2025/08/18 by Mahdi Abdollahpour, Abdollahpour, Mahdi, Marco Bertuletti +10 · 2 citations
Computer Science · Engineering · Neuroscience · #Advanced MIMO Systems Optimization #Advanced Wireless Communication Technologies #Antenna (radio) #Artificial neural network #Baseband #Brain Tumor Detection and Classification #C-RAN #Channel (broadcasting) #Enhanced Data Rates for GSM Evolution #Radio access network #Ran #Scalability #Telecommunications link #Wireless Signal Modulation Classification
paper · pdf · doi:10.1109/globecom59602.2025.11432060
openalex publication_date 2025/12/08 · openalex created_date 2026/03/20 · openalex updated_date 2026/07/14
Artificial intelligence approaches for base-band processing for radio receivers have demonstrated significant performance gains. Most of the proposed methods are characterized by high compute and memory requirements, hindering their deployment at the edge of the Radio Access Networks (RAN) and limiting their scalability to large bandwidths and many antenna 6G systems. In this paper, we propose a low-complexity, model-driven neural network-based receiver, designed for multiuser multiple-input multiple-output (MU-MIMO) systems and suitable for implementation at the RAN edge. The proposed solution is compliant with the 5G New Radio (5G NR), and supports different modulation schemes, bandwidths, number of users, and number of base-station antennas with a single trained model without the need for further training. Numerical simulations of the Physical Uplink Shared Channel (PUSCH) processing show that the proposed solution outperforms the state-of-the-art methods in terms of achievable Transport Block Error Rate (TBLER), while reducing the Floating Point Operations (FLOPs) by 66×, and the learnable parameters by 396×.