UNIVERSITY OF CALGARY. Distributed Energy Minimization in Heterogeneous Cellular Networks. Seyedmohammad Naghibi A THESIS

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1 UNIVERSITY OF CALGARY Distributed Energy Minimization in Heterogeneous Cellular Networks by Seyedmohammad Naghibi A THESIS SUBMITTED TO THE FACULTY OF GRADUATE STUDIES IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE DEPARTMENT OF COMPUTER SCIENCE CALGARY, ALBERTA November, 2015 Seyedmohammad Naghibi 2015

2 UNIVERSITY OF CALGARY FACULTY OF GRADUATE STUDIES The undersigned certify that they have read, and recommend to the Faculty of Graduate Studies for acceptance, a thesis entitled Distributed Energy Minimization in Heterogeneous Cellular Networks submitted by Seyedmohammad Naghibi in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE. Supervisor, Dr. Majid Ghaderi Department of Computer Science Dr. Carey Williamson Department of Computer Science Dr. Geoffrey Messier Department of Electrical and Computer Engineering Date

3 Abstract Heterogeneous networks are designed to increase the capacity for cellular data traffic. Selforganization is a key element of heterogeneous cellular networks. In this thesis, we present a randomized algorithm that addresses two challenges in HetNets, namely energy saving and throughput maximization, in a self-organizing manner. More specifically, the proposed algorithm seeks to maximize an objective function that balances the trade-off between the downlink bit rate of users, and the energy consumption of base stations. To achieve this goal, we deactivate under-utilized picocells to save energy, and adjust low-power Almost Blank Subframes to utilize the frequency spectrum and minimize the interference between macrocells and picocells. An important feature of our algorithm is its distributed design, which eliminates the need for a central device to coordinate the base stations. In fact, the base stations directly interact with each other in a locally defined neighborhood to drive the system toward the optimal state. ii

4 Table of Contents Abstract ii Table of Contents iii List of Tables v List of Figures vi List of Symbols vii 1 Introduction Motivation Objectives Solution Optimality Adaptation to Modern Networks Pico-cell Protection Contributions Organization Background and Related Work User Association Static Association Methods Load-Aware Association Methods Interference Management Interference Types Interference Coordination Interference Management in LTE Self-Organizing Networks Related Works on Self-Organization User Association Interference Coordination Gibbs Sampling Based Methods Optimization Techniques Convex Optimization Discrete Optimization Basic Definitions Gibbs Sampling Simulated Annealing Sample Usage Methodology Overview System Model General Assumptions Network Topology Frame Structure User Rates BS Power Consumption Optimization Objective iii

5 4.3 Solution Overview Gibbs Sampler Distributed Algorithm Simulation Results Simulation Environment Simulation Parameters Implementation Remarks States of BSs After Convergence Energy-Throughput Trade-off Rate Utility Function Sleep Mode and ABS Subframes Dense Deployment of Pico-BSs Numerical Analysis of Convergence Temperature Function Update Rate Initial Temperature Duration Conclusion Thesis Summary Future Work Bibliography iv

6 List of Tables 4.1 Notations of the model Simulation parameters Global performance measures Statistics of macro-bss Statistics of pico-bss Effect of the energy cost on the converged state Effect of rate utility function on user throughputs Effect of ABS subframes and pico-bs sleep mode on final states Effect of ABS subframes and pico-bs sleep mode on global objective Global objective of grid of pico-bss Effect of the temperature function on convergence Effect of update rate on convergence of the system Effect of the initial temperature on convergence Effect of duration of Gibbs Sampling on grid on pico-bss Effect of duration of Gibbs Sampling on the small HetNet v

7 List of Figures and Illustrations 1.1 A heterogeneous network G network architecture G network architecture Cellular user association Different types of interference Reuse patterns Interference coordination using both frequency and time domains Variable power allocation Almost blank subframe Reduced power ABS subframes A convex function Monte Carlo integration Markov chain representation of a random field A sample network Tow-tier neighborhood graph Frames and subframes Frame structure of a macro-bs Frame structure of a pico-bs Multiple macro-bss interfering with a user Interference on a user during a frame Interaction graph neighborhood The simulated HetNet scenario Neighborhood system The network after convergence Throughput vs. energy State of the network with sum rate function Effect of ABS subframes and pico-bs sleep mode on objective Global objective of grid of pico-bss Dense deployment of pico-bss Neighborhood graph of the pico-bss Hibernating every other BS in the pico-bs grid Optimal state of pico-bss Convergence of logarithmic temperature function Convergence of linear temperature function Convergence of quadratic temperature function Effect of duration of the algorithm on global objective vi

