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Found 9 papers, 1 papers with code
Joint Design of ISAC Waveform under PAPR Constraints
In this paper, we formulate the precoding problem of integrated sensing and communication (ISAC) waveform as a non-convex quadratically constrainted quadratic program (QCQP), in which the weighted sum of communication multi-user interference (MUI) and the gap between dual-use waveform and ideal radar waveform is minimized with peak-to-average power ratio (PAPR) constraints.
Gradient-Based Markov Chain Monte Carlo for MIMO Detection
However, optimal MIMO detection is associated with a complexity that grows exponentially with the MIMO dimensions and quickly becomes impractical.
SAFARI: Sparsity enabled Federated Learning with Limited and Unreliable Communications
It is demonstrated that SAFARI under unreliable communications is guaranteed to converge at the same rate as the standard FedAvg with perfect communications.
Learn to Compress CSI and Allocate Resources in Vehicular Networks
The centralized decision unit employs a deep Q-network to allocate resources and then sends the decision results to all vehicles.
Learn to Allocate Resources in Vehicular Networks
Meanwhile, there exists an optimal number of continuous feedback and binary feedback, respectively.
Deep Learning based Wireless Resource Allocation with Application to Vehicular Networks
The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve the problem to a certain level of optimality.
Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning
This paper investigates the spectrum sharing problem in vehicular networks based on multi-agent reinforcement learning, where multiple vehicle-to-vehicle (V2V) links reuse the frequency spectrum preoccupied by vehicle-to-infrastructure (V2I) links.
Information Theory Information Theory
Deep Learning based End-to-End Wireless Communication Systems with Conditional GAN as Unknown Channel
We propose to use a conditional generative adversarial net (GAN) to represent channel effects and to bridge the transmitter DNN and the receiver DNN so that the gradient of the transmitter DNN can be back-propagated from the receiver DNN.
Information Theory Information Theory
Toward Intelligent Vehicular Networks: A Machine Learning Framework
As wireless networks evolve towards high mobility and providing better support for connected vehicles, a number of new challenges arise due to the resulting high dynamics in vehicular environments and thus motive rethinking of traditional wireless design methodologies.
