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Title: Impact of CSI Feedback Strategies on LTE Downlink and Reinforcement Learning Solutions for Optimal Allocation
Authors: Chiumento, Alessandro ×
Desset, Claude
Pollin, Sofie
Van der Perre, Liesbet
Lauwereins, Rudy #
Issue Date: 18-Feb-2016
Publisher: Institute of Electrical and Electronics Engineers
Series Title: IEEE Transactions on Vehicular Technology vol:PP issue:99 pages:1-1
Article number: TVT.2016.2531291
Abstract: The constant increase in wireless handheld devices and the prospect of billion of connected machines has brought the cellular community to research many different technologies able to deliver high datarate and quality of service to the mobile users. One of the problems, usually overlooked by the community, is that more devices means higher signalling necessary to coordinate transmission and to allocate resources effectively. Particularly, channel state information of the users’ channels is necessary in order for the base station to assign frequency resources. On the other hand, this feedback information comes at a cost of uplink bandwidth which is traditionally not considered. In this work, we analyse the impact that reduced user feedback information has on an LTE network. A model, which considers the trade-off between downlink performance and uplink overhead is presented. We introduce different feedback allocation strategies, which follow the same structure as the ones in the LTE standard, and study their effects on the network for varying number of users and different resource allocation strategies. We show that dynamically allocating feedback resources can be beneficial for the network. In order for the base station to determine which feedback allocation strategy is the most beneficial, in specific network conditions, we propose two reinforcement learning algorithms. The first solution allows the base station to allocate one homogeneous feedback strategy valid for all the users served, while, the second more complex solution determines a different strategy for each user based on its channel conditions. The reinforcement learning methods show that, even in dynamic scenarios, each base station is capable of determining an optimal operating point autonomously, hence optimally balancing feedback overhead and benefits.
ISSN: 0018-9545
Publication status: published
KU Leuven publication type: IT
Appears in Collections:ESAT- TELEMIC, Telecommunications and Microwaves
Electrical Engineering - miscellaneous
Associated Section of ESAT - INSYS, Integrated Systems
× corresponding author
# (joint) last author

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