Broadcast Channels - Stanford University

EE360: Multiuser Wireless Systems and Networks Lecture 4 Outline Announcements Project proposals due 1/27 Makeup lecture for 2/10 (previous Friday 2/7, time TBD) Multiuser Detection Multiuser OFDM Techniques Cellular System Overview Design Considerations Standards Review of Last Lecture Duality connects BC and MAC channels Used to obtain capacity of one from

the other Duality and dirty paper coding are used to obtain the capacity of a broadcast MIMO channel. MIMO MAC capacity known from general formula MIMO BC capacity uses DPC optimized based on duality with MIMO MAC. DPC complicated to implement in practice. Multiuser Detection Multiuser Detection In all CDMA systems and in TD/FD/CD cellular systems, users interfere with each other.

In most of these systems the interference is treated as noise. Systems become interference-limited Often uses complex mechanisms to minimize impact of interference (power control, smart antennas, etc.) Multiuser detection exploits the fact that the structure of the MUD System Model Synchronous Case X y(t)= s1(t)+ s2(t)+ s3(t)+ n(t)

MF 1 sc1(t) X MF 2 sc2(t) X MF 3 y1+I1 y2+I2 Multiuser Detector y3+I3 sc3(t) Matched filter integrates over a symbol time and samples

MUD Algorithms Multiuser Receivers Optimal MLSE Suboptimal Linear Decorrelator Non-linear MMSE Multistage Decision -feedback Successive interference cancellation Optimal Multiuser

Detection Maximum Likelihood Sequence Estimation Detect bits of all users simultaneously (2M possibilities) Matched (Verdu86) filter bank followed by the VA VA uses fact that Ii=f(b y1+I1 j, ji) X MF 1 Complexity still high: (2M-1 Viterbi Algorithm states) sc1(t)

s1(t)+s y2+I2 algorithm extends 2(t)+s3(t) In asynchronous Searches for ML X MF 2 case, over 3 bit sc2(t)times bit sequence y +I 3 3 VA samples MFs in round robin fasion MF 3 X

sc3(t) Suboptimal Detectors Main goal: reduced complexity Design tradeoffs Near far resistance Asynchronous versus synchronous Linear versus nonlinear Performance versus complexity Limitations under practical operating conditions Common methods Decorrelator MMSE

Multistage Decision Feedback Successive Interference Cancellation Mathematical Model Simplified Baseband system model (BPSK) signal for the kth user is: sk t xk i ck i sk t iT k i 0 sk(i) is the ith input symbol of the kth user

ck(i) is the real, positive channel gain sk(t) is theKsignature waveform containing the PN y t sequence sk t n t k is the transmission delay; for synchronous k 1 CDMA, k=0 for all users Received signal at baseband Matched Filter Output Sampled output of matched filter for T the kyth user: k y t sk t dt

0 K T j k 0 T ck xk x j c j sk t s j t dt sk t n t dt 1st term - desired 2nd term - MAI 3rd term - noise Assume 0 information T

two-user case (K=2), and r s1 t s2 t dt 0 Symbol Detection Outputs are: y c x 1 1 1 of the matched filters rc2 x2 z1 y2 c2 x2 rc1 x1 z2 xk sgn yk Detected If

symbol for user k: user 1 much stronger than user 2 (near/far problem), the MAI rc1x1 of user 2 is very large Decorrelator Matrix representation y RW x z where y=[y1,y2,,yK]T, R and W are KxK matrices Components of R are cross-correlations between codes W is diagonal with W k,k given by the channel gain ck z is a colored Gaussian noise vector 1 1 ~

~ Solve y R y W x R z xk sgn yk for x by inverting R Analogous to zero-forcing equalizers Multistage Detectors Decisions are 2nd produced by 1

x1 2 sgn y1 rc2 x2 1 x2 2 sgn y2 rc1 x1 1 stage: and so on st x 1 , x 1 2 1 stage

