EE360: Multiuser Wireless Systems and Networks

Lecture 5 OutlineAnnouncements

Project proposals due 1/27 Makeup lecture for 2/10 (previous Friday 2/7,

time TBD) Reading, Supplemental reading, and class

pace

Small cells, HetNets, and SoNShannon Capacity of Cellular

SystemsArea Spectral EfficiencyMultiuser Detection in cellularMIMO in Cellular

Review of Last LectureMultiuser Detection

Tradeoff in performance versus complexityMultiuser OFDM Techniques

OFDMA most commonCellular System Overview

Reuse frequencies to increase spectral efficiency

Sophisticated PHY layer Centralized control

Standards 4G is LTE: uses OFDMA and MIMO to achieve 50-100 Mbps 10-20 MHz of spectral available: hard to

compete with WiFi

Rethinking “Cells” in Cellular

Traditional cellular design “interference-limited”MIMO/multiuser detection removes interferenceCooperating BSs form MIMO array: what is a cell?Distributed antennas move BS towards boundaryRelays change cell shape and boundariesMobile relaying, virtual MIMO, analog network coding.Small cells create a cell within a cell

SmallCell

Relay

DAS

Coop MIMO

How should cellularsystems be designed?

Will gains in practice bebig or incremental; incapacity or coverage?

NextLecture

Are small cells the solution to increase cellular system capacity?

Yes, with reuse one and adaptive techniques (Alouini/Goldsmith 1999)

A=.25D2p Area Spectral Efficiency

S/I increases with reuse distance (increases link capacity).Tradeoff between reuse distance and link spectral efficiency

(bps/Hz).Area Spectral Efficiency: Ae=SRi/(.25D2p) bps/Hz/Km2.

The Future Cellular Network: Hierarchical Architecture

MACRO: solving initial coverage issue, existing network

FEMTO: solving enterprise & home coverage/capacity issue

PICO: solving street, enterprise & home coverage/capacity issue

10x Lower HW COST

10x CAPACITY Improvem

ent

Near 100%COVERAGE

MacrocellPicocell Femtocell

Today’s architecture•3M Macrocells serving 5 billion users

Managing interferencebetween cells is hard

Deployment Challenges

Deploying One MacrocellEffort(MD –

Man Day)New site verification 1 On site visit: site details verification

0.5

On site visit: RF survey 0.5New site RF plan 2 Neighbors, frequency, preamble/scrambling code plan

0.5

Interference analyses on surrounding sites

0.5

Capacity analyses 0.5 Handover analyses 0.5Implementation on new node(s)

0.5

Field measurements and verification

2

Optimization 2Total activities 7.5 man

days

5M Pico base stations in 2015 (ABI)• 37.5M Man Days = 103k Man Years• Exorbitant costs• Where to find so many engineers?

Small cell deployments require automated self-configuration

via software

Basic premise of self-organizing networks (SoN)

SON for LTE small cells

Node Installation

Initial Measurement

s

Self Optimizatio

n

SelfHealing

Self Configuration Measurement

SON Server

SoNServer

Macrocell BS

Mobile GatewayOr Cloud

Small cell BS

X2

X2X2

X2

IP Network

Algorithmic Challenge: Complexity

Optimal channel allocation was NP hard in 2nd-generation (voice) IS-54 systems

Now we have MIMO, multiple frequency bands, hierarchical networks, …

But convex optimization has advanced a lot in the last 20 years

Innovation needed to tame the complexity

Cellular System Capacity

Shannon CapacityShannon capacity does no incorporate reuse distance.Wyner capacity: capacity of a TDMA

systems with joint base station processing

User Capacity Calculates how many users can be supported for a given performance specification.Results highly dependent on traffic, voice

activity, and propagation models.Can be improved through interference

reduction techniques.

Area Spectral EfficiencyCapacity per unit area

In practice, all techniques have roughly the same capacity for voice, butflexibility of OFDM/MIMO supports more heterogeneous users

Defining Cellular Capacity

Shannon-theoretic definition Multiuser channels typically assume user

coordination and joint encoding/decoding strategies

Can an optimal coding strategy be found, or should one be assumed (i.e. TD,FD, or CD)?

What base station(s) should users talk to? What assumptions should be made about base

station coordination? Should frequency reuse be fixed or optimized? Is capacity defined by uplink or downlink? Capacity becomes very dependent on

propagation model Practical capacity definitions (rates or

users) Typically assume a fixed set of system

parameters Assumptions differ for different systems:

comparison hard Does not provide a performance upper bound

Approaches to Date Shannon Capacity

TDMA systems with joint base station processing

Multicell CapacityRate region per unit area per cellAchievable rates determined via Shannon-

theoretic analysis or for practical schemes/constraints

Area spectral efficiency is sum of rates per cell

User Capacity Calculates how many users can be supported for a given performance specification.Results highly dependent on traffic, voice

activity, and propagation models.Can be improved through interference

reduction techniques. (Gilhousen et. al.)