8 List of Symbols, Abbreviations and Nomenclature Symbol 3GPP ABS ANR AP BS CSB CSG eicic enb feicic ICIC LTE MCMC MRF NRT PPP RF RNC SINR SON UE WLAN Definition 3rd Generation Partnership Project Almost Blank Subframe Automatic Neighbor Relation Access Point Base Station Cell Selection Bias Closed Subscriber Group Enhanced Inter-Cell Interference Coordination Evolved Node B Further Enhanced Inter-Cell Interference Coordination Inter-Cell Interference Coordination Long Term Evolution Markov Chain Monte Carlo Markov Random Field Neighbor Relation Table Poisson Point Process Radio Frequency Radio Network Controller Signal to Interference and Noise Ratio Self-Organizing Network User Equipment Wireless Local Area Network vii

9 Chapter 1 Introduction 1.1 Motivation Cellular data traffic has recently seen a rapid growth due to the proliferation of data-enabled mobile devices such as smartphones, tablets, and cellular modems. In 2014, 800 million smartphone subscriptions were added, and by the end of 2020, it is expected that 5.4 billion mobile broadband subscriptions will be added worldwide [6]. Mobile data traffic also grew remarkably fast and mobile networks carried nearly 30 exabytes of traffic in 2014, almost 30 times the size of the entire global Internet in Global mobile traffic will surpass 290 exabytes by 2019 [22], considering the increasing demand for online video streaming, video calls, and cloud-based services. Heterogeneous networks (HetNets) are introduced as a solution to cope with the everrising data demand, especially at cell edges and indoor environments, where about 70 percent of today s data traffic is generated [5]. In HetNets, in addition to the traditional macrocells, low-power small-cells are added to extend service coverage, with ranges from 10 meters to a few kilometers [1]. The term picocell refers to small cells typically with ranges from a few hundred meters to two kilometers, which are deployed to improve coverage in places where the macrocell signal is weak (Figure 1.1). Femto-cells are small-cells designed for use in a home or office building, with ranges in the order of 10 meters. As opposed to picocells, femtocells usually require subscription and do not serve public users. The faster data speeds of HetNets come at the expense of network management complexity. With many more cells to manage, it is cumbersome and inefficient to manually set up and optimize the network. Automatic and intelligent ways are preferable to configure and optimize network parameters, such as channel allocation, interference coordination, and 1

10 Figure 1.1: A simple HetNet. Traditional macrocells are served by high-power base stations, usually mounted on ground-based masts. Picocells provide coverage for smaller areas such as buildings or small neighborhoods. Femtocells are designed for a home or small office. power control. The ability of a network to organize itself and optimize its own parameters with minimal human effort is known as self-organization. This ability is considered to be one of the key elements of future heterogeneous cellular networks [62]. Another challenge arising from the growth of cellular networks is the energy consumption of base stations. By the end of 2017, according to [14], more than 3000 small cells are needed to support a dense traffic in a city just over 33 km 2. Having too many unsupervised picocells is shown to reduce the overall efficiency of the network [60]. Utilization of base stations in dense urban environments fluctuates throughout the day. According to one report, the traffic load at 6 a.m. is less than 20% of the peak rate at 10 p.m. [30]. Subsequently, the number of base stations needed to satisfy user demand varies over time. Actually, picocells have a built-in capability to enter sleep mode to save energy when their presence is not necessary [12]. 2

11 1.2 Objectives With the above points in mind, this work addresses two challenges in HetNets, namely energy saving and throughput maximization, in a self-organizing fashion. We consider a heterogeneous network composed of macrocells and picocells. Pico base stations are capable of entering sleep mode when the demand is low according to the network policies. In active mode, pico base stations transmit at their maximum power, while in sleep mode they do not serve any user. Macro base stations, on the other hand, are always active. However, they are capable of transmitting at various power levels to mitigate the interference on other cells and lower their energy consumption. In general, having more active picocells and high-power macrocell signals leads to a higher network throughput. The downside is that this throughput enhancement increases the energy consumption of the network. We define an objective function that balances the trade-off between network throughput and energy consumption of BSs. We then develop a solution to maximize this function to find the optimal state of the network. In particular, we determine which picocells must remain active and which ones should enter sleep mode to find the optimal balance. Moreover, we adjust the transmission power of macro base stations to mitigate the interference on picocell users and maximize the objective function. In the following, we express the desirable features of our solution Solution Optimality The problems of user association and inter-cell interference coordination are generally nonconvex [39, 59, 69]. Such optimizations usually involve solving an NP-hard integer program and therefore an efficient solution to find the optimal answer cannot be found. One approach to get around this issue is relaxing the constraints and transforming the problem into a convex optimization [72]. Sometimes, another step is taken consisting of using the output of a convex optimization problem as an intermediate result and converting it to a feasible solution for 3

