Multiple Access in Cell-Free Networks: Outage Performance, Dynamic Clustering, and Deep Reinforcement Learning-Based Design
Bibliographic record
Abstract
During the last few decades, wireless communication technologies and services have radically changed the way we live and interact at the personal, social, local and global levels. Such changes were mainly driven by the continuous emergence of innovative wireless communication services and products. These services and products represents a direct upshot of enduring research outcomes within the area. Nevertheless, the blessing of such innovation was accompanied by extremely high demands in forms of data traffic, per-user transmission rate, minimum transmission delay and in the number of wireless devices per unit area. Tackling these issues through cellular network densification was faced by many technical issues related to high interference levels, tedious user scheduling processes, and complicated network resource allocation algorithms. Trying to address these imperative technical issues in future wireless networks, this thesis develops several innovative enabling techniques for massive wireless multiple access. Specifically, we commence this work by introducing a new concept of partial spectrum overlapping among active users equipment (UEs). The proposed scheme represents a trade-off between fully orthogonal multiple access schemes (e.g. time division multiple access [TDMA], frequency division multiple access (FDMA) and orthogonal frequency division multiple access (OFDMA)) and that of non-orthogonal multiple access (NOMA). Second, we develop several innovative dynamic cell-free network architectures that support massive wireless connectivity through adaptive access points (APs)/base stations (BSs) coordination and/or cooperation. The proposed network models are then evaluated under different state-of-the-art enabling wireless techniques such as millimeter wave (mmWave) channel links and massive multiple-input multiple-output (mMIMO) systems. Furthermore, the performance of the proposed architectures is investigated through the derivation of several closed-form expressions of exact and/or asymptotic performance metrics (example, probability of outage, asymptotic outage, instantaneous rate and outage-capacity). Finally, for practical control and monitoring of the proposed access techniques and network models, we develop several low-complexity deep reinforcement learning (DRL)-based modeling frameworks that can efficiently learn the solution of several combinatorial optimization problems related to network partitioning (clustering) and uplink/downlink beamforming. This is achieved through innovative nested DRL designs that utilizes continuous and discrete deep neural networks (DNN) agents based on the nature of the problem. Several operating scenarios of the proposed techniques are evaluated through extensive Monte-Carlo simulations (Matlab and Python) with practical parameters and assumptions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".