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Found 9 papers, 0 papers with code
RIS-Aided Interference Cancellation for Joint Device-to-Device and Cellular Communications
This paper considers an RIS-aided joint D2D and cellular communication system where the RIS is exploited to cancel interference to the D2D links and maximize the minimum signal-to-interference plus noise (SINR) of the device pairs and cellular users.
Decision-Directed Hybrid RIS Channel Estimation with Minimal Pilot Overhead
We also perform a detailed spectral efficiency analysis for both the pilot-directed and decision-directed frameworks.
One-Bit Massive MIMO Precoding for Frequency-Selective Fading Channels
While block-wise processing (BWP) can effectively address the inter-symbol-interference (ISI) in frequency-selective fading channels, its computational complexity and processing delay can be too high for practical implementation.
Leveraging Deep Neural Networks for Massive MIMO Data Detection
Massive multiple-input multiple-output (MIMO) is a key technology for emerging next-generation wireless systems.
Deep Learning for Estimation and Pilot Signal Design in Few-Bit Massive MIMO Systems
In this paper, we propose a deep learning framework for channel estimation, data detection, and pilot signal design to address the nonlinearity in such systems.
Machine Learning-based Reconfigurable Intelligent Surface-aided MIMO Systems
Reconfigurable intelligent surface (RIS) technology has recently emerged as a spectral- and cost-efficient approach for wireless communications systems.
DNN-based Detectors for Massive MIMO Systems with Low-Resolution ADCs
Low-resolution analog-to-digital converters (ADCs) have been considered as a practical and promising solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems.
Linear and Deep Neural Network-based Receivers for Massive MIMO Systems with One-Bit ADCs
Based on the reformulated ML detection problem, we propose a model-driven deep neural network-based (DNN-based) receiver, whose performance is comparable with an existing support vector machine-based receiver, albeit with a much lower computational complexity.
Linear Receivers for Massive MIMO Systems with One-Bit ADCs
In this letter, we propose three linear receivers including Bussgang-based Maximal Ratio Combining (BMRC), Bussgang-based Zero-Forcing (BZF), and Bussgang-based Minimum Mean Squared Error (BMMSE) for massive MIMO systems with one-bit analog-to-digital converters (ADCs).
