On Deep Learning for Radio Resource Management in A Non-stationary Radio Environment

2020/05/05 by Suren Sritharan, Sritharan, Suren, Harshana Weligampola +3
Computer Science · Engineering · #Advanced MIMO Systems Optimization #Cognitive Radio Networks and Spectrum Sensing #FOS: Electrical engineering #Signal Processing (eess.SP) #Wireless Communication Networks Research #eess.SP #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2005.02501

20 pages

arxiv created 2020/05/05 · openalex publication_date 2020/05/05 · arxiv updated 2020/05/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper studies practical limitations of learning methods for resource management in non-stationary radio environment. We propose two learning models carefully designed to support rate maximization objective under user mobility. We study the effects of practical systems such as latency and reliability on the rate maximization with deep learning models. For common testing in the non-stationary environment we present a generic dataset generation method to benchmark across different learning models versus traditional optimal resource management solutions. Our results indicate that learning models have practical challenges related to training limiting their applications. The models need environment-specific design to reach the accuracy of an optimal algorithm.

Citations

• Adam: A Method for Stochastic Optimization
• An Iteratively Weighted MMSE Approach to Distributed Sum-Utility Maximization for a MIMO Interfering Broadcast Channel
• An overview of gradient descent optimization algorithms
• A Brief Survey of Deep Reinforcement Learning

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