English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Tuning cellular network performance against always occurring wirelessimpairments can dramatically improve reliability to end users. In this paper,we formulate cellular network performance tuning as a reinforcement learning(RL) problem and provide a solution to improve the performance for indoor andoutdoor environments. By leveraging the ability of Q-learning to estimatefuture performance improvement rewards, we propose two algorithms: (1) closedloop power control (PC) for downlink voice over LTE (VoLTE) and (2)self-organizing network (SON) fault management. The VoLTE PC algorithm uses RLto adjust the indoor base station transmit power so that the signal tointerference plus noise ratio (SINR) of a user equipment (UE) meets the targetSINR. It does so without the UE having to send power control requests. The SONfault management algorithm uses RL to improve the performance of an outdoorbase station cluster by resolving faults in the network through configurationmanagement. Both algorithms exploit measurements from the connected users,wireless impairments, and relevant configuration parameters to solve anon-convex performance optimization problem using RL. Simulation results showthat our proposed RL based algorithms outperform the industry standards todayin realistic cellular communication environments.

A Framework for Automated Cellular Network Tuning with Reinforcement Learning