Jongho Oh

Breadth-First Signal Decoder: A Novel Maximum-Likelihood Scheme for Multi-Input–Multi-Output Systems

A number of decoding schemes have recently been proposed to perform maximum-likelihood (ML) detection for multi-input-multi-output (MIMO) systems. In this paper, employing a ldquobreadth-firstrdquo search algorithm for closet points in a lattice, we propose a novel ML decoding scheme called the breadth-first signal decoder (BSIDE). Through analysis and computer simulations, it is shown that the BSIDE has the same bit-error-rate performance as the conventional ML decoders while allowing significantly lower computational complexity. In addition, we introduce a simple tuning scheme that allows the BSIDE to have a performance-complexity tradeoff capability as necessary.

Decoding With Hypothesis Testing: A Near ML Decoding Scheme for MIMO Systems

A near maximum-likelihood (NML) scheme for the decoding of multiple-input-multiple-output (MIMO) systems is addressed by incorporating the technique of hypothesis testing in the searching procedure. The proposed decoding scheme selects the best node based on the node metric, determines one child node of the best node via hypothesis testing, and connects the best node with some sibling nodes of the child node. From simulation results, it is confirmed that the proposed scheme has a lower computational complexity than other NML decoders and that the performance difference between the proposed and maximum-likelihood schemes is negligibly small.

Decoding of MIMO Systems with Hypothesis Testing Technique

In this paper, we propose a near maximum likelihood (ML) decoding scheme for multiple input multiple output (MIMO) systems. Based on the multiple hypothesis testing problem, the proposed decoding scheme provides a higher efficiency than other conventional near ML decoding schemes by using some characteristics of the channel matrix. Numerical results show that, despite the proposed scheme has a lower computational complexity than other near ML decoders, the performance difference between the ML and proposed scheme is negligibly small.

A Hybrid ML Decoding Scheme for Multiple Input Multiple Output Signals on Partitioned Tree

In this paper, we propose a novel ML decoding scheme based on the combination of depth- and breadth-first search methods on a partitioned tree for multiple input multiple output (MIMO) systems. The proposed scheme first partitions the searching tree into several stages, each of which is then searched by a depth- or breadth-first search method, maximally exploiting the advantages of both the depth- and breadth-first search methods. Numerical results indicate that, when the depth- and breadth-first search algorithms are adopted appropriately, the proposed scheme exhibits substantially lower computational complexity than conventional ML decoders while maintaining the ML bit error performance.

Asymptotic Performance of Weak Signal Detectors in Multiplicative and First-Order Markov Additive Noise Environment

The use of an independent sample model may cause a considerable performance degradation in detection applications of modern high data rate communication systems exhibiting dependence among interference components. In this paper, we address the detection of weak known signals in observations corrupted by multiplicative and first-order Markov additive noise. The asymptotic performance of several detectors are obtained and compared, confirming that the dependence among interference components need to be taken into account to maintain performance accordingly.

Analysis of Weak Signal Detectors Under Moving Average of Impulsive Noise

The performance of weak signal detectors is addressed in additive noise described by the first order moving average (FOMA) of an impulsive process. The decision regions of the maximum likelihood (ML) and suboptimum ML (S-ML) detectors are derived in the FOMA model. The ML and S-ML detectors are employed in the antipodal signaling system, and compared in terms of the bit-error-rate performance in impulsive environment. Numerical results show that the S-ML detector, despite its reduced complexity and simpler structure, exhibits practically the same performance as the ML detector. It is also shown that the performance gap between detectors for FOMA and independent and identically distributed noise becomes larger as the degree of noise impulsiveness increases.

Analysis of Optimum Weak Signal Detectors under Moving Average Model of Non-Gaussian Noise

The maximum likelihood (ML) and suboptimum ML (S-ML) detectors are derived in the first order moving average model. The ML and S-ML detectors are employed in the antipodal signaling system, and compared in terms of the bit-error-rate in impulsive environment. Numerical results show that the S-ML detector, despite reduced complexity and simpler structure, exhibits practically the same performance as the ML detector

Weak signal detection performance of selective rake receivers in fading channels

We derive the optimum and suboptimum selective rake receivers (SRRs) for fading environment exhibiting impulsive nature. Simulation results confirm that, when the noise is impulsive, the SRRs designed for impulsive noise offer performance improvement over those optimized for Gaussian environment. The suboptimum SRR is observed to exhibit almost the same performance as the optimum SRR.

Performance comparison of signal detectors for dependent Gaussian channels

The detection of weak signals is addressed in additive noise described by the first order moving average of a Gaussian process. We derive decision regions of the maximum likelihood (ML) and suboptimum ML (SML) detectors, and obtain specific examples of the ML and SML decision regions. The ML and SML detectors are employed in the antipodal signaling system, and compared in terms of the bit-error-rate in the dependent Gaussian noise environment. Numerical results show that the SML detector, despite its reduced complexity and simpler structure, exhibits practically the same performance as the optimum ML detector.