Increasing demand for massive device connectivity in underserved regions drives the development of advanced low Earth orbit (LEO) satellite communication systems. Beam-hopping LEO systems without connection establishment provide a promising solution for achieving both demand-aware resource allocation and low access latency. However, integrating beam-hopping with grant-free random access presents unique challenges due to sporadic and unpredictable device activity, fundamentally differing from scheduled systems with deterministic resource allocation. This paper investigates beam-hopping pattern design for grant-free random access systems to dynamically allocate satellite resources according to traffic demands across serving cells. We formulate a binary optimization problem that maximizes the minimum successful transmission probability across cells, which captures performance in systems with unpredictable device activity. To solve this problem, we propose novel beam-hopping design algorithms that alternately enhance the collision avoidance rate to mitigate intra-cell collisions and the decoding success probability to manage inter-cell interference within an alternating optimization framework. Specifically, the algorithms employ a bisection method to optimize illumination allocation for each cell based on demand, while using the alternating direction method of multipliers (ADMM) to optimize beam-hopping patterns for maximizing decoding success probability. Furthermore, we enhance the ADMM by replacing the strict binary constraint with two equivalent continuous-valued constraints. Simulation results demonstrate the superiority of the proposed algorithms compared to other beam-hopping methods and verify robustness in managing traffic demand imbalance.

This paper studies a data detection problem for multiple-input multiple-output (MIMO) communication systems with hardware impairments. To facilitate maximum likelihood (ML) data detection without knowledge of nonlinear and unknown hardware impairments, we develop a novel likelihood function estimation method based on data augmentation and boosting. In our method, we generate multiple augmented datasets by injecting noise with various distributions into seed data consisting of online received signals. We then estimate the likelihood function (LF) using each augmented dataset based on the expectationmaximization algorithm. We linearly combine the multiple LF estimates obtained from these datasets based on their reliability levels. Simulation results demonstrate that the ML detection combined with our LF estimation method outperforms existing methods, while also highlighting the effectiveness of our data augmentation approach.

This paper addresses a data detection problem for multiple-input multiple-output (MIMO) communication systems with hardware impairments. To facilitate maximum likelihood (ML) data detection without knowledge of nonlinear and unknown hardware impairments, we develop novel likelihood function (LF) estimation methods based on data augmentation and boosting. The core idea of our methods is to generate multiple augmented datasets by injecting noise with various distributions into seed data consisting of online received signals. We then estimate the LF using each augmented dataset based on either the expectation maximization (EM) algorithm or the kernel density estimation (KDE) method. Inspired by boosting, we further refine the estimated LF by linearly combining the multiple LF estimates obtained from the augmented datasets. To determine the weights for this linear combination, we develop methods that take different approaches to measure the reliability of the estimated LFs. Simulation results demonstrate that both the EM- and KDE-based LF estimation methods offer significant performance gains over existing LF estimation methods. Our results also show that the effectiveness of the proposed methods improves as the size of the augmented data increases.

This paper considers a data detection problem in multiple-input multiple-output (MIMO) communication systems with hardware impairments. To address challenges posed by nonlinear and unknown distortion in received signals, two learning-based detection methods, referred to as model-driven and data-driven, are presented. The model-driven method employs a generalized Gaussian distortion model to approximate the conditional distribution of the distorted received signal. By using the outputs of coarse data detection as noisy training data, the model-driven method avoids the need for additional signaling overhead beyond traditional pilot overhead for channel estimation. An expectation-maximization algorithm is devised to accurately learn the parameters of the distortion model from noisy training data. To resolve a model mismatch problem in the model-driven method, the data-driven method employs a deep neural network (DNN) for approximating a-posteriori probabilities for each received signal. This method uses the outputs of the model-driven method as noisy labels and therefore does not require extra training overhead. To avoid the overfitting problem caused by noisy labels, a robust DNN training algorithm is devised, which involves a warm-up period, sample selection, and loss correction. Simulation results demonstrate that the two proposed methods outperform existing solutions with the same overhead under various hardware impairment scenarios.