Queue-Aware Uplink Arbitration-based Contention and Downlink Resource Allocation for Multi-APs for IEEE 802.11ax WLANs

Shen, Li-Hsiang; Liao, Kuan-Hsun; Feng, Kai-Ten

doi:10.1109/wcnc51071.2022.9771963

Cited by 8 publications

(6 citation statements)

References 16 publications

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“…We evaluate both fixed changes in the number of stations as well as random ones. In the latter case, the number of stations arriving or departing is randomly chosen from the ranges [1,5], [1,15], or [1,30], which represents small, moderate, and large network dynamicity, respectively. The second network configuration parameter, the number of RUs, is selected from the set {4, 8, 16, 32}.…”

Section: B Simulation Scenariomentioning

confidence: 99%

“…After training in fixed-increase scenarios, we test the operation of RL-OBO in three scenarios with varying network dynamics. We select the number of new station arrivals randomly from the ranges [1,5], [1,15], and [1,30] to reflect small, moderate, and high network dynamicity, respectively. Additionally, starting from step 1100, stations start leaving the network.…”

Section: ) Testingmentioning

confidence: 99%

“…Additionally, starting from step 1100, stations start leaving the network. The number of stations leaving the network is again randomly selected from the ranges [1,5], [1,15], and [1,30], respectively. We also set the minimum number of stations as n s = n RU , because if the number of stations is less than the number of available RUs, the resources would be underutilized, leading to low observed efficiency.…”

Section: ) Testingmentioning

confidence: 99%

“…E-OBO allows UORA to adjust its operation to the Results are gathered at the end of the measuring interval ζ. The number of stations arriving or departing (since step 1100) is randomly chosen from the ranges [1,5], [1,15], and [1, 30], for the three scenarios respectively. number of competing stations, resulting in high values of the five measured metrics (Figure 7).…”

Section: E E-obo Performancementioning

confidence: 99%

“…Table 1). The efficiency of UORA can be improved by adaptive grouping [6], [7], spatial clustering [8], sub- channel hopping [9], complementary probability instead of backoff [10], additional carrier sensing [11], retransmission awareness [12], OBO modifications [2], [13], grouping-based channel access [14], and considering adjacent channel interference [15] 1 . None of the above research uses ML methods, although the application of reinforcement learning (RL) to improve UORA operation is suggested as future work by Kim et al [13].…”

Section: Introductionmentioning

confidence: 99%

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Improving IEEE 802.11ax UORA Performance: Comparison of Reinforcement Learning and Heuristic Approaches

2022

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Machine learning (ML) has gained attention from the network research community because it can help solve difficult problems and potentially lead to groundbreaking achievements. In the Wi-Fi domain, ML is applied to solve challenges such as efficient channel access and fair coexistence with other technologies in unlicensed bands. In this paper, we address the performance of uplink orthogonal frequency division multiple random access (UORA) in IEEE 802.11ax networks. Optimization of UORA is a good case for applying ML because of its inherent complexity and dependence on situation and time-dependent parameters. In particular, we use deep reinforcement learning to tune UORA parameters. Our simulation results show that even though the ML-based solution leads to close to optimal results, its operation is comparable to a much simpler, non-ML heuristic. Therefore, we conclude that ML-based solutions to improve IEEE 802.11 performance need not exceed well-designed heuristics.

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