Distributed Multiple Access for the Uplink of Multi-cell OFDMA Networks
Bibliographic record
Abstract
With the global deployment of the 4G (4th generation) wireless networks, it is expected that in the near future wireless data services will account for most of the traffic in the cellular networks.These data services may be characterized by highly diversified QoS (Quality of Service) requirements such as delay, data rate, and reliability.It may not be a good approach to use the current BS (Base Station)-centric scheduling as the universal scheduling method for all kinds of data services.Besides, centralized scheduling will require a substantial amount of overhead when the number of wireless devices is very large, due to the fact that in centralized scheduling the BSs need to collect the CSI (Channel State Information) of these devices to make appropriate decisions.This may be very challenging at BSs especially when the channel coherence time is low.Therefore, in this thesis we explore a distributed scheduling method for the next-generation cellular networks, in which the scheduling decisions are performed jointly by the wireless devices and the BSs in order to reduce the complexity at the BSs and to reduce the protocol overhead in terms of the bandwidth dedicated for the control signalling.We propose a distributed multi-channel multiple access protocol for the uplink data transfer in TDD (Time-Division Duplex) mode for a multi-cell network.The protocol may be used as a low-overhead scheduling solution for delay-tolerant services especially with large numbers of devices, and can coexist with the legacy centralized scheduling schemes.In the proposed protocol, wireless devices play a key part by iii First, I would like to express my sincere appreciation and respect for my supervisor Prof. Halim Yanikomeroglu for his generous efforts in guiding this thesis research.Without his valuable supervision, this work would not have been possible.All the results of this thesis came from many face to face discussions with Prof. Halim Yanikomeroglu.During this research period, I was also impressed by his expertise in wireless communications.He taught me not only the broad range of wireless communications but also the details and latest advancements of many research directions.He also showed to me the management skills of relating academic research work to industrial requirements.I am very lucky to have such an excellent professor and a fruitful research group in my thesis research.I also want to express gratitude to the valuable help and suggestions from all the members of our research group.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".