Interference-aware resource allocation in high-density WLANs
Bibliographic record
Abstract
IEEE 802.11 wireless local area networks (WLANs) become more and more popular and widely deployed in public hotspots, enterprise environments, and residential areas to provide seamless coverage and improve user connectivity.However, the high density of Access Points (APs) and the stations (STAs) associated to each AP results in increased intra-cell and inter-cell interference, which can cause high collision rates, long backoff intervals, and degraded received signalto-interference-plus-noise ratio (SINR).To manage such interference, it is required to explore medium access control (MAC) enhancement, efficient channel assignment, and load balancing techniques, which can improve the performance experienced by users.In this work, we develop and evaluate interference-aware enhanced MAC and radio resource allocation algorithms in 802.11WLANs, aiming to increase the network throughput.To improve the achievable throughput in a single-cell WLAN, a channel-aware adaptive carrier sensing multiple access with collision avoidance (CSMA/CA) scheme is developed to take advantage of multi-user diversity, while supporting distributed and asynchronous operation.By dynamically adjusting the contention window of each STA according to its channel state, this approach prioritizes STAs who gain most from using a channel and enhances channel utilization.A three-dimensional Markov chain is developed to model and evaluate the proposed adaptive CSMA/CA, which significantly improves throughput, especially in a large network.To manage the inter-cell interference in a multi-cell WLAN, the channel assignment and AP-STA association are investigated.Applying difference-of-convex-functions (DC) programming, two different optimization problems are solved aiming to minimize interference sum utility and maximize STA throughput.Distributed schemes are also developed in which APs and STAs can self-configure channel selection and association to mitigate the interference and thereby improve the network throughput.It is shown that the proposed approaches are highly efficient and robust with fast convergence and low complexity.To balance the load and provide service customization in a multi-cell WLAN, AP-STA association and airtime control are studied in a virtualized network, where physical APs are shared by multiple Internet service providers (ISPs).More specifically, an optimization problem is formulated on the STAs' transmission probabilities to maximize the overall network throughput, while providing airtime usage guarantees for the ISPs.The algorithm to reach the optimal transmission probability and detailed implementation are also discussed.Illustrative results confirm the superior and robust performance of the developed association and airtime control scheme.x List of Tables 2.1 IEEE 802.11PHY layers . . . . . . . . .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".