. Techno. Info. and Commun. Universidad Politecnica
More information