ecse 6592 wireless ad hoc and sensor networks spatial diversity in wireless networks hsin-yi shen...
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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
Information-Theoretic results for multiple antenna channels (1)
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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