A weighted diversity combining technique for cyclostationarity detection based spectrum sensing in cognitive radio networks

This paper presents a weighted diversity combining technique for the cyclostationarity detection based spectrum sensing of orthogonal frequency division multiplexing signals in cognitive radio. In cognitive radio systems, secondary users must detect a desired signal in an extremely low SNR environment. In such environments, multiple antenna techniques (space diversity) such as a maximum ratio combining are not effective because an energy of target signal is also extremely weak and it is difficult to acquire the signal synchronization of some received signals. A cyclic autocorrelation function (CAF) is used for traditional cyclostationarity detection based spectrum sensing, and in this paper, the CAFs of the received signals are combined whereas the received signals themselves are combined in general space diversity techniques. In this paper, a CAF SNR is defined using the signal and noise components of the CAF, and we attempt to improve sensing performance to use a different weight for each component of the CAF. The presented results are compared with some conventional results and show that the presented technique can improve the spectrum sensing performance.