Spectrum Sensing and SINR Estimation in an IEEE 802.11s Dynamic Spectrum Access Wireless Mesh Network

Wireless Mesh Networks (WMNs) that make opportunistic use of licensed spectrum (also called Cognitive Radio Ad Hoc Networks or CRAHNs) need to perform spectrum sensing (SS) to find an advantageous channel assignment. However, current SS methods for cognitive radios typically rely on binary decisions on the presence or absence of interference, or the presence or absence of a licensed user. Interference estimation in WMNs also relies on binary measures such as the presence or absence of conflicts. Yet, performance can be significantly improved if measures have smaller resolutions, i.e., are more fine-grained than binary decisions. On the matter of when SS is performed, existing work tends to require a trade-off between SS time and data transmission. This work presents Signal-to-Interference-and-Noise-Ratio estimation for Dynamic Spectrum Access WMNs that is granular and accurate. We also propose to use the idle time inherent in the backoff mechanism of Enhanced Distributed Channel Access (EDCA) for performing SS, which has the benefit of causing no disruption to data transmissions. The estimation method we suggest that uses the SS results employs the maximum a posteriori estimate, which is the same as the maximum likelihood estimator. We show that this is the best possible estimator as it is the minimum variance unbiased estimator and it is efficient since the Cramer-Rao bound is satisfied by equality. The performance is evaluated in terms of confidence intervals. We show that the time available during EDCA backoff for performing sensing is sufficient. We also show the margin of error that can be achieved based on the number of sensing windows employed.