Transmit Antenna Selection for Downlink Transmission in a Massively Distributed Antenna System Using Convex Optimization
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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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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it