12 the original integer problem [29]. Others apply heuristic algorithms hoping that reasonably good results can be found. We seek to address these limitations by using a method that is mathematically proven to give the optimal answer Adaptation to Modern Networks One of the strategies to facilitate network expansion is to simplify the network structure. Recently, some standalone entities pertaining to previous generations of cellular networks have been removed, with their functionality merged into other existing entities. As a result, there are fewer devices, but they are more complex and sophisticated. For instance, Radio Network Controller (RNC) is a device belonging to 3rd Generation cellular networks that is responsible for controlling a group of base stations (or NodeBs, in 3G terminology) [9]. Its functionality includes radio resource management and mobility management, such as managing the handover process when User Equipment (UE) moves from one cell to another (Figure 1.2). LTE networks eliminate the need for RNC, and embed its functionality in more complicated base stations, known as evolved-nodebs [7]. In order for enodebs to carry out these responsibilities, they should be able to communicate with each other directly, without a central controller. For this purpose, the X2 interface is introduced in enbs to convey messages directly (Figure 1.3). This makes it possible to deploy new base stations with fewer worries, e.g. having to deal with RNCs. This architecture encourages network protocol designers to devise distributed network management schemes. We, too, are inspired to exploit the new features in LTE networks, one of which is the aforementioned enb-to-enb link Pico-cell Protection Deploying both macrocells and small-cells in a heterogeneous network requires some considerations to be made. Strong signals from adjacent macro base stations may overpower small base station signals received at small-cell users and make them experience degraded 4

13 Figure 1.2: Overall view of a 3G mobile network. The Core Network is the central part of the system that routes data and telephone calls and provides various other services to customers. Radio Access Network resides between user devices and the core network. NodeBs are connected to the core network through Radio Network Controllers. Each RNC controls a number of NodeBs and performs radio resource management and mobility management. NodeBs cannot communicate directly with each other. service quality. In order for macro and small cells to coexist peacefully, the concept of Almost Blank Subframes (ABS) is introduced. The idea of ABS subframes is to dedicate a portion of each radio frame for small-bs transmissions, and have the macro-bss fully (or partially) restrained from transmitting data signals during those subframes to reduce the interference on small-cell users. The ratio of ABS to non-abs subframes depends on the network policy, and is studied in the literature [29, 72]. Some of proposed algorithms (for example [72]) synchronize all the base stations and assign a network-wide ratio to all of them. Assigning a single ratio to every base station in the network is inefficient, and may cause some macrocells to underutilize their spectrum, and others to interfere overwhelmingly with their neighboring small-cells. As opposed to this global configuration, we aim to adjust this parameter on a per base station basis. That means, we let each BS individually decide how many ABS subframes are required, based on the users and BSs in its local neighborhood. 5

14 Figure 1.3: Overall view of a 4G mobile network. Evolved-NodeBs are connected to the core network using S1 interface. They can also directly communicate with each other through X2 interface. This interface can be used by enodebs to share information in a local neighborhood. 1.3 Contributions In this thesis, we develop and evaluate a distributed algorithm to efficiently balance the trade-off between network throughput and energy consumption of base stations in a heterogeneous cellular network. Energy saving is primarily achieved by putting under-utilized pico base stations into sleep mode. The proposed method is based on the framework in [15], and uses Gibbs Sampling which is analytically proven to drive the network to the optimal state, in which the desired throughput-energy balance is obtained. In our protocol, base stations work in a self-organizing manner to find the optimal network configuration. There is no need for a central management entity to find the optimal state, and base stations only need to exchange information and measurements in a locally defined neighborhood in order to reach the globally optimal state in a distributed fashion. More specifically, our algorithm: 6

15 determines which base stations should be in sleep mode to minimize the energy consumption without having significant throughput loss. finds the optimal ratio of ABS subframes for each macro base station, to lessen the interference on adjacent picocells. assigns to each macro-bs the optimal RF output power level during ABS subframes in order to avoid wasting resources. determines the ratio of subframes that ought to be allocated to each user of a base station. We simulate the proposed algorithm on two different network topologies, study the effect of each parameter, and report the findings through graphs and tables. The results show that our algorithm provides considerable throughput enhancement and energy savings. To the best of our knowledge, this is the first work that incorporates Gibbs Sampling to dynamically regulate pico-bss and adjust ABS subframes in HetNets. 1.4 Organization The rest of this thesis is organized as follows. Chapter 2 reviews the background and related work in cellular networks. Static user association based on Reference Signal Received Power is described, and its ineffectiveness in HetNets is demonstrated. Next, load-aware user association schemes are discussed as a solution to best utilize the available frequency spectrum, and standard mechanisms to implement smart user association are studied. Then we enumerate different scenarios in which interference can degrade the service quality experienced by mobile users in heterogeneous cellular networks. We illustrate how the emergence of picocells and femtocells has introduced new interference scenarios that did not exist in traditional homogeneous networks. Then we explain interference coordination methods that 7

