Traffic-Matching Revenue-Rate Maximization Scheduling for Downlink OFDMA

Abstract

In this paper, we study the weighted sum rate (w- rate) maximization algorithms with proportional rate fairness (PRF) and delay constraints for the downlink orthogonal frequency division multiple access (OFDMA) system. First, based on the Lagrangian duality optimization tool, we design a weighted sum rate maximization scheme based on instantaneous transmit power and bit error rate (BER) constraints. Second, to meet the rate fairness constraint imposed by different users' diverse traffic demands, we design a fast algorithm to search for the optimal weight factors, and implement the traffic-matching duality scheme to achieve the long-term target PRF and enhanced revenue rate. Third, we provide analytical channel throughput formulas for equal power allocation (EPA) and waterfilling (WF) power allocations schemes, and also evaluate the actual throughput taking into account the traffic random arrival process, limited buffer size, and transmission delay deadline. Simulation results show that the proposed traffic-matching duality scheme can achieve a significantly higher revenue rate than the fixed weight duality scheme which only tries to maximize the revenue rate but does not match the incoming traffic.