Successive Interference Cancellers Successively subtract off strongest detected bits b1 c1 x1 rc2 x2 z1 MF output: b2 c2 x2 rc1 x1 z2 x1 sgn b1 Decision x made sgn y rc for x strongest user: sgn c2 x2 MAI rc1 x1 from x1 z 2 the weaker Subtractthis

user: 2 all 2 1 1 MAI can be subtracted is user 1 decoded correctly Parallel Interference Cancellation Similarly uses all MF outputs Simultaneously subtracts off all of the users signals from all of the others works

better than SIC when all of the users are received with equal strength (e.g. under power control) Performance of MUD: AWGN Performance of MUD Rayleigh Fading Traditional Power Control On uplink, users have different channel gains If all users transmit at same power h3 h1 user (Pi=P), interference from near

drowns out far user P1 power control forces each signal to have the same received power P3 Traditional Channel inversion: Pi=P/hi h2 P2 Near Far Resistance Received signals are received at different powers MUDs

should be insensitive to nearfar problem Linear receivers typically near-far resistant Disparate power in received signal doesnt affect performance Nonlinear MUDs must typically take into account the received power of Synchronous vs. Asynchronous Linear MUDs dont need synchronization Basically project received vector onto

state space orthogonal to the interferers Timing of interference irrelevant Nonlinear MUDs typically detect interference to subtract it out If only detect over a one bit time, users must be synchronous Can detect over multiple bit times for asynch. users Channel Estimation (Flat Fading) Nonlinear MUDs typically require the channel gains of each user Channel obtain:

estimates difficult to Channel changing over time Must determine channel before MUD, so estimate is made in presence of interferers Imperfect estimates can State Space Methods Antenna techniques can also be used to remove interference (smart antennas) Combining antennas and MUD in a powerful technique for interference rejection

Optimal joint design remains an open problem, especially in Multipath Channels In channels with N multipath components, each interferer creates N interfering signals Multipath signals typically asynchronous MUD must detect and subtract out N(M-1) signals Desired signal also has N components, which should be combined via a RAKE.

MUD in multipath greatly increased Channel estimation a nightmare Summary of MUD MUD a powerful technique to reduce interference Optimal under ideal conditions High complexity: hard to implement Processing delay a problem for delayconstrained apps Degrades in real operating conditions Much research focused on complexity reduction, practical constraints, and real channels

Smart antennas seem to be more practical Multiuser OFDM Techniques Multiuser OFDM MCM/OFDM divides a wideband channel into narrowband subchannels to mitigate ISI In multiuser systems these subchannels can be allocated among different users Orthogonal allocation: Multiuser OFDM (OFDMA) Semiorthogonal allocation: Multicarrier CDMA

Adaptive techniques increase the spectral efficiency of the Multicarrier CDMA Multicarrier CDMA combines OFDM and CDMA Idea is to use DSSS to spread a narrowband signal and then send each chip over a different subcarrier DSSS time operations converted to frequency domain Greatly system

reduces complexity of SS FFT/IFFT replace synchronization and despreading Multicarrier DSCDMA The data is serial-to-parallel converted. Symbols on each branch spread in time. Spread c(t) signals transmitted via S/P convert

... OFDM s(t) Get spreading frequency IFFT in both time and c(t) P/S convert Cellular System Overview Frequencies (or time slots or codes) are reused at spatially-separated locations exploits power falloff with distance. Base stations perform centralized control functions (call setup, handoff, routing, etc.)

Best efficiency obtained with minimum reuse 8C32810.43-Cimini-7/98 Basic Design Considerations Spectral Sharing Reuse Distance TD,CD or hybrid (TD/FD) Frequency reuse Distance between cells using the same frequency, timeslot, or code

Smaller reuse distance packs more users into a given area, but also increases co-channel interference Cell radius Decreasing the cell size increases system capacity, but complicates routing and handoff Cellular Evolution: 1G-3G Japan 1 Gen 1st Gen st nd Gen 22nd Gen 3rd rd Gen

3 Gen TACS Americas Europe NMT/TACS/Other PDC GSM W-CDMA/EDGE (EDGE in Europe and Asia outside Japan) AMPS TDMA CDMA EDGE

