Cooperative MIMO Communications

1
Cooperative MIMO Communications

• Hsin-Yi Shen
• January 23, 2009

2
Outline

• Introduction
• Cooperative Diversity
• Our Contribution
• Asynchronous cooperative MIMO communication
• Overhead Analysis
• Cooperative MIMO systems with space-time block
  codes (STBC) and code combining
• Cooperative MIMO systems with Multiple Carrier
  Frequency Offset
• Conclusion

3
Introduction

• Fading effects and channel variation often
  degrade data transmission in wireless
  environments
• MIMO degree-of-freedom gain diversity gains
• However, MIMO requires multiple antennas at
  transmitter and receiver
• Cooperative diversity gt achieve spatial
  diversity with even one antenna per-node (eg
  MISO, SIMO, MIMO)
• Main idea recruit nearby idle nodes to assist
  transmitting and receiving data
• Cooperative MIMO special case of coop. diversity
• Achieve MIMO gains even with one antenna
  per-node.
• Eg open-spectrum meshed/ad-hoc networks, sensor
  networks, backhaul from rural areas

4
MIMO vs Cooperative MIMO
Tx
Rx
.
8x8 MIMO gt 64 channel estimations required
Source node
Destination node
Transmitting cluster
Transmitting cluster
Receiving cluster
We decompose 8x8 MIMO into a 8x1 MISO problems
soft combining. gt only 8 per-node channel
estimations required
5
Cooperative MIMO Communication with Meshed
Backhaul Networks
Inter-cluster transmission
Intra-cluster transmission
user
6
Outline

• Introduction
• Cooperative Diversity
• Our Contribution
• Asynchronous cooperative MIMO communication
• Overhead Analysis
• Cooperative MIMO systems with space-time block
  codes (STBC) and code combining
• Cooperative MIMO systems with Multiple Carrier
  Frequency Offset
• Conclusion

7
Cooperative Diversity

• Motivation
• In MIMO, size of the antenna array must be
  several times the wavelength of the RF carrier
• unattractive choice to achieve receiver diversity
  in small handsets/cellular phones
• Cooperative diversity Transmitting nodes use
  idle nodes as relays to reduce multi-path fading
  effect in wireless channels
• Methods
• Amplify and forward
• Decode and forward
• Coded Cooperation
• Application Virtual MIMO

8
Cooperative Diversity Schemes
Amplify and forward
Decode and forward
Coded cooperation
decode
amplify
0101
N1 bits
N2 bits
forward
Frame 1
Frame 2
Relay node
Relay node
Relay node
Destination node
Destination node
Source node
Source node
Source node
N1 bits
N2 bits
Frame 1
Frame 2
Source Signal
Source Signal
9
Our Design for Cooperative MIMO
Coded cooperation
Amplify and forward
Decode and forward
decode
N1 bits
N2 bits
0101
amplify
Frame 2 Frame 1
forward
Relay node
Relay node
Relay node
Tx
Rx
N1 bits
N2 bits
Rx
Tx
Tx
Frame 1 Frame 2
Source node
Rx cluster
Destination node
Tx cluster
10
Outline

• Introduction
• Cooperative Diversity
• Our Contribution
• Asynchronous cooperative MIMO communication
• Overhead Analysis
• Cooperative MIMO systems with space-time block
  codes (STBC) and code combining
• Cooperative MIMO systems with Multiple Carrier
  Frequency Offset
• Conclusion

11
Cooperative MIMO Phase 1 2
PHASE 1 Source node broadcasts symbol (to ALL
cluster members and destination)
Destination node
Source node
Rx cluster
Tx cluster
PHASE 2 Inter-cluster Tx-cluster detects
rexmits symbol to Rx cluster AND destination.
Source node
Rx cluster
Destination node
Tx cluster
12
Cooperative MIMO Phase 3
PHASE 3 Rx-cluster destination do MISO
soft-symbol detection. Rx-cluster transmits soft
symbols sequentially to destination
Source node
Destination node
Tx cluster
Rx cluster
Destination combines the soft symbols from
Rx-cluster
Source node
Destination node
Rx cluster
Tx cluster
13
Handling Asynchrony

• Synchronization techniques are required in most
  current cooperative schemes
• The lack of synchronization may result in inter
  symbol interference (ISI) and dispersive channels
  
• Different propagation delays due to distance
  variation
• We allow 1-symbol asynchrony (see next slide)

14
1-symbol asynchrony
Indirect path
Direct path
Rx cluster node has to do MISO soft-detection
while tolerating this max multi-path delay!

