Bandwidth and power efficient wireless spectrum sensing networks
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Opportunistic spectrum reuse is a promising solution to the two main causes of spectrum scarcity: most of the radio frequency (RF) bands are allocated by static licensing, and many of them are underutilized. Frequency spectrum can be more efficiently utilized by allowing communication systems to find out unoccupied spectrum and to use it harmlessly to the licensed users. Reliable sensing of these spectral opportunities is perhaps the most essential element of this technology. Despite significant work on spectrum sensing, further performance improvement is needed to approach its full potential. In this dissertation, wireless spectrum sensing networks (WSSNs) are investigated for reliable detection of the primary (licensed) users, that enables efficient spectrum utilization and minimal power consumption in communications. Reliable spectrum sensing is studied in depth in two parts: a single sensor algorithm and then cooperative sensing are proposed based on a spectral covariance sensing (SCS). The first novel contribution uses different statistical correlations of the received signal and noise in the frequency domain. This detector is analyzed theoretically and verified through realistic simulations using actual digital television signals captured in the US. The proposed SCS detector achieves significant improvement over the existing solutions in terms of sensitivity and also robustness to noise uncertainty. Second, SCS is extended to a distributed WSSN architecture to allow cooperation between 2 or more sensors. Theoretical limits of cooperative white space sensing under correlated shadowing are investigated. We analyze the probability of a false alarm when each node in the WSSN detects the white space using the SCS detection and the base station combines individual results to make the final decision. The detection performance compared with that of the cooperative energy detector is improved and fewer sensor nodes are needed to achieve the same sensitivity. Third, we propose a low power source coding and modulation scheme for power efficient communication between the sensor nodes in WSSN. Complete analysis shows that the proposed scheme not only minimizes total power consumption in the network but also improves bit error rate (BER).