Controlled Out-of-Band Device-to-Device Communication in Cellular Networks Using a Backup Channel in Television White Space

In this article, we address the problem of spectrum scarcity in cellular networks (CNs). We propose a backup channel (BuC) for cellular users (CUs) located in the same macro-cell under the control of a single macro base station (eNB). This BuC operates in television white space and is detected by the CUs through a cognitive radio energy-detection channel-sensing technique with a certain probability of success. When all regular channels with the cellular eNB are occupied, the CUs within the same coverage area of the macro eNB can utilize the sensed BuC to establish a controlled out-of-band device-to-device link for communication. The BuC bypasses the eNB for data communication and reduces the burden on the core of the CN. This leads to improved cellular eNB capacity. In the proposed system model, each CU and eNB is equipped with two antennas for communication in two separate bands, i.e., cellular and TV bands. Simulations show significant reductions in the blocking probability and probability of call delay.

Distributed Deep Learning with RIS Grouping for Accurate Cascaded Channel Estimation

Reconfigurable Intelligent Surface (RIS) panels are envisioned as a key technology for sixth-generation (6G) wireless networks, providing a cost-effective means to enhance coverage and spectral efficiency. A critical challenge is the estimation of the cascaded base station (BS)-RIS-user channel, since the passive nature of RIS elements prevents direct channel acquisition, incurring prohibitive pilot overhead, computational complexity, and energy consumption. To address this, we propose a deep learning (DL)-based channel estimation framework that reduces pilot overhead by grouping RIS elements and reconstructing the cascaded channel from partial pilot observations. Furthermore, conventional DL models trained under single-user settings suffer from poor generalization across new user locations and propagation scenarios. We develop a distributed machine learning (DML) strategy in which the BS and users collaboratively train a shared neural network using diverse channel datasets collected across the network, thereby achieving robust generalization. Building on this foundation, we design a hierarchical DML neural architecture that first classifies propagation conditions and then employs scenario-specific feature extraction to further improve estimation accuracy. Simulation results confirm that the proposed framework substantially reduces pilot overhead and complexity while outperforming conventional methods and single-user models in channel estimation accuracy. These results demonstrate the practicality and effectiveness of the proposed approach for 6G RIS-assisted systems.