16 are currently employed. We depict how time domain, frequency domain, and power allocation techniques can be exercised to share the same spectrum by multiple neighboring cells. Specifically, we demonstrate how Almost Blank Subframes can alleviate the interference between neighboring macrocells and picocells using time domain and power allocation adjustments. Different types of self-organizing schemes, including centralized and distributed methods, are also reviewed, and their strengths and weaknesses are discussed. Chapter 3 explains the optimization techniques used in the proposed method. Our method aims to solve two types of optimization problems: A group of convex optimization problems are solved locally at each base station, and the results are exchanged in a neighborhood of base stations to distributedly solve a larger non-convex combinatorial optimization problem. In this chapter, we begin with defining convex optimization problems, and briefly reviewing the techniques that are used to solve such problems efficiently. Then we scrutinize Gibbs Sampling as the basis of our algorithm, which is used to solve the nonconvex combinatorial problem. In order to explain Gibbs Sampling, we first review some essential concepts such as Monte Carlo method, Markov chains, and Markov random fields. By showing the equivalence of Gibbs fields and Markov random fields, we explain how a Markov field defined over a neighborhood system can be randomly sampled to converge to its steady state. We also demonstrate why the information of two-tier neighbors is needed for each node of the Markov field to generate a new sample. At the end of Chapter 3, an example is given on how to apply Gibbs Sampling to solve network optimization problems in a distributed manner, regardless of their convexity. We describe the proposed method in Chapter 4. In this chapter, we first specify our assumptions. Then we define the signal transmission model at macrocells and picocells. Next, we describe how users are associated with base stations, and calculate the signal to interference and noise ratio (SINR) of users during different subframes, which is used to formulate user throughputs. The power consumption of macro and pico base stations 8

17 is also modeled in both active mode and sleep mode. Using these models, we define the objective function that balances the trade-off between transmission rates of users and power consumption of base stations, and formulate the optimization problem and its constraints. Finally, an iterative Gibbs Sampling based algorithm is proposed to distributedly solve the problem by randomly sampling from a distribution that converges to the optimal state. Numerical results are evaluated in Chapter 5, and the effect of each parameter of the algorithm on the objective function is analyzed. This chapter shows how the total transmission rate of the system can be improved by adjusting the duration and power of ABS subframes. We also examine the energy saving that can be achieved by deactivating picocells, and evaluate how this power consumption reduction affects the objective function. Finally, we study the convergence of the proposed algorithm and investigate how different parameters can determine the speed and accuracy of the convergence. Chapter 6 concludes the thesis, and discusses limitations of the simulations presented in this work. At the end, several areas for future work are suggested. 9

18 Chapter 2 Background and Related Work In this chapter, we explain some of the challenges in resource management of cellular networks, and review standards and proposed solutions to overcome them. In Section 2.1, we discuss different approaches to associate users to base stations in cellular networks. Section 2.2 describes different scenarios where interference can impair user bit rates in heterogeneous networks, and reviews approaches to address this problem. Section 2.3 discusses the importance of self-organization in cellular networks, and revisits three architectural types of self-organizing networks. At the end of this chapter, a review of the literature on the discussed topics is provided in Section User Association In this section, we look at the methods for how mobile users are associated with base stations in cellular networks Static Association Methods The most intuitive way to associate a user to a base station is to assess the received signal strength value. In this mode, which is widely used in pre-lte cellular networks, each UE listens to all reference signals coming from base stations and chooses the one with the strongest value (Figure 2.1a). This simple scheme requires no cooperation with BSs and only depends on the cell transmit power and the channel between the BS and the user. In this approach, user-i selects a base station BS-i using the following relation: BS i = arg max(p j H ij ) j B 10

