Contact: niranjan@ece.ualberta.ca

Robust Wireless Communication Robust Wireless Communication System for Maritime MonitoringSystem for Maritime Monitoring

Thomas S. John, Thomas S. John,

Department of Electrical Engineering, Department of Electrical Engineering,

Stanford University.Stanford University.

A. Nallanathan A. Nallanathan Department of Electrical and Computer EngineeringDepartment of Electrical and Computer Engineering

National University of Singapore. National University of Singapore.

IntroductionIntroduction

Communications in maritime protection via the ability of Communications in maritime protection via the ability of rapidly field flexible, wireless, ad-hoc mobile networks.rapidly field flexible, wireless, ad-hoc mobile networks.

Inaugural partner project COASTS (Inaugural partner project COASTS (CCoalition oalition OOperational perational AArea rea SSurveillance and urveillance and TTargeting argeting SSystem).ystem).

COASTS mission is to develop low cost, unclassified COASTS mission is to develop low cost, unclassified unattended sensor networks.unattended sensor networks.

Provide real-time information for tactical and remote Provide real-time information for tactical and remote command-and-control.command-and-control.

Wireless Technology: Combination of 802.11 and 802.16.Wireless Technology: Combination of 802.11 and 802.16.

Wi-Fi 802.11n and WiMax 802.16Wi-Fi 802.11n and WiMax 802.16

802.11n Wi-Fi standard is to emerge in Mid-2006.802.11n Wi-Fi standard is to emerge in Mid-2006.

Date rate for 802.11n 100 Mbits/s.Date rate for 802.11n 100 Mbits/s.

WiMax 802.16 is going to be the real future of wireless WiMax 802.16 is going to be the real future of wireless (peer to peer communication, range upto 50km).(peer to peer communication, range upto 50km).

MIMO-OFDM is recommended for Wi-Fi and WiMax.MIMO-OFDM is recommended for Wi-Fi and WiMax.

Maritime Protection: Combination of 802.11 and 802.16.Maritime Protection: Combination of 802.11 and 802.16.

Hence, MIMO-OFDM transceiver design becomes Hence, MIMO-OFDM transceiver design becomes important.important.

MIMO-OFDMMIMO-OFDM

OFDM is a potential scheme for high data rate wireless OFDM is a potential scheme for high data rate wireless transmission.transmission.

OFDM can be combined with multiple transmit and OFDM can be combined with multiple transmit and receive antennas: MIMO-OFDMreceive antennas: MIMO-OFDM

MIMO-OFDM ReceiverMIMO-OFDM Receiver

Several detection schemes have been proposed for MIMO Several detection schemes have been proposed for MIMO systems. Ex: ZF nulling and IC with ordering, MMSE nulling systems. Ex: ZF nulling and IC with ordering, MMSE nulling and IC with ordering, etc…and IC with ordering, etc…

However, performance is inferior to ML detection.However, performance is inferior to ML detection. ML detection: Complexity grows exponentially with ML detection: Complexity grows exponentially with

number of Transmit antennas.number of Transmit antennas. To reduce the complexity, Sphere decoding.To reduce the complexity, Sphere decoding. All are hard-decision algorithms. Suffer performance loss All are hard-decision algorithms. Suffer performance loss

when concatenated with channel decoder.when concatenated with channel decoder. List sphere decoding with soft output.List sphere decoding with soft output. But complexity is higher than hard-decision decoding.But complexity is higher than hard-decision decoding. We use SMC methodology to obtain near-optimal We use SMC methodology to obtain near-optimal

performance with low complexity.performance with low complexity.

System Model: TransmitterSystem Model: Transmitter

Receiver structureReceiver structure

Problems in Conventional SMCProblems in Conventional SMC

Conventional Sequential Monte Carlo (SMC) detectors: Conventional Sequential Monte Carlo (SMC) detectors: Based on Sequential importance sampling and resampling.Based on Sequential importance sampling and resampling.

Resampling is important in SMC to counter the inherent Resampling is important in SMC to counter the inherent problem of degeneracy (as SIS algorithm progresses, it problem of degeneracy (as SIS algorithm progresses, it tends to carry more imputed trajectories of low importance tends to carry more imputed trajectories of low importance weights that do not contribute significantly to the final weights that do not contribute significantly to the final estimation)estimation) . .

Problems with resampling: Problems with resampling:

(a) (a) impoverished trajectory diversityimpoverished trajectory diversity(b)(b) loss of independence among imputed trajectories. loss of independence among imputed trajectories.

To solve this problem, we terminate the phase trellis of To solve this problem, we terminate the phase trellis of differentially encoded data at predetermined indices.differentially encoded data at predetermined indices.

System Model: Transmitter (Cont’d)System Model: Transmitter (Cont’d)

Termination period is K, i.e., at every transition bits Termination period is K, i.e., at every transition bits are inserted to terminate at the desired state.are inserted to terminate at the desired state.

