Relay Selection for Cognitive Massive MIMO Two-Way Relay Networks
Bibliographic record
Abstract
We analyze relay selection for an underlay cognitive radio (CR) two-way relay network (TWRN) with zero-forcing (ZF) transmission and receiving. The source and the destination nodes are massive multiple-input multiple-output (MIMO) enabled. Relays will perform amplify and forwarding (AF) while the destination and source nodes perform self interference cancellation. We first obtain asymptotic signal-to-interference-plus-noise ratio (SINR) values under the power scaling at the relay and end nodes. Then, we derive optimal power allocation schemes for the end nodes to satisfy the interference constraints at the primary user (PU). Based on these optimal values, we analyze the effect of relay selection on the sum rate. With the use of massive MIMO, the SINR and the sum rate will only depend on the pathloss coefficients of the channels and average noise levels. Thus, the relay selection can be done at the deployment stages of the system and most of the time it simplifies to selection of the relay with the highest number of antennas. Our simulation results validate the analytical asymptotic results and qualify CR massive MIMO TWRNs as a possible candidate for future wireless systems.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".