Throughput optimization for IRS-assisted multi-user NOMA URLLC systems

Zhang, Can; Cui, Miao; Zhang, Guangchi

doi:10.1007/s11276-023-03326-4

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…The exponential increment in connected devices within wireless networks will risk throughput degradation due to simultaneous data packet transmissions. To combat this, the enhancements in network performance have become crucial, i.e., Zhang et al [1] maximize throughput by considering an intelligent reflecting surface (IRS) assisted multi-user non-orthogonal multiple access (NOMA) ultra-reliable low-latency communication (URLLC) systems. Ramalingam et al [2] designed a novel routing protocol that takes advantage of cluster head selection (CHS) and elephant herd optimization (EHO).…”

Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning-based contention window optimization for IEEE 802.11 networks

Tu,

Ma,

2024

Preprint

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This study focuses on optimizing the contention window (CW) in IEEE 802.11 networks using deep reinforcement learning (DRL) to enhance the effectiveness of the contention mechanism. Recent research has employed a deep Q-learning network (DQN) as one type of DRL for CW size selection tasks to maximize network throughput. However, a notable limitation of DQN is the substantial overestimation error, which means the predicted reward value significantly deviates from the actual value. To address this issue, our study introduces the smart exponential-threshold-linear with double deep Q-learning network (SETL-DDQN) in a wireless networks scenario, with the aim to mitigate the overestimation error via the CW threshold size optimization with the help of a DDQN-based approach during the learning phase. We experimented with our proposed SETL-DDQN in both static and dynamic scenarios and conducted an analysis to solve the overestimation problem, then enhance the long-term simulation stability. Our experimental results demonstrate that SETL-DDQN achieves more efficient packet transmissions than related existing mechanisms.

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Section: Introductionmentioning

confidence: 99%