Optimal Multiband Joint Detection for Spectrum Sensing in Cognitive Radio Networks

• Published in: IEEE transactions on signal processing Vol. 57; no. 3; pp. 1128 - 1140
• Main Authors: Zhi Quan, Shuguang Cui, Sayed, A.H., Poor, H.V.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.03.2009; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Cognitive radio; Constraint optimization; cooperative sensing; Detection; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Fading; Frequency; Frequency bands; hypothesis testing; Information, signal and communications theory; Interference; Interference constraints; Mathematical analysis; Miscellaneous; multiband joint detection; Networks; nonlinear optimization; Optimization; Radio; Radiofrequency interference; Shadow mapping; Signal and communications theory; Signal detection; Signal processing; Signal, noise; spectrum sensing; Studies; Telecommunications and information theory; Throughput; Wideband; hypothesis testing; Small signal behavior; Information rate; Optimal detection; Optimization; nonlinear optimization; Frequency band; Linear combination; Wide band; Convexity; Multiple frequency; Signal detection; Fading; multiband joint detection; Small signal; Space diversity; Cognitive radio; Information transmission; Cognitive system; Joint detection; Mathematical transformation; Simulation; Optimal solution; spectrum sensing; Signal processing; cooperative sensing; Shadowing; Radio communication; Software radio
• ISSN: 1053-587X; 1941-0476; 1941-0476
• DOI: 10.1109/TSP.2008.2008540

Spectrum sensing is an essential functionality that enables cognitive radios to detect spectral holes and to opportunistically use under-utilized frequency bands without causing harmful interference to legacy (primary) networks. In this paper, a novel wideband spectrum sensing technique referred to as multiband joint detection is introduced, which jointly detects the primary signals over multiple frequency bands rather than over one band at a time. Specifically, the spectrum sensing problem is formulated as a class of optimization problems, which maximize the aggregated opportunistic throughput of a cognitive radio system under some constraints on the interference to the primary users. By exploiting the hidden convexity in the seemingly nonconvex problems, optimal solutions can be obtained for multiband joint detection under practical conditions. The situation in which individual cognitive radios might not be able to reliably detect weak primary signals due to channel fading/shadowing is also considered. To address this issue by exploiting the spatial diversity, a cooperative wideband spectrum sensing scheme refereed to as spatial-spectral joint detection is proposed, which is based on a linear combination of the local statistics from multiple spatially distributed cognitive radios. The cooperative sensing problem is also mapped into an optimization problem, for which suboptimal solutions can be obtained through mathematical transformation under conditions of practical interest. Simulation results show that the proposed spectrum sensing schemes can considerably improve system performance. This paper establishes useful principles for the design of distributed wideband spectrum sensing algorithms in cognitive radio networks.