Enabling Seamless Connectivity in Consumer Electronics: A DRL Approach for Scalable Cell-Free mMIMO Handover Under O-RAN
Bibliographic record
Abstract
Scalability challenges in modern networks arise from the increasing density of User Equipment (UE) and Access Points (APs), compounded by hardware heterogeneity and complexity. Cell-Free Massive MIMO (CF mMIMO) addresses these issues by adopting a UE-centric paradigm, supported by the flexible O-RAN architecture. However, the integration of these technologies in the context of UE mobility requires further investigation. This study optimizes CF mMIMO deployment in O-RAN by proposing a Deep Reinforcement Learning (DRL) approach to maximize throughput while balancing the competing constraints of latency and handover costs. Compared to cellular, fixed, and ubiquitous baselines, the DRL model achieves 30% and 45% higher spectral efficiency, respectively. Furthermore, a 22% reduction in unnecessary handovers has been achieved by deploying the proposed DRL model. The results highlight significant implications for consumer electronics, enabling reliable connectivity in dense Internet of Things (IoT) and Augmented Reality (AR) environments through enhanced Quality of Experience (QoE).
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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 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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; 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".