We leverage the Multiplicative Weight Update (MWU) method to develop a decentralized algorithm that significantly improves the performance of dynamic time division duplexing (D-TDD) in small cell networks. The proposed algorithm adaptively adjusts the time portion allocated to uplink (UL) and downlink (DL) transmissions at every node during each scheduled time slot, aligning the packet transmissions toward the most appropriate link directions according to the feedback of signal-to-interference ratio information. Our simulation results reveal that compared to the (conventional) fixed configuration of UL/DL transmission probabilities in D-TDD, incorporating MWU into D-TDD brings about a two-fold improvement of mean packet throughput in the DL and a three-fold improvement of the same performance metric in the UL, resulting in the D-TDD even outperforming Static-TDD in the UL. It also shows that the proposed scheme maintains a consistent performance gain in the presence of an ascending traffic load, validating its effectiveness in boosting the network performance. This work also demonstrates an approach that accounts for algorithmic considerations at the forefront when solving stochastic problems.

翻译：我们利用乘法权重更新（MWU）方法开发了一种去中心化算法，显著提升了小蜂窝网络中动态时分双工（D-TDD）的性能。该算法根据信干比信息的反馈，在每个调度时隙内自适应调整每个节点分配给上行（UL）和下行（DL）传输的时间比例，使数据包传输方向与最合适的链路方向对齐。仿真结果表明，与D-TDD中（传统）固定的UL/DL传输概率配置相比，将MWU融入D-TDD可使下行平均数据包吞吐量提升两倍，上行相同性能指标提升三倍，甚至使D-TDD在上行性能上超越静态TDD。研究还表明，所提方案在业务负载递增的情况下仍能保持一致的性能增益，验证了其提升网络性能的有效性。本研究同时展示了在求解随机问题时，将算法考量置于前沿的一种方法路径。