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 proposes a novel joint source-channel coding (JSCC) approach for robust digital semantic communications. When employing a binary-output JSCC encoder with digital modulation, end-to-end training becomes challenging due to the unpredictable dynamics of channel conditions. To address this challenge, we first develop a new demodulation method which assesses the uncertainty of the demodulation output to improve the robustness of the digital semantic communication system. We then devise a robust training strategy which enhances the robustness and flexibility of the JSCC encoder and decoder against diverse channel conditions. To this end, we model the relationship between the encoder’s output and decoder’s input using binary symmetric erasure channels and then sample the parameters of these channels from diverse distributions. Using simulations, we demonstrate the superior performance of the proposed JSCC approach for image classification and reconstruction tasks compared to existing JSCC approaches.

This paper presents a robust beam alignment technique for millimeter-wave communications in low signal-to-noise ratio (SNR) regimes. The basic strategy of this technique involves the repeated transmission of beam candidates, aimed at minimizing the beam misalignment probability induced by noise. In this strategy, however, the beam training overhead becomes impractically significant when both the numbers of beam candidates and beam repetitions are large. To address this challenge, the presented technique aims at optimizing both the selection of beam candidates and the number of repetitions for each candidate based on channel prior information. In the presented technique, a deep neural network is employed to learn the prior probability of the optimal beam at each location. The beam misalignment probability is then analyzed based on the channel prior, and a practical algorithm is developed to find the optimal beam repetition strategy to minimize the beam misalignment probability. Simulation results using the DeepMIMO dataset demonstrate the superior performance of the presented technique in dynamic low-SNR communication environments compared to existing beam alignment techniques.

This paper presents a novel split learning (SL) framework, referred to as SplitMAC, which reduces the latency of SL by leveraging simultaneous uplink transmission over multiple access channels. The key strategy is to divide devices into multiple groups and allow the devices within the same group to simultaneously transmit their smashed data and device-side models over the multiple access channels. The optimization problem of device grouping to minimize SL latency is formulated, and the benefit of device grouping in reducing the uplink latency of SL is theoretically derived. By examining a two-device grouping case, two asymptotically-optimal algorithms are devised for device grouping in low and high signal-to-noise ratio (SNR) scenarios, respectively. By merging these algorithms, a near-optimal device grouping algorithm is proposed to cover a wide range of SNR. Although our theoretical analysis holds only for the two-device case, our SL framework is also extended to consider practical fading channels and to support a general group size. Simulation results demonstrate that our SL framework with the proposed device grouping algorithm is superior to existing SL frameworks in reducing SL latency.