Energy efficient and fair resource allocation for LTE‐unlicensed uplink networks: A two‐sided matching approach with partial information. Issue 10 (22nd August 2018)

• Record Type: Journal Article
• Authors: Gao, Yuan; Hu, Haonan; Wu, Yue; Chu, Xiaoli; Zhang, Jie
• Other Names: Pervaiz Haris guestEditor.; Imran Muhammad Ali guestEditor.; Mumtaz Shahid guestEditor.; Dulaimi Anwer‐al guestEditor.; Thomos Nikolaos guestEditor.
• Is Part Of: Transactions on emerging telecommunications technologies. Volume 29:Issue 10(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-08-22
• Subjects: Telecommunication -- Periodicals; 384.05
• DOI: 10.1002/ett.3500
• Languages: English
• ISSNs: 2161-5748

Abstract

Abstract: Long‐Term Evolution–unlicensed (LTE‐U) has recently attracted worldwide interest to meet the explosion in cellular traffic data. By using carrier aggregation, licensed and unlicensed bands are integrated to enhance transmission capacity while maintaining reliable and predictable performance. As there may exist other conventional unlicensed band users, such as WiFi users, LTE‐U users have to share the same unlicensed bands with them. Thus, an optimized resource allocation scheme to ensure the fairness between LTE‐U users and conventional unlicensed band users is critical for the deployment of LTE‐U networks. In this paper, we investigate an energy efficient resource allocation problem in LTE‐U coexisting with other wireless networks, which aims at guaranteeing fairness among the users of different radio access networks. We formulate the problem as a multiobjective optimization problem and propose a semidistributed matching framework with a partial information‐based algorithm to solve it. We demonstrate our contributions with simulations in which various network densities and traffic load levels are considered. Abstract : We developed semidistributed two‐step matching with partial information‐based algorithm to solve the EE optimization problem of an LTE‐U and WiFi scenario. The proposed algorithm outperforms the greedy algorithm based resource allocation scheme and random resource allocation scheme in terms of EE, percentage of users served, and system throughput. Furthermore, we have analyzed the computational complexity of the proposed algorithm theoretically and by simulations, thereby showing the complexity is reasonable for real‐world deployment.