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Cooperative MIMO Communications

Cooperative MIMO Communications. Hsin-Yi Shen January 23, 2009. Outline. Introduction Cooperative Diversity Our Contribution Asynchronous cooperative MIMO communication Overhead Analysis Cooperative MIMO systems with space-time block codes (STBC) and code combining

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Cooperative MIMO Communications

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  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 => 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 => 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. => only 8 per-node channel estimations required

  5. BS BS BS BS BS 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. n Asynchronous MISO detection yri(t) Soft Quantization h(t) DFE Sampling rate = n*data 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 > 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 = t1+t2+t3 or capacity = 1/(t1+t2+t3) • Then compute capacity ratio with respect to direct Tx capacity • Assumptions: • The size of transmitting cluster is M+1 and the size of receiving cluster is N+1 (including the source node and destination node). • Each node in source cluster transmits with equal power P/(M+1)

  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. 5x5 MIMO 4x4 MIMO N=4 5x5 Coop 4x4 Coop N=3 3x3 MIMO The size of receiving cluster (N+1) more important factor for capacity ratio (than Tx cluster size M+1) Capacity ratio decreases as SNR increases. Compared to the equivalent MIMO case, the capacity ratio is smaller due to node cooperation overheads The relation of capacity ratio and major system factors Note: Tx cluster size (M+1) & Rx cluster size (N+1), 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 =>cluster 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 => 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 for unsuccessful attempt Energy for successful attempt Energy Consumption Proposed system utilizes both transmitter and receiver diversity => 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)=> 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 register=> 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 =>suitable 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,τt(i)) of Hr is the path gain of symbol xi transmitted at time t by sending node τt(i), where λ is path-loss component and αi,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. => 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 => 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 => 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.

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