The growing demand for high-throughput and lowlatency wireless applications in 5 G and beyond, such as autonomous driving and extended reality, has increased the need for accurate beam prediction in millimeter-wave (mmWave) and subterahertz (sub-THz) systems. Machine learning offers a promising solution, but practical deployment is often limited by model complexity and data labeling requirements. This paper investigates two complementary techniques to address these challenges: knowledge distillation (KD) and active learning (AL). KD enables compact models to approximate the performance of larger models, while AL reduces labeling effort by selecting informative training samples. We evaluate both techniques using a vehicle-toinfrastructure scenario from the DeepSense 6G dataset. Results show that KD improves Top-1 beam prediction accuracy by up to 12%, while AL reduces labeled data requirements by up to 30%. These findings highlight the potential of KD and AL for enabling efficient, scalable machine learning solutions in next-generation wireless systems.
Diffusion models have emerged as a powerful image priors for inverse problems. However, their performance can degrade significantly when there is a distribution shift between training and testing datasets. Quantifying distribution shifts is thus essential for ensuring the reliability of diffusion model priors. Existing shift detection methods typically require access to clean data, which is often unavailable in inverse problems. We propose a measurement-domain KL divergence estimator for linear inverse problems, enabling shift quantification directly from corrupted measurements. We prove that the proposed metric can recover the KL divergence between training and test distributions using only noisy observations and pretrained diffusion models. Our method provides a practical, unsupervised approach for detecting distribution shifts in inverse problems such as image deblurring—without relying on clean images.
In this paper, we propose a reflection design solution for a single-user reconfigurable intelligent surface aided mmWave MIMO communication system with multiple streams, by formulating it as a spectral efficiency maximization problem. A heuristic low-complexity reflection design (LCRD) method is proposed in the form of a non-iterative algorithm. It is then compared to benchmark methods and is found to demonstrate an extremely low complexity rendering it highly efficient for practical applications with only a slight trade-off in accuracy as compared to more computationally intensive methods.
This paper introduces a novel approach designed to significantly reduce this cost by mapping the spatial Digital beamforming is a critical technology for next-generation satellite communications. Conventional solutions in current missions primarily rely on hybrid analog-digital architectures due to the significant power consumption challenges of a fully-digital implementation. For future systems, Fast Fourier Transform (FFT)-based beamforming has been proposed as an efficient digital alternative, especially for managing many beams. However, the use of a FFT beamforming alone is not compatible with non-uniform antennas and beams. This inflexibility is a major problem for modern satellite systems that need beam reconfigurability to meet changing traffic demands. The NonUniform FFT (NUFFT) offers the needed flexibility for nonuniform grids, but it involves higher computational costs due to extra processing steps. This paper presents a new method to lower this cost by mapping the spatial samples onto a hexagonal grid instead of the traditional Cartesian grid. Taking advantage of the efficient 2D sampling efficiency of the hexagonal lattice, our approach uses circularly symmetric windows more effectively. This efficiency allows for more accurate signal reconstruction with fewer operations. The hexagonal transformation offers a more power-efficient and adaptable way to achieve real-time, dynamic beam steering required in advanced satellite systems.
Signal folding is an inherent problem encountered in phase reconstruction and sampling hardware. The precision of analogue-to-digital converter (ADC) measurements depends on the available dynamic range and number of ADC bins. Recent developments in hardware design have taken advantage of signal continuity to obtain precise measurements of high dynamic range signals using low-cost ADCs with modulo-sampling. The existing reconstruction algorithms provide batch-based methods for point estimates of the original signal, up to an unknown additive constant. We present a Bayesian model using a state-space model (SSM) capable of running in an online setting. Modulosampling and ADC quantisation non-linearities are modelled in the particle filter likelihood. We provide bounds to adaptively select the required sampling range to account for modulosampling discontinuities. This model accurately reconstructs a signal measured on real hardware, and significantly out-performs existing methods on a noisy dataset.
