Highly Efficient Resource Allocation Techniques in 5G for NOMA-based Massive MIMO and Relaying Systems
Bibliographic record
Abstract
The explosive proliferation of smart devices in the 5-th generation (5G) network expects 1,000-fold capacity enhancement, leading to the urgent need of highly resource-efficient technologies. Non-orthogonal multiple access (NOMA), a promising spectral efficient technology for 5G to serve multiple users concurrently, can be combined with massive multiple input multiple output (MIMO) and relaying technology, to achieve highly efficient communications. Hence, this thesis studies the design and resource allocation of NOMA-based massive MIMO and relaying systems.\nDue to hardware constraints and channel condition variation, the first topic of the thesis develops efficient antenna selection and user scheduling algorithms for sum rate maximization in two MIMO-NOMA scenarios. In the single-band scenario, the proposed algorithm improves antenna search efficiency by limiting the candidate antennas to those are beneficial to the relevant users. In the multi-band scenario, the proposed algorithm selects the antennas and users with the highest contribution total channel gain. Numerical results show that our proposed algorithms achieve similar performance to other algorithms with reduced complexity.\nThe second part of the thesis proposes the relaying and power allocation scheme for the NOMA-assisted relaying system to serve multiple cell-edge users. The relay node decodes its own message from the source NOMA signal and transmits the remaining part of signal to cell-edge users. The power allocation scheme is developed by minimizing the system outage probability. To further evaluate the system performance, the ergodic capacity is approximated by analyzing the interference at cell-edge users. Numerical results proves the performance improvement of the proposed system over conventional orthogonal multiple access mechanism.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".