• Difference between the direct 1-hop path is at
  most 1-symbol time
• Why? the send cluster member is at most ½ symbol
  time away from sender
• The detect-and-rexmit step is assumed to be
  near-instantaneous

15
Asynchronous MISO detection
yri(t)
Soft Quantization
h(t)
DFE
Sampling rate ndata rate

• Multi-path requires equalization. We choose the
  DFE equalizer.
• Handles fast deep fades well. But linear
  equalizers can also be used (instead of DFE)
• We tap the channel at n-times the symbol rate.
• Even though we control max-delay spread
• with more randomly positioned Tx cluster nodes,
  
• more taps allows us to resolve indirect paths
  in the equalization
• Tradeoff need to tap faster if many Tx cluster
  nodes

16
Overall Receiver Structure (Rx-cluster
destination)
Destination node
Receiving cluster
17
Bit error rate with different SNR

• Coop MIMO increase diversity gain and degrees of
  freedom compared to SISO
• Cluster sizes gt 3
• BER curve for proposed system is better than 3x3
  MIMO system

18
Outline

• Introduction
• Cooperative Diversity
• Our Contribution
• Asynchronous cooperative MIMO communication
• Overhead Analysis
• Cooperative MIMO systems with space-time block
  codes (STBC) and code combining
• Cooperative MIMO systems with Multiple Carrier
  Frequency Offset
• Conclusion

19
Overhead analysis

• Analysis starts from AWGN channel capacity
  formula
• Three phases with transmission times t1, t2, t3.
• Total time t1t2t3 or capacity 1/(t1t2t3)
• Then compute capacity ratio with respect to
  direct Tx capacity
• Assumptions
• The size of transmitting cluster is M1 and the
  size of receiving cluster is N1 (including the
  source node and destination node).
• Each node in source cluster transmits with equal
  power P/(M1)

20
Analysis of capacity ratio -Phase I

• Phase I Broadcasting

Transmission time for Phase I
Source node transmits to cluster members and
destination
Destination node
Source node
21
Analysis of capacity ratio -Phase II

• Phase II Inter-Cluster Transmission

Transmission time for Phase II
Inter-cluster transmission between transmitting
cluster and receiving cluster
Source node
Receiving cluster
Destination node
Transmitting cluster
Transmitting cluster
22
Analysis of capacity ratio -Phase III

• Phase III intra-cluster transmission in
  destination cluster

Transmission time for Phase III
Intra-cluster transmission for soft symbols
Source node
Destination node
Transmitting cluster
Receiving cluster
Note Q is of bits to represent a hard symbol
as soft symbol
23
Capacity ratio

• Total transmission time and the capacity is
• Thus the system capacity ratio is

24
The relation of capacity ratio and major system
factors
Note Tx cluster size (M1) Rx cluster size
(N1), incl. of src/dest
25
Outline

• Introduction
• Cooperative Diversity
• Our Contribution
• Asynchronous cooperative MIMO communication
• Overhead Analysis
• Cooperative MIMO systems with STBC and code
  combining
• Cooperative MIMO systems with Multiple Carrier
  Frequency Offset
• Conclusion

26
Cooperative MIMO system with STBC and code
combining

• Key Challenges in Cooperative MIMO
• node coordination in sending and receiving group
• gtcluster recruiting algorithm and asynchronous
  scheme
• Achieve distributed MIMO gain by utilizing both
  transmitter and receiver diversity
• distributed space-time coding in senders
• data combining in the destination
• Solution
• distributed implementation of space-time block
  codes (STBC) in sending group
• STBC only change the order of information bits
• gt suitable for distributed implementation
• code combining in receiving group
• Use convolution code and Viterbi decoder
• provide not only spatial diversity but the MIMO
  diversity

27
Proposed DesignStep 1 Broadcasting

• Before transmission, the sending and receiving
  group have been formed
• The source node encodes information bits by FEC
  and broadcasts to select neighbor nodes
• Number of nodes required by STBC is selected
• Gives order for selected helper nodes so each
  helper node will choose the corresponding row in
  space-time block code (STBC) matrix.

Receiving Group
Sending Group
Source node broadcasts data and sends control
message to destination node to forms receiving
group
28
Proposed DesignStep 2 STBC MIMO transmission

• The helper nodes in sending group use the
  corresponding row in STBC code matrix to change
  the permutation of data bits
• Transmit space-time coded data to the receiving
  group
• Note STBC is applied properly with distributed
  implementation because of knowing exact sending
  group size and assigning order to each node

Sending group
Receiving Group
(b) MIMO transmission
29
Proposed DesignStep 3 Data Collection
Combining

• Each node in the receiving group decodes the
  space-time block coded (STBC) data.
• After decoding for STBC, the helper nodes in
  receiving group relay their copies to the
  destination node.
• The destination detects them as soft symbols.
• Then the destination uses code combining and
  chooses the most possible codeword based on
  received soft symbols.

