Cross-layer optimisation of network performance over multiple-input multiple-output wireless mobile channels
Bibliographic record
Abstract
In this study a wireless multiple-input multiple-output (MIMO) communication system operating over a fading channel is considered. Data packets are stored in a finite size buffer before being released into the time-varying MIMO wireless channel. The main objective of this work is to satisfy a specific quality of service (QoS) requirement, i.e. the probability of data loss because of both erroneous wireless transmission and buffer overflow, as well as to maximise the system throughput. The theoretical limit of ergodic capacity in MIMO time-variant channels can be achieved by adapting the transmission rate to the capacity evolving process. In this study, the channel capacity evolving process has been described by a suitable autoregressive model based on the capacity time correlation and a finite state Markov chain (FSMC) has been derived. The joint effect of channel outage at the physical layer and the buffer overflow at the medium access control layer has been considered to describe the probability of data loss in the system. The optimal transmission strategy must minimise that probability of data loss and has been derived analytically through the Markov decision process (MDP) theory. Analytical results show the significant improvements of the proposed optimal transmission strategy in terms of both system throughput and probability of data loss.
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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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".