Deepak Kumar Ray

The emerging 6G technologies will bring billions of IoT sensors which are going to demand little radio spectrum. The resulting high level of connectivity poses some novel problems, among them network congestion, and privacy: because energy-consumption data might be shared unintentionally, it provides insight into personal behavioral patterns. In addition, the disproportionate access to highly developed infrastructure can contribute to the need to continue the digital divide between urban and rural areas. This paper presents a decentralized infrastructure combining federated learning and Hyperledger Fabric to deal with these issues. To implement it, PyTorch is used to perform distributed learning tasks, and MATLAB is used to generate the synthetic spectrum traces that reflect the real-world CBRS conditions based on the NTIA field measurements between 2021 and 2024. These traces were validated by Kolmogorov–Smirnov test ( p = 0.87). The framework outperforms conventional centralized DSS benchmarks while complying with 3GPP Release 18 transparency and ETSI ESG principles. The framework is a modular, open-source system with sharding that scales horizontally to over 5,000 nodes. The proposed solution successfully addresses the scarcity of the spectrum with the use of cooperative resource sharing through congested urban vehicle-to-everything (V2X) networks, remote solar-powered microgrids, and so on. The findings verify that the next generation 6G networks are capable of not only providing extremely high data rates but also improved privacy and fair connectivity in different environments.

Summary: The future generation of wireless communications systems uses the generalized frequency division multiplexing (GFDM) due to its viable candidate waveform to perform multiple user scheduling. It is a non‐orthogonal waveform susceptible to intercarrier and intersymbol interference (ISI); still, it offers flexible pulse shaping that enhances the efficiency of user scheduling. Here, to accomplish spatial diversity, the multiple‐input multiple‐output (MIMO) is incorporated with GFDM to enhance the performance; there is also an extra inter‐antenna interference problem that limits the model's performance. The issue concerning inter‐antenna interference can be resolved by adopting pilot‐based information transfer. The detection of a signal at the receiver based on the minimum mean square error has the issue of computational complexity while enhancing the estimation quality of a detector. Hence, a low‐complexity channel estimation technique is proposed in this research using the V‐degree polynomial‐based channel estimation technique, wherein the cubic order computation is reduced to square order. Also, the proposed adaptive pulse shaping technique, wherein the filter coefficient is optimized using the gazelle optimization algorithm (GOA) to provide optimal pulse shaping filter parameters by employing bit error rate (BER) as the objective function. The performance of a proposed V‐degree polynomial‐based channel estimation is analyzed based on various assessment measures like BER and MSE and acquired the minimal values of 5.75E‐05 and 2.07E‐05, respectively.