A Cooperative Spectrum Sensing Technique with Dynamic Frequency Boundary Detection and Information-Entropy-Fusion for Primary User Detection

The invention of cognitive radio concept is aimed to overcome the spectral scarcity issues of emerging radio systems by exploiting under-utilization of licensed spectrum. As the cognitive users (secondary users) are only allowed to use a licensed spectrum in the absence of its rightful owner, the ability to accurately sense the presence of the rightful owners (primary users) is highly essential. The traditional way of having individual secondary users perform their own spectrum sensing is vulnerable to the presence of noise and shadowing of propagation channel. Cooperative spectrum sensing emerges as an attractive alternative that exploits the inherent geospatial diversity of multiple cognitive radios to enhance the robustness of sensing accuracy. Two specific issues in cooperative spectrum sensing are discussed in this paper. The first issue is on dynamic detection of primary user's bands. A dynamic band clustering (DBC) algorithm that uses K-means clustering technique is proposed in this paper. The proposed algorithm reduces the number of erroneous narrow subbands resulting from spurious noise. This in turn minimizes the number of subbands to be detected and hence the overall sensing time. The second issue is on reducing the overheads required to facilitate fusion center operation. A novel entropy-based maximal ratio combining for decision-fusion center is also proposed in this paper. Based on extensive simulation studies, the proposed fusion technique is shown to be comparable to conventional information-fusion techniques. The performance offered by the proposed technique is achieved with significant reduction in the bandwidth overheads.