Chapter 6: Intercell Interference Coordination: Towards A Greener Cellular Network

1. HANDBOOK ON GREEN INFORMATION AND COMMUNICATION SYSTEMS Chapter 6:Intercell Interference Coordination: Towards A Greener Cellular Network Duy T. Ngo, Duy H. N. Nguyen, and Tho Le-Ngoc McGill University Montreal, QC, Canada

2. Introduction • Intercell interference (ICI) is a critical issue in cellular communication systems. • With universal frequency reuse, it is even more urgent to find effective solutions to this problem. • Energy efficiency is crucial: • Environmental effects of operating a huge number of cellular networks with high energy consumption • Battery-powered user terminals have relatively short operating time • Towards a greener design, effective coordination of the intercell interference is key to • Minimize the carbon footprint • Maximize the overall network performance

3. Introduction • Cell coordination offers tremendous advantages over the traditional approaches that typically treat interference on a per-cell basis. • This chapter reviews the state-of-the-art techniques that manage the intercell interference in multicell networks • Homogeneous networks • Coordinated multipoint transmission and reception (CoMP) • Small-cell heterogeneous networks (femtocells) • Energy-efficient interference coordination

4. Frequency Reuse and Interference Issue in Homogeneous Cellular Systems • Broadcast nature of wireless medium results in the fundamental problem of interference. • Fractional frequency reuse • Reduced spectral efficiency • Universal frequency reuse • More spectrally efficient • Only if intercell interference (ICI) is properly control. • CDMA systems • All users (UEs) share the same spectrum and are interfered. • OFDMA systems • Joint subchannel assignment and power control is required to maximize system performance and reduce ICI.

5. BS Coordination for Interference Management in Homogeneous Multicell Systems • Conventionally, interference is usually controlled on a per-cell basis. • The ICI is treated as background noise by each cell, and the base station (BS) of which has no intention to control the interference induced to other cells. • Base station (BS) coordination is a more effective means to mitigate cochannel interference in multicell networks. • Coordinated multipoint transmission and reception (CoMP) takes advantage of the inter-cell transmissions to enhance the overall system performance.

6. Classification and Design Requirements for CoMP • Depending on the extent of coordination among cells, CoMP schemes can be classified into 3 categories: • Joint Signal Processing (JP): multiple BSs are transmitting/receiving data signals to/from the UEs. • Interference Coordination (IC): each UE transmits/receives data signals to/from its single serving BS. ICI is jointly controlled. • Interference Aware (IA): ICI is not controlled, but is utilized to adjust the transmitting/receiving strategy at each BS. This scheme is a strategic noncooperative game (SNG). • Different CoMP schemes impose different requirements on • Data and signaling exchanges. • Channel state information (CSI) knowledge needed at the coordinated BSs.

7. Co-channel Deployment of Heterogeneous Small-cell Networks • Key Benefits • Higher capacity via larger area spectral efficiency • Better coverage with lower power consumption • Offload traffic for macrocell • More cost-effective compared to cell-partitioning approach • Small cells (i.e. femtocells) deployed at a home, connected to backhaul via residential wireline links (e.g. DSL). • Range of less than 50m and serve a dozen active users

8. Cross-tier Interference in Femtocell Deployment Cross-tier interference can be severe and hard to control • Scenario A: • A victim cell-edge macrocell user (MUE) is strongly interfered by the downlink transmission of a nearby femtocell BS. • Scenario B: • An MUE located far away from its serving macrocell BS transmits at high power in the uplink to compensate the path losses. • This may jam the transmission of a nearby victim femtocell user (FUE).

9. Challenges in Managing Interference for Femtocell Networks • It is more challenging to mitigate inteference in femtocell than in traditional homogeneous settings. • Unplanned deployment: Femtocells are deployed randomly without network planning that is normally taken place. Femtocell BSs and users can be moved or switched on/off at any time. • Access priority: Prioritized MUEs, the spectrum owner, need to be protected from cross-tier interference induced by lower-tier FUEs. • Limited control/signaling: Residential network infrastructure only provide limited capacity for the exchange of control and signaling information. Delay can be a major issue.

10. Interference Management in Femtocell Networks: Design Requirements • Femtocell deployment: A paradigm shift from the traditional centralized macrocell approaches to a more uncoordinated and autonomous solution • Available centralized solutions may not be applicable. • Distributed interference management approaches are preferable in practical applications so that • MUEs are robustly protected with their QoS requirements always maintained; and • FUEs effectively exploit residual network capacity to optimize their own performance.

