Effective relaying mechanisms in future device to device communication : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in School of Food and Advanced Technology at Massey University, Palmerston North, New Zealand
Bibliographic record
Abstract
Future wireless networks embrace a large number of assorted network-enabled devices such as mobile phones, sensor nodes, drones, smart gears, etc., with different applications and purpose, but they all share one common characteristic which is the dependence on strong network connectivity.Growing demand of internet-connected devices and data applications is burdensome for the currently deployed cellular wireless networks.For this reason, future networks are likely to embrace cutting-edge technological advancements in network infrastructure such as, small cells, device-to-device communication, non-orthogonal multiple access scheme (NOMA), multiple-input-multiple out, etc., to increase spectral efficiency, improve network coverage, and reduce network latency.Individual devices acquire network connectivity by accessing radio resources in orthogonal manner which limits spectrum utilisation resulting in data congestion and latency in dense cellular networks.NOMA is a prominent scheme in which multiple users are paired together and access radio resources by slicing the power domain.While several research works study power control mechanisms by base station to communicate with NOMA users, it is equally important to maintain distinction between the users in uplink communication.Furthermore, these users in a NOMA pair are able to perform cooperative relaying where one device assists another device in a NOMA pair to increase signal diversity.However, the benefits of using a NOMA pair in improving network coverage is still overlooked.With a varierty of cellular connected devices, use of NOMA is studied on devices with similar channel characteristics and the need of adopting NOMA for aerial devices has not been investigated.Therefore, this research establishes a novel mechanism to offer distinction in uplink communication for NOMA pair, a relaying scheme to extend the coverage of a base station by utilising NOMA pair and a ranking scheme for ground and aerial devices to access radio resources by NOMA.viii How can I forget some of my close friends, Ayesha, Saman, Shan, Ali, Abhipray, Van and Amardeep who listened to my endless stories, shared laughs and failures.Finally,
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".