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Found 10 papers, 2 papers with code
From OTFS to AFDM: A Comparative Study of Next-Generation Waveforms for ISAC in Doubly-Dispersive Channels
Next-generation wireless systems will offer integrated sensing and communications (ISAC) functionalities not only in order to enable new applications, but also as a means to mitigate challenges such as doubly-dispersive channels, which arise in high mobility scenarios and/or at millimeter-wave (mmWave) and Terahertz (THz) bands.
Image Patch-Matching with Graph-Based Learning in Street Scenes
Matching landmark patches from a real-time image captured by an on-vehicle camera with landmark patches in an image database plays an important role in various computer perception tasks for autonomous driving.
AFDM vs OTFS: A Comparative Study of Promising Waveforms for ISAC in Doubly-Dispersive Channels
This white paper aims to briefly describe a proposed article that will provide a thorough comparative study of waveforms designed to exploit the features of doubly-dispersive channels arising in heterogeneous high-mobility scenarios as expected in the beyond fifth generation (B5G) and sixth generation (6G), in relation to their suitability to integrated sensing and communications (ISAC) systems.
A Slow-Shifting Concerned Machine Learning Method for Short-term Traffic Flow Forecasting
However, a crucial challenge in traffic flow forecasting is the slow shifting in temporal peaks between daily and weekly cycles, resulting in the nonstationarity of the traffic flow signal and leading to difficulty in accurate forecasting.
Low-Complexity Memory AMP Detector for High-Mobility MIMO-OTFS SCMA Systems
Efficient signal detectors are rather important yet challenging to achieve satisfactory performance for large-scale communication systems.
Spatiotemporal Capsule Neural Network for Vehicle Trajectory Prediction
An accurate vehicle trajectory prediction benefits communication traffic management and network resource allocation for the real-time application of the V2X network.
OTFS Signaling for SCMA With Coordinated Multi-Point Vehicle Communications
This paper investigates an uplink coordinated multi-point (CoMP) coverage scenario, in which multiple mobile users are grouped for sparse code multiple access (SCMA), and served by the remote radio head (RRH) in front of them and the RRH behind them simultaneously.
Intelligent Resource Allocation in Joint Radar-Communication With Graph Neural Networks
In this paper, we propose a framework for intelligent vehicles to conduct JRC, with minimal prior knowledge of the system model and a tunable performance balance, in an environment where surrounding vehicles execute radar detection periodically, which is typical in contemporary protocols.
Deep Reinforcement Learning for Time Allocation and Directional Transmission in Joint Radar-Communication
In addition, experimental results show that the trained deep reinforcement learning agents are robust to changes in the number of vehicles in the environment.
Spectrum Learning-Aided Reconfigurable Intelligent Surfaces for 'Green' 6G Networks
In the sixth-generation (6G) era, emerging large-scale computing based applications (for example processing enormous amounts of images in real-time in autonomous driving) tend to lead to excessive energy consumption for the end users, whose devices are usually energy-constrained.
