scholarly journals Lyapunov optimization machine learning resource allocation approach for uplink underlaid D2D communication in 5G networks

2021 ◽  
Author(s):  
Krishna Pandey ◽  
Rajeev Arya
Keyword(s):  
Machine Learning ◽  
Resource Allocation ◽  
D2d Communication ◽  
5G Networks ◽  
Learning Resource ◽  
Lyapunov Optimization ◽  
Allocation Approach

scholarly journals Wireless Edge Machine Learning: Resource Allocation and Trade-offs

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Mattia Merluzzi ◽  
Paolo Di Lorenzo ◽  
Sergio Barbarossa
Keyword(s):  
Machine Learning ◽  
Resource Allocation ◽  
Learning Resource ◽  
Trade Offs

scholarly journals Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks

2021 ◽  
Author(s):  
Sherief Hashima ◽  
Basem Elhalwany ◽  
Kohei Hatano ◽  
Kaishun Wu ◽  
Ehab Mahmoud Mohammed
Keyword(s):  
Machine Learning ◽  
Network Architecture ◽  
D2d Communication ◽  
Neighbor Discovery ◽  
5G Networks ◽  
Research Directions ◽  
Fifth Generation ◽  
Research Activities ◽  
Network Overhead

Device-to-device (D2D) communication is a promising paradigm for the fifth generation 2 (5G) and beyond 5G (B5G) networks. Although D2D communication provides several benefits, 3 including limited interference, energy efficiency, reduced delay, and network overhead, it faces a lot 4 of technical challenges such as network architecture, and neighbor discovery, etc. The complexity 5 of configuring D2D links and managing their interference, especially when using millimeter-wave 6 (mmWave), inspire researchers to leverage different machine-learning (ML) techniques to address 7 these problems towards boosting the performance of D2D networks. In this paper, a comprehensive 8 survey about recent research activities on D2D networks will be explored with putting more 9 emphasis on utilizing mmWave and ML methods. After exploring existing D2D research directions 10 accompanied with their existing conventional solutions, we will show how different ML techniques 11 can be applied to enhance the D2D networks performance over using conventional ways. Then, still 12 open research directions in ML applications on D2D networks will be investigated including their 13 essential needs. A case study of applying multi-armed bandit (MAB) as an efficient online ML tool 14 to enhance the performance of neighbor discovery and selection (NDS) in mmWave D2D networks 15 will be presented. This case study will put emphasis on the high potency of using ML solutions 16 over using the conventional non-ML based methods for highly improving the average throughput 17 performance of mmWave NDS.

scholarly journals Spectral and Energy Efficient D2D Communication Underlay 5G Networks

2020 ◽  
Vol 16 (1) ◽  
pp. 57-65 ◽  
Author(s):  
Selmi Sawsan ◽  
Bouallègue Ridha
Keyword(s):  
Resource Allocation ◽  
Power Consumption ◽  
Energy Efficient ◽  
Random Network ◽  
Network Capacity ◽  
Theory Model ◽  
Spectrum Efficiency ◽  
D2d Communication ◽  
5G Networks ◽  
Resource Allocation Algorithm

4G is now deployed all over the world, but requirements are about to change rapidly face to the exponential growth on devices number, local service applications and spectrum scarce. To deal with that, 5G networks integrated Device To Device (D2D) communication as a key technology in its evolving architecture. From 3GPP Rel-12 to Rel-16, D2D succeeded to improve network capacity by enhancing spectrum reuse, data rates and reducing end-to-end latency. However, despite all these advantages, it implies new challenges in 5G system design as interference, spectrum and energy consumption. As a contribution, we propose in this paper a joint spectrum and energy efficient resource allocation algorithm for D2D communications. This approach maximizes the total spectrum efficiency and reduces UEs power consumption. Contrarily to most of previous studies on resource allocation problems considering only centralized and pure strategies approaches, we propose a distributed algorithm based on new mathematical game theory model as an interpretation of mixed strategy non cooperative game. We extend our previous research, by focusing on power consumption issue. Our proposed solution enhances joint SE/EE tradeoff by minimizing interferences and power consumption via a smart RB allocation. This new approach allows users to adopt more accurate strategies and maximize their utilities according to the random network behavior.

scholarly journals A Machine Learning Resource Allocation Solution to Improve Video Quality in Remote Education

2021 ◽  
pp. 1-21
Author(s):  
Ioan-Sorin Comsa ◽  
Andreea Molnar ◽  
Irina Tal ◽  
Per Bergamin ◽  
Gabriel-Miro Muntean ◽  
...  
Keyword(s):  
Machine Learning ◽  
Resource Allocation ◽  
Video Quality ◽  
Remote Education ◽  
Learning Resource
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