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Optimal Joint Fronthaul Compression and Beamforming Design for Networked ISAC Systems
The primary objective is to minimize the total transmit power while meeting the signal-to-interference-plus-noise ratio (SINR) requirements for communication and sensing under fronthaul capacity constraints, resulting in a joint fronthaul compression and beamforming design (J-FCBD) problem.
Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Framework
In this paper, we firstly reveal the fact that the federated ADMM is essentially a client-variance-reduced algorithm.
Quantized Federated Learning under Transmission Delay and Outage Constraints
Federated learning (FL) has been recognized as a viable distributed learning paradigm which trains a machine learning model collaboratively with massive mobile devices in the wireless edge while protecting user privacy.
Energy-Efficient Design of IRS-NOMA Networks
Combining intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA) is an effective solution to enhance communication coverage and energy efficiency.
Optimal Resource Allocation for Delay Minimization in NOMA-MEC Networks
We adopt the partial offloading policy, in which each user can partition its computation task into offloading and locally computing parts.
