Recent Advances in Cellular D2D Communications
Bibliographic record
Abstract
Device-to-device (D2D) communications have attracted a great deal of attention from researchers in recent years.It is a promising technique for offloading local traffic from cellular base stations by allowing local devices, in physical proximity, to communicate directly with each other.Furthermore, through relaying, D2D is also a promising approach to enhancing service coverage at cell edges or in black spots.Besides improving network performance and service quality, D2D can open up opportunities for new proximity-based services and applications for cellular users.However, there are many challenges to realizing the full benefits of D2D.For one, minimizing the interference between legacy cellular and D2D users operating in underlay mode is still an active research issue.With the 5th generation (5G) communication systems expected to be the main data carrier for the Internet-of-Things (IoT) paradigm, the potential role of D2D and its scalability to support massive IoT devices and their machine-centric (as opposed to human-centric) communications need to be investigated.New challenges have also arisen from new enabling technologies for D2D communications, such as non-orthogonal multiple access (NOMA) and blockchain technologies, which call for new solutions to be proposed.This special issue aims to present a collection of exciting papers, reporting the most recent advances in cellular D2D communications.Through invited and open call submissions, a total of ten excellent articles have been accepted, following a rigorous review process that required a minimum of three reviews and at least one revision round for each paper.The list of accepted articles includes one review and nine original research articles on addressing many of the aforementioned challenges and beyond.The first paper by Höyhtyä, Apilo and Lasanen [1] is a review article that analyzed the latest energy consumption models of 3GPP standardized LTE (long-term evolution) and WiFi interfaces, with recommendations on energy saving options for D2D communications in a set of application scenarios.Distributed resource sharing and allocation are amongst the most important issues in cellular D2D networks.Hong, Wang, Cai and Leung [2] investigated the issue of fairness in cooperative D2D computational resource sharing, and proposed a blockchain-based credit system where user's computational task cooperation are recorded on public blockchain ledger as transactions, and their credit balance can be easily accessed from the ledger.The performance of the proposed credit system is demonstrated by incorporating it into a connectivity-aware task scheduling scheme to enforce fairness among users in the D2D network.Radio resource is another resource type that must be efficiently managed.The next four papers explore different strategies for allocating radio resources such spectrum and transmit power for D2D communications.Jiang, Wang, Ren and Xu [3] studied the problem of spectrum resource and transmit power allocation for underlay multicast D2D communications, and presented a heuristic and low-complexity resource and power allocation scheme that aims to maximize overall energy efficiency, while satisfying the QoS (quality of service) requirements of both cellular and D2D users.Similarly, for underlay D2D communications, Ban [4] proposed a practical scheme with low Future Internet 2018, 10, 10 1 www.mdpi.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.020 |
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".