© Elsevier1 Chapter 6: Intercell Interference Coordination: Towards A Greener Cellular Network Duy T. Ngo, Duy H. N. Nguyen, and Tho Le-Ngoc McGill University.

© Elsevier 1

Chapter 6:Intercell Interference Coordination:

Towards A Greener Cellular Network

Duy T. Ngo, Duy H. N. Nguyen, and Tho Le-Ngoc

McGill UniversityMontreal, QC, Canada

HANDBOOK ON GREEN INFORMATION AND COMMUNICATION SYSTEMS

© Elsevier 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

© Elsevier 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

© Elsevier 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.

© Elsevier 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.

© Elsevier 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.

© Elsevier 7

Co-channel Deployment of Heterogeneous Small-cell Networks

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

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

© Elsevier 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).

© Elsevier 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.

© Elsevier 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.

© Elsevier 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.

][ˆ

]1[min

tPtP ii

ii

),(max 0 iiiP PPUi

),(),(max 0 iiiiiiP PPCPPUi

© Elsevier 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

iiii PPC ),(

© Elsevier 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.

maxk

)(

m k

)(),(,),(i P,

P P :subject to

)]P(1log[ max

k

kkmimkmiw

© Elsevier 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

© Elsevier 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.

iqw

© Elsevier 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

2||||min q

iKi

qw

© Elsevier 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

q Ki

q

q

i

2||||min w

© Elsevier 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.

22 ||||||||minj

q

j

q

i qHq

Kjq

Kiq hWw

jq

© Elsevier 19

CoMP for Power Minimization (4)

CoMP is more power-efficient than frequency reuse scheme.

© Elsevier 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

qKi

i1logmax

© Elsevier 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.

K

ii

1

1logmax

2||||1logmaxj

q

j

q

qHq

Kjq

Kii hW

© Elsevier 22

CoMP for Rate Maximization (3)

CoMP extracts higher sum-rate than frequency reuse scheme.

© Elsevier 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:

imicbii PaU

iii

)()](exp[1

1tot )(

iiiiii gtItP ,min* ][),max(]1[

jfjjij PaWU )(tot )1log()(

© Elsevier 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.

© Elsevier 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.

minii

© Elsevier 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

MUE index

1 2 3 4 5 6 7 8 9 10

Target SINR

1.578

1.507

1.440

1.376

1.315

1.256

1.200

1.147

1.095

1.047

Achieved

SINR

1.578

1.507

1.440

1.376

1.315

1.256

1.200

1.147

1.095

1.047

© Elsevier 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.

© Elsevier 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.

© Elsevier 29

Tradeoff between Spectral and Energy Efficiency

Spectral Efficiency (SE)

Energy Efficiency (EE)

With circuit power Pc

b/s/Hz]][)(1[log = 2SE WPW

R

b/J][])(1[Wlog

= 2EE P

WP

P

R

)1(2 SE

SEEE

b/J][ EEcPP

R

Tradeoff relation:

© Elsevier 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

) P,(P max arg = )(Pf i-iiPi-n i

k

)(

)((k)i,i

i

g1log =R

ki

ki

I

P

)(kiI

ci

i

PP

R

i

) P,(P i-ii

© Elsevier 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%

i

,ia,ia P

)(

M

RL =(P)u i

i

f

iaiiiaiiciaaP PcPPuPPu

iiiii ),(),(max ,,,

iiijjP Pci

][maxmax ,

© Elsevier 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.

© Elsevier 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.