Next Article in Journal
Near-Field Communication in Biomedical Applications
Previous Article in Journal
Joint Image Encryption and Screen-Cam Robust Two Watermarking Scheme
Open AccessArticle

D2D Mobile Relaying Meets NOMA—Part I: A Biform Game Analysis

1
NEST Research Group, LRI Lab., ENSEM, Hassan II University of Casablanca, Casablanca 20000, Morocco
2
Laoratoire de Reacherche en Informatique, Sorbonne Université, CNRS, LIP6, F-75005 Paris, France
3
Department of Computer Science, University of Quebec at Montreal, Montreal, QC H2L 2C4, Canada
4
TICLab, International University of Rabat, Rabat 11100, Morocco
5
School of Computer Science, Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(3), 702; https://doi.org/10.3390/s21030702
Received: 28 November 2020 / Revised: 10 January 2021 / Accepted: 13 January 2021 / Published: 20 January 2021
(This article belongs to the Special Issue Device to Device (D2D) Communication)
Structureless communications such as Device-to-Device (D2D) relaying are undeniably of paramount importance to improving the performance of today’s mobile networks. Such a communication paradigm requires implementing a certain level of intelligence at device level, allowing to interact with the environment and select proper decisions. However, decentralizing decision making sometimes may induce some paradoxical outcomes resulting, therefore, in a performance drop, which sustains the design of self-organizing, yet efficient systems. Here, each device decides either to directly connect to the eNodeB or get access via another device through a D2D link. Given the set of active devices and the channel model, we derive the outage probability for both cellular link and D2D link, and compute the system throughput. We capture the device behavior using a biform game perspective. In the first part of this article, we analyze the pure and mixed Nash equilibria of the induced game where each device seeks to maximize its own throughput. Our framework allows us to analyse and predict the system’s performance. The second part of this article is devoted to implement two Reinforcement Learning (RL) algorithms enabling devices to self-organize themselves and learn their equilibrium pure/mixed strategies, in a fully distributed fashion. Simulation results show that offloading the network by means of D2D-relaying improves per device throughput. Moreover, detailed analysis on how the network parameters affect the global performance is provided. View Full-Text
Keywords: D2D-relaying; 5G/B5G/6G; biform game; self-organized devices; Nash equilibrium; distributed reinforcement learning; NOMA/OMA D2D-relaying; 5G/B5G/6G; biform game; self-organized devices; Nash equilibrium; distributed reinforcement learning; NOMA/OMA
Show Figures

Figure 1

MDPI and ACS Style

Driouech, S.; Sabir, E.; Ghogho, M.; Amhoud, E.-M. D2D Mobile Relaying Meets NOMA—Part I: A Biform Game Analysis. Sensors 2021, 21, 702. https://doi.org/10.3390/s21030702

AMA Style

Driouech S, Sabir E, Ghogho M, Amhoud E-M. D2D Mobile Relaying Meets NOMA—Part I: A Biform Game Analysis. Sensors. 2021; 21(3):702. https://doi.org/10.3390/s21030702

Chicago/Turabian Style

Driouech, Safaa; Sabir, Essaid; Ghogho, Mounir; Amhoud, El-Mehdi. 2021. "D2D Mobile Relaying Meets NOMA—Part I: A Biform Game Analysis" Sensors 21, no. 3: 702. https://doi.org/10.3390/s21030702

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop