No abstract

This paper proposes a cyclic autocorrelation function (CAF) diversity combining technique for spectrum sensing using test statistics shared among multiple receive antennas with time-averaged weights. The proposed technique computes a weight factor by averaging CAF, and the weight factor is employed to improve the performance of signal detection. The proposed results are compared with some conventional techniques, and they show that the signal detection performance can be improved without the increasing computational complexities in comparison with the conventional techniques.

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.Index Terms-Cognitive radio network, cyclostationarity detection based spectrum sensing, space diversity, OFDM signal.

Low computational complexity spectrum sensing based on cyclostationarity for multiple receive antennas is proposed. The proposed technique does not calculate certain test statistics at all receive antennas, as opposed to conventional techniques that perform calculations at all receive antennas. Therefore, a low computational complexity can be achieved for sensing. Numerical examples verify that the proposed technique can obtain favorable sensing performance, even with the low computational complexity.

No abstract