Cross-Layer Resource Allocation and Scheduling in Bidirectional Cooperative Multichannel Relaying Networks
Bibliographic record
Abstract
Efficient allocation of the scarce resource spectrum is a fundamental problem in mobile communications.Cooperative Relaying (CoR) and Network Coding (NC) are two promising techniques for improving the performance of next-generation wireless networks.In this thesis, cross-layer resource allocation is studied for relay-assisted bidirectional multichannel wireless networks.We model the network in which two nodes, User Equipment (UE) and eNodeB (eNB), exchange information with the assistance of a Relay Station (RS).The UEs and eNB can choose between different transmission schemes: direct transmission, pure CoR, or via the combination of NC and CoR (NC/CoR).Novel optimization frameworks are proposed for the resource allocation problem for such networks.First, the achievable rate regions are characterized, and the joint optimization problem of power allocation and transmission scheme selection for maximizing the bidirectional stability region is defined.A hybrid transmission scheme with adaptive resource allocation is proposed to dynamically select the best transmission strategy and the optimal resource allocation at each bidirectional transmission frame.The simulation results show that the hybrid scheme with joint power control and channel allocation improves the system performance considerably.Second, the joint resource allocation and relay selection problem is studied for bidirectional Orthogonal Frequency Division Multiple Access (OFDMA) relay networks.The problem of joint channel allocation/pairing, relay selection, and First of all, I would like to thank my advisor, Professor Ioannis Lambadaris, for his continuous inspiration, guidance and support on my thesis work.In the past few years, he constantly encouraged me to focus on the most important and challenging problems, and instructed me how to find a way toward innovative ideas and creative solutions.He also gave me a lot of freedom to choose the favorite research topics to strengthen my interest and enthusiasm in research.I am also grateful to all the colleagues and students in the Broadband Networks Lab at the Department of Systems and Computer Engineering for their enjoyable discussions and shared research work with me on communications concepts and interesting ideas.I would like to thank Dr. Hassan Halabian for his collaborative
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".