Abstract
A decentralized channel access strategy is proposed for a multichannel low-power cognitive radio (CR) system. CRs are equipped with rechargeable batteries to harvest radio-frequency (RF) energy from the primary user (PU) transmission. At any time, secondary users (SUs) can exploit either the spatiotemporal spectrum opportunities for transmission or the PU active periods for RF energy harvesting. An optimal channel-selection probability vector is obtained to achieve the maximum SU throughput by finding sufficient spectrum-access and energy-harvesting opportunities while accounting for SU contention. The authors have a nonconvex optimization problem. It is decomposed to obtain a convex optimization subproblem, but an exhaustive search is required for the master problem. Alternatively, an iterative algorithm is proposed wherein the authors define a combined metric to consider the spectrum-access and energy-harvesting opportunities on different PU channels. A convex optimization problem is solved with a closed-form solution in each iteration. Numerical results show that the proposed channel-selection policy enables an SU to achieve better expected throughput through an enhanced battery-recharging probability.
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