Robust Wireless Communication System for Maritime Monitoring

1
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
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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) .
• 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 (Contd)

• 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 (Contd)

• 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.

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
• , K3.
• QPSK modulation (M4)
• Channel bandwidth of 800 kHz is divided into N64
  subchannels.
• Symbol duration Guard interval
• Uniform (UNI), typical urban (TU), and hilly
  terrain (HT) delay profiles.
• Doppler frequency of 40 Hz.
• L3, delay spreads are ,
  and
• MMSE channel estimation.
• Number of Turbo iterations 4

14
Simulation Results (Contd)

• BER of Convolutionalcoded MIMOOFDM (SISO
  Channel decoder MAP Algorithm)

15
Simulation Results (Contd)

• BER of LDPCcoded MIMOOFDM (SISO Channel
  decoder Message passing algorithm)

16
Simulation Results (Contd)
17
Simulation Results (Contd)
18
Simulation Results (Contd)
19
Simulation Results (Contd)
20
Simulation Results (Contd)
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.