Resource allocation in wireless systems with relay-based cooperation and energy harvesting
Bibliographic record
Abstract
Wireless communication networks are subject to exponential growth as a result of proliferation of smart phones, diverse wireless services and Internet of Things (IoT) applications. This extensive growth of wireless networks can significantly increase energy consumption, and escalating environmental pollution and energy costs have already created an urge for green communication. Therefore, we need to be proactive in designing environment friendly communication technologies and efficient resource allocation solutions, which will potentially drive the future generation of wireless communication. In this thesis, we focus on two promising communication technologies, namely cooperative relaying, which improves energy and spectral efficiency by providing spatial diversity, and energy harvesting technology, which can improve sustainability by utilizing renewable energy sources. The objective of this thesis is to address a number of key challenges in the design of efficient resource allocation techniques for wireless systems based on these two communication technologies. Firstly, we address the problem of energy efficiency maximization for downlink orthogonal frequency division multiple access (OFDMA)-based cooperative networks. The power and subcarrier allocation policies are jointly optimized with quality of service (QoS) provisioning. Afterwards, we investigate frequency reuse in OFDMA device-to-device (D2D) cooperative systems in which D2D pairs are classified based on the level of proximity with each other. We propose different scenarios of downlink communications and provide efficient frequency allocation techniques. Moreover, resource allocation algorithms with low complexity and signaling overhead are developed. Next, we focus on energy limitation of the relay nodes in cooperative systems. Using wireless energy harvesting to power the relay nodes, we propose an efficient resource allocation algorithm. As wireless energy harvesting technology is only effective for charging small nodes in communication systems, finally, we focus on the issue of charging the wireless nodes with renewable energy. We investigate the problem of resource allocation in energy harvesting systems considering the fact that the energy harvested from environment may not be enough to satisfy the QoS of all users due to its random nature. Two different utility functions are introduced and both offline and online schemes are devised to address this problem.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".