Power Allocation and Group Assignment for Reducing Network Coding Noise in Multi-Unicast Wireless Systems
Bibliographic record
Abstract
In this paper, we consider physical-layer network coding (PNC) in a multi-unicast wireless cooperative network with a single relay. We aim to deal with the NC noise, i.e., an additional noise term due to applying NC, with the objective of improving the network data rate. Our approaches are based on relay power-allocation and group-allocation techniques. To this end, we provide a mathematical framework for the achievable information rate of the system with the notion of power assignment at the relay. Based on this framework, we present a novel power-allocation scheme to maximize the total information rate among all the source-destination communication sessions in the network. Further, we provide a closed-form solution for the two-unicast case. Simulation results show that the proposed relay power allocation can significantly help alleviate the adverse effects of NC noise. Next, we propose a group-allocation scheme to assign sessions to different groups for performing PNC at the relay. We combine power allocation and group allocation to further improve performance. The formulated joint optimization problem is NP-hard. Therefore, a suboptimal heuristic algorithm is proposed and implemented at the relay to solve this problem. From the simulation results, the proposed joint group assignment and power-allocation scheme achieves up to 64% overall data rate gain for the multi-unicast system compared with a single-group system with no relay power assignment. This observation shows that PNC can be efficiently harnessed in a multi-unicast cooperative network by exploiting proposed approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".