Sending group
Receiving group
(c) Data Collection and Code Combining
30
BER and energy consumption
Although cooperative MIMO communication has more
control-message overhead, the total power
consumption is low due to low BER and fewer
retransmissions
As sending/receiving groups increase, BER
decreases faster because of transmitter and
receiver diversity
31
Energy Consumption

• Energy for unsuccessful attempt
• Energy for successful attempt

Proposed system utilizes both transmitter and
receiver diversity gt lowest power consumption
when transmission power is the same
Total Energy
32
Outline

• Introduction
• Cooperative Diversity
• Our Contribution
• Asynchronous cooperative MIMO communication
• Overhead Analysis
• Cooperative MIMO systems with STBC and code
  combining
• Cooperative MIMO systems with Multiple Carrier
  Frequency Offset
• Conclusion

33
Cooperative MIMO Systems with Multiple Carrier
Frequency Offsets

• Key challenges in proposed cooperative MIMO
  system design
• Each sending node has individual electronic
  circuit for carrier frequency generation
• Distortion from multiple carrier frequency
  offsets
• Most of current techniques consider single
  carrier frequency offset, such Phase Lock Loop
  (PLL)gt Multiple CFO estimation is desired
• The sending group implements space-time block
  codes (STBC) in a distributed manner
• Each receiving node will receive STBC-coded
  signal under the distortion of multiple carrier
  frequency offsets
• Solution
• Estimation of the multiple carrier frequency
  offsets
• Use uncorrelated pilot symbols
• MMSE Detection of space-time block coded (STBC)
  data under multiple carrier frequency offsets

34
Using PN sequence as uncorrelated pilot symbols

• Each receiver needs to estimate the multiple
  carrier frequency offsets (CFO) from senders
• We propose to use pseudo-random noise (PN)
  sequence as uncorrelated pilot symbols for
  multiple CFO estimation
• Use shift register to generate PN sequence
• Different initial state in shift registergt
  generate uncorrelated sequence
• Thus each receiver only requires information on
  shift register length and initial state of shift
  register in each sender to obtain uncorrelated
  pilot symbols gtsuitable for distributed
  implementation
• To send pilot symbols, source node first decides
  the length of shift register and assigns the
  initial state of the shift register for each
  sending node
• Include this information in MIMO RTS so receiving
  nodes can obtain pilot symbol information

35
Design of estimation algorithm for multiple CFOs

• Sending group starts pilot symbol transmission
  and all receiving nodes use the received mixed
  signal of pilot symbols for multiple CFO
  estimation
• Assume M sending nodes and N receiving nodes
• Denote pilot symbols and carrier frequency offset
  in sending node i as pi and fi
• Thus receiving signal at receiving node r is
  
• 
  where n is the symbol index
• Then compute the discrete-time Fourier Transform
  (DTFT) of the received signal
• The cross-correlation of the DTFT of receiving
  signal and the DTFT of pilot symbols is
• The above function has maximum at lag 0 and can
  use to estimate CFO

36
Iterative estimation algorithm for multiple CFOs

• In each iteration, use estimated information from
  last iteration and estimate the multiple CFOs
  sequentially
• Algorithm stops when small estimation error or
  large of iterations

37
STBC decoding under Multiple Carrier Frequency
Offset

• After obtaining the information of multiple CFOs,
  the receiving nodes need to detect receiving
  signals.
• With STBC-coded data x, the received signal at
  receiving node r, yr, is given by
  ,while N is noise and Hr is the matrix of
  path gain
• The element in position (t,tt(i)) of Hr is the
  path gain of symbol xi transmitted at time t by
  sending node tt(i),
• where ? is path-loss component and ai,r is
  fading gain
• Hr become non-orthogonal and time-variant matrix
  due to impefect carriers
• We propose to use a linear MMSE detector to
  detect the STBC-coded data under multiple carrier
  frequency offsets

38
Detection algorithm of STBC decoding under
multiple CFOs

• At time kc, the signal received at receiving node
  r is
• To simplify the computational complexity in
  receiving node r ,use
• The mean square value of detection error is
• So the MMSE detector can be rewritten as
• Thus the linear MMSE detector is applied to
  received signal,
• and the ith element in the output vector above is
  detected as xi

39
BER Simulation result

• Compare proposed system with a) Cooperative code
  combining without STBC and b) Cooperative MIMO
  systems without code combining
• Proposed system has best performance
• No full transmitter diversity guaranteed due to
  multiple CFO and non-orthogonal path gain matrix
• But STBC coding, FEC code combining and the
  proposed linear MMSE detector still improves BER
  performance.

40
Comparison of simulation result with different
estimation algorithms

• Compare with results of no CFO estimation and
  non-iterative estimation
• No CFO estimation cannot detect symbols due to
  distortion of multiple carrier frequency offsets
• Non-iterative estimation cannot precisely
  estimate when multiple senders
• Iterative estimation estimate precisely even
  under multiple senders. gt Performance of
  proposed iterative algorithm is significantly
  better.

