In this paper, a multidimensional-correlation-based sensing scheduling algorithm, (CORN) 2 , is developed for cognitive radio networks to minimize energy consumption. A sensing quality metric is defined as a measure of the correctness of spectral availability information based on the fact that spectrum sensing information at a given space and time can represent spectrum information at a different point in space and time. The scheduling algorithm is shown to achieve a cost of sensing (e.g., energy consumption, sensing duration) arbitrarily close to the possible minimum, while meeting the sensing quality requirements. To this end, (CORN) 2 utilizes a novel sensing deficiency virtual queue concept and exploits the correlation between spectrum measurements of a particular secondary user and its collaborating neighbors. The proposed algorithm is proved to achieve a distributed and arbitrarily close to optimal solution under certain, easily satisfied assumptions. Furthermore, a distributed Selective-(CORN) 2 (S-(CORN) 2) is introduced by extending the distributed algorithm to allow secondary users to select collaboration neighbors in densely populated cognitive radio networks. In addition to the theoretically proved performance guarantees, the algorithms are evaluated through simulations.

Cognitive radio networks enable opportunistic shar ing of bandwidth/spectrum. In this paper, we introduce optimal control and scheduling algorithms for multi-hop cognitive radio networks to maximize the throughput of secondary users while stabilizing the cognitive radio network subject to collision rate constraints required by primary users. We show that by em ploying our proposed optimal algorithm, the achievable network throughput can be arbitrarily close to the optimal value. To reduce complexity, we propose a class of feasible suboptimal algorithms that can achieve at least a fraction of the optimal throughput. In addition, we also analyze the optimal algorithm in the fixed-routing scenario and deduce the corresponding lower bound of average end-to-end delay across a link set.

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Abstract-In this paper, we propose a cross-layer scheduling algorithm that achieves a throughput "ǫ-close" to the optimal throughput in multi-hop wireless networks with a tradeoff of O( 1 ǫ ) in delay guarantees. The algorithm aims to solve a joint congestion control, routing, and scheduling problem in a multihop wireless network while satisfying per-flow average end-toend delay guarantees and minimum data rate requirements. This problem has been solved for both backlogged as well as arbitrary arrival rate systems. Moreover, we discuss the design of a class of low-complexity suboptimal algorithms, effects of delayed feedback on the optimal algorithm, and extensions of the proposed algorithm to different interference models with arbitrary link capacities.