Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Applications of Repeated Games in Wireless Networks: A Survey

A repeated game is an effective tool to model interactions and conflicts for players aiming to achieve their objectives in a long-term basis. Contrary to static noncooperative games that model an interaction among players in only one period, in repeated games, interactions of players repeat for multiple periods; and thus the players become aware of other players' past behaviors and their future benefits, and will adapt their behavior accordingly. In wireless networks, conflicts among wireless nodes can lead to selfish behaviors, resulting in poor network performances and detrimental individual payoffs. In this paper, we survey the applications of repeated games in different wireless networks. The main goal is to demonstrate the use of repeated games to encourage wireless nodes to cooperate, thereby improving network performances and avoiding network disruption due to selfish behaviors. Furthermore, various problems in wireless networks and variations of repeated game models together with the corresponding solutions are discussed in this survey. Finally, we outline some open issues and future research directions.Comment: 32 pages, 15 figures, 5 tables, 168 reference

Game theory for collaboration in future networks

Cooperative strategies have the great potential of improving network performance and spectrum utilization in future networking environments. This new paradigm in terms of network management, however, requires a novel design and analysis framework targeting a highly flexible networking solution with a distributed architecture. Game Theory is very suitable for this task, since it is a comprehensive mathematical tool for modeling the highly complex interactions among distributed and intelligent decision makers. In this way, the more convenient management policies for the diverse players (e.g. content providers, cloud providers, home providers, brokers, network providers or users) should be found to optimize the performance of the overall network infrastructure. The authors discuss in this chapter several Game Theory models/concepts that are highly relevant for enabling collaboration among the diverse players, using different ways to incentivize it, namely through pricing or reputation. In addition, the authors highlight several related open problems, such as the lack of proper models for dynamic and incomplete information games in this area.info:eu-repo/semantics/acceptedVersio

URLLC for 5G and Beyond: Requirements, Enabling Incumbent Technologies and Network Intelligence

The tactile internet (TI) is believed to be the prospective advancement of the internet of things (IoT), comprising human-to-machine and machine-to-machine communication. TI focuses on enabling real-time interactive techniques with a portfolio of engineering, social, and commercial use cases. For this purpose, the prospective 5{th} generation (5G) technology focuses on achieving ultra-reliable low latency communication (URLLC) services. TI applications require an extraordinary degree of reliability and latency. The 3{rd} generation partnership project (3GPP) defines that URLLC is expected to provide 99.99% reliability of a single transmission of 32 bytes packet with a latency of less than one millisecond. 3GPP proposes to include an adjustable orthogonal frequency division multiplexing (OFDM) technique, called 5G new radio (5G NR), as a new radio access technology (RAT). Whereas, with the emergence of a novel physical layer RAT, the need for the design for prospective next-generation technologies arises, especially with the focus of network intelligence. In such situations, machine learning (ML) techniques are expected to be essential to assist in designing intelligent network resource allocation protocols for 5G NR URLLC requirements. Therefore, in this survey, we present a possibility to use the federated reinforcement learning (FRL) technique, which is one of the ML techniques, for 5G NR URLLC requirements and summarizes the corresponding achievements for URLLC. We provide a comprehensive discussion of MAC layer channel access mechanisms that enable URLLC in 5G NR for TI. Besides, we identify seven very critical future use cases of FRL as potential enablers for URLLC in 5G NR