Dr. Bar-Ness's Research Interests

MIMO Systems

Multiple-input multiple-output (MIMO) wireless systems have multiple antennas at both receiver and transmitter. Under the condition of uncorrelated antenna elements and Rayleigh fading, capacity of the system can be increased linearly with the number of antennas. Most of the theoretical works in the past assumed that kind of simplified channel model, but in practice some correlation among the antenna elements may exist that can reduce the channel capacity.Moreover, most papers in the past have focused on frequency-flat fading channels, whereas frequency selective channels have recently received significant attention due to the high demand for high data rate communications.

Phase Noise in OFDM System

Orthogonal frequency division multiplexing (OFDM) has been widely adopted and implemented in wire and wireless communications. In comparison to single carrier transmission, OFDM is quite effective to eliminate inter-symbol interference (ISI) caused by channel multipath fading hostility while providing high transmission data rate with high spectral efficiency. Moreover, OFDM receiver becomes relatively simple with one-tap channel equalizer and simple hardware. Hence, it is very well suited to the future high data rate wireless multimedia communications. The disadvantage of OFDM, however, is its sensitivity to both frequency offset and phase noise. Caused by the frequency difference between the transmitter and the receiver, or by Doppler shift, frequency offset has been thoroughly analyzed and many methods have been proposed for its estimation and correction. Unlike frequency offset which is deterministic, phase noise is a random process caused by the fluctuation of the receiver and transmitter oscillators. Phase noise causes leakage of DFT which subsequently destroys the orthogonalities among subcarrier signals, leading to the significant performance degradation.

Our research work on phase noise includes:

We present the OFDM performance analysis in the presence of phase noise in both single-user and multiple access environments.
For single-user case, we provide exact closed-form expression of signal to interference plus noise ratio (SINR) and the corresponding lower and upper bounds of SINR for small phase noise.
For multi-user case, we derive a closed-form BER expression with BPSK modulation.
With the understandings of phase noise effects, we also propose several methods to mitigate both single and multiple phase noise.

Phase noise suppression algorithm
Simultaneous CPE and ICI correction
Multiple phase noise correction for OFDM/SDMA

The effect of phase noise on OFDM channel estimation. We propose joint channel and frequency offset estimation in the presence of phase noise

Hybrid ARQ

Turbo Coding

Due to the recursive and iterative nature of turbo decoding algorithm, the computational decoding delay of turbo codes may not be acceptable. To reduce the decoding delay in turbo decoding, one may use a short frame size code at the expense of performance degradation. This is a plausible option in low data rate systems because the time span of the codeword is the dominant factor in decoding delay. However, in high data rate systems, such as multi-mega bps, the computational decoding delay is dominant and it may be required to reduce the computational decoding delay.

To reduce the computational decoding delay of turbo codes, we propose a parallel algorithm for MAP decoders that use multiple processors to perform sub-block MAP decoding in parallel. However, unlike the previously published parallel algorithm with sub-block overlapping, we utilized the forward and backward variables computed in the previous iteration to provide boundary distributions for each sub-block MAP decoder. The proposed algorithm is based on the belief propagation paradigm, where each sub-block take messages from the neighbors, update their belief and send them back to the neighbors. We obtain at least two advantages over the previous parallel MAP scheme. First, there are no additional computations due to overlapping. Second, communicating with their neighbors, each sub-block MAP decoder can converge to optimal values since information in one sub-block propagates through the entire network by message passing.

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