Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
D2D Communications in 6G Heterogeneous Ultra Dense Networks
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

D2D Communications in 6G Heterogeneous Ultra Dense Networks

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 4932

Special Issue Editor

Dr. Fotis Foukalas

E-Mail Website
Guest Editor
Department of Informatics and Telecommunications, University of Thessaly, 38221 Volos, Greece
Interests: communications and networking; signal and information processing

Special Issue Information

Dear Colleagues,

While a wireless transmission distance will become much shorter in case of ultra dense networks, D2D communication is envisioned to continuously evolve into the 6G wireless communication networks. Such an evolution is able to efficiently support a much larger and more diverse set of devices and applications. Heterogeneous ultra dense networks (HUDNs) integrated with D2D communications is considered as one of the main elements of the future 6G networks. D2D communications in HUDNs can improve users’ quality of experience while reducing both power consumption and latency and increasing the network throughput, enhancing proximity services. HUDNs can also give access through the unlicensed spectrum and thereby increase the overall spectral efficiency. Moreover, D2D communication can enable various differnet vertical applications such as vehicular and public safety communications.

Dr. Fotis Foukalas
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • device-to-device
  • heterogeneous ultra dense networks
  • vertical applications

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

10 pages, 442 KiB  
Communication
A Self-Regulating Power-Control Scheme Using Reinforcement Learning for D2D Communication Networks
by Tae-Won Ban
Sensors 2022, 22(13), 4894; https://doi.org/10.3390/s22134894 - 29 Jun 2022
Cited by 1 | Viewed by 1541
Abstract
We investigate a power control problem for overlay device-to-device (D2D) communication networks relying on a deep deterministic policy gradient (DDPG), which is a model-free off-policy algorithm for learning continuous actions such as transmitting power levels. We propose a DDPG-based self-regulating power control scheme [...] Read more.
We investigate a power control problem for overlay device-to-device (D2D) communication networks relying on a deep deterministic policy gradient (DDPG), which is a model-free off-policy algorithm for learning continuous actions such as transmitting power levels. We propose a DDPG-based self-regulating power control scheme whereby each D2D transmitter can autonomously determine its transmission power level with only local channel gains that can be measured from the sounding symbols transmitted by D2D receivers. The performance of the proposed scheme is analyzed in terms of average sum-rate and energy efficiency and compared to several conventional schemes. Our numerical results show that the proposed scheme increases the average sum-rate compared to the conventional schemes, even with severe interference caused by increasing the number of D2D pairs or high transmission power, and the proposed scheme has the highest energy efficiency. Full article
(This article belongs to the Special Issue D2D Communications in 6G Heterogeneous Ultra Dense Networks)
►▼ Show Figures

Figure 1

32 pages, 4427 KiB  
Article
Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
by Iago Diógenes do Rego and Vicente A. de Sousa, Jr.
Sensors 2021, 21(23), 7899; https://doi.org/10.3390/s21237899 - 27 Nov 2021
Cited by 1 | Viewed by 2395
Abstract
This work explores interference coordination techniques (inter-cell interference coordination, ICIC) based on fractional frequency reuse (FFR) as a solution for a multi-cellular scenario with user concentration varying over time. Initially, we present the problem of high user concentration along with their consequences. Next, [...] Read more.
This work explores interference coordination techniques (inter-cell interference coordination, ICIC) based on fractional frequency reuse (FFR) as a solution for a multi-cellular scenario with user concentration varying over time. Initially, we present the problem of high user concentration along with their consequences. Next, the use of multiple-input multiple-output (MIMO) and small cells are discussed as classic solutions to the problem, leading to the introduction of fractional frequency reuse and existing ICIC techniques that use FFR. An exploratory analysis is presented in order to demonstrate the effectiveness of ICIC techniques in reducing co-channel interference, as well as to compare different techniques. A statistical study was conducted using one of the techniques from the first analysis in order to identify which of its parameters are relevant to the system performance. Additionally, another study is presented to highlight the impact of high user concentration in the proposed scenario. Because of the dynamic aspect of the system, this work proposes a solution based on machine learning. It consists of changing the ICIC parameters automatically to maintain the best possible signal-to-interference-plus-noise ratio (SINR) in a scenario with hotspots appearing over time. All investigations are based on ns-3 simulator prototyping. The results show that the proposed Q-Learning algorithm increases the average SINR from all users and hotspot users when compared with a scenario without Q-Learning. The SINR from hotspot users is increased by 11.2% in the worst case scenario and by 180% in the best case. Full article
(This article belongs to the Special Issue D2D Communications in 6G Heterogeneous Ultra Dense Networks)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop