Beam management is an essential operation of next-generation (xG) wireless networks to compensate severe signal attenuation and ensure reliable communications over millimeter wave (mmWave) and terahertz (THz) bands. The primary objective of beam management is to determine beam directions that are properly aligned with the signal propagation paths. Conventional beam management techniques typically rely on geometric channel parameters such as angles, delays, and path gains. However, due to their lack of contextual awareness of the surrounding environment, these techniques fall short in handling the piecewise continuous changes in the geometric channel parameters caused by sudden obstructions or variations in scatterers. In this paper, we propose a novel beam management framework that utilizes environmental information extracted from sensor data (e.g., images) and pilot measurements to optimize beam directions. The main idea of the proposed scheme is to utilize visual channel parameters, including the positions of user equipment (UE), reflection points, and scatterers, which provide a direct visualization of the propagation environment. By tracking the visual channel parameters, dynamic changes in scatterers and propagation paths can be monitored over time. To analyze multimodal data and track these parameters, we exploit large multimodal model (LMM), a generative artificial intelligence (AI) model specialized in extracting correlated features across multimodal data and generating subsequent data. Simulation results show that the proposed scheme can accurately track the visual channel parameters and enhance data rate.

We consider the optimal design of the frame-based irregular repetition slotted ALOHA (IRSA) protocol for minimizing the average age of information (AAoI) in grant-free massive machine-type communications (mMTC). To this end, we first characterize the AAoI as a function of the number of user elements (UEs), the frame duration, and the repetition distribution of IRSA. We present this characterization for IRSA schemes with packet recovery at the end of frame and packet generation either at the beginning of the frame or just in time before first transmission in a frame. We also propose and characterize the AAoI of a novel early packet recovery method which further reduces the average age of information. In all cases, the analysis reveals that, as a function of normalized channel traffic (defined as the ratio of number of UEs to frame duration), the AAoI first decreases linearly due to more frequent updates received from the UEs, and increases sharply beyond a critical point due to packet recovery failures caused by collisions. We then consider the problem of minimizing AAoI by optimizing over the normalized channel traffic and repetition distribution for all the proposed sampling and recovery schemes. The optimization problem is challenging since the objective function is semi-analytical and can only be completely characterized using simulations. In an asymptotic regime where the number of UEs as well as the frame size is large, we characterize the AAoI using upper and lower bounds. We also obtain a locally optimal normalized channel traffic and repetition distribution using differential evolution. Based on the insights obtained from the asymptotic analysis, we also propose a pragmatic approach to obtain a normalized channel traffic and repetition distribution for AAoI reduction in the non-asymptotic case. Finally, we empirically show that our AAoI minimizing schemes outperform conventional throughput optimal schemes.

Giant Magneto-Resistance (GMR) sensor-elements are usually configured in a Wheatstone bridge form. In this work, a new simple interface circuit with a microcontroller is proposed to identify the fractional-resistance change of GMR sensor using the discharge times of the capacitor. An Op-amp is used to avoid the effect of the mismatch of unshielded-resistance of the GMR sensor. This method just uses three conversion modes to make the output immune to the pin-resistance of the microcontroller. Simulation and emulation studies of the proposed interface are performed. A worst-case nonlinearity of 0.35% is observed. A hardware model of the interface is tested with the GMR sensor and the associated results are also reported in this paper.