利用乘法权重更新方法提升小小区网络中的动态时分双工性能
Boosting Dynamic TDD in Small Cell Networks by the Multiplicative Weight Update Method
AI总结：
本文利用乘法权重更新方法提出去中心化算法，自适应调整上下行时间分配，使动态时分双工在下行吞吐量提升两倍、上行提升三倍，并优于静态时分双工。
摘要：
我们利用乘法权重更新（MWU）方法开发了一种去中心化算法，该算法显著提升了小小区网络中动态时分双工（D-TDD）的性能。所提出的算法在每个调度时隙内自适应地调整每个节点分配给上行链路（UL）和下行链路（DL）传输的时间比例，根据信号与干扰比信息的反馈，将数据包传输引导至最合适的链路方向。我们的仿真结果表明，与（传统的）D-TDD中固定的UL/DL传输概率配置相比，将MWU引入D-TDD可使下行链路的平均数据包吞吐量提高两倍，上行链路的同一性能指标提高三倍，从而使D-TDD在上行链路中甚至优于静态时分双工（Static-TDD）。结果还表明，在流量负载递增的情况下，所提出的方案保持了持续的性能增益，验证了其在提升网络性能方面的有效性。这项工作还展示了一种在解决随机问题时将算法考量置于前沿的方法。
英文摘要：
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.
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