Partially Overlapped Channel Detection in Heterogeneous Cognitive Networks

A Partially Overlapped WiFi Channel (POC) is a type of WiFi channel whose spectrum is partially overlapping with other carriers. It has been empirically demonstrated that the throughput of heterogeneous cognitive network can be improved by utilizing POCs. POC detection is a prerequisite to POC utilization. Unfortunately, the existing Clear Channel Assessment (CCA) methods such as energy-based detection and preamble detection cannot accurately detect the POC in heterogeneous cognitive networks. As a result, POCs will not be used by most WiFi users. The spectrum in POCs is therefore under-utilized and wasted. In this article, we propose to detect a POC by statistically analyzing the bit-level information inside the payload of WiFi frames. The proposed approach is based on a series of measurements on bit errors under real-world IEEE 802.11ac channels. A POC can be accurately detected by analyzing the correlation between an unknown WiFi channel and a given POC in terms of their bit-error vectors. Our approach is evaluated by detecting fifty $\frac{1}{2}$ -overlap WiFi channels among a hundred different real-world WiFi channels. The final results show that, our approach can achieve an accuracy of 96% and a false positive rate of 8% on POC detection, which is much better than the existing CCA methods.