Performance analysis and resourceallocation in underlaid device-to-device cellular networks
Bibliographic record
Abstract
Device-to-device (D2D) communications enables direct connection between nearby cellular users without traversing through the base-station (BS).Potential benefits of D2D communications in cellular networks are multi-folds, ranging from enhancing spectrum efficiency, reducing network congestion, shortening packet delay, saving power, to enabling location-based applications and services.D2D-enabled networking not only has been required for public safety networks when the cellular coverage is not available, but also developed as supporting technologies for Internet of Things (IoT) and Vehicle-to-Everything (V2X) connections.As D2D is allowed to operate in the same spectrum with cellular users, the more resources (e.g., time-frequency) are shared between D2D and cellular transmission, the more interference will be present, yet higher potential spectral efficiency gains will be provided if the interference can be effectively managed.This thesis considers underlaid D2D schemes where the D2D links fully reuse time-spectrum resources that currently occupied by the cellular transmission.Our focus is to investigate the benefits offered by underlaid D2D in terms of spectral efficiency gains via performance analysis and sum-rate maximizing resource allocation algorithms.First, this thesis develops a Gaussian-Mixture (GM) model to represent the aggregate interference at a typical D2D receiver in underlaid D2D cellular networks.From information theoretical point-of-view, the corresponding D2D link can be thought of as an additive quadrature GM channel.We then study the characterization of optimal input and the computation of capacity of such GM channel under an average power constraint.It is shown that the capacity-achieving input distribution has a uniformly distributed phase, while the optimal amplitude distribution includes a finite number of mass points.Our numerical examples illustrate that, in many cases, the capacityachieving distribution consists of only one or two mass points.Second, the analysis of achievable sum-rate offered by D2D communications is extended from link to network level.Both single-and multi-cell settings are considered in which multiple D2D links reuse the channel (time-frequency resources) currently occupied by one cellular uplink transmission.In addition, we assume full-duplex (FD) operation at D2D links to ease the channel assignment for underlaid D2D as FD D2D only requires one carrier frequency for both transmitting and receiving signals.Utilizing stochastic geometry based models to capture the randomness and mobility of D2D/cellular users, analytical sum-rate expressions are derived and applied to investigate the effects of network parameters on the achieved sum-rates.It is demonstrated that, from an average throughput perspective, FD D2D brings performance improvements as compared to the half-duplex (HD) counterpart and pure cellular systems (in absence of D2D).Third, the single-antenna multi-cell network model is expanded to include multi-antenna trans-Ngoc, for his support, encouragement, and advice during my graduate study at McGill University.Without his assistance and dedicated involvement in every steps throughout the process, this thesis would have never been accomplished.I also learned very much from his vast knowledge, genuine enthusiasm toward research, hard work, and his sense of humour.I am grateful to Dr. Nghi Tran, whose suggestions and enormous knowledge in wireless communications and mathematics have benefited me tremendously.I would also like to thank Prof. Benoit Champagne, Prof. Jun Cai
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".