Energy efficient and fair resource allocation for LTE‐unlicensed uplink networks: A two‐sided matching approach with partial information

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.

Extended belief rule-based method for the assessment of matching between rescue tasks and volunteers

“…With the increase in information uncertainty and data complexity in modern analysis problems, decision-makers often have difficulty accounting for bilateral preferences. This uncertainty with respect to preference information has, therefore, been widely studied in terms of operational decision support (Bikhchandani, 2017;Chandrashekhar and Bhasker, 2011;Gao et al, 2018;Lee and Schwarz, 2017). Notably, Zhang et al (2019) used a disappointment theorybased approach to solve TSMDM problems, which allows matching objects to cause incomplete preference information.…”