Transmit Antenna Selection for Downlink Transmission in a Massively Distributed Antenna System Using Convex Optimization
Bibliographic record
Abstract
The use of multiple antennas in a spatial multiplexing multiple-input multiple-output (SM-MIMO) system can increase the capacity linearly with the number of antennas, M. However, the radio-frequency (RF) chain associated with each antenna increases the system hardware cost considerably. Antenna selection is a signal processing technique that helps to reduce the system complexity and cost of the RF front-end. This paper describes the novel concept of transmit antenna selection method for the massively distributed antenna system, which is conceived as a technique to increase the data-rate beyond the Long Term Evolution (LTE) and LTE Advanced (LTEA) technologies. In this work, convex optimization is used to determine the optimum antennas for the massively distributed MIMO, to achieve the best compromise between the achievable capacity and system complexity. Specifically, the interior-point algorithm from optimization theory is utilized. For the case of an extremely large antenna array, we observe from the simulations that antenna selection is dependent only upon the large scale fading (LSF). So complexity of the antenna selection algorithm reduces to O(M) if the bucket sorting algorithm is employed. Simulation results confirm that our proposed method works well in a massively distributed antenna system, and its performance is close to the optimal antenna selection algorithm.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".