Wyner Uplink Capacity

Linear or hexagonal cells

Received signal at base station (N total users)

Propagation for out-of-cell interference captured by a

Average power constraint: E Capacity CN defined as largest achievable

rate (N users)

Linear ArrayTheorem: for

Optimal scheme uses TDMA within a cell - Users transmit in 1/K timeslots; power KP

Treats co-channel signals as interference:

)(*lim aCCNN

Results Alternate TDMA

CDMA w/ MMSE

15

• Channel Reuse in Cellular Systems• Motivation: power falloff with transmission distance• Pro: increase system spectral efficiency• Con: co-channel interference (CCI) • “Channel”: time slot, frequency band, (semi)-orthogonal code ...

• Cellular Systems with different multiple-access techniques• CDMA (IS-95, CDMA2000): weak CCI, channel reuse in every cell

• codes designed with a single and narrow autocorrelation peak • TDMA (GSM), FDMA (AMPS): much stronger CCI

• a minimum reuse distance required to support target SINR

• Channel reuse: traditionally a fixed system design parameter

Channel Reuse in Cellular Systems

16

• Tradeoff• Large reuse distance reduces CCI• Small reuse distance increases bandwidth allocation

• Related work• [Frodigh 92] Propagation model with path-loss only

channel assignment based on sub-cell compatibility• [Horikawa 05] Adaptive guard interval control

special case of adaptive channel reuse in TDMA systems

• Current work• Propagation models incorporating time variation of wireless channels

static (AWGN) channel, fast fading and slow fading• Channel reuse in cooperative cellular systems (network MIMO)

compare with single base station processing

Adaptive Channel Reuse

17

• Linear cellular array, one-dimensional, downlink, single cell processing

best models the system along a highway [Wyner 1994]

• Full cooperation leads to fundamental performance limit• More practical scheme: adjacent base station cooperation

System Model

18

Channel Assignment

• Intra-cell FDMA, K users per cell, total bandwidth in the system K·Bm

• Bandwidth allocated to each user • maxium bandwidth Bm, corresponding to channel reuse in each cell • may opt for a fraction of bandwidth, based on channel strength

• increased reuse distance, reduced CCI & possibly higher rate

19

• Path loss only, receive power

A: path loss at unit distance γ : path-loss exponent

• Receive SINR

L: cell radius. N0: noise power

• Optimal reuse factor

tAP

NLL ddd

022

Single Base Station Transmission: AWGN

dPAdP tr )(

),(1log maxarg dBm

),( d

• Observations• Mobile close to base station -> strong channel, small reuse distance• Reuse factor changes (1 -> ½) at transition distance dT = 0.62 mile

20

• Environment with rich scatters• Applies if channel coherence time shorter

than delay constraint

• Receive power g: exponentially distributed r.v.

• Optimal reuse factor

• Lower bound: random signal Upper bound: random interference

Rayleigh Fast Fading Channel

dPgAP tr

),,(1log maxarg gdB gm E

• Observations• AWGN and fast fading yield similar performance

reuse factor changes (1 -> ½) at transition distance dT = 0.65 mile• Both “sandwiched” by same upper/lower bounds (small gap in between)

21

• Stringent delay constraint, entire codeword falls in one fading state

• Optimal reuse factor

• Compare with AWGN/slow fading:optimal reuse factor only depends on distance between mobile and base station

Rayleigh Slow Fading Channel

),,(1log maxarg gdBm

• Observations• Optimal reuse factor random at each distance, also depends on fading• Larger reuse distance (1/τ > 2) needed when mobiles close to cell edge

22

• Adjacent base station cooperation, effectively 2×1 MISO system• Channel gain vectors: signal interference

• Transmitter beamforming• optimal for isolated MISO system with per-base power constraint• suboptimal when interference present• an initial choice to gain insight into system design

Base Station Cooperation: AWGN

2

2

)2( 0

00

dLdh

2

2

02

02

2,12

dLd

L

LI

h

)()()( jjj hhw w

23

• no reuse channel in adjacent cell: to avoid base station serving user and interferer at the same time

• reuse factor ½ optimal at all d: suppressing CCI without overly shrinking the bandwidth allocation

• bandwidth reduction (1-> ½) over-shadows benefit from cooperation

Performance Comparison

Observations

• Advantage of cooperation over single cell transmission: only prominent when users share the channel; limited with intra-cell TD/FD [Liang 06]

• Remedy: allow more base stations to cooperate in the extreme case of full cooperation, channel reuse in every cell

Area Spectral Efficiency

BASESTATION

S/I increases with reuse distance. For BER fixed, tradeoff between reuse

distance and link spectral efficiency (bps/Hz).