19 where B is the set of base-stations, P j is the transmit power of BS-j, and H ij is the channel loss between user-i and BS-j. Connecting to a base station purely based on the received signal strength might be a reasonable choice for homogeneous cellular networks. Since all of the BSs in these types of networks have more or less the same level of transmit power, choosing the strongest signal implicitly balances the network load over all BSs, assuming the base stations are located according to the expected user distribution pattern. Other basic and load-independent association mechanisms have also been proposed. In [36], a Picocell First scheme is suggested to bring BSs closer to users and offload more data to picocells. In this scheme, a UE connects to the strongest picocell, provided that this strongest picocell signal is above a certain threshold, which is a tunable parameter. If the signals coming from all of the picocells are too weak and do not meet the minimum required quality, then the user equipment has to connect to a macrocell Load-Aware Association Methods Range Expansion In heterogeneous networks, in contrast to homogeneous networks, relying only on the received power can result in a poorly balanced network. Although the density of picocells is usually higher than macrocells in any covered area, maximum transmit power of picocells is considerably less than that of macrocells. Considering the free space radio wave propagation model, in which received power is inversely related to the square of the distance, small-cell transmissions are overpowered by macrocell signals as the distance increases from the smallcell. Therefore, except for the areas very close to the small-cells, macrocell transmissions are dominant. This stronger power tempts most of the UEs to connect to the bigger and geographically farther BSs, hence extremely increasing the traffic load on sparse macrocells and leaving the small-cells underutilized. This is obviously inefficient, and wastes resources. It also contradicts one of the important rationales for implementing heterogeneous networks, 11

20 (a) Without range expansion (b) With range expansion Figure 2.1: User association. The light blue area is added to the range of the picocell after range expansion. which is offloading the macrocell traffic to small-cells when possible. To overcome this problem, the concept of range expansion is introduced by 3GPP [53]. In this mechanism, UEs tend to favor small-cells over macrocells in spite of their lower received power. Particularly, instead of just comparing the received signal power, each base station is associated with a Cell Selection Bias (CSB). When deciding which base station to connect to, UEs calculate the sum of signal power and cell selection bias, and connect to the cell that yields the maximum value: BS i = arg max(p j H ij + α j ) j B where α j is the cell selection bias for BS-j. Using this technique, some of the users that would have connected to a macrocell without considering α j are now biased toward associating to a small-cell. This can be interpreted as if the range of the small-cell was expanded to cover a wider area (Figure 2.1b). Modern cellular networks tend to dynamically balance the load on base stations according to the distribution of user traffic over the geographical area. This can be achieved, for example, by means of cell selection bias. The network can dynamically monitor the load and assign higher CSB values to under-utilized cells in order for them to attract mobile users, and at the same time reduce the CSB value for overcrowded cells to offload some of their users 12

21 to adjacent cells. In Section 2.4, we review some of the proposed load-aware cell association techniques. 2.2 Interference Management A mobile user can usually detect signals from multiple base stations on the same frequency channel. While one of them provides useful data, the other ones (from BSs not associated with the user) add up destructively and interfere with the main signal Interference Types Figure 2.2 illustrates 3 different scenarios in which interference can severely damage the wireless link quality of a UE. Cell Edges In the first case, the user is located in the boundary region of two neighboring cells (Figure 2.2a). Therefore, the signal from both BSs is weak, and the user can connect to either of the BSs without any significant advantage. In this area, the user receives the weakest signal, while experiencing the strongest interference, because moving in either direction makes one of the signals stronger and the other one weaker. This makes the signal to interference and noise ratio (SINR), and consequently the user throughput, very low. The user in Figure 2.2a is called a cell-edge user. This type of interference is common between heterogeneous and homogeneous networks, and does not happen to users in a short distance from base stations. Macro Pico Interference HetNets introduce other interference types that did not exist in homogeneous networks. As can be seen in Figure 2.2b, the UE is not at the edge of the macrocell. This user is associated to the picocell, either because it is getting a stronger signal from it, or because of 13

22 the scheduling policy of the network to offload macrocell user to small cells. As the range of the cells implies, the macro-bs signal is still powerful enough to damage the signal quality of the user. In fact, the user could have been connected to the macrocell if the pico-bs had not been deployed there. So again the interference is significant, and the throughput is degraded. This means that picocell users have to be protected from macro-bss. Especially, it gets worse when the user is connected to the picocell due to range expansion. In this case, the macro-bs has a higher signal level, and SINR of the user is remarkably low, which renders the channel almost useless for the user if it is not handled properly. Note that this type of interference does not happen in homogeneous networks, where there is at most one powerful and dominant signal at any point. Macro Femto Interference Another type of interference can also occur when a macrocell and a small-cell are involved [34]. Unlike the previous type, this one happens when there is a femtocell, instead of a picocell, and works in a similar but opposite fashion. A pico-bs, like macro-bss, accepts association requests from all customers of a cellular provider, and its purpose is to cover crowded areas, blind spots or macrocell edges. Femto-BSs, on the other hand, work in a private manner, and only serve authorized users registered in a Closed Subscription Group (CSG). They are also smaller in size, and designed to provide cellular access to a limited number of users, like users in a small building. Non-registered users, no matter how close, are denied by the femto-bs and their association requests get rejected. As a result, they have to connect to a macro (or pico) base station, in the presence of the stronger nearby femto-bs. This again results in a very low signal to noise ratio, like the previous case. This time, macrocell users have to be protected from the small base station. Figure 2.2c illustrates this kind of interference. 14

