Generalized CSMA/CA for OFDMA systems: protocol design, throughput analysis, and implementation issues

Kwon, Hyuk‐Jun; Seo, Hanbyul; Kim, Seonwook; Lee, Byeong Gi

doi:10.1109/twc.2009.080816

Cited by 91 publications

(6 citation statements)

References 10 publications

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“…We consider a generic single-user multi-resource system. The resources could be, e.g., non-overlapping portions of the spectrum [30]- [32], eigen-channels of a spatial multiplexing transceiver [33], relays in a multi-relay system [34], etc.…”

Section: A Channel Model For a Resourcementioning

confidence: 99%

Outage Performance and Novel Loss Function for an ML-Assisted Resource Allocation: An Exact Analytical Framework

Simmons,

Yacoub

2024

Trans. Mach. Learn. Comm. Netw.

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In this paper, we present Machine Learning (ML) solutions to address the reliability challenges likely to be encountered in advanced wireless systems (5G, 6G, and indeed beyond). Specifically, we introduce a novel loss function to minimize the outage probability of an ML-based resource allocation system. A single-user multi-resource greedy allocation strategy constitutes our application scenario, for which an ML binary classification predictor assists in selecting a resource satisfying the established outage criterium. While other resource allocation policies may be suitable, they are not the focus of our study. Instead, our primary emphasis is on theoretically developing this loss function and leveraging it to train an ML model to address the outage probability challenge. With no access to future channel state information, this predictor foresees each resource's likely future outage status. When the predictor encounters a resource it believes will be satisfactory, it allocates it to the user. The predictor aims to ensure that a user avoids resources likely to undergo an outage. Our main result establishes exact and asymptotic expressions for this system's outage probability. These expressions reveal that focusing solely on the optimization of the per-resource outage probability conditioned on the ML predictor recommending resource allocation (a strategy that -at face value -looks to be the most appropriate) may produce inadequate predictors that reject every resource. They also reveal that focusing on standard metrics, like precision, false-positive rate, or recall, may not produce optimal predictors. With our result, we formulate a theoretically optimal, differentiable loss function to train our predictor. We then compare predictors trained using this and traditional loss functions namely, binary cross-entropy (BCE), mean squared error (MSE), and mean absolute error (MAE). In all scenarios, predictors trained using our novel loss function provide superior outage probability performance. Moreover, in some cases, our loss function outperforms predictors trained with BCE, MAE, and MSE by multiple orders of magnitude. Additionally, when applied to another ML-based resource allocation scheme (a modified greedy algorithm), our proposed loss function maintains its efficacy.

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Section: A Channel Model For a Resourcementioning

confidence: 99%

Outage Performance and Novel Loss Function for an ML-Assisted Resource Allocation: An Exact Analytical Framework

Simmons,

Yacoub

2024

Trans. Mach. Learn. Comm. Netw.

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“…Even before the development of 802.11ax, several mechanisms had been proposed for the adoption of OFDMA in Wi-Fi networks [11,12,13,14]. Kwon et al proposed to exploit OFDMA in the process of channel access by conducting backoff in both the time and frequency domains [11]. The proposed protocol reduces the frame collision rate, and thus improves channel use for the legacy systems.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of the IEEE 802.11ax MAC Protocol for Heterogeneous Wi-Fi Networks in Non-Saturated Conditions

Lee

2019

Sensors

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The IEEE 802.11ax standard, which realizes multi-user transmission based on orthogonal frequency division multiple access (OFDMA), has been highlighted as a key technology to meet high future demand for Wi-Fi systems. Since this standard is still being developed, performance analysis through mathematical modeling is of paramount importance; however, existing studies have several limitations. Firstly, most of these consider only the saturation network throughput, whereas for 802.11ax in particular, the access delay of the nodes needs to be studied carefully, since they no longer acquire the medium independently but depend solely on the access point’s (AP’s) schedule. Secondly, since the network performance may be affected to a greater extent by legacy nodes than by 802.11ax nodes, it is desirable to consider various heterogeneous cases of networks with existing legacy nodes. In this paper, a new analytical framework for the 802.11ax MAC protocol is provided. Markov-chain-based models are developed to represent the behavior of the 802.11ax nodes, and both non-saturated traffic conditions and co-existence with the legacy nodes are considered. Through both analysis and MATLAB simulations, it is shown that the proposed model accurately evaluates the throughput and the delay performance under various network conditions.

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“…In the following subsections, the mechanism and analytical model of the proposed medium access control algorithms and related assumptions are presented. In terms of the various evaluation criteria used for our algorithms and previous research, and used for comparison between the previous research [21][22][23][24] and our algorithm is proposed as Table 2. The MU transmission and MU access represent feature of simultaneous channel access and concurrent data transmission of STAs by taking advantage of the frequency orthogonality of OFDMA.…”

Section: Proposed Opportunistic Medium Accessmentioning

confidence: 99%

Interference-Aware Adaptive Beam Alignment for Hyper-Dense IEEE 802.11ax Internet-of-Things Networks

Kwon

Kim

et al. 2018

Preprint

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Generalized CSMA/CA for OFDMA systems: protocol design, throughput analysis, and implementation issues

Cited by 91 publications

References 10 publications

Outage Performance and Novel Loss Function for an ML-Assisted Resource Allocation: An Exact Analytical Framework

Outage Performance and Novel Loss Function for an ML-Assisted Resource Allocation: An Exact Analytical Framework

Performance Analysis of the IEEE 802.11ax MAC Protocol for Heterogeneous Wi-Fi Networks in Non-Saturated Conditions

Interference-Aware Adaptive Beam Alignment for Hyper-Dense IEEE 802.11ax Internet-of-Things Networks

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