Effects of quantization and channel errors on sequential detection in cognitive radios

This paper studies the impact of quantization and reporting channel errors on the performance of a sequential detection (SD) scheme for cooperative spectrum sensing in cognitive radios. The analyzed system model is a distributed parallel detection network in which the sensors quantize their local log-likelihood ratios (LLRs) and transmit them to the FC which makes the decision sequentially. The quantized local LLRs are transmitted through erroneous reporting channels. These reporting channels are modeled as binary symmetric channels (BSCs) that cause errors with a certain bit error probability (BEP). The quantization and reporting channel errors are shown to increase the average sample number (ASN) of the SD. Later the performances of the SD and fixed sample size (FSS) test are compared to show the superiority of the SD over the FSS test even in the presence of quantization and reporting channel errors regardless of the BEP. Even the ASN of the SD with 1-bit quantization is shown to be smaller than the ASN of an unquantized FSS test with the same error probabilities in the considered scenario.