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Cookie-Jar: An Adaptive Re-configurable Framework for Wireless Network Infrastructures

Published: 02 July 2024 Publication History

Abstract

5G advancements like Massive Multiple Input Multiple Output (MIMO) bring high capacity and low latency, but also intensify interference challenges. Static and dynamic coordination techniques address this, often at the cost of increased power draw. We introduce Cookie-Jar (CJ), an interference coordination (IC) framework using reinforcement learning for multi-goal optimization. By dynamically adjusting network, power, and topology parameters based on realtime conditions, CJ improves Signal to Noise and Interference Ratio (SINR) while minimizing power consumption. Simulated 5G experiments showcase CJ's potential, achieving a 15% SINR improvement with near-identical power draw compared to existing methods.

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    cover image ACM Conferences
    CF '24: Proceedings of the 21st ACM International Conference on Computing Frontiers
    May 2024
    345 pages
    ISBN:9798400705977
    DOI:10.1145/3649153
    This paper is authored by an employee(s) of the United States Government and is in the public domain. Non-exclusive copying or redistribution is allowed, provided that the article citation is given and the authors and agency are clearly identified as its source.

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    Published: 02 July 2024

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    1. Machine learning
    2. Wireless Networks

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