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Found 11 papers, 0 papers with code
Interference-Aware Super-Constellation Design for NOMA
Non-orthogonal multiple access (NOMA) has gained significant attention as a potential next-generation multiple access technique.
Selective Experience Sharing in Reinforcement Learning Enhances Interference Management
We propose a novel multi-agent reinforcement learning (RL) approach for inter-cell interference mitigation, in which agents selectively share their experiences with other agents.
Modulation and Coding for NOMA and RSMA
In addition to addressing challenges in finite-alphabet NOMA, this paper offers new insights and provides an overview of code-domain NOMA, trellis-coded NOMA, and RSMA as key NGMA candidates.
Deep Autoencoder-based Z-Interference Channels with Perfect and Imperfect CSI
The proposed structure significantly enhances the performance of the ZIC both for the perfect and imperfect CSI.
Deep Reinforcement Learning for Interference Management in UAV-based 3D Networks: Potentials and Challenges
In this paper, we propose leveraging deep reinforcement learning for interference management to tackle this shortcoming.
Ultra-Low-Power IoT Communications: A novel address decoding approach for wake-up receivers
As this module continuously monitors the received ambient energy for potential paging of the device, its contribution to WuR's power consumption is crucial.
SVD-Embedded Deep Autoencoder for MIMO Communications
SVDembedded DAE largely outperforms theoretic linear precoding in terms of BER.
Secure Precoding in MIMO-NOMA: A Deep Learning Approach
A novel signaling design for secure transmission over two-user multiple-input multiple-output non-orthogonal multiple access channel using deep neural networks (DNNs) is proposed.
Multi-Objective DNN-based Precoder for MIMO Communications
Numerical results demonstrate that, compared to the conventional solutions, the proposed DNN-based precoder reduces on-the-fly computational complexity more than an order of magnitude while reaching near-optimal performance (99. 45\% of the averaged optimal solutions).
Power Allocation in Cache-Aided NOMA Systems: Optimization and Deep Reinforcement Learning Approaches
To address the power allocation problem, two novel methods are proposed.
Deep Learning based Precoding for the MIMO Gaussian Wiretap Channel
A novel precoding method based on supervised deep neural networks is introduced for the multiple-input multiple-output Gaussian wiretap channel.
