Enhancing 5G Massive MIMO Systems Using a Compressive Sensing-Based Approach
Bibliographic record
Abstract
The capacity of massive multiple-input multiple-output (MIMO) systems is significantly enhanced through the use of abundant antennas at the base station (BS), supporting a multitude of users.However, in Time Division Duplex (TDD) mode, the multiplexing of pilots inevitably leads to Pilot Contamination (PC).In this study, a novel approach to interference alignment and pilot purification in a large MIMO system was proposed, utilizing the principles of compressive sensing and deep learning.The primary aim of the Compressive Sensing-Based Location Scheduling System (CSLSS) is to mitigate PC issues while optimizing the pilot sequence for the user.By introducing an additional sequence, the pilot set is enhanced using our location-based decontamination technique.Furthermore, a Feed-Forward Convolutional Neural Network (FFCNN) is employed for interference alignment in the MIMO system.Experimental results indicate significant improvements in the parameters under consideration.The sum rate showed an increase of 74.6%, the Bit Error Rate (BER) improved by 57.8%, the Signal-to-Interference-plus-Noise Ratio (SINR) rose by 79.8%, and the spectral efficiency of the channel improved by 98% in terms of interference alignment.In addition, a compressive sensing-based parameter showed an enhancement of 98%.The Mean Square Error (MSE) for CSLSS was reduced by 64.5% for the Signal-to-Noise Ratio (SNR), and the BER for the proposed compressive sensing method improved by 41.2% for SNR.The throughput also improved, with an increase of 97.5% for SNR and 97.8% for the number of antennas used in the BS.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 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".