CoMP Transmission in Downlink NOMA-Based Heterogeneous Cloud Radio Access Networks
Bibliographic record
Abstract
In this paper, we investigate the integration between the coordinated multipoint (CoMP) transmission and the non-orthogonal multiple access (NOMA) in downlink heterogeneous cloud radio access networks (H-CRANs). In H-CRAN, low-power high-density small remote radio heads (SRRHs) are underlaid by high-power low-density macro RRH (MRRH). However, co-channel deployment of the different RRHs gives rise to the problem of inter-cell interference that significantly affects system performance especially the cell-edge users. Thus, the users are first categorized into Non-CoMP users and CoMP users based on the relation between the useful signal to the dominant interference signal. The Non-CoMP user is the user equipment (UEs) having high signal-to-interference-plus-noise-ratio (INR) and hence associates with only one RRH. On the other hand, the CoMP user, cell-edge user, is the UE that experiences less distinctive received power with the best two RRHs. In the proposed CoMP-NOMA framework, each RRH schedules CoMP-UE and non-CoMP-UE over the same transmission channel using NOMA. We first design an analytical framework based on tools from the stochastic geometry to evaluate the performance of the proposed framework (CoMP-NOMA) which is based on H-CRAN in terms of the average achievable data rate for each NOMA UE. We then examine the spectral efficiency of the proposed CoMP-NOMA based H-CRAN. Simulation results are provided to validate the accuracy of the analytical models and to reveal the superiority of the proposed CoMP-NOMA framework compared with conventional CoMP orthogonal multiple access (CoMP-OMA) techniques. By reaping the benefits of both JT-CoMP and NOMA, we prove that the proposed framework can successfully deal with the inter-cell interference by using CoMP and improve the network's spectral efficiency through NOMA technique. We also show that, with an appropriate power allocation coefficient setting at the Non-CoMP-UEs, a fairness performance can be achieved between the CoMP-UEs and the Non-CoMP-UEs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".