Adaptive OFDMA has recently been recognized as a promising technique for providing high spectral potency in future broadband wireless systems. The analysis over the last decade on adaptive OFDMA systems has targeted on adapting the allocation of radio resources, such as subcarriers and power, to the instantaneous channel conditions of all users. However, such “quick” adaptation requires high computational complexity and excessive signaling overhead. This hinders the deployment of adaptive OFDMA systems worldwide. This paper proposes a slow adaptive OFDMA theme, in which the subcarrier allocation is updated on a a lot of slower timescale than that of the fluctuation of instantaneous channel conditions. Meanwhile, the data rate needs of individual users are accommodated on the fast timescale with high likelihood, thereby meeting the requirements except occasional outage. Such an objective encompasses a natural probability constrained programming formulation, which is thought to be intractable. To circumvent this problem, we formulate safe tractable constraints for the matter based mostly on recent advances in likelihood constrained programming. We have a tendency to then develop a polynomial-time algorithm for computing an optimal solution to the reformulated downside. Our results show that the proposed slow adaptation scheme drastically reduces each computational cost and management signaling overhead in comparison with the conventional quick adaptive OFDMA. Our work can be viewed as an initial attempt to use the chance constrained programming methodology to wireless system designs. Given that the majority wireless systems can tolerate an occasional dip in the standard of service, we have a tendency to hope that the proposed methodology can find further applications in wireless communications.
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