Locally Optimum and Suboptimum Detector Performance in a Non-Gaussian Interference Environment

摘要

As the normally assumed white Gaussian interference is the most destructive in terms of minimizing channel capacity, substantial improvement can usually be obtained if the real-world interference environment (non-Gaussian) is properly taken into account. In this report, the performance of the locally optimum Bayes detector (LOBD) is compared with the performance of various ad hoc nonlinear detection schemes. The known results are reviewed and then it is demonstrated that these theoretical results may be misleading due to the assumptions that are required in order to derive them analytically. For a particular type of broadband impulsive noise, the critical assumptions of 'sufficiently' small signal level and large number of samples (large time-bandwidth product so that the Central Limit Theorem applies) are removed; the first, analytically, and the second, by computer simulation. The thus derived performance characteristics are then compared, especially as the signal level increases; that is, the locally optimum detector may not remain 'near optimum' in actual operational situations.