23 (a) Cell-edge between 2 macro-bss (b) Macro-pico (c) Macro-femto Figure 2.2: Different types of interference. Solid lines represent association and dotted lines represent interference. 15

24 (a) Reuse factor 1 (b) Reuse factor 1 /4 Figure 2.3: Reuse patterns Interference Coordination In cellular networks, interference can be coordinated using one or a combination of the following schemes. Frequency Domain In this scheme, adjacent cells are assigned different frequency bands (or groups of channels) to prevent interfering with each other. However, because of signal attenuation, a single band can still be used in two different cells if they are located far enough apart. This is called frequency reuse. How often the same band can be reused is controlled by a parameter of the network design, called reuse factor. This parameter is indicated by 1 /K, where K is the cluster size, i.e. the number of close cells that should have distinct sets of channels. The frequency pattern of these cells is repeated over and over again to cover all the network. With frequency reuse enabled, if the total available bandwidth is B Hz, each cell is assigned B K Hz. There is a trade-off in choosing K, which influences the network capacity and interference. Using a high value of K, each cell gets a small portion of the bandwidth. Therefore, the capacity goes down, and so does the interference. A low value for K gives a high cell capacity, since each BS gets a wider bandwidth, but it also means that users experience more interference, due to close co-channel base stations. 16

25 Not all values of K can result in a valid reuse pattern. In fact, K has to be of the form K = i 2 + ij + j 2, where i 0 and j i. Common values of K are 4, 7 and 12. It should be noted that selecting the reuse factor depends on the cell radius, density of BSs, and their RF output power. To increase efficiency, each cellular tower usually has a number of directional antennas (instead of only one omnidirectional antenna), and together, they cover the whole 360 degrees. The area covered by each of these antennas is called a sector. In this case, reuse factor is specified by N /K, where N is the number of sectors per BS tower. Each sector can then use B NK reuse patterns. Hz of the bandwidth. A common value for N is 3. Figure 2.3 shows two sample Instead of evenly distributing the bandwidth over all the cells (or sectors), assigning channels to cells can be based on user demands. Moreover, channels can dynamically be assigned to cells, controlled by a central scheduler, or distributedly. In heterogeneous networks, frequency domain separation can be done by assigning different frequency bands to different tiers, and separating pico or femtocells from macrocells. Time Domain To further increase the granularity of resources, we can divide each frequency channel into time slots of a predefined size. This way we have a two-dimensional table of resources. This enables two neighboring cells to employ the same channel in different time slots, which increases the flexibility of scheduling. Figure 2.4 shows two neighboring base stations that are using different resources with overlapping frequencies to serve their users, without any interference. Power Allocation The utilization of the network can be further increased by controlling how much power is used on each resource block. As an example, consider Figure 2.5. If we only apply frequency domain and time domain coordination, the same resource block (frequency channel and time 17

26 Figure 2.4: Interference coordination using both frequency and time domains. slot) cannot be used by both cells. As we can see, user-1 is within a short distance of BS-A, and the interference of BS-B on it is negligible. User-2, however, is far from its serving BS, and receives a large amount of interference from BS-B. BS-A can send data to user-1 with high SINR, even by using a low transmit power, whereas it has to boost its output power in order to send data to user-2. As illustrated in Figure 2.5, both BSs can use the entire spectrum if they keep the RF output power low for their close users (1 and 4), and only use high power for their distant users (2 and 3). Of course, they need to interact with each other in order to use different resource blocks for their cell-edge users Interference Management in LTE To maximize spectrum efficiency, LTE is designed with frequency reuse factor of 1. This means there is no frequency band separation between neighboring cells, and all cells can use all the frequency channels. This way there is no need for band-assignment during cellplanning, and new base stations can be added easily and without requiring major changes. The disadvantage is that there is a high probability of a resource block used by two adjacent cells, which may result in excessive interference on users. Here, we briefly explain the most 18

27 Figure 2.5: Variable power allocation. Darker resource blocks indicate high output power and are allocated to cell-edge users. recent solutions proposed by 3GPP to coordinate the interference in LTE networks. To achieve the interference coordination provided by these mechanisms, base stations need to directly talk to each other and exchange information about their users and resources. This can be accomplished through X2 interface in the base stations designed for LTE networks. For more information on X2 interface, see [10]. Inter-Cell Interference Coordination Inter-Cell Interference Coordination (ICIC) was introduced in 3GPP Release-8 in It is designed to address interference on cell-edge users. This mechanism can be implemented in three different ways as described below. In the simplest case, neighboring cells can use resources in a mutually exclusive manner. This means that no adjacent cells transmit to their users at the same frequency channel and time slot. This eliminates inter-cell interference in neighboring cells and greatly improves SINR at cell-edges. The downside is that resources are not fully utilized, and it impacts the total throughput of the network. To improve this, in the second method, base stations use all their resources to schedule nearby users. For cell-edge users, however, they negotiate with their neighboring BSs to 19

