Enhanced Clear Channel Assessment Based on Statistical Characteristics

In the cognitive radio systems, sensor nodes perform a backoff process as soon as the clear channel assessment (CCA) detects a busy channel. In doing so they may neglect the implicit information of the failed CCA detection and further cause the redundant sensing. The blind backoff process in the CSMA/CA will cause lower channel utilization. This paper proposes an enhanced carrier sensing algorithm based on statistical characteristics to enhance the carrier sensing mechanism for the original CSMA/CA. An enhanced CCA module based on statistical characteristics is developed to evaluate the efficiency of radio resource management (RRM). Both analytical and simulation results show that the proposed algorithm performs better than existing CCA mechanism in IEEE 802.11 standards, which in turn significantly improves throughput, and reduces backoff time, average medium access control (MAC) delay and power consumption of CCA detection.