Fairness in Wireless Networks:Issues, Measures and Challenges

By Huaizhou Shi, R. Venkatesha Prasad, Ertan Onur, Ignas G. Niemegeers. 2014

425 citations recorded by the source corpus.

The pervasiveness of wireless technology has indeed created massive opportunity to integrate almost everything into the Internet fabric. This can be seen with the advent of Internet of Things and Cyber Physical Systems, which involves cooperation of massive number of intelligent devices to provide intelligent services. Fairness amongst these devices is an important issue that can be analysed from several dimensions, e.g., energy usage, achieving required quality of services, spectrum sharing, and so on. This article focusses on these viewpoints while looking at fairness research. To generalize, mainly wireless networks are considered. First, we present a general view of fairness studies, and pose three core questions that help us delineate the nuances in defining fairness. Then, the existing fairness models are summarized and compared. We also look into the major fairness research domains in wireless networks such as fair energy consumption control, power control, topology control, link and flow scheduling, channel assignment, rate allocation, congestion control and routing protocols. We make a distinction amongst fairness, utility and resource allocation to begin with. Later, we present their inter-relation. At the end of this article, we list the common properties of fairness and give an example of fairness management. Several open research challenges that point to further work on fairness in wireless networks are also discussed. Indeed, the research on fairness is entangled with many other aspects such as performance, utility, optimization and throughput at the network and node levels. While consolidating the contributions in the literature, this article tries to explain the niceties of all these aspects in the domain of wireless networking.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

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Citations in the corpus

Fair Resource Allocation in Federated Learning (2019, background): “, 2008) and wireless communications (Eryilmaz & Srikant, 2006; Nandagopal et al., 2000; Sanjabi et al., 2014; Shi et al., 2014).”
A Survey on Resource Allocation for 5G Heterogeneous Networks: Current Research, Future Trends, and Challenges (2020, background): “In addition to the required QoS and spectrum sharing, fairness among different users is another important index in wireless RA problems [64].”
Proactive Resource Management for LTE in Unlicensed Spectrum: A Deep Learning Perspective (2017, methodology): “7 shows the (a) average airtime allocated for the LTE-LAA network, (b) average airtime allocated for WLAN, (c) proportion of served total network traffic load, and (d) Jain’s fairness index as a function of T resulting from our proposed scheme as well as from a centralized solution considering a pro”
What should 6G be? (2019, methodology): “The Gini index and the Lorenz curve could be involved to evaluate the service fairness provided by the BigCom and should be treated as crucial indicators of UE in 6G [54].”
Prospective Multiple Antenna Technologies for Beyond 5G (2019, background): “While the proportional fairness metric is often used in cellular networks [12], [70], the picture can be quite different in cell-free networks, where the UEs’ SINRs are distributed in a very different way.”
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2019, methodology): “To this end, based on the utility function α-fairness [226] used in fair resource allocation in wireless networks, the authors of [221] define a fair-oriented goal q-FFL for joint resource optimization.”
Review of Internet of Things (IoT) in Electric Power and Energy Systems (2018, background): “The fair channel assignment approaches ensure fair allocation of radio resources to links or flows to achieve seamless transmission [102].”