Delay-Constrained Optimal Transmission With Proactive Spectrum Handoff in Cognitive Radio Networks

Abstract

In this paper, we investigate the transmission strategy for cognitive radios (CRs), which opportunistically operate on various primary users’ channels with the aid of proactive spectrum handoff. In particular, a secondary user (SU) in the CR network proactively predicts the future spectrum status and decides whether to keep idle, or stay in the current channel, or switch to a new channel to resume its transmission. A problem of completing a target data packet size of $\mathcal {V}$ bits within a predefined deadline $D$ time slots is formulated as a discrete-time Markov decision process, in which the SU aims at minimizing its expected total cost, i.e., transmission cost, handoff cost, and overtime penalty. We solve the problem using dynamic programming, and propose a general optimal transmission with the proactive spectrum handoff (OTPH) algorithm whose complexity is $2\times |\mathcal {V}|\times D$ . Furthermore, we prove that for a convex penalty function, the optimal handoff-aided transmission exhibits a threshold structure. A monotone OTPH algorithm with a complexity of ${\max }(|\mathcal {V}|,D)$ is used in this case. Simulation results verify that our proposed scheme achieves both the minimal total cost and the highest data transmission efficiency as compared with the traditional always staying and always changing schemes.