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Boosting Dynamic TDD in Small Cell Networks by the Multiplicative Weight Update Method

Jiaqi Zhu, Nikolaos Pappas, Howard H. Yang

TL;DR

This work tackles the challenge of dynamic UL/DL balance in dynamic TDD for dense small-cell networks by introducing a decentralized Multiplicative Weight Update (MWU) algorithm. The MWU approach uses SIR feedback and per-UE queue states to adapt the UL/DL time shares in each slot, guiding transmissions toward the higher-quality direction. Results show that MWU-enabled D-TDD achieves about 2× mean packet throughput gains in DL and 3× in UL compared with fixed D-TDD, even surpassing Static-TDD in UL, and maintains gains under increasing traffic. The study highlights the value of embedding algorithmic considerations into stochastic wireless optimization to improve spectral efficiency in heterogeneous networks.

Abstract

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.

Boosting Dynamic TDD in Small Cell Networks by the Multiplicative Weight Update Method

TL;DR

This work tackles the challenge of dynamic UL/DL balance in dynamic TDD for dense small-cell networks by introducing a decentralized Multiplicative Weight Update (MWU) algorithm. The MWU approach uses SIR feedback and per-UE queue states to adapt the UL/DL time shares in each slot, guiding transmissions toward the higher-quality direction. Results show that MWU-enabled D-TDD achieves about 2× mean packet throughput gains in DL and 3× in UL compared with fixed D-TDD, even surpassing Static-TDD in UL, and maintains gains under increasing traffic. The study highlights the value of embedding algorithmic considerations into stochastic wireless optimization to improve spectral efficiency in heterogeneous networks.

Abstract

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.
Paper Structure (13 sections, 4 equations, 6 figures, 1 algorithm)

This paper contains 13 sections, 4 equations, 6 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Network Topology
  4. Traffic Pattern
  5. Channel Access Scheme
  6. S-TDD
  7. D-TDD
  8. Performance Metric
  9. Multiplicative Weight Update Algorithm for TDD Configuration
  10. Signal-to-Interference Ratio (SIR)
  11. Multiplicative Weight Update Algorithm
  12. Numerical Analysis
  13. Conclusion

Figures (6)

  • Figure 1: Illustration of a small cell network with random traffic arrivals. D-TDD with Multiplicative Weight Update (MWU) algorithm is adopted in this example, where SAPs schedule their uplink (white block) and downlink (black block) transmissions independently at each time slot.
  • Figure 2: Mean packet throughput per UE versus SIR threshold: (a) transmissions in downlink and (b) transmissions in uplink.
  • Figure 3: Mean packet throughput per UE versus number of UEs per SAP: (a) transmissions in downlink and (b) transmissions in uplink.
  • Figure 4: Mean time delay per UE versus number of UEs per SAP: (a) transmissions in downlink and (b) transmissions in uplink.
  • Figure 5: Pr(Q = 0) at each time slot per UE versus the number of UEs per SAP: (a) transmissions in downlink and (b) transmissions in the uplink.
  • ...and 1 more figures