Performance Analysis and Optimization of Grant-Free Random Access With Capture Effect for Cell-Free Massive MIMO
Bibliographic record
Abstract
To accommodate the proliferation of Internet-of-Things (IoT) applications, next-generation wireless communication networks, particularly the sixth-generation (6G), are expected to offer excellent support for the massive access of machine-type communication (MTC). In this paper, we investigate the grant-free random access (GFRA) employing orthogonal preambles in cell-free massive multiple-input multiple-output (mMIMO), which shows immense potential for enabling massive connectivity. In particular, we take into account the capture effect, defined as successful decoding despite preamble collisions, when the received signal-to-interference-plus-noise ratio (SINR) exceeds a predefined threshold. To this end, we develop an analytical framework to model GFRA with the capture effect adopting stochastic geometry. Subsequently, approximate analytical expressions for the received SINR and the access success probability for the typical GFRA frame structure are derived. Furthermore, leveraging these theoretical expressions, we formulate an optimization problem to determine the optimal preamble length that maximizes effective throughput. Simulation results validate the accuracy of our theoretical analyses and demonstrate the superior access performance of the optimized frame structure, whereas a frame structure with a constant preamble length does not consistently attain maximum effective throughput across varying user densities.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".