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Found 12 papers, 5 papers with code
Optimized Detection with Analog Beamforming for Monostatic Integrated Sensing and Communication
In this paper, we formalize an optimization framework for analog beamforming in the context of monostatic integrated sensing and communication (ISAC), where we also address the problem of self-interference in the analog domain.
QoS prediction in radio vehicular environments via prior user information
Moreover, we are extending prior art by showing how longer prediction horizons can be supported.
From Empirical Measurements to Augmented Data Rates: A Machine Learning Approach for MCS Adaptation in Sidelink Communication
In this paper, we propose a machine learning approach to predict suitable MCS levels.
Toward an AI-enabled Connected Industry: AGV Communication and Sensor Measurement Datasets
This paper presents two wireless measurement campaigns in industrial testbeds: industrial Vehicle-to-vehicle (iV2V) and industrial Vehicle-to-infrastructure plus Sensor (iV2I+), together with detailed information about the two captured datasets.
Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies
The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities.
GPU-Accelerated Machine Learning in Non-Orthogonal Multiple Access
Non-orthogonal multiple access (NOMA) is an interesting technology that enables massive connectivity as required in future 5G and 6G networks.
GPU-accelerated partially linear multiuser detection for 5G and beyond URLLC systems
In this feasibility study, we have implemented a recently proposed partially linear multiuser detection algorithm in reproducing kernel Hilbert spaces (RKHSs) on a GPU-accelerated platform.
Deep Learning Beam Optimization in Millimeter-Wave Communication Systems
We propose a method that combines fixed point algorithms with a neural network to optimize jointly discrete and continuous variables in millimeter-wave communication systems, so that the users' rates are allocated fairly in a well-defined sense.
Deep Learning Based Hybrid Precoding in Dual-Band Communication Systems
To overcome the issue of large signalling overhead in the mmWave band, the proposed method exploits the spatiotemporal correlation between sub-6GHz and mmWave bands, and it predicts/tracks the RF precoders in the mmWave band from sub-6GHz channel measurements.
Hybrid Model and Data Driven Algorithm for Online Learning of Any-to-Any Path Loss Maps
Pure data-driven methods can achieve good performance without assuming any physical model, but their complexity and their lack of robustness is not acceptable for many applications.
Towards optimal nonlinearities for sparse recovery using higher-order statistics
In this context, we analyze the Bayesian mean-square-error (MSE) for two types of estimators: (i) a linear estimator and (ii) a structured estimator composed of a linear operator followed by a Cartesian product of univariate nonlinear mappings.
Energy-Efficient Classification for Anomaly Detection: The Wireless Channel as a Helper
In the traditional approach, the sensors encode their observations and transmit them to a fusion center by means of some interference avoiding channel access method.