28 Figure 2.6: ABS subframes (red) are dedicated for small-cells. Regular (blue) subframes are used by both cells. make sure no resource block is commonly used by the two cells. This greatly improves the spectrum utilization over the previous method. In the third scheme, dynamic power allocation can maximize the resource utilization in the network. In this case, in addition to time domain and frequency domain interference coordination, signals on each resource should be transmitted at a power level calculated according to the channel conditions between the BS and UEs (same as in Figure 2.5). ICIC was introduced before the existence of HetNets, so it does not provide a solution for the kinds of interference emerged by deploying small cells. Enhanced Inter Cell Interference Coordination To address new challenges of interference mitigation in heterogeneous networks, enhanced- ICIC was introduced in 3GPP Release-10 in E-ICIC added a time-domain separation scheme to the existing ICIC, to protect small-cell users from interference of macrocells. In particular, a certain number of subframes, known as Almost Blank Subframes, are dedicated to picocells (Figure 2.6). During ABS subframes, macrocells refrain from transmitting any data signal to their connected users. They still transmit necessary control signals in order to 20

29 (a) eicic scheme (b) feicic scheme Figure 2.7: RF output power of a macrocell in almost blank subframes manage their cells, though. Even these control signals are sent using a lower power level than that of regular frames. This is why they are called almost blank subframes. In these ABS subframes, picocells can reach their users without the massive interference from macrocells. One good approach to exploit ABS subframes by picocells is to allocate them for the users that are connected to the picocell through range expansion mechanism, because these are the users who suffer most from macrocell interference. Further Enhanced Inter Cell Interference Coordination Further Enhanced ICIC was introduced by 3GPP Release-11 in 2013 to address some of the drawbacks of eicic. Almost blank subframes provide a good protection for picocells, but at the cost of wasting some resources in macrocells. By applying eicic, picocells can transmit on the whole range of subframes, whereas macrocells, which usually have more users, have to stay completely blank on ABS subframes. One of the main new features of feicic over eicic is introducing reduced power almost blank subframes. In this scheme, instead of being totally silent on data channels, macrocells keep transmitting data even on data channels (although with a lower power) in order to at least serve their center users (Figure 2.7). This ensures that wasted capacity of the macrocells is minimized. 2.3 Self-Organizing Networks The problems of associating users to cells, and allocating a cell s resources to its associated users, are essentially correlated. To allocate resources among users, a cell needs to know 21

30 how many users are connected to it, and what the channel condition between each user and the base station is like. This way each base station can maximize the throughput of its cell according to some utility definition. In addition, to wisely associate users to base stations, information about load and congestion of different cells is required. What a network operator would like to maximize is the aggregate throughput of the network, not individual cells. To attain this goal, these two problems (user association and resource allocation) should be tackled together. This requires BSs to be aware of other BSs using some sort of communication to exchange information. For example, suppose we want to configure the amount of almost blank subframes for a base station. A static configuration might waste the resources on the macro-bs, by allocating too many ABS subframes, or it can starve micro-bs users by assigning insufficient dedicated subframes. By exchanging information and employing a dynamic approach, the system can configure the ratio of ABS subframes to optimize the network operation. As another example, to enhance the throughput of the users in cell edges, as discussed earlier, neighboring cells need to exchange information in order to allocate proper resources and transmit power. The urge to facilitate network planning has led to the rise of Self-Organizing Networks (SONs). The concept of SON was introduced in 3GPP Release 8 to automate management, planning and configuration parameter adjustment of the network, and to optimize and accelerate the process. Base stations have many configuration parameters, some of which are discussed above, e.g., the ratio of ABS subframes, frequency bands, RF output power, antenna tilt, etc. A SON tunes these parameters for each BS using information from the BS itself, other cells, and also user measurements, with the goal of optimizing the network including coordinating interference and maximizing throughput. Other functionalities of a SON include plug and play deployment of base stations. Without a self-organizing paradigm, many parameters should be set to install each new base station. These parameters reside not only in the new BS, but also in other cells that are going 22

