On throughput maximization in multichannel cognitive radio networks via generalized access strategy

Spectrum access strategy plays a critical role in multichannel cognitive radio networks (CRNs). However, the CRNs cannot obtain the maximal throughput, when the existing access strategies, including overlay, underlay, and hybrid access strategies, are applied to multichannel CRNs. In this paper, we present a generalized access strategy in a multichannel CRN smart home environment, in which a secondary user (SU) system selects part of channels for sequential spectrum sensing, and accesses these channels based on the sensing results. Moreover, it accesses the remaining channels directly. We then formulate a two-phase optimization framework, which takes the sensing channel selection, sensing time allocation, and the power allocation into consideration, to maximize the gross average throughput of the multichannel CRN. In the sensing phase, a generalized access strategy algorithm (GAS) is first proposed, where we prove that only part of channels needs to be selected for spectrum sensing to achieve the maximum throughput. An optimal stopping rule is proposed to determine the optimal number of selected sensing channels. In addition, a completed hybrid access strategy algorithm is further investigated where the SU system senses all channels. An approximation algorithm is also presented to achieve suboptimal results with low computational complexity. In the transmission phase, the transmission powers of all channels are optimized via convex algorithms. Numerical experiments show that, compared with the existing schemes, the proposed schemes are able to achieve considerable throughput improvement.