Interference Suppression and Energy Efficiency Improvement With Massive MIMO and Relay Selection in Cognitive Two-Way Relay Networks
Bibliographic record
Abstract
We analyze a relay assisted wireless communication link between two underlay massive multiple-input multiple-output (MIMO) terminals. Specifically, an amplify and forwarding (AF) two-way relay is optimally selected to maximize the sum rate and to keep the interference on the primary user (PU) below an interference threshold. We first obtain asymptotic signal-to-interference-plus-noise ratio (SINR) values for two scenarios: (1) the relays and the two end nodes use transmit power scaling and (2) only the end nodes use transmit power scaling. For these two cases, we derive optimal power allocations subject to the PU interference constraints. With these optimal power allocations, we analyze how relay selection impacts the outage, the sum rate, and the energy efficiency of the network. For the first scenario, the outage can be reduced to zero with appropriate power allocation and the relay selection can be done offline. For the second scenario, outage will depend on the instantaneous channel state between the relays and the PU. Furthermore, for a realistic power consumption model, we analyze the energy efficiency and show that relay selection will increase it.
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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) | 0.000 | 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".