Power control refined: Addressing the problems in optimization-based distributed algorithms for opportunistic spectrum sharing

Transmission power control is one of the key enabling technologies in opportunistic spectrum sharing for secondary users to optimize their performance without incurring undesirable interference on primary users. Various optimization models and algorithms for different scenarios and design objectives have hence been proposed in related work. While ideally these algorithms can find optimal solutions for power control, in practice it is very likely that the output powers are suboptimal or even infeasible if these algorithms are applied in a distributed environment. The reasons are due to various practical considerations such as the overheads of power training, use of the control channel, and dynamics of primary user activity. To address the problem of unfit outputs thus obtained, we investigate in this paper low-complexity algorithms that can be used in tandem with these distributed algorithms by quickly adapting undesirable solutions for use by secondary users such that feasibility is restored and/or optimality is improved. Compared with the approach of finding a new solution based on linear approximation of the optimization problem, we show through evaluations that the proposed algorithm is simple yet effective in achieving the desired goal.