Distributionally Robust Slow Adaptive OFDMA with Soft QoS via Linear Programming

Being the predominant air interface of next-generation wireless standards, orthogonal frequency division multiple access (OFDMA) is well known for its flexibility in allocating subcarriers to different mobile users according to their different fast channel variations. Numerous research studies have demonstrated that OFDMA can bring substantial capacity gain when the subcarriers are optimally allocated. Nonetheless, practical systems can hardly afford optimal subcarrier allocation, because frequent re-optimization performed at the same timescale as fast fading variation would lead to excessively high computational and signaling costs. As a result, most practical systems settle for low-complexity schemes that operate far from the optimum, thus making them unable to enjoy the large capacity gain predicted by theoretical studies. To address this problem, we propose a novel alternative, termed the slow adaptive OFDMA, to drastically reduce the computational and signaling costs. The proposed scheme adapts subcarrier allocation at a much slower timescale than that of channel fading variation, yet achieves similar system capacity and quality of service (QoS) levels as the optimal fast adaptive OFDMA. Moreover, it possesses several attractive features. First, neither prediction of channel state information nor specification of channel fading distribution is needed for subcarrier allocation. As such, the algorithm is robust against any mismatch between actual channel state/distributional information and the one assumed. Secondly, although the optimization problem arising from our proposed scheme is non-convex in general, based on recent advances in chance-constrained optimization, we show that it can be approximated by a certain linear program with provable performance guarantees. In particular, we only need to handle an optimization problem that has the same structure as the fast adaptive OFDMA problem, yet we are able to enjoy lower computational and signaling costs. Last but not the least, instead of relying on standard but abstract linear program solvers such as the interior-point method to solve the aforementioned linear program, we can exploit its special structure and design a provably efficient algorithm for it. The proposed algorithm not only has a transparent engineering interpretation but is also easy to implement at the base stations of practical systems.