CoMP Transmission in Downlink NOMA-Based Heterogeneous Cloud Radio Access Networks
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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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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 it