Interference Suppression Through Adaptive Subset Antenna Transmission in Interference Limited MIMO Wireless Environments
Bibliographic record
Abstract
We consider spatially multiplexed MIMO transmission in the presence of co-channel interference with subset transmit antenna selection. Several algorithms for antenna selection have been proposed in the literature for point-to- point MIMO systems. However, performance of such antenna selection techniques in interference-limited environments is less well understood. In this paper, we propose to use transmission techniques with antenna selection to minimize the effect of co-channel interference. We propose several simplified transmit antenna selection algorithms. V-BLAST detection is used and channel state information (CSI) is assumed to be known only at the receiver. A simple algorithm to adaptively obtain information about the number of transmitted streams and the best antenna subset that minimizes the symbol error rate is also proposed. Several examples to demonstrate the effectiveness of proposed selection algorithms in interference limited environments are presented. Simulation results show that for low to moderate interference power, significant improvement in the system performance is achievable with the use of transmit antenna selection algorithms. It is found that employing transmit antenna selection algorithms, and adaptation of the number of transmitted streams and the signal constellation sizes can significantly enhance the performance of MIMO systems with co-channel interference. The performance improvement is more significant in spatially correlated fading channels.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 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".