Boosting Dynamic TDD in Small Cell Networks by the Multiplicative Weight Update Method
CoRR(2024)
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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