11. Interference Management Techniques in CDMA-based Homogeneous Cellular Networks (1) • Power control is effective for CDMA-based systems • SINR/Power balancing: can be implemented distributively, but diverges with infeasible SINR targets: • Game-theoretical approach:users selfishly optimize their own performance, giving Nash equilibrium (NE), but not Pareto-efficient. • Game with pricing can substantially enhance the NE.

12. Interference Management Techniques in CDMA-based Homogeneous Cellular Networks (2) • Using pricing scheme that is linearly proportional to SINR, i.e., , NE is unique and Pareto-efficient for single-cell settings. • Observe: SINRs should not be fixed but adjusted to the extent that the system capacity can still support. • A high SINR is translated into better throughput and reliability • A low SINR implies lower data rates. • Jointly optimize SINR and power to achieve Pareto optimality by • Re-parametrization via the left Perron-Frobenius eigenvectors • A locally computable ascent direction

13. Interference Management Techniques in OFDMA-based Homogeneous Cellular Networks (1) • Optimize over 2 dimensions • Joint subchannel assignment and power allocation • Typical design problem: • Common approach: • Step 1:Given fixed power allocation P, find optimal subchannel assignment i* • Step 2:Given fixed subchannel assignment i, find optimal power P* • Go back to Step 1 and repeat until convergence.

14. Interference Management Techniques in OFDMA-based Homogeneous Cellular Networks (2) • Game theoretical approach with “virtual referee” • This referee mandatorily changes the game rules whenever needed, and helps improve the outcome of the game. • Transmit power of UEs with unfavorable channel conditions are reduced. • UEs generating significant interference to others may be prohibited from using certain subchannels. • Low-complexity and heuristic approaches • Affordable computational complexity • Reduced feedback overhead • Suitable for practical applications

15. Coordinated Multipoint Transmission and Reception (CoMP) • Consider a network with Q cells and K users. • CoMP allows the data signals to a UE to be sent from multiple BSs. • CoMP utilizes space division multiple access (SDMA) • Each BS can send data signals to multiple connected UEs by means of precoding • Beamformer for UE i at BS q. • Assuming each UE is assigned to a known subset of BSs.

16. CoMP for Power Minimization (1) • Interference Aware (IA) • Each UE is assigned to only one BS • BS adjusts its beamformers to ensure a set of target SINR at its connected UEs. • CoMP under IA scheme is a strategic noncooperative game. • Players: BSs • Admissible set of strategies: Constraints on the SINR at each UE. • Utility function: Transmit power at the BSs • The beam patterns are always unchanged, regardless the ICI power allocation game. • Characterization of the NE: existence and uniqueness. • Fully distributed implementation

17. CoMP for Power Minimization (2) • Joint Signal Processing (JP) and Interference Coordination (IC) • Joint optimization to minimize transmit power across coordinated BS • Solution is Pareto-optimal. • Convex optimization, easy to find the optimal solution. • Multicell problem can be reformulated as a single cell problem well-known algorithms can be adopted. • Drawbacks: • Centralized implementation • Signaling and synchronization between BSs

18. CoMP for Power Minimization (3) • Consider a new game • Distributed implementation as in IA scheme • Optimal solution as in IC scheme • New utility function with pricing: where : pricing factor charged on ICI caused by BS q to its unconnected UEs IA scheme with pricing • Under the right pricing scheme, the new game approaches optimal performance offered by IC scheme.

19. CoMP for Power Minimization (4) CoMP is more power-efficient than frequency reuse scheme.

20. CoMP for Rate Maximization (1) • Interference Aware: • Each UE is assigned to only one BS • BS adjusts its beamformers to maximize the data rate to its connected UEs. • CoMP under IA scheme is a strategic noncooperative game. • Players: BSs • Admissible set of strategies: Power constraint on the beamformers • Utility function: Data rate at the BSs • Nonconcave utility function difficult to analyze • Apply zero-forcing (ZF) at each BS • Simplify the game into a power iterative waterfilling game • Easier to character of the NE: existence and uniqueness

21. CoMP for Rate Maximization (2) • Joint Signal Processing (JS) and Interference Coordination (IC) • Joint optimization to maximize the data rate to all the UEs • Nonconvex optimization problem • Difficult to find global optimum • Approximation technique to find locally optimal solutions • Solution approaches are usually centralized. • IA scheme with pricing: new utility function with pricing • Under the right pricing, the network sum rate monotonically increases to a local maximum.

