ee360: multiuser wireless systems and networks lecture 5 outline announcements l project proposals...
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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 0.5
New 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 0.5
Implementation on new node(s)
0.5
Field measurements and verification
2
Optimization 2
Total 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
MeasurementSON
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 Array
Theorem:
for
Optimal scheme uses TDMA within a cell - Users transmit in 1/K timeslots; power KP
Treats co-channel signals as interference:
)(*lim CCNN
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 dd
d
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
dL
dh
2
2
02
02
2,12
dL
dL
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 dK
d d gS
r a bt( )
( / ),
1
ASE vs. Cell Radius
Cell Radius R [Km]
101
100A
vera
ge
Are
a S
pec
tra
l E
ffic
ien
cy[B
ps/
Hz/
Km
2 ]
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
D=4R
D=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
systemsOptimal 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
76
14
2
3
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 g 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
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 1
2
1
),(
),(
j
N
i ji
ij
N
ii
i
upDfupD
f
SINR
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.
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