DRL at the MAC Layer
Bibliographic record
Abstract
In a wireless communications system, the medium access control (MAC) layer, which is also regarded as a sublayer of the radio link control layer in the transmission protocol stack, is responsible for controlling the radio medium access so that multiple devices can coexist and effectively access/leverage the radio resources for their communications (e.g. avoiding packet collisions). This chapter starts with the application of deep reinforcement learning (DRL) for the resource allocation problem at the MAC layer. We then discuss different ideas for using DRL-based MAC frameworks for different networks, e.g. 802.11-based wireless local area networks, Internet of Things, and cellular networks such as 5G and B5G networks. In the rest of the chapter, we will investigate how DRL can enable inter-networking among multiple wireless systems with different MAC protocols/standards. For each topic, we will discuss the key aspects of DRL such as the state space, the action space, the reward function choice, the convergence, the complexity, and also how they are tailored to each specific problem.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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; both teacher heads agree on what is shown here.
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".