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cs.IT2025
Deep Autoencoder-Based Constellation Design in Multiple Access Channels
Stepan Gorelenkov, Mojtaba Vaezi
In multiple access channels (MAC), multiple users share a transmission medium to communicate with a common receiver. Traditional constellations like quadrature amplitude modulation…
cs.IT2025
Interference-Aware Super-Constellation Design for NOMA
Mojtaba Vaezi, Xinliang Zhang
Non-orthogonal multiple access (NOMA) has gained significant attention as a potential next-generation multiple access technique. However, its implementation with finite-alphabet in…
cs.LG2025
Selective Experience Sharing in Reinforcement Learning Enhances Interference Management
Madan Dahal, Mojtaba Vaezi
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. E…