Generalized Coordinated Multipoint (GCoMP)-Enabled NOMA: Outage,\n Capacity, and Power Allocation
Bibliographic record
Abstract
A novel generalized coordinated multi-point transmission (GCoMP)-enabled\nnon-orthogonal multiple access (NOMA) scheme is proposed. In particular, the\ntraditional joint transmission CoMP scheme is generalized to be applied for all\nuser-equipments (UEs), i.e. both cell-centre and cell-edge users within the\ncoverage area of cellular base stations (BSs). Furthermore, every BS applies\nNOMA for all UEs associated to it using the same frequency sub-band (i.e. all\nUEs associated to a BS forms a single NOMA cluster). To evaluate the proposed\nscheme, we derive a closed-form expression for the probability of outage for a\nUE with different orders of BS cooperation. Important insights on the proposed\nsystem are extracted by deriving an approximate (asymptotic) expressions for\nthe probability of outage and outage capacity. Furthermore, an optimal\ntransmission power allocation scheme that jointly allocates transmission power\nfractions from all cooperating BSs to all connected UEs is developed and\ninvestigated for the proposed system. Findings show that NOMA with a large\nnumber of UEs is feasible when the GCoMP technique is used over all UEs within\nthe network coverage area. Also, the performance degradation caused by a large\nNOMA cluster size is significantly mitigated by increasing the number of\ncooperating BSs. In addition, for given feasible system parameters and a given\nNOMA cluster, the lower the available power budget, the higher is the number of\nBSs that apply NOMA for their cluster members and the lower the number of BSs\nthat use water-filling for power allocation.\n
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".