22. CoMP for Rate Maximization (3) CoMP extracts higher sum-rate than frequency reuse scheme.

23. Advanced Interference Coordination Techniques for CDMA-based Femtocells (1) • Joint power and admission control for distributed interference management with dynamic pricing combined with admission control • Net utility for MUE I to robustly protect the performance of all active MUEs: • Update of power for MUE i: • Net utility for FUE j to balance the achieved throughput and the power expenditure:

24. Advanced Interference Coordination Techniques for CDMA-based Femtocells (2) • For non-congested network, the proposed algorithm quickly converges to an equilibrium with the target SINRs achieved for all MUEs. • For congested network, admission control can remove some FUEs, resulting in a noticeable growth in SINRs of the remaining FUEs. • Removal of FUEs does not significantly affect the transmit powers and SINRs of MUEs.

25. Advanced Interference Coordination Techniques for CDMA-based Femtocells (3) • Using convex optimization, distributed joint power and SINR allocation is devised such that • All users attain their respective SINRs that are always optimal in Pareto sense, • Every MUE i is protected with . • Every FUE j has its utility globally maximized. • Key steps: • Characterize Pareto-optimal boundary of the SINR feasible region • Use load-spillage parametrization to realize every SINR point lying on such a boundary • Determine a unique operating SINR point, based upon the specific network utility function of FUEs and the minimum SINR requirements of MUEs, • Adapt transmit power according to Foschini-Miljanic's algorithm to attain such a design target.

26. Advanced Interference Coordination Techniques for CDMA-based Femtocells (4) • Proposed algorithm converges to global optima for different utilities. • Performance of the femtocell network optimized • Minimum SINRs prescribed for MUEs always guaranteed

27. Advanced Interference Coordination Techniques for OFDMA-based Femtocells (1) • Joint allocation of resource block and transmit power • Utility of each femtocell BS includes system capacity and other sources of interferences (i.e., femtocell to macrocell, macrocell to femtocell, and femtocell to femtocell). • Formulated game belongs to the class of exact potential game, shown to always converge to a NE when a best response adaptive strategy is applied. • Solution is an iterative process: • Step 1: Optimal resource block allocation is determined given a transmit power policy. • Step 2: Waterfilling allocation of power for femtocells is computed for a fixed resource block allocation. • Go back to Step 1 and repeat until convergence.

28. Advanced Interference Coordination Techniques for OFDMA-based Femtocells (2) • Joint subchannel and transmit power allocation scheme • Femto BSs are allowed to transmit on the same subchannel with MUEs as long as interference is limited to an acceptable level • Maximizing capacity of cognitive radio network (e.g., femtocell) • ICI among different cognitive radio cells is controlled. • Lagrangian dual method: • Original design problem is decomposed into multiple subproblems in the dual domain • Each problem is solved by an efficient algorithm. • Duality gap approaches zero when the number of OFDMA subchannels is sufficiently large. • Proposed solution outperforms the fixed subchannel allocation scheme.

29. Tradeoff between Spectral and Energy Efficiency • Spectral Efficiency (SE) • Energy Efficiency (EE) • With circuit power Pc • Tradeoff relation:

30. Energy-efficient Interference Management for Multicarrier Multicell Networks • Given interference power on subchannel k, data rate of user i across all subchannels is • EE of user i: • Given the power allocation of all other users, P-i, each user i is required to solve the best-response problem: • As is strictly quasiconcave in Pi, there exists at least one NE in this power control game. • Under certain conditions, the NE is unique in frequency-selective channels

31. Energy-efficient Joint Power Control and BSAssignment in CDMA-based Multicell Networks • Utility of user i received at its assigned BS ai: • Two-dimensional space: • Transmit power Pi • Base station ai • Power control game with a linear pricing: • Original problem is reduced to: • Improvement in EE with linear pricing is above 25%

32. Chapter Summary • Two conflicting goals in cellular network deployment: spectral v.s energy efficiency • With universal frequency reuse, new communication paradigms are needed to proactively deal with intercell interference (ICI). • Effective coordinating of ICI is the key to optimizing the two design goals towards a greener cellular network. • For conventional homogeneous networks, CoMP schemes efficiently coordinate or even take advantage of the ICI. • For heterogenous networks, advanced interference management mechanisms help mitigate cross-tier interference in mixed macrocell/femtocell deployment. • Current advances in ICI coordination improve the energy efficiency of cellular networks while maintaining a good tradeoff with spectral efficiency goal.

33. Some Potential Research Directions • Design CoMP schemes that deal with quantization errors, fast-varying channels and CSI feedback delay • Tradeoff between achieving optimal performance and incurring low computational complexity in CoMP • Distributed implementation of robust CoMP schemes with only local CSI required • Address energy-efficiency criterion in standardization of CoMP techniques • Determine the optimal cell sizes and locations to deploy femtocell BSs, taking into account the energy expended for the backhaul and signaling overhead • With cooperative relays, power-efficient resource allocation techniques (e.g., energy-efficient modulation, selective relaying) should be devised and adapted.