Jinsoo Bae

Rank-based detection of weak random signals in a multiplicative noise model

Abstract Multiplicative noise is known to be useful in modeling some environment, which is difficult to describe by additive noise model. In this paper, nonparametric detection of weak random signals in multiplicative noise is considered. The locally optimum detector based on signs and ranks of observations is derived for good weak-signal detection performance under any noise probability density function. The detector has similarities to the locally optimum detector for random signals in multiplicative noise. It is shown that the nonparametric detector asymptotically has almost the same performance as the locally optimum detector.

Nonparametric detection of known signals based on ranks in multiplicative noise

Abstract Multiplicative noise is known to be useful in modeling multipath propagation which is important in mobile communication systems. In this paper, nonparametric detection of known signals in multiplicative noise is considered. The locally optimum detector is derived based on signs and ranks of observations for good weak-signal detection performance under any specified noise probability density function. This detector has similarities to the locally optimum detector for known signals in multiplicative noise. The asymptotic performance of this nonparametric detector is as good as that of the locally optimum detector. With approximate score functions, we can construct the approximate locally optimum rank detector.

Maximum length cellular automaton sequences and its application

Abstract In this paper, the maximum length linear binary cellular automaton (CA) sequence is considered. It is shown that all possible maximum length linear binary CA sequences, which are equivalent to maximum length linear binary feedback shift register (LFSR) sequences, can be constructed using linear simple CAs. A table of configurations of the n -cell maximum length simple CAs with its characteristic polynomials is obtained for 76 ⩽ n ⩽ 120. An application of the CAs to stream ciphers is indicated. In other applications, a maximum length LFSR may be replaced by a maximum length linear binary CA.

A known-signal detector based on ranks in weakly dependent noise

Abstract Locally optimum rank detection of known signals under a weakly dependent noise model is considered in this paper. The weakly dependent noise model is known to be useful for modeling inter-user interference, which is important in the synthesis and analysis of mobile communication systems. For good weak-signal detection performance under any specified noise probability density function, the locally optimum detector is derived based on signs and ranks of the observation. It is also shown that the locally optimum rank detector has the same asymptotic performance as the locally optimum rank detector which uses the actual values of the observations.

Nonparametric detection of known and random signals based on zero-crossings

Abstract In this paper, we consider the problem of nonparametric detection of signals in noisy observations, where zero-crossings are used to form the test statistic. We apply zero-crossings to known and random signal detection problems, and investigate the performance of detectors via computer simulation under several noise environments. In known signal detection, the zero-crossing detector shows quite good performance when the signal to noise ratio is moderate-to-high. In the correlated random signal case, the zero-crossing detector also shows quite good performance. To show this more explicitly, we compare the performance characteristics of the zero-crossing detector with those of the linear correlator and sign correlator detectors in the known signal case and with those of the correlation and sign-correlation detectors in the random signal case. Because of its simplicity and easy hardware implementation, the zero-crossing detector may be useful in many real applications at low cost.

A known signal detection scheme using rank statistics in multiplicative noise

Multiplicative noise is known to be useful in modeling multipath propagation which is important in the analysis of mobile communication systems. In this paper, nonparametric detection of known signals in multiplicative noise is considered. The locally optimum detector is derived based on signs and ranks of observations for good weak-signal detection performance under any specified noise probability density function. This detector has similarities to the locally optimum detector for known signals in multiplicative noise. The asymptotic performance of this nonparametric detector is as good as that of the locally optimum detector. With approximate score functions, we can construct the locally suboptimum rank detector.

On rank-based non-parametric detection of composite signals in purely additive noise

Abstract In this paper, rank-based detection of composite signals in additive noise is considered. Based on signs and ranks of observations, the locally optimum detector is derived for weak-signal detection. This detector has similarities to the locally optimum detector for composite signals in additive noise. The asymptotic performance of this detector is shown to be as good as that of the locally optimum detector.

Locally optimum user detection in Nakagami interference

Detection of the existence of a desired user is considered in this paper. We assume that the signal to noise ratio is high enough to ignore the effects of noise compared with those of the interference by other users. The inter-user interference and user signals are modeled by the Nakagami model. The observation model for this situation is proposed, the locally optimum test statistic is derived under the model, and the asymptotic performance of the test statistic is compared with that of the envelope detector. We show that the locally optimum detector has performance better than the conventional envelope detector.

Asymptotic performance of a user detection scheme in Nakagami interference

Abstract Detection of the existence of a desired user is considered in this paper. We assume that the signal to noise ratio is high enough to ignore the effects of noise compared with those of the interference by other users. The inter-user interference and user signals are modeled by the Nakagami model. The observation model for this situation is proposed, the locally optimum test statistic is derived under the model, and the asymptotic performance of the test statistic is compared with that of the envelope detector. We show that the locally optimum detector has performance better than the conventional envelope detector.

Noise canceling with autoassociative memory trained by order statistics

In this paper, noise canceling using an autoassociative memory is considered for possible applications to constant signal detection. The authors use order statistics to help the neural network learn the noise characteristics. In essence, the performance of this neural network is shown to not depend on the distribution of noise, based on simulations for six well known noise probability density functions.<<ETX>>