Secrecy Based Resource Allocation for D2D Communication Using Tabu Search Algorithm
Bibliographic record
Abstract
Device-to-device (D2D) communications have been pro- posed as one of the key technologies to improve the spectral efficiency in the future fifth generation (5G) of wireless mobile communication systems through resource sharing with cellular networks such that it can offload the part of cellular traffic onto the D2D network. However, intra-cell interference in D2D underlying cellular systems may decrease the performance of wireless network. In this paper, we investigate the subcarrier allocation issue when employing physical layer security capacity of D2D pairs and cellular users (CUs). When secrecy-capacity take into consideration, D2D communications can help the cellular system to decrease intra-cell interference. In our optimization task, we formulate the subcarrier allocation problem to maximize the system secrecy-capacity while guaranteeing the minimum data rate requirements for all D2D pairs and CUs. Such optimization is NP-hard problem with nonlinear constraints and optimal solution can be found through complicated methods such as exhaustive search or branch-and-bound. We, therefore, propose tabu search (TS) meta-heuristic algorithm to globally find the optimal subcarrier allocation solution to maximize system secrecy-capacity. Simulation results show that the proposed TS scheme achieves a higher performance than other algorithms in term of system secrecy- capacity.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".