EUROPEAN TRANSACTIONS ON TELECOMMUNICATIONS Eur. Trans. Telecomms. 2011; 22:458–470 Published online 2 August 2011 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/ett.1496 RESEARCH ARTICLE Round-robin power control for the weighted sum rate maximisation of wireless networks over multiple interfering links † Chung Shue Chen 1 *, Kenneth W. Shum 2 and Chi Wan Sung 3 1 Department of Networks and Networking, Alcatel-Lucent Bell Labs, Route de Villejust, 91620 Nozay, France 2 Institute of Network Coding, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong 3 Department of Electronic Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong ABSTRACT We study the weighted sum rate maximisation problem of wireless networks consisting of multiple source–destination pairs. The optimisation problem is to maximise a weighted sum of data rates by adjusting the power of each user. The problem is in general a non-convex optimisation problem that will lead to multiple local maxima. A Gauss–Seidel type iterative power control algorithm is presented. The proposed algorithm has the favourable properties that only simple oper- ations are needed in each iteration and the convergence is fast. Performance investigation under some benchmark examples and different user densities has shown its effectiveness. A survey and comprehensive comparison with today’s best-known solutions is provided. Finally, we derive and prove some simple and interesting optimal binary power allocation strategies under special cases of the problem if the network can be represented by a certain approximation. Copyright © 2011 John Wiley & Sons, Ltd. *Correspondence Chung Shue Chen, Department of Networks and Networking, Alcatel-Lucent Bell Labs, Route de Villejust, 91620 Nozay, France. E-mail: cschen@ieee.org † A part of this work was presented in [1] at the IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC’09) in Tokyo, Japan. Received 21 April 2011; Accepted 23 May 2011 1. INTRODUCTION Because of today’s high demand of broadband service and new applications, wireless networking is facing the challenge of supporting fast increasing data traffic [2]. To enhance spectrum utilisation efficiency in broadband wire- less networks, system-wide radio resource optimisation is a key approach. Different from traditional power control designed mainly for voice-centric services or meeting cer- tain signal-to-interference-plus-noise ratio (SINR) targets [3–6], system capacity or sum rate maximisation is partic- ularly favourable in wireless data networks. In [7], such an optimisation for a single-cell code division multiple access system is formulated. For the multicell case, a fully dis- tributed algorithm that can provide reasonable sum rate in a fading environment is proposed in [8]. The same problem, assuming static link gains, is formulated as an optimisation problem and studied in [9–11]. Results of optimal power allocation are useful to various interference-limited wire- less systems including mobile cellular, wireless ad hoc, cognitive radio [12, 13] and digital subscriber line [14] networks. It is known that the sum rate optimisation problem is in general non-convex and difficult to solve [15]. A cen- tralised algorithm based on multiplicative linear fractional programming (MLFP), namely MAPEL (MLFP-based power allocation), is devised in [16] and is guaranteed to converge to the global maximum. It is a special case of the polyblock approximation method for generalised linear fractional programming [17]. Although it is able to find global optimal solution, its computation time grows expo- nentially with the number of users. Nevertheless, we can use it as a benchmark in evaluating other heuristic or sub- optimal algorithms for small-scale systems. Note that the MAPEL algorithm has been generalised to multichannel systems in [18]. There are different approaches to find approximate solu- tions to the sum rate maximisation problem. One is to reduce the problem to some convex problems by relax- ation. The above power control problem is reduced to 458 Copyright © 2011 John Wiley & Sons, Ltd.