Area Spectral Efficiency: Ae=SRi/(.25D2p) bps/Hz/Km2.

A=.25D2p =

ASE with Adaptive Modulation

Users adapt their rates (and powers) relative to S/I variation.

S/I distribution for each user based on propagation and interference models.

Computed for extreme interference conditions. Simulated for average interference conditions.

The maximum rate Ri for each user in a cell is computed from its S/I distribution. For narrowband system use adaptive MQAM

analysis

d d i

S S /

Propagation Model Two-slope path loss model:

Slow fading model: log-normal shadowing

Fast fading model: Nakagami-mModels Rayleigh and approximates

Ricean.

ASE maximized with reuse distance of one!Adaptive modulation compensate for

interference

S d Kd d g

Sr a bt( )

( / ),

1

ASE vs. Cell Radius

Cell Radius R [Km]

101

100Aver

age

Are

a Sp

ectr

al

Effic

ienc

y[B

ps/H

z/K

m2 ]

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

D=4RD=6R

D=8R

fc=2 GHz

MUD, Smart Antennasand MIMO in Cellular

MUD in CellularIn the uplink scenario, the BS RX must decode all K desired users, while suppressing other-cell interference from many independent users. Because it is challenging to dynamically synchronize all K desired users, they generally transmit asynchronously with respect to each other, making orthogonalspreading codes unviable.

In the downlink scenario, each RX only needs to decode its own signal, while suppressing other-cell interference from just a few dominant neighboring cells. Because all K users’ signals originate at the base station, the link is synchronous and the K – 1 intracell interferers can be orthogonalized at the base station transmitter. Typically, though, some orthogonality is lost in the channel.

MIMO in Cellular:Performance Benefits Antenna gain extended battery life,

extended range, and higher throughput

Diversity gain improved reliability, more robust operation of services

Interference suppression (TXBF) improved quality, reliability, and robustness

Multiplexing gain higher data rates

Reduced interference to other systems

Optimal use of MIMO in cellular systems, especially given practical constraints, remains an open problem

8C32810.46-Cimini-7/98

5

5

5

5

5

5

761

4

23

Sectorization and Smart Antennas

1200 sectoring reduces interference by one third

Requires base station handoff between sectors

Capacity increase commensurate with shrinking cell size

Smart antennas typically combine sectorization with an intelligent choice of sectors

Beam Steering

Beamforming weights used to place nulls in up to NR directionsCan also enhance gain in direction of

desired signalRequires AOA information for signal and

interferers

SIGNAL

INTERFERENCE

BEAMFORMINGWEIGHTS

SIGNAL OUTPUT

INTERFERENCE

Multiplexing/diversity/interference cancellation

tradeoffs

Spatial multiplexing provides for multiple data streams

TX beamforming and RX diversity provide robustness to fading

TX beamforming and RX nulling cancel interference Can also use DSP techniques to remove interference

post-detection

Stream 1

Stream 2

Interference

Optimal use of antennas in wireless networks unknown

Diversity vs. Interference Cancellation

+

r1(t)

r2(t)

rR(t)

wr1(t)

wr2(t)

wrR(t)

y(t)

x1(t)

x2(t)

xM(t)

wt1(t)

wt2(t)

wtT(t)

sD(t)

Nt transmit antennas NR receive antennas

Romero and Goldsmith: Performance comparison of MRC and IC Under transmit diversity, IEEE Trans. Wireless Comm., May 2009

Diversity/IC Tradeoffs NR antennas at the RX provide NR-

fold diversity gain in fadingGet NTNR diversity gain in MIMO

system

Can also be used to null out NR interferers via beam-steeringBeam steering at TX reduces

interference at RX Antennas can be divided between

diversity combining and interference cancellation

Can determine optimal antenna array processing to minimize outage probability

Diversity Combining Techniques

MRC diversity achieves maximum SNR in fading channels.

MRC is suboptimal for maximizing SINR in channels with fading and interference

Optimal Combining (OC) maximizes SINR in both fading and interferenceRequires knowledge of all desired

and interferer channel gains at each antenna

SIR Distribution and Pout

Distribution of obtained using similar analysis as MRC based on MGF techniques.

Leads to closed-form expression for Pout.Similar in form to that for MRC

Fo L>N, OC with equal average interference powers achieves the same performance as MRC with N −1 fewer interferers.

