Taehun An

Decoding With Expected Length and Threshold Approximated (DELTA): A Near-ML Scheme for Multiple-Input–Multiple-Output Systems

In this paper, we propose a near maximum likelihood (ML) scheme for the decoding of multiple-input-multiple-output (MIMO) systems. By employing the metric-first search method, Schnorr-Euchner enumeration, and branch-length thresholds in a single frame systematically, the proposed technique provides efficiency that is higher than those of other conventional near-ML decoding schemes. From simulation results, it is confirmed that the proposed scheme has computational complexity lower than those of other near-ML decoders while maintaining the bit error rate (BER) very close to the ML performance. The proposed scheme, in addition, possesses the capability of allowing flexible tradeoffs between the computational complexity and BER performance.

Spectrum sensing based on nonlinear combining for cognitive radio with receive diversity

In this paper, employing the generalized likelihood ratio test detector on each antenna branch and exploiting nonlinear diversity combining strategies, we propose a class of spectrum sensing schemes for cognitive radio with receive diversity. The performance characteristics of the proposed scheme are analyzed in comparison with those of the conventional schemes in Gaussian noise with various fading condition. In addition, the detection and false alarm probabilities of the proposed scheme are derived in Gaussian noise with Nakagami fading channels.

Detection of Signals With Observations in Multiple Subbands: A Scheme of Wideband Spectrum Sensing for Cognitive Radio With Multiple Antennas

We address detection schemes of spectrum sensing for cognitive radio with multiple receive antennas operating over a wideband channel composed of a multitude of subbands. By taking the observations in all subbands into consideration in the likelihood functions for sensing a subband, the test statistics of the proposed schemes are functions of the sample covariance matrix in the subband under consideration and that in the subband exhibiting the lowest power spectral density. The false alarm and detection probabilities of the proposed schemes are analyzed theoretically and confirmed via simulations when the numbers of observations are the same for all the subbands. It is shown through computer simulations that the proposed schemes can provide considerable performance gains over conventional schemes for wideband spectrum sensing when the observations are spatially correlated and temporally independent/dependent.

Likelihood ratio test for wideband spectrum sensing

In this paper, we propose a novel detection scheme of spectrum sensing for cognitive radio with multiple receive antennas operating over a wideband channel composed of a multitude of subbands. By taking the observations in all subbands into consideration in the likelihood ratio test for sensing a subband, the proposed scheme can provide better performance than other conventional schemes. It is shown through computer simulations that the proposed scheme can provide considerable performance gains over conventional schemes for wideband spectrum sensing.

Spectrum Sensing with Receive Diversity for Cognitive Radio Operating over Wideband Channel

A detection scheme of spectrum sensing is discussed for cognitive radio with multiple receive antennas operating over a wideband channel composed of a multitude of sub bands. By taking the observations in all sub bands into consideration in the likelihood ratio test for sensing a sub band, the proposed scheme can provide better performance than other conventional schemes.

A Metric-First Scheme for MIMO Signal Decoding with Branch Length Threshold

In this paper, we propose a near maximum likelihood (ML) scheme for the decoding of multiple input multiple output systems. Based on the metric-first search method and by employing Schnorr-Euchner enumeration and branch length thresholds, the proposed scheme provides a higher efficiency than other conventional near ML decoding schemes. From simulation results, it is confirmed that the proposed scheme has lower computational complexity than other near ML decoders while maintaining the bit error rate very close to the ML performance. The proposed scheme in addition possesses the capability of allowing flexible tradeoffs between the computational complexity and BER performance.

Non-intrusive Appliance Load Monitoring with Feature Extraction from Higher Order Moments

A pattern recognition (PR) system is addressed for nonintrusive appliance load monitoring. For the effective recognition of two home appliances (specifically, an electric iron and a cook top), we consider a novel feature extraction method employing higher order moments of power signals from the appliances. Through simulation results, we have confirmed that the PR system with the features from the proposed higher order moment technique and kernel discriminant analysis can effectively separate the two appliances.

Cooperative spectrum sensing based on generalized likelihood ratio test under impulsive noise circumstances

We propose nonlinear schemes for the spectrum sensing in cooperative cognitive radio networks under impulsive noise circumstances. By jointly employing the order statistics, generalized likelihood ratio test, and counting rule in the framework of spectrum sensing, the proposed scheme exhibits a better performance than the conventional counterparts. From simulation results, it is confirmed that the proposed scheme provides significant performance improvements over the conventional schemes.

A Novel Spectrum Sensing Scheme for the Cognitive Radio under Impulsive Noise Circumstances

In the study in [1], a novel spectrum sensing scheme has been proposed and the performance characteristics of the proposed scheme have been analyzed in Gaussian noise environment. The test statistic of the proposed scheme is the sum of one or more order statistics of the log-likelihood ratios obtained at the branches of a receiver. In this paper, the performance characteristics of the proposed scheme are addressed in impulsive noise environment. It is demonstrated that the proposed scheme provides significant performance gain and higher level of performance stability over the conventional schemes in non-Gaussian impulsive noise environment.

Acquisition of spread spectrum codes with consecutive correlator outputs in the presence of fractional Doppler frequency offset

A novel code acquisition scheme is proposed for the acquisition in time of spread spectrum codes in the presence of fractional Doppler frequency offset (FDFO). In the proposed acquisition scheme, the decision variable for detection is formed by combining two consecutive correlator outputs so that the influence of the reduction in the correlation peak due to the FDFO can be alleviated. Numerical results show that the proposed acquisition scheme can offer better mean-time-to-synchronization performance than the conventional scheme based on the cell-by-cell detection.