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Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach
Christo Kurisummoottil Thomas, Walid Saad, Yong Xiao
A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing (e.g., edge computing), and artificial intelligence (AI…
Disentangling Learnable and Memorizable Data via Contrastive Learning for Semantic Communications
Christina Chaccour, Walid Saad
Achieving artificially intelligent-native wireless networks is necessary for the operation of future 6G applications such as the metaverse. Nonetheless, current communication schem…
Neuro-Symbolic Causal Reasoning Meets Signaling Game for Emergent Semantic Communications
Christo Kurisummoottil Thomas, Walid Saad
Semantic communication (SC) aims to communicate reliably with minimal data transfer while simultaneously providing seamless connectivity to heterogeneous services and users. In thi…
Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks
Sihua Wang, Mingzhe Chen, Christopher G. Brinton +3
This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices…
Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Design
Minsu Kim, Walid Saad, Mohammad Mozaffari +1
In this paper, a green-quantized FL framework, which represents data with a finite precision level in both local training and uplink transmission, is proposed. Here, the finite pre…
Neuro-Symbolic Artificial Intelligence (AI) for Intent based Semantic Communication
Christo Kurisummoottil Thomas, Walid Saad
Intent-based networks that integrate sophisticated machine reasoning technologies will be a cornerstone of future wireless 6G systems. Intent-based communication requires the netwo…