WCDMA Global strategy based on W-CDMA and EDGE networks, common IP based network, and dual mode W-CDMA/EDGE phones. cdma2000 was the initial standard, which evolved To WCDMA 1-2 G Cellular Design: Voice Centric Cellular coverage designed for voice service Area outage, e.g. < 10% or < 5%. Minimal, but equal, service everywhere. Cellular

systems are designed for voice 20 ms framing structure Strong FEC, interleaving delays. Spectral around Efficiency and decoding 0.04-0.07 bps/Hz/sector IS-54/IS-136 (TD) FDD separates uplink and downlink. Timeslots

cells. FDD allocated between different separates uplink and downlink. One of the US standards for digital cellular IS-54 in 900 MHz (cellular) band. IS-136 in 2 GHz (PCS) band. IS-54 compatible with US analog GSM (TD with FH) FDD

separates uplink and downlink. Access is combination of FD,TD, and slow FH Total BW Channels divided into 200Khz channels. reused in cells based on signal and interference measurements. All signals modulated with a FH code. FH codes within a cell are orthogonal. FH codes in different cells are semi-orthgonal FH mitigates frequency-selective fading via coding. FH averages interference via the pseudorandom hop pattern

IS-95 (CDMA) Each user assigned a unique DS spreading code Orthogonal codes on the downlink Semiorthogonal codes on the uplink Code is reused in every cell No frequency planning needed Allows for soft handoff is code not use in neighboring cell Power in control required due to nearfar problem

3G Cellular Design: Voice and Data Goal (early 90s): A single worldwide air interface Yeah, Bursty right Data => Packet Transmission Simultaneous transmisison Need 384 Need with circuit voice

to widen the data pipe: Kbps outdoors, 1 Mbps indoors. to provide QOS Evolve from best effort to statistical or guaranteed 3G GSM-Based Systems EDGE: Packet data with adaptive modulation and coding 8-PSK/GMSK at 271 ksps supports 9.02 to 59.2 kbps per time slot with up to 8 time-slots

Supports IP peak rates over 384 kbps centric for both voice and data 3G CDMA Approaches W-CDMA and cdma2000 cdma2000 used a multicarrier overlay for IS95 compatibility WCDMA designed for evolution of GSM systems Current 3G services based on WCDMA Voice, streaming, high-speed data

Multirate service via variable power and spreading Different services can be mixed on a single code CA for a user CC CD 38 Features of WCDMA Bandwidth Spreading codes Scrambling codes Data Modulation Data rates 5, 10, 20 MHz Orthogonal variable spreading factor (OVSF) SF: 4-256 DL- Gold sequences. (len18) UL- Gold/Kasami

sequences (len-41) DL - QPSK UL - BPSK 144 kbps, 384 kbps, 2 Mbps 4G Evolution LTE most recent cellular standard: 200 networks worldwide LTE Penetration (Sept. 2013) Predicted by Ericsson to be 60% by 2018, serving 1 billion phones Long-Term Evolution (LTE) OFDM/MIMO Much higher data rates (50-100 Mbps) Greater

spectral efficiency (bits/s/Hz) Flexible use of up to 100 MHz of spectrum Low packet latency (<5ms). Rethinking Cells in Cellular How should cellular Smallsystems be designed? Cell Coop MIMO Relay DAS

Traditional Will gains in practice be big or incremental; in capacity or coverage? cellular design interference-limited MIMO/multiuser detection can remove interference Cooperating BSs form a MIMO array: what is a cell? Relays change cell shape and boundaries Distributed antennas move BS towards cell boundary Small cells create a cell within a cell Mobile relaying, virtual MIMO, analog network coding. Are small cells the solution to increase cellular system capacity? Yes, with reuse one and adaptive techniques (Alouini/Goldsmith 1999) Area Spectral Efficiency

A=.25D2p 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 Todays architecture MACRO: 3M Macrocells serving 5 billion solving initial users coverage issue, existing network 10x Lower HW COST PICO: solving street,

enterprise & home coverage/cap acity issue FEMTO: solving enterprise & Picocell Macrocell home coverage/cap Femtoc ell 10x Near 100% CAPACITY COVERAGE Improvem ent interference Managing between cells is hard

Deployment Challenges Deploying One Macrocell New site verification Effort (MD Man Day) 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 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 selforganizing networks (SoN) SON for LTE small cells Mobile Gateway Or Cloud Node Installation Self

Healing SoN Server Initial Measurement s IP Network Self Configuration Measurement SON Server Self Optimizatio n X2 X2

Small cell BS Macrocell BS X2 X2 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

