NOMA Integrated with Enabling Technologies and Practical Challenges
Bibliographic record
Abstract
Non-orthogonal multiple access (NOMA) schemes allow multiple users to share the same resource and separate the users in either the power-domain (PD -NOMA) or in the code domain (CD-NOMA).To make NOMA a reality in networks, several important practical challenges need to be considered.This thesis addresses some of these challenges in both the PD-NOMA and CD-NOMA space.In PD-NOMA systems integrated with mmWave technology, the thesis considers the practical constraint of the end user processing capabilities, modelled through an successive interference cancellation (SIC) decoding capability constraint that captures the number of other user's signals any given user can decode in the SIC decoding procedure.6G networks are expected to include a mix of users of varying processing capabilities, all needing to access the same spectrum.To solve the rate maximization problem when each user has different processing capabilities, the thesis proposes low-complexity heuristics to maximize the sum-rate after factoring in each individual users SIC decoding capability constraint.However, these algorithms are based on the instantaneous channel conditions of users and need to be run on a millisecond granularity.To address this, a machine learning based neural network approach is proposed that takes this complexity offline where the neural network is trained on simulated and past network data, and the trained network is directly applied to solve the clustering problem in live networks.The thesis also addresses the practical constraint around the availability of CSI by exploiting the growing field of integrated communication and sensing solutions using a camera equipped base station to aid the user clustering process in NOMA.In CD-NOMA systems using the widely promoted sparse code multiple access (SCMA) scheme for uplink (UL) NOMA, the thesis studies the PAPR problem in UL SCMA-OFDM systems.A novel link between the obtained PAPR statistics and the SCMA modulation scheme and the placement of the sub-carriers (SC's) that carry the SCMA codewords is presented.The thesis highlights unique opportunities that SCMA-OFDM systems present to the widely studied PAPR problem due to the
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".