Reinforcement Learning-Based Weak Signal Detection from Compressed Measurements in Massive MIMO Systems

In this paper, we consider optimizing a compressive measurement matrix (CMM) in a massive multiple-input multiple-output (MIMO) system that provides reliable detection capability of both strong and weak signals. To achieve this goal, we propose a reinforcement learning framework, wherein the base st...

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Bibliographic Details
Main Authors: Pavel, Md. Saidur R., Zhang, Yimin D.
Format: Conference Proceeding
Language:English
Subjects:
Base stations
compressive measurement matrix
Computers
direction-of-arrival estimation
Estimation
Massive MIMO
Reinforcement learning
Reliability
Simulation
weak signal
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Description
Summary:In this paper, we consider optimizing a compressive measurement matrix (CMM) in a massive multiple-input multiple-output (MIMO) system that provides reliable detection capability of both strong and weak signals. To achieve this goal, we propose a reinforcement learning framework, wherein the base station acts as an agent and interacts with the environment to design the CMM by selecting appropriate actions based on a well-defined reward function. Our proposed framework yields improved weak signal detection capabilities. The optimized CMM obtained through the proposed method can then be utilized to reduce the dimension of the received signal, making it practical to implement a massive MIMO system by reducing the number of required radio frequency front-end circuits.
ISSN:2576-2303
DOI:10.1109/IEEECONF59524.2023.10476805