cDERSA: Cognitive D2D Enabled Relay Selection Algorithm to Mitigate Blind-Spots in 5G Cellular Networks
Bibliographic record
Abstract
Blind-spots, where wireless signals do not reach within the coverage range, often emerge in a dynamic environment due to obstacles, geographical location or mobility of cellular users (CUs). Thus greatly reducing the overall system performance in terms of coverage and throughput. Relay-aided cognitive Device to Device (cD2D) communication system underlying the 5G cellular network can help mitigate blind-spots. Cognitive capability helps D2D users to acquire the spectrum opportunistically for proximity communication and establish a semi-independent network underlying the 5G network, which not only offloads 5G-New Radio (NR) base station but also enhances the overall system performance. In this work, we have developed a relay-aided cognitive D2D network that helps CUs falling into the blind-spots to retain access to the 5G network and increase wireless coverage. Relay selection requires mutual consent between the relay and the device in the blind-spot. The in-coverage devices are tempted to act as relays through incentive-based mechanism. For enhanced system performance a suitable match among the devices in blind-spots and the relays is required. cD2D enabled relay selection algorithm (cDERSA) is proposed in this work, in which a cognitive D2D user (cDU), which is a CU falling in the blind-spot, establishes a relayed cD2D link to access 5G-NR gNodeB. All cDUs as well as the tempted relays, i.e. cognitive D2D relays (cDRs), first scan their surroundings for devices capable of D2D communication and based on multi-criteria objective functions, build a priority table. A stable marriage problem is formulated and solved using a unique, stable, distributed, and efficient matching algorithm based on the Gale-Shapley algorithm. A new incentive mechanism is also developed to keep relays motivated to share their resources. Simulation is performed and their results show improvement in throughput and average user satisfaction, which validates our proposed cDERSA.
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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 | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".