MIMO Techniques in Cellular How should MIMO be fully used in cellular systems? Shannon capacity requires dirty paper coding or IC Network MIMO: Cooperating BSs form an antenna array Downlink is a MIMO BC, uplink is a MIMO MAC Can treat interference as known signal (DPC) or 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 Optimal use of MIMO in cellular systems, especially given practical constraints, remains an open problem Sectorization and Smart Antennas 5 2 5 3 5

8C32810.46-Cimini-7/98 7 6 5 1 4 5 5 1200 sectoring reduces interference by one third Requires base station handoff between sectors Capacity increase commensurate with shrinking cell size

Beam Steering SIGNAL INTERFERENCE INTERFERENCE SIGNAL OUTPUT BEAMFORMING WEIGHTS Beamforming weights used to place nulls in up to NR directions Can also enhance gain in direction of desired signal Requires AOA information for signal and MUD in Cellular

In 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 orthogonal spreading 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. MUD in Cellular Goal: decode interfering signals to remove them from desired signal

Interference cancellation decode strongest signal first; subtract it from the remaining signals repeat cancellation process on remaining signals works best when signals received at very different power levels Optimal multiuser detector (Verdu Algorithm) cancels interference between users in parallel complexity increases exponentially with the number of users Other techniques trade off performance and complexity decorrelating detector 7C29822.051-Cimini-9/97 Successive Interference Cancellers

Successively subtract off strongest detected bits b1 c1 x1 rc2 x2 z1 b2 c2 x2 rc1 x1 z2 MF output: x1 sgn b1 Decision made x 2 sgn y2 for rc1 x1 strongest user: sgn c2MAI x2 rc1 from x1 x1 the z 2 weaker user: Subtract this all MAI can be subtracted is user 1 decoded correctly

MAI is reduced and near/far problem Parallel Interference Cancellation Similarly uses all MF outputs Simultaneously subtracts off all of the users signals from all of the others works better than SIC when all of the users are received with equal strength Performance of MUD: AWGN Optimal Multiuser Detection Maximum

Likelihood Sequence Estimation Detect bits of all users simultaneously (2M possibilities) Matched (Verdu86) filter bank followed by the VA VA uses fact that Ii=f(b y1+I1 j, ji) X MF 1 Complexity still high: (2M-1 Viterbi Algorithm states) sc1(t) s1(t)+s y2+I2 algorithm extends

2(t)+s3(t) In asynchronous Searches for ML X MF 2 case, over 3 bit sc2(t)times bit sequence y +I 3 3 VA samples MFs in round robin fasion MF 3 X sc3(t)

Tradeoffs Diversity vs. Interference Cancellation x1(t) x2(t) wt1(t) wt2(t) r1(t) r2(t) wr1(t) wr2(t) sD(t) + xM(t)

wtT(t) Nt transmit antennas rR(t) wrR(t) NR receive antennas Romero and Goldsmith: Performance comparison of MRC and IC Under transmit diversity, IEEE Trans. Wireless Comm., May 2009 y(t) Diversity/IC Tradeoffs N antennas at the RX provide NRfold diversity gain in fading R Get NTNR diversity gain in MIMO

system Can also be used to null out NR interferers via beam-steering Beam steering at TX reduces interference at RX Antennas can be divided between diversity combining and interference cancellation Diversity Combining Techniques MRC diversity achieves maximum SNR in fading channels.

MRC is suboptimal for maximizing SINR in channels with fading and interference 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 Fo in form to that for MRC

L>N, OC with equal average interference powers achieves the Performance Analysis for IC Assume that N antennas perfectly cancel N-1 strongest interferers General fading assumed for desired signal Rayleigh fading assumed for interferers Performance impacted by remaining interferers and noise SINR and Outage Probability The

MGF for the interference can be computed in closed form pdf is obtained from MGF by differentiation Can express outage2 probability ( y ) / P in Pout |Y of ( X ( y signal )) 1 and e y P terms desired interference as Pout 1 e ( y 2 2

Unconditional s ) / Ps y / Ps e fY ( y )dy 0 Pout obtained as 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 Summary Multiuser detection removes interference: tradeoffs between complexity and performance Multiuser OFDM the basis for current cellular and WiFi systems. Cellular systems have evolved from voiceonly to sophisticated high-speed data bandwidth limited. HetNets the key to increasing capacity of cellular systems require automated selforganization (SoN)

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