Design of sensing and power allocation strategies for energy-aware multi-channel cognitive radio networks

Abstract

Frequency spectrum and energy are two key resources of green cognitive radio networks with battery-powered wireless terminals. The issues of how to utilize sparse frequency spectrum and limited energy resource pose challenges to the design of sensing and power allocation strategies that affect both throughput and energy consumption. In this paper, we first construct an utility function that incorporates throughput as reward and energy consumption as cost for a time slotted multi-channel cognitive radio network. An optimization problem to maximize the utility function is formulated involving optimization of both sensing parameters (sensing duration and local test threshold) and power allocation strategy. The problem is non-convex, however, we decouple it into two separate convex problems and propose an iterative algorithm to obtain a suboptimal solution. The simulation results show that our iterative algorithm converges fast and performs better than an “only power allocation optimization” approach and an existing approach that ignores energy efficiency.