1. Contact: niranjan@ece.ualberta.ca Robust Wireless Communication System for Maritime Monitoring Robust Wireless Communication System for Maritime Monitoring Thomas S. John, Department of Electrical Engineering, Stanford University. A. Nallanathan Department of Electrical and Computer Engineering National University of Singapore.

2. Introduction  Communications in maritime protection via the ability of rapidly field flexible, wireless, ad-hoc mobile networks.  Inaugural partner project COASTS (Coalition Operational Area Surveillance and Targeting System).  COASTS mission is to develop low cost, unclassified unattended sensor networks.  Provide real-time information for tactical and remote command-and-control.  Wireless Technology: Combination of 802.11 and 802.16.

3. Wi-Fi 802.11n and WiMax 802.16  802.11n Wi-Fi standard is to emerge in Mid-2006.  Date rate for 802.11n 100 Mbits/s.  WiMax 802.16 is going to be the real future of wireless (peer to peer communication, range upto 50km).  MIMO-OFDM is recommended for Wi-Fi and WiMax.  Maritime Protection: Combination of 802.11 and 802.16.  Hence, MIMO-OFDM transceiver design becomes important.

4. MIMO-OFDM  OFDM is a potential scheme for high data rate wireless transmission.  OFDM can be combined with multiple transmit and receive antennas: MIMO-OFDM

5. MIMO-OFDM Receiver  Several detection schemes have been proposed for MIMO systems. Ex: ZF nulling and IC with ordering, MMSE nulling and IC with ordering, etc…  However, performance is inferior to ML detection.  ML detection: Complexity grows exponentially with number of Transmit antennas.  To reduce the complexity, Sphere decoding.  All are hard-decision algorithms. Suffer performance loss when concatenated with channel decoder.  List sphere decoding with soft output.  But complexity is higher than hard-decision decoding.  We use SMC methodology to obtain near-optimal performance with low complexity.

6. System Model: Transmitter

7. Receiver structure

8. Problems in Conventional SMC  Conventional Sequential Monte Carlo (SMC) detectors: Based on Sequential importance sampling and resampling.  Resampling is important in SMC to counter the inherent problem of degeneracy (as SIS algorithm progresses, it tends to carry more imputed trajectories of low importance weights that do not contribute significantly to the final estimation).  Resampling is important in SMC to counter the inherent problem of degeneracy (as SIS algorithm progresses, it tends to carry more imputed trajectories of low importance weights that do not contribute significantly to the final estimation).  Problems with resampling: (a) impoverished trajectory diversity (b) loss of independence among imputed trajectories.  To solve this problem, we terminate the phase trellis of differentially encoded data at predetermined indices.

9. System Model: Transmitter (Cont’d)  Termination period is K, i.e., at every transition bits are inserted to terminate at the desired state.  This terminated state acts as the initial state for next symbols.  Consider (K-1) M-PSK symbols that are differentially encoded to yield the sub-trellis:  complete sub-trellis:

10. System Model: Transmitter (Cont’d)  For MIMO-OFDM, the serial concatenation of sub-trellises yield:  Sequence is demultiplexed to yield,  sent through the conventional OFDM transmitter.

11. Transmission Grid: Example   When does not divide, we see that there is at least one termination state at any frequency.  These terminated states serve as pilot symbols to estimate the channel parameter  The phase of these pilots could be made to cycle through each of the M states in sequence. Frequency Space : Data Symbol : Pilot Symbol

12. Receiver Structure  Turbo structure: SISO NR-SMC detector (inner) and SISO channel decoder (outer).  SISO NR-SMC inputs: channel estimates, the symbol prior probabilities and the samples  SISO NR-SMC output: a posteriori symbol probabilities  SISO Channel decoder delivers an update LLR of code bits from priori LLR.  SISO NR-SMC detector and channel decoder exchange “extrinsic” information.

13. Simulation Results Parameters: , K=3.  QPSK modulation (M=4)  Channel bandwidth of 800 kHz is divided into N=64 sub– channels.  Symbol duration: Guard interval:  Uniform (UNI), typical urban (TU), and hilly terrain (HT) delay profiles.  Doppler frequency of 40 Hz.  L=3, delay spreads are, and  MMSE channel estimation.  Number of Turbo iterations: 4

14. Simulation Results (Cont’d) BER of Convolutional–coded MIMO–OFDM (SISO Channel decoder: MAP Algorithm)

15. Simulation Results (Cont’d) BER of LDPC–coded MIMO–OFDM (SISO Channel decoder: Message passing algorithm)

16. Simulation Results (Cont’d)

21. Conclusions  Periodic termination of differential phase trellis enhance the trajectory diversity and retard weight degeneracy.  This allows us to circumvent the resampling step.  SMC for Convolutional coded and LDPC coded MIMO-OFDM system employing periodically terminated DQPSK gives the performance close to perfectly known channel bound within 1 dB and 0.75 dB respectively.  For a given number of transmit and receive antennas, an even distribution of antenna elements between the transmitter and receiver achieves the best BER performance.