Information-Guided Antenna Selection and Activation for Spatial Modulation MIMO Systems
Bibliographic record
Abstract
Each transmit antenna in spatial modulation (SM) based communication is uniformly activated, which can lead to poor channels for transmission, causing performance degradation. Though antenna selection can be used to tackle this problem caused by uniform antenna activation (U-AA), it requires additional antenna elements, high computational complexity, and signalling overhead. This paper proposes an irregular antenna activation (I-AA) technique, which activates an antenna with a probability that is proportional to a channel with the highest gain. In the proposed method, consecutive equal bits in a bit sequence are exploited to choose an antenna index for transmission. Since different numbers of consecutive equal bits occur with different probabilities, this makes each of the available antennas to be randomly activated with different probabilities. Taking advantage of available channel state information at the transmitter, we develop a rate-optimized I-AA method to utilize better channels for transmission. In prominent variants of SM, the use of I-AA is shown to yield higher throughput and smaller error rates compared to operations with the conventional U-AA. Moreover, a joint rate and Euclidean-distance optimized antenna selection (REAS) and a rate-optimized low-complexity AS (RLAS) for SM with I-AA are proposed. The use of I-AA without AS in prominent variants of the SM technique is shown to achieve better error rate performance compared to U-AA with AS. Also, the use of I-AA in REAS and RLAS yields improvements in the data rates and error rates compared to U-AA with AS, at a negligible extra complexity and signalling overhead.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".