Multi-Dimensional Small-Scale Cooperative Spectrum Sensing Approach for Cognitive Radio Receivers

Cognitive radio (CR) is one of the most important emerging technologies that have been introduced to meet the heavy data traffic of future wireless networks. Effective implementation of CR networks strongly requires devolution of efficient spectrum sensing (SS) techniques. Recently many SS techniques based on the utilization of multiple antenna elements (MAE) and matched filtering (MF) at the CR receiver are emerged due to their high detection performance. In this paper, a multi-dimensional small-scale cooperative SS approach based on the use of MAE, MF, and DOA estimation is proposed. The multi-dimensional detection is performed by identifying both the primary user (PU) signal and its DOA, which corresponds to the temporal domain and spatial domain, respectively. The use of MAE provides many versions of the received PU signal, which considered as the backbone of many signal processing operations such as DOA estimation of PU signals. DOA estimation is based on calculating time difference of arrival (TDOA) between the output correlation signals coming from fast convolution blocks. The received PU signal versions are also used as inputs for separate MF detectors to obtain various decisions taking the advantage of the spatial diversity at the CR receiver antennas. Moreover, the estimated DOA of the PU signal is used to make a combination of the signal versions emanating from each antenna branch, using the delay and sum combiner to produce a strong signal with high signal to noise ratio (SNR). This strong signal is also used as input to a separate MF to produce an accurate decision. Finally, the principle of cooperation is achieved by combining these various decisions to obtain a reliable decision. Several simulation scenarios are carried out to verify the superiority of the proposed SS approach compared to the traditional MAE based SS technique under extremely low SNR regimes and high interference. Moreover, the simulations and theoretical closed-form expressions are highly matched together.