31 to cooperate with it. In a self-organized network, these parameters are set by software delivered to network operators by infrastructure vendors. Once the new BS is powered on, it gets registered to the network, detects its neighbors, and declares its existence to the neighbors. Likewise, removing BSs can be automated using these strategies. In case of failure in one BS, the network gets informed and tolerates the loss by adjusting the parameters of other BSs to cope with the situation until the problem is fixed. Without self-organization, even detecting failures would be difficult. Implementing a self-organizing network can be done centrally or distributedly. In central paradigms, all the base stations send their own measurements and the information obtained from UEs to some central entity. Having the information from a wide region, this entity calculates the proper parameters for each cell according to some network operator s policy, and sends back the tuned parameters to each BS. These schemes are provided to 2G, 3G, and 4G network operators by 3rd party suppliers. The software on the central entity should be multi-technology aware, since in each geographical area operators with different technologies or generations may co-exist and they should peacefully operate without disrupting each other. They also should be aware of multiple vendors, since radio devices even in one network come from different vendors and they are not necessarily fully compatible on their own, and need a 3rd party software to coordinate them. 3GPP Release 8 introduced distributed self-organizing schemes for LTE networks. Base stations, known as enodebs or enbs in LTE networks, have an interface (possibly virtual) dedicated for directly talking to other enbs and exchanging load and interference related information. This enables the BSs to share the required information in a local neighborhood, and without needing a central entity, they can optimize their own parameters. To this end, Automatic Neighbor Relations (ANR) is specified by 3GPP and is implemented in enbs. Each enodeb usually runs multiple cell sectors. Each cell broadcasts its global identifier to announce its existence. An ANR-enabled enodeb maintains a Neighbor Relation Table 23

32 (NRT) for each cell, which stores identifications of its neighboring cells. Neighbor detection function of ANR finds the neighboring cells newly installed in the network. Likewise, neighbor removal function detects and removes outdated cells. In addition to direct talking, an enb can instruct its UEs to perform measurements on neighboring cells. These measurements will be sent back to enb to update the NRT. Backward compatibility was one of the design goals of ANR. To facilitate co-existence with previous generations of networks, an enb can also instruct UEs to perform measurements on different frequency bands and technologies, like 2G, 3G, and even WiMax, provided that the UE supports those technologies. Each of the aforementioned families of SON technologies (central versus distributed) has its own benefits and disadvantages. As mentioned earlier in the introduction, modern cellular networks tend to simplify the network structure and facilitate network expansion. As a result, some entities are removed and their functionality is integrated into other entities to have fewer devices. For instance, Radio Network Controller is removed and its role is embedded into enbs in LTE networks. This strategy justifies delegating other common tasks to enbs and removing central devices. Moreover, there are some issues with central approaches that have to be handled. For example, consider the amount of control traffic that has to be sent to the central scheduler, which can be multiple hops away. This traffic consumes bandwidth that could otherwise be used for user data transfer. Another concern when the coordinator is far from base stations is the excessive latency. This can make the self-organizing protocol slow to react to network changes. In conclusion, distributed SON is preferable to centralized schemes. Communicating in a local neighborhood can alleviate these problems, although it introduces its own difficulties and challenges. There are also hybrid SON approaches that are a mixture of centralized and distributed mechanisms. For example, scheduling can be done frequently in a local neighborhood, and less frequent in a wider geographical area. 24

33 2.4 Related Works on Self-Organization The problem of resource management has been investigated since the early days of modern cellular networks. A survey of channel allocation schemes in 2G networks is presented in [50], and compares their complexity and performance. With the technology advances and new features regularly added to current standards, this problem is going to remain a crucial challenge in the future of mobile networks. A survey of the existing 4G cell association and power control schemes is provided in [45], and suggestions are given to make them suitable for future 5G networks, which will require higher data rates and lower latency. In the following, we review various self-organization methods in the literature. In Section 2.4.1, we investigate user association methods. Section revisits interference management schemes, and Section reviews the related algorithms that are methodologically similar to our work User Association Range Expansion Based Schemes Various strategies for user-cell association have been proposed. Some of them are based on range expansion concept of LTE [29, 61, 70]. In these papers, algorithms are designed to adjust cell bias values for the purpose of load balancing. In [13], Bao and Liang proposed a spectrum allocation scheme for heterogeneous cellular networks. They assume that every user equipment in a cell receives equal resources, and do not solve the problem of intra-cell resource allocation. The distribution of base stations is modeled by a homogeneous Poisson Point Process (PPP) for each tier of BSs. UEs are also modeled by a PPP distribution, and a user is considered to be covered if it receives a signal above a certain threshold from a base station. By maximizing the probability of coverage, they compute how much of the spectrum should be given to each tier. They also address the user association problem by finding a cell bias value that is achieved in Nash equilibrium. 25