ECSE 6592 Wireless Ad Hoc and Sensor Networks Spatial Diversity in Wireless Networks Hsin-Yi Shen Nov 3, 2005.

ECSE 6592 Wireless Ad Hoc and Sensor

Networks Spatial Diversity in Wireless Networks

Hsin-Yi Shen

Nov 3, 2005

Introduction

Main characteristic in wireless channels- randomness in users’ transmission channels and randomness in users’ geographical locations

Diversity- Convey information through multiple independent instantiations of random attenuations

Spatial diversity- through multiple antennas or multiple users

Wireless Channel Characteristics Three kind of attenuations-path loss,

shadowing loss, fading loss Path loss: Signals attenuate due to distance Shadowing loss : absorption of radio waves

by scattering structures Fading loss :constructive and destructive

interference of multiple reflected radio wave paths

Attenuation in Wireless Channels

Wireless Channel Characteristics Key parameters of wireless channels-

coherence time, coherence bandwidth If symbol period>coherence time, the channel

is time selective If symbol period< channel delay spread, the

channel is frequency selective

MIMO Channel Model

H(k;l) is the lth tap of the Mr x Mt channel response matrix, z is noise vector

Theoretical Consideration Information-Theoretic results for multiple-

antenna channels Information-Theoretic results for multi-user

channels Diversity order Design consideration

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Design Consideration

Space time code with low decoding complexity and achieving maximum diversity order

Trade-off between diversity order and rate If system is delay-constrained, design with

high diversity order and lower data rate Fairness for resource sharing between users Cross layer design

Signal transmission

Transmitter Techniques- spatial multiplexing, space-time trellis code and block codes

Receiver techniques- joint equalization with channel estimation, space-time code decoding

Spatial Multiplexing (Bell Labs Layered Space-Time Architecture, BLAST) Multiple transmitted data streams are separated and

detected successfully using a combination of array processing (nulling) and multi-user detection (interference cancellation) techniques

A broadband channel scenario using a MIMO generalization of classical decision feedback equalizer (DFE)

The nulling operation is performed as “feed-forward filter” and the interference cancellation operation is performed by the “feedback filter”

Spatial Multiplexing-continued May have error propagation The presence of antenna correlation and the

lack of scattering richness in the propagation environment reduce the achievable rates of spatial multiplexing techniques

Enhancement: Use MMSE interference cancellation, perform ML detection for first few streams

Space time coding

Improve downlink performance without requiring multiple receive antennas

Easily combined with channel coding Do not require channel state information at

the transmitter Robust against non-ideal operating

conditions

Space-time Trellis codes

Maps information bit stream into Mt streams of symbols

Decoding complexity increases exponentially as a function of the diversity level and transmission rate

Example:

Space time block codes

Cons and pros of space time block codes Achieve full diversity at full transmission rate for any

signal constellation Does not require CSI at the transmitter ML decoding involves only linear processing at the

receiver Does not provide coding gain A rate-1 STBC cannot be constructed for any

complex signal constellation with more than two transmit antennas

Simple decoding rule valid only for flat-fading channel where channel gain is const over two consecutive symbols

Tradeoff between diversity and throughput BLAST achieves max spatial multiplexing

with small diversity gain Space time codes achieves max diversity

gain with no multiplexing gain Linear dispersion codes (LDC): achieve

higher rate with polynomial decoding complexity for a wide SNR range

Build in the diversity into the modulation

Build Diversity into modulation

Receiver techniques

Coherent and non-coherent techniques Coherent technique require channel state

information by channel estimation or training sequences and feed this to joint equalization/decoding algorithm

Non-coherent techniques does not require CSI and more suitable for rapidly time-varying channels

Joint Equalization/Decoding techniques M-BCJR algorithm: at

each trellis step, only M active states associated with the highest metrics are retained

Significant reduction in the number of equalizer/decoder states

Sphere decoder

Suitable for codes with lattice structures Perform ML search with low computation

complexity

Joint Equalization/Decoding of space time Block codes Eliminate inter-antenna interference using a

low complexity linear combiner Single-carrier frequency domain equalizer

(SC-FDE)

Performance of SC-FDE

Non-coherent techniques

Does not require channel estimation Include blind identification and detection

schemes Exploit channel structure (finite impulse

response), input constellation (finite alphabet), output (cyclostationarity) to eliminate training symbols

Use ML receiver which assumes statistics about channel state but not knowledge of the state itself

Summary in signal transmission Mitigate fading effect by using space diversity Use MIMO to realize spatial rate multiplexing

gains Use equalization techniques (ex: M-BCJR,

SC-FDE) to mitigate channel frequency selectivity

Use channel estimation and tracking, adaptive filtering, differential transmission/detection to mitigate time selectivity

Networking issues

Medium sharing resource allocation Mobility and routing Hybrid networks

Resource allocation

Allocation criteria: rate-based criteria and job-based criteria

Rate-based criteria provide average data rates to users which satisfy certain properties

Job-based criteria schedule data delivery in order to optimize various QoS guarantees based on the job requests

Resource allocation-Rate-Based QoS criteria Utilize the multi-user diversity inherently available in

wireless channels Schedule users when their channel state is close to

peak rate it can support => inherent unfairness Keep track of the average throughput Tk(t) and rate

Rk(t), transmit the user with the largest Rk(t)/ Tk(t) among the active users

If channel is slow time-varying, introduce random phase rotations between the antennas to simulate fast fading

Impact of spatial diversity

Multi-antenna diversity provide greater reliability by smoothening channel variations

Multi-user diversity utilize the channel variability across users to increase throughput

Choose diversity techniques according to channel conditions, mobility and application constraints

For example, low delay-applications with high reliability requirement may use multi-antenna diversity with space time codes

Hybrid Networks

Two approaches to increasing TCP efficiency in hybrid networks

Reduce error rate in wireless channel by using more sophisticated coding schemes, such as space-time codes

Use explicit loss notification (ELN) to inform the sender that the packet loss occurred due to wireless link failure rather than congestion in wired part

Space time code and TCP throughput STBC-enhanced 802.11a achieves a

particular throughput value at a much lower SNR value than the standard 802.11a

STBC modify the SNR region under which a particular transmission rate should be chosen

STBC increase the transmission range and improve robustness of WLANs

STBC-enhanced 802.11a

The difference between STBC 802.11a and 802.11a becomes smaller when channel quality is sufficiently good

STBC-802.11a can switch to faster transmission mode at much lower SNR values

Conclusion

In wireless networks, power and spectral bandwidth are limited

Limitation on signal processing at terminal and requirement of sophisticated resource allocation techniques due to variation in capacity

Spatial diversity improves data rates and reliability of individual links

Space time codes improves link capacity and system capacity through resource allocation

Future works

Space time code design Implementation issues-low-cost multiple RF

chains and low-power parallelizable implementation of STC receiver signal processing algorithm

Receiver signal processing-the development of practical adaptive algorithm that can track rapid variation of large number of taps in MIMO channel and/or equalizer

Standardization activities

Reference

[1]S. N. Diggavi, N. Al-Dhahir, A. Stamoulis, and A.R. Calderbank, “Great Expectations: The Value of Spatial Diversity in Wireless Networks,” Proceeding of The IEEE, Vol. 92, No. 2, pp219-270, Feb 2004

[2] Sergio Verdu, “Multiuser Detection,” Cambridge University Press, 1998