Sparse Activity Detection for Massive Connectivity in Cellular Networks: Multi-Cell Cooperation Vs Large-Scale Antenna Arrays
Why this work is in the frame
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Bibliographic record
Abstract
Sparse device activity detection for machine-type communications has attracted increasing attention in recent studies. However, most of the previous works focus on the single-cell case. This paper studies the impact of the inter-cell interference on the device activity detection problem with non-orthogonal signatures in multi-cell systems by employing the computationally efficient approximate message passing algorithm (AMP). Specifically, this paper studies the impact of the inter-cell interference by either treating it as noise or recovering it, showing that it is always beneficial to recover the interference at each base station (BS). Two network architectures, namely BSs with large antenna arrays and network with multi-cell cooperation, are compared in terms of their effectiveness in overcoming inter-cell interference. This paper provides an analytical characterization of probabilities of false alarm and missed detection. Simulation results show that large-scale antenna array is effective in improving the performance of all users whereas cooperation is effective in improving the performance of cell-edge users. In terms of the detection performance of the 95-percentile users, simulation results under a typical network setting show that having twice as many antennas provides almost the same benefit as multi-cell cooperation.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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