Resource Allocation in Spectrum-Sharing OFDMA Femtocells With Heterogeneous Services

By Haijun Zhang, Chunxiao Jiang, Norman Charles Beaulieu, Xiaoli Chu, X. Wen, Meixia Tao. IEEE Transactions on Communications, 2014

391 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/bab52e7abaaa8645aee13c107df871da2a707469

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Heterogeneous Vehicular Networking: A Survey on Architecture, Challenges, and Solutions (2015, neutral): “limited [70], service-oriented schemes [71], etc.”
Resource Allocation for Cognitive Small Cell Networks: A Cooperative Bargaining Game Theoretic Approach (2015, background): “Traditional deployment of macrocell base stations (MBS) suffers from poor quality of service (QoS) and coverage for indoor and cell edge users, especially for potential use of high carrier frequency in 5G [1].”
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, neutral): “Upon combining macrocells, microcells, picocells [157] and femtocells [158], [159], HetNets are capable of providing a seamless wireless coverage ranging from outdoor environments to office buildings and even to underground areas by selecting another RAT when a RAT fails, and HetNets can also provid”
Energy-Efficient Resource Allocation for Downlink Non-Orthogonal Multiple Access Network (2016, background): “Among them, NOMA takes advantage of spectrum efficiency by allowing multiple users to occupy the same subchannel, which is different from the resource allocation in OFDM systems [9]–[11].”
A Survey on Resource Allocation for 5G Heterogeneous Networks: Current Research, Future Trends, and Challenges (2020, background): “Rate/capacity-based maximization RA problems: In [87], for a two-tier OFDMA-based heterogeneous macro-femto network, a subchannel and PA problem for co-channel femtocells was modeled as a mixed-integer programming problem to maximize the capacity for both delay-sensitive users and delay-tolerant use”