This terminated state acts as the initial state for next This terminated state acts as the initial state for next symbols. symbols.

Consider (K-1) M-PSK symbols that are differentially Consider (K-1) M-PSK symbols that are differentially encoded to yield the sub-trellis:encoded to yield the sub-trellis:

complete sub-trellis:complete sub-trellis:

,initial

,, 1 ,

, 0

, 1, , 1

p

p jp j p j

a j

d j K

thp1

, 0{ } K

p p j j

thK2log M

1K

thp

1, 1{ }Kp j jd

System Model: Transmitter (Cont’d)System Model: Transmitter (Cont’d)

For MIMO-OFDM, the serial concatenation of sub-trellises For MIMO-OFDM, the serial concatenation of sub-trellises yield: yield:

Sequence is demultiplexed to yield ,Sequence is demultiplexed to yield ,

sent through the conventional OFDM transmitter.sent through the conventional OFDM transmitter.

1

01

01

0

; 0 mod

; 0 mod

T

T

T

N N

K

p TpN N

N N

K

p final Tp

N N K

S

N N K

1

0

TN NS

1

, 0

N

i c iS

0, , 1Tc N

1

, 0

N

i c iS

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.

5, 8 and 3TN N K

Frequency

Space

: Data Symbol

: Pilot Symbol

R TN Ni

H

K TN N

Receiver StructureReceiver Structure

Turbo structure: SISO NR-SMC detector (inner) and SISO Turbo structure: SISO NR-SMC detector (inner) and SISO channel decoder (outer).channel decoder (outer).

SISO NR-SMC inputs: channel estimates, the symbol SISO NR-SMC inputs: channel estimates, the symbol prior probabilities and the samplesprior probabilities and the samples

SISO NR-SMC output: a posteriori symbol probabilitiesSISO NR-SMC output: a posteriori symbol probabilities

SISO Channel decoder delivers an update LLR of code bits SISO Channel decoder delivers an update LLR of code bits from priori LLR.from priori LLR.

SISO NR-SMC detector and channel decoder exchange SISO NR-SMC detector and channel decoder exchange “extrinsic” information.“extrinsic” information.

10

ˆ{ } N

i iH

Pr( )md a 10}

Ni i

{Y

10Pr( |{ } )N

m i id a

Y

Simulation ResultsSimulation Results

Parameters:Parameters: , K=3., K=3. QPSK modulation (M=4)QPSK modulation (M=4) Channel bandwidth of 800 kHz is divided into N=64 sub–Channel bandwidth of 800 kHz is divided into N=64 sub–

channels. channels. Symbol duration: Guard interval:Symbol duration: Guard interval: Uniform (UNI), typical urban (TU), and hilly terrain (HT) Uniform (UNI), typical urban (TU), and hilly terrain (HT)

delay profiles.delay profiles. Doppler frequency of 40 Hz. Doppler frequency of 40 Hz. L=3, delay spreads are , andL=3, delay spreads are , and MMSE channel estimation.MMSE channel estimation. Number of Turbo iterations: 4Number of Turbo iterations: 4

4T RN N

80 s 20 s

1.06d s 5.04d s 1.06d s

Simulation Results (Cont’d)Simulation Results (Cont’d)

BER of Convolutional–coded MIMO–OFDM BER of Convolutional–coded MIMO–OFDM (SISO Channel decoder: MAP Algorithm)(SISO Channel decoder: MAP Algorithm)

Simulation Results (Cont’d)Simulation Results (Cont’d)

BER of LDPC–coded MIMO–OFDM (SISO Channel BER of LDPC–coded MIMO–OFDM (SISO Channel decoder: Message passing algorithm)decoder: Message passing algorithm)

Simulation Results (Cont’d)Simulation Results (Cont’d)

Simulation Results (Cont’d)Simulation Results (Cont’d)

Simulation Results (Cont’d)Simulation Results (Cont’d)

Simulation Results (Cont’d)Simulation Results (Cont’d)

Simulation Results (Cont’d)Simulation Results (Cont’d)

ConclusionsConclusions Periodic termination of differential phase trellis enhance Periodic termination of differential phase trellis enhance

the trajectory diversity and retard weight degeneracy.the trajectory diversity and retard weight degeneracy.

This allows us to circumvent the resampling step.This allows us to circumvent the resampling step.

SMC for Convolutional coded and LDPC coded MIMO-OFDM SMC for Convolutional coded and LDPC coded MIMO-OFDM system employing periodically terminated DQPSK gives system employing periodically terminated DQPSK gives the performance close to perfectly known channel bound the performance close to perfectly known channel bound within 1 dB and 0.75 dB respectively.within 1 dB and 0.75 dB respectively.

For a given number of transmit and receive antennas, an For a given number of transmit and receive antennas, an even distribution of antenna elements between the even distribution of antenna elements between the transmitter and receiver achieves the best BER transmitter and receiver achieves the best BER performance.performance.