Dynamic TDD DL/UL reconfiguration based on shift

Binyong, Lin; Chuai, Gang

doi:10.1109/compcomm.2016.7924964

Cited by 2 publications

(2 citation statements)

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“…Recent prior-art proposals seek to utilize the standardized pattern update flexibility. In [8,9], a predefined set of radio frame configurations is adopted, with different possible DL and UL symbol ratios and pre-determined structures (akaa frame-book). Thus, BSs dynamically select those patterns from the frame-book which best satisfy their individual link selection criteria, e.g., the currently buffered traffic.…”

Section: A State Of the Art Dynamic Tdd Studiesmentioning

confidence: 99%

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Online Radio Pattern Optimization Based on Dual Reinforcement-Learning Approach for 5G URLLC Networks

2020

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The fifth generation (5G) radio access technology is designed to support highly delaysensitive applications, i.e., ultra-reliable and low-latency communications (URLLC). For dynamic time division duplex (TDD) systems, the real-time optimization of the radio pattern selection becomes of a vital significance in achieving decent URLLC outage latency. In this study, a dual reinforcement machine learning (RML) approach is developed for online pattern optimization in 5G new radio TDD deployments. The proposed solution seeks to minimizing the maximum URLLC tail latency, i.e., min-max problem, by introducing nested RML instances. The directional and real-time traffic statistics are monitored and given to the primary RML layer to estimate the sufficient number of downlink (DL) and uplink (UL) symbols across the upcoming radio pattern. The secondary RML sub-networks determine the DL and UL symbol structure which best minimizes the URLLC outage latency. The proposed solution is evaluated by extensive and highly-detailed system level simulations, where our results demonstrate a considerable URLLC outage latency improvement with the proposed scheme, compared to the state-of-the-art dynamic-TDD proposals.

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Section: A State Of the Art Dynamic Tdd Studiesmentioning

confidence: 99%

“…• Unlike the state-of-the-art relevant TDD solutions [7][8][9][10][11][12][13][14][15][16][17][18][19], the proposed solution considers the joint capacity and latency statistics to optimize the URLLC outage latency performance. It is fully compliant with the current 3GPP 5G-NR standard specifications for dynamic-TDD deployments.…”

Section: Paper Contributionmentioning

confidence: 99%