Performance Analysis for IC

Assume that N antennas perfectly cancel N-1 strongest interferersGeneral fading assumed for

desired signalRayleigh fading assumed for

interferers

Performance impacted by remaining interferers and noiseDistribution of the residual

interference dictated by order statistics

SINR and Outage Probability

The MGF for the interference can be computed in closed formpdf is obtained from MGF by

differentiation

Can express outage probability in terms of desired signal and interference as

Unconditional Pout obtained as

sPyyYout eyXPP /)(2 2

1))((|

0

//)( )(12

dyyfeeP YPyPy

outss

Obtain closed-form expressions for most fading distributions

OC vs. MRC for Rician fading

IC vs MRC as function of No. Ints

Fig1.eps

Diversity/IC Tradeoffs

Distributed Antennasin Cellular

Distributed Antennas (DAS) in Cellular

Basic Premise:Distribute BS antennas throughout cell

Rather than just at the centerAntennas connect to BS through

wireless/wireline links

Performance benefitsCapacityCoveragePower consumption

DAS

1p

2p3p

4p

5p6p

7p

Average Ergodic Rate Assume full CSIT at BS of gains for all antenna ports Downlink is a MIMO broadcast channel with full CSIR Expected rate is

Average over user location and shadowing

DAS optimizationWhere to place antennasGoal: maximize ergodic rate

2

12 ),(1log)( N

Ii

ishucsit upD

fSEEPC a

Solve via Stochastic Gradients

Stochastic gradient method to find optimal placement

1. Initialize the location of the ports randomly inside the coverage region and set t=0.

2. Generate one realization of the shadowing vector f(t) based on the probabilistic model that we have for shadowing

3. Generate a random location u(t), based on the geographical distribution of the users inside the cell

4. Update the location vector as

5. Let t = t +1 and repeat from step 2 until convergence. tP

tt PtftuCP

PP )),(),((1

Gradient Trajectory N = 3 (three nodes) Circular cell size of

radius R = 1000m Independent log-

Normal shadow fading

Path-loss exponent: a=4

Objective to maximize : average ergodic rate with CSIT

Power efficiency gains Power gain for optimal placement versus central placement

Three antennas

Non-circular layout For typical path-loss exponents 2<α<6, and

for N>5, optimal antenna deployment layout is not circular

N = 12, α = 5 N = 6, α = 5

Interference Effect Impact of intercell interference

is the interference coefficient from cell j Autocorrelation of neighboring cell codes for CDMA

systems Set to 1 for LTE(OFDM) systems with frequency reuse

of one.

6

1 12

1

),(

),(

j

N

i ji

ij

N

ii

i

upDfupD

f

SINR a

a

j

Interference Effect

The optimal layout shrinks towards the center of the cell as the interference coefficient increases

Power AllocationPrior results used same fixed power for all nodes

Can jointly optimize power allocation and node placement

Given a sum power constraint on the nodes within a cell, the primal-dual algorithm solves the joint optimization

For N=7 the optimal layout is the same: one node in the center and six nodes in a circle around it. Optimal power of nodes around the central node unchanged

Power Allocation Results

For larger interference and in high path-loss, central node transmits at much higher power than distributed nodes

N = 7 nodes

Area Spectral Efficiency

Average user rate/unit bandwidth/unit area (bps/Hz/Km2)Captures effect of cell size on spectral efficiency

and interference• ASE typically increases as cell size decreases

• Optimal placement leads to much higher gains as cell size shrinks vs. random placement

Virtual MIMO andCoMP in Cellular

Virtual/Network MIMO in Cellular

Network MIMO: Cooperating BSs form a MIMO arrayDownlink is a MIMO BC, uplink is a MIMO

MACCan treat “interference” as known signal

(DPC) or noiseCan cluster cells and cooperate between

clusters

Mobiles can cooperate via relaying, virtual MIMO, conferencing, analog network coding, …

Design Issues: CSI, delay, backhaul, complexity

Many open problemsfor next-gen systems

Will gains in practice bebig or incremental; incapacity or coverage?

Open design questions

Single ClusterEffect of impairments (finite capacity, delay) on the backbone

connecting APs:Effects of reduced feedback (imperfect CSI) at the APs.Performance improvement from cooperation among mobile

terminalsOptimal degrees of freedom allocation

Multiple ClustersHow many cells should form a cluster?How should interference be treated? Cancelled spatially or

via DSP?How should MIMO and virtual MIMO be utilized: capacity vs.

diversity vs interference cancellation tradeoffs

Cooperative Multipoint (CoMP)

"Coordinated multipoint: Concepts, performance, and field trial results" Communications Magazine, IEEE , vol.49, no.2, pp.102-111, February 2011

Part of LTE Standard - not yet implemented

Summary HetNets the key to increasing capacity of

cellular systems – require automated self-organization (SoN)

Smart antennas, MIMO, and multiuser detection have a key role to play in future cellular system design.

Limited results for Shannon capacity of cellular systems Challenge is how to deal with interference

Area spectral efficiency a good metric for capturing impact of small cells and frequency reuse

Distributed antennas (DAS) leads to large performance gains, CoMP not so promising.