41
Simulation result of energy consumption

• We compare the energy consumption in different
  system design
• Energy consumption in cooperative FEC system is
  lower than it in cooperative relay because
  cooperative code combining improves BER
  performance and require less retransmission.
• The proposed system has lower energy consumption
• Proposed system uses more control messages in
  node coordination
• But it also has better BER and require less
  retransmission
• Thus proposed system provides reliable low-power
  transmission

42
Conclusion

• Cooperative communication systems achieve lower
  transmission power, extend battery life, and
  improve network connectivity and throughput
• Our works consider to design the cooperative MIMO
  communication step by step
• Cluster recruiting algorithm form clusters for
  cooperative MIMO communication (shown in the
  thesis)
• Asynchronous cooperative transmission deal with
  the synchronization problem in sending nodes
• Overhead analysis Consider the system overhead
  and performance analysis
• Cooperative MIMO with STBC and code combining
  fully utilize both transmitter and receiver
  diversity to achieve MIMO gain
• Cooperative MIMO system with multiple carrier
  frequency offset Consider the distributed
  senders and provide CFO estimation and signal
  detection scheme for proposed system design
• Theoretical analysis and formulas are provided in
  dissertation

43
Related Publications

• IEEE DCDIS, Guelph, Canada, July27-29, 2005,
  Cluster Recruiting for Ad Hoc Cooperative
  Networks, Hsin-Yi Shen, Babak Azimi-Sadjadi, and
  Alejandra Mercado
• IEEE WiOPT, April 16-20, 2007, Limassol, Cyprus,
  Asynchronous Cooperative MIMO Communications,
  Hsin-Yi Shen and Shivkumar Kalyaraman
• IEEE Globecom 2007 Ad-hoc and Sensor Networking
  Symposium - Globecom 2007 Ad-hoc and Sensor
  Networking Symposium "A MAC Protocol for
  Cooperative MIMO Transmissions in Sensor
  Networks" Haiming Yang, Hsin-Yi Shen, Biplab
  Sikdar
• IEEE Globecom 2008 Ad Hoc, Sensor and Mesh
  Networking Symposium - IEEE Globecom 2008 Ad Hoc,
  Sensor and Mesh Networking Symposium "A
  Distributed System for Cooperative MIMO
  Transmissions" Hsin-Yi Shen, Haiming Yang, Biplab
  Sikdar, Shivkumar Kalyanaraman
• The 28th IEEE International Conference on
  Computer Communications - INFOCOM
  Mini-Conference, "A Threshold Based MAC Protocol
  for Cooperative MIMO Transmissions", Haiming
  Yang, Hsin-Yi Shen, Biplab Sikdar,  Shivkumar
  Kalyanaraman
• Hsin-Yi Shen, Shivkumar Kalyanaraman and Biplab
  Sikdar, Asynchronous Cooperative MIMO
  Communications System Design and Overhead
  Analysis, submitted to IEEE IEEE Transactions on
  Wireless Communications

44
Comparison of Cooperative Diversity Scheme

• Decode and Forward
• Simple and adaptable to channel condition (power
  allocation)
• If detection in relay node unsuccessful gt
  detrimental for detection in receiver (adaptive
  algorithm can fix the problem)
• Receiver need CSI between source and relay for
  optimum decoding
• Amplify and Forward
• Achieve full diversity
• Performance better than direct transmission and
  decode-and-forward
• achieve the capacity when number of relays tend
  to infinity
• Coded Cooperation
• transmit incremental redundancy for partner
• Automatic manage through code design
• no feedback required between the source and relay
• Rely on full decoding at the relay gt cannot
  achieve full diversity!
• Not scalable to large cooperating groups.
• Other methods are proposed to use spatial
  diversity by node cooperation

45
Energy consumption analysis

• The energy consumption for an unsuccessful
  transmission attempt is
• And energy consumption for a successful
  transmission is
• where Emrts, Emcts, Eack, Errts and Escts are
  the energy spent on sending MRTS, MCTS, ACK, RRTS
  and SCTS packets.
• Ecol energy spent by each receiving node
  during data collection.
• M, N size of source and destination clusters,
  respectively
• Ebr energy spent on broadcasting data to
  nodes in the sending group.
• Edata energy spent on data transmission
  between sending/receiving groups.
• Assume the length of all control messages is Lc
  and the size of a data packet is L.
• The data rate is R and a convolutional code with
  rate Rc

46
Energy consumption analysis- Cont

• Thus the equation of unsuccessful and successful
  transmission can be rewritten as
• And the total energy consumption for transmission
  in cooperative MIMO system is
• where Pe is the packet error probability.