Resource allocation for uplink non-orthogonal multiple access in virtualized wireless networks
Bibliographic record
Abstract
Wireless networks are strained by an exponential growth in mobile network traffic and new applications, such as the internet-of-things (IoT) paradigm and smart cities, are amplifying the problem as the density of networks increases. At the same time, network providers are faced with increasing infrastructure and service deployment costs which are not being offset by increased revenues. Multi-carrier non-orthogonal multiple access (NOMA) and virtualized wireless networks (VWN) are being positioned as promising techniques to jointly meet the needs of future network users and service providers by promoting the mutualization of network hardware and sharing of spectrum resources. With NOMA, sub-carriers can be shared by several users concurrently, with resulting reduction in spectrum requirements via increased spectral efficiency and re-use, increased power efficiency, and network density. Under VWN, hardware and radio resources are shared by several service providers with groups of users isolated from one another by minimum quality of service guarantees. The use of NOMA in VWNs has not been extensively studied and, due to the nature of wireless channels and user mobility, careful dynamic resource allocation is required to maintain system and user performance.The purpose of this work is to study NOMA-based VWNs and propose efficient resource allocation algorithms to leverage the available gains for users and service and infrastructure providers. Specifically, the use of NOMA for uplink transmissions is examined due to the many proposed use-cases, such as distributed sensor networks, for which uplink traffic far outweighs downlink and the greater capability of base stations in processing concurrent user signals. Initially, performance of NOMA VWN in single-input single-output channels with perfect processing of received signals is examined. With the goal of minimizing required transmit power for battery-dependent devices, an efficient iterative algorithm is presented. The proposed algorithm is then extended to multiple-input multiple-output systems and a sensitivity analysis to increased interference from imperfect processing of received signals is presented. Since many of the proposed use-cases support critical applications such as health and public safety monitoring, we then examine the use of NOMA VWN subject to reliability constraints. The resource allocation problem is mapped to its robust counterpart and the techniques of chance-constrained robust optimization are used to develop an efficient iterative algorithm which minimizes required transmit power subject to user rate and outage constraints. In each of these scenarios, simulation results are presented demonstrating the performance of the proposed algorithms and the improvement compared to traditional orthogonal multiple access is evaluated.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".