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Minimum Description Feature Selection for Complexity Reduction in Machine Learning-based Wireless Positioning
In this work, we design a novel positioning neural network (P-NN) that utilizes the minimum description features to substantially reduce the complexity of deep learning-based WP.
Complexity Reduction in Machine Learning-Based Wireless Positioning: Minimum Description Features
A recent line of research has been investigating deep learning approaches to wireless positioning (WP).
Dynamic and Robust Sensor Selection Strategies for Wireless Positioning with TOA/RSS Measurement
For dynamic sensor selection, two greedy selection strategies are proposed, each of which exploits properties revealed in the derived CRLB expressions.
A Decentralized Pilot Assignment Algorithm for Scalable O-RAN Cell-Free Massive MIMO
Radio access networks (RANs) in monolithic architectures have limited adaptability to supporting different network scenarios.
Channel Estimation via Successive Denoising in MIMO OFDM Systems: A Reinforcement Learning Approach
Our methodology includes a new successive channel denoising process based on channel curvature computation, for which we obtain a channel curvature magnitude threshold to identify unreliable channel estimates.
