Recently, interest has been shown in cognitive radio (CR) systems since they can op- portunistically access unused spectrum bands thereby increasing usable communication capacity. Spectrum sensing has been identified as a key function to ensure that CR can detect spectrum holes. In a CR network, a fast and accurate spectrum sensing scheme is important. Spectrum sensing can be viewed as a signal detection problem. Most of the existing spectrum sensing schemes are based on fixed sample size detectors which means that their sensing time is preset and fixed. However, the work of Wald [27] showed that a detector based on sequential detection requires less average sensing time than a fixed sample size detector. In this thesis, we have applied the method of sequential detection to reduce the average sensing time. Simulation results have shown that, compared to the fixed sample size energy detector, a sequential detector can reduce sensing time by up to 85% in the AWGN channel for the same detection performance. In order to limit sensing time, especially in a fading environment, a truncated sequential detector is developed. The simulation results show that the truncated sequential detector requires less sensing time than the sequential detector, but the performance degrades due to truncation. Finally, a cooperative spectrum sensing scheme is used where each individual sensor uses a sequential detector. The combining rule used at the fusion center is a selection combining rule. Simulation results show that the proposed cooperative spectrum sensing scheme can reduce the sensing time compared to the individual spectrum sensing scheme.

No description available.

This paper deals with CUSUM tests for monitoring of intensity parameter of a Poisson process if this process can be observed in a restricted manner only at pregiven equidistant time points. In this case the process can be monitored by means of a CUSUM test for the parameter of a corresponding Poisson distribution. For rational reference parameter values the computation of average run length is reduced to that of solving of a system of simultaneous linear equations. The performance of obtained CUSUM tests is discussed by means of corresponding examples.

This paper deals with sequential likelihood ratio tests based on grouped observations. It is demonstrated that the method of conjugated parameter pairs known from the non-grouped case can be extended to the grouped case obtaining Waldlike approximations for the OC- and ASN- function. For near hypotheses so-called F-optimal groupings are recommended. As example an SPRT based on grouped observations for the parameter of an exponentially distributed random variable is considered.

This paper deals with the computation of OC- and ASN-function of sequential probability ratio tests in the multi-parameter case. In generalization of the method of conjugated parameter pairs Wald-like approximations are presented for the OC- and ASN-function. These characteristics can be used describing robustness properties of a sequential test in case of nuisance parameters. As examples tests are considered for the mean and the variance of a normal distribution.

This paper deals with CUSUM tests for monitoring of intensity parameter of a Poisson process if this process can be observed in a restricted manner only at pregiven equidistant time points. In this case the process can be monitored by means of a CUSUM test for the parameter of a corresponding Poisson distribution. For rational reference parameter values the computation of average run length is reduced to that of solving of a system of simultaneous linear equations. The performance of obtained CUSUM tests is discussed by means of corresponding examples.

This paper deals with the computation of OC- and ASN-function of sequential probability ratio tests in the multi-parameter case. In generalization of the method of conjugated parameter pairs Wald-like approximations are presented for the OC- and ASN-function. These characteristics can be used describing robustness properties of a sequential test in case of nuisance parameters. As examples tests are considered for the mean and the variance of a normal distribution.

This paper deals with sequential likelihood ratio tests based on grouped observations. It is demonstrated that the method of conjugated parameter pairs known from the non-grouped case can be extended to the grouped case obtaining Waldlike approximations for the OC- and ASN- function. For near hypotheses so-called F-optimal groupings are recommended. As example an SPRT based on grouped observations for the parameter of an exponentially distributed random variable is considered.