Performance analysis of full‐duplex cellular two‐way relay networks with the <i>N</i>th worst relay selection under channel estimation error and cochannel interference
Bibliographic record
Abstract
Abstract This paper considers a full‐duplex (FD) cellular multiuser two‐way multirelay network, wherein one N a ‐antenna base station communicates bidirectionally with M single‐antenna users via L single‐antenna amplify‐and‐forward relays. A practical scenario where cochannel interference (CCI) and channel estimation error (CEE) present in the network is considered for the investigation. Considering the CEE and interferers at the relays, we formulate the N th worst relay selection scheme, which consists the best relay selection as a special case. With such relay selection strategy, we derive the tight lower bound (LB) expressions for the overall outage probability, average bit error rate, and ergodic sum rate with CEE and CCI under Rayleigh fading. Furthermore, we deduce the asymptotic expressions for the overall outage probability and average bit error rate at high signal‐to‐noise ratio. Based on the asymptotic outage expression, we present some practical cases of interest, from which the impacts of CCI, CEE, and residual self‐interference (RSI) (imposed on the FD nodes) on system diversity order are presented. It is shown via the numerical results that CEE, CCI, and RSI have detrimental impact on the system performance. Our results also reveal that the impacts of CEE, CCI, and RSI can be alleviated by increasing the number of relays. We also obtain some insights on the self‐interference cancelation requirements for the FD mode. The numerical and simulation results verify our theoretical analysis.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".