Matching theory for future wireless networks: fundamentals and applications

By Yunan Gu, Walid Saad, Medhi Bennis, Merouane Debbah, Zhu Han. IEEE Communications Magazine, 2014

511 citations recorded by the source corpus.

The emergence of novel wireless networking paradigms such as small cell and cognitive radio networks has forever transformed the way in which wireless systems are operated. In particular, the need for self-organizing solutions to manage the scarce spectral resources has become a prevalent theme in many emerging wireless systems. In this article, the first comprehensive tutorial on the use of matching theory, a Nobel Prize winning framework, for resource management in wireless networks is developed. To cater for the unique features of emerging wireless networks, a novel, wireless-oriented classification of matching theory is proposed. Then the key solution concepts and algorithmic implementations of this framework are exposed. The developed concepts are applied in three important wireless networking areas in order to demonstrate the usefulness of this analytical tool. Results show how matching theory can effectively improve the performance of resource allocation in all three applications discussed.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/bde62852a04efb2363f1ffd9d466ac1e9f002a42

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

User-Centric Cell-Free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions (2021, background): “Stable matching theory is a framework that provides mathematically tractable solutions for the combinatorial problem of matching players into distinct sets [249].”
Dynamic Proximity-Aware Resource Allocation in Vehicle-to-Vehicle (V2V) Communications (2016, methodology): “To this end, the framework of matching theory [9] is a promising approach for resource management in V2V communication.”
Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues (2016, background): “Besides the coalition formation game and the contract game model, the matching game model, a Nobel-prize winning framework, is promising as well, since it provides mathematically tractable solutions for the combinatorial probl em of matching players in two distinct sets, depending on the individual ”
Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks (2017, background): “Therefore, the problem of computing task assignment is a many-to-many matching problem [199].”
An Overview of Service Placement Problem in Fog and Edge Computing (2020, methodology): “As other formulations found in the literature, we quote: Matching Game [131], Markov Decision Process (MDP) [132], stochastic optimization [133], petri nets [134], potential games [135], quadratic assignment problem [136], and general convex optimization [137].”
Cell-Free Massive MIMO: A New Next-Generation Paradigm (2019, methodology): “efficiently, the solutions may rely on matching theory [43], greedy heuristic methods [44], bioinspired algorithms or reinforcement learning [45].”
Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing (2018, methodology): “To tackle this, we invoke matching theory which provides an efficient and low-complexity way to solve integer programming problems [34], [35] and has been utilized in various wireless communication systems [22], [35]–[38].”
Survey of Fog Computing: Fundamental, Network Applications, and Research Challenges (2018, unknown): “Besides, a game theoretic approach [134]”
A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems (2018, background): “Other relevant game-theoretic tools include contract theory [208], to design incentive mechanisms and matching theory [209] to study network planning problems.”
Proactive edge computing in latency-constrained fog networks (2017, methodology): “DA algorithm described in Algorithm 3, converges to a two-sided stable matching of UNs to cloudlets [14].”
Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access (2017, background): “ system-wide global utility) [28], [37], [48], [49] settings. Matching algorithms have focused on allocating channels to users so that a certain utility is maximized (e.g., user sum rate) [38], [39], [41]. Graph coloring formulations have concerned with modeling the spectrum access problem as a grap”
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial (2017, background): “Therefore, the problem of computing task assignment is a many-to-many matching problem [145].”