With recent advances on the dense low-earth orbit (LEO) constellation, LEO satellite network has become one promising solution to providing global coverage for Internet-of-Things (IoT) services. Confronted with the sporadic transmission from randomly activated IoT devices, we consider the random access (RA) mechanism, and propose a grant-free RA (GF-RA) scheme to reduce the access delay to the mobile LEO satellites. A Bernoulli-Rician message passing with expectation maximization (BR-MP-EM) algorithm is proposed for this terrestrial-satellite GF-RA system to address the user activity detection (UAD) and channel estimation (CE) problem. This BR-MP-EM algorithm is divided into two stages. In the inner iterations, the Bernoulli messages and Rician messages are updated for the joint UAD and CE problem. Based on the output of the inner iterations, the expectation maximization (EM) method is employed in the outer iterations to update the hyper-parameters related to the channel impairments. Finally, simulation results show the UAD and CE accuracy of the proposed BR-MP-EM algorithm, as well as the robustness against the channel impairments.

This paper considers a low-complexity Gaussian Message Passing (GMP) scheme for a coded massive Multiple- Input Multiple-Output (MIMO) systems with Non-Orthogonal Multiple Access (massive MIMO-NOMA), in which a base station with Ns antennas serves Nu sources simultaneously in the same frequency. Both Nu and Ns are large numbers, and we consider the overloaded cases with Nu > Ns. The GMP forMIMO-NOMA is a message passing algorithm operating on a fully-connected loopy factor graph, which is well understood to fail to converge due to the correlation problem. In this paper, we utilize the largescale property of the system to simplify the convergence analysis of the GMP under the overloaded condition. First, we prove that the variances of the GMP definitely converge to the mean square error (MSE) of Linear Minimum Mean Square Error (LMMSE) multi-user detection. Secondly, the means of the traditional GMP will fail to converge when Nu/Ns < (√2 - 1)-2 ≈ 5.83. Therefore, we propose and derive a new convergent GMP called scale-and-add GMP (SA-GMP), which always converges to the LMMSE multi-user detection performance for any Nu/Ns > 1, and show that it has a faster convergence speed than the traditional GMP with the same complexity. Finally, numerical results are provided to verify the validity and accuracy of the theoretical results presented.

MIMO-NOMA combines multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) techniques to address heterogeneous challenges, such as massive connectivity, low latency, and high reliability in the 5G cellular communication system and beyond. In this paper, a coded MIMO-NOMA system with capacity-approaching performance and low implementation complexity is proposed. Specifically, the proposed MIMO receiver consists of a linear minimum mean-square error (LMMSE) multi-user detector and a bank of single-user message-passing decoders, which decompose the overall NOMA signal recovery into distributed low-complexity computations with iterative processing. An asymptotic extrinsic information transfer analysis is employed to model the overall performance, and a novel class of multi-user irregular repeat-accumulate channel codes that match with the LMMSE multi-user detector in the iterative decoding process are constructed for the system. As a result, the proposed coded MIMO-NOMA system achieves asymptotic performance within 0.2 dB from the theoretical capacity. Simulation results validate the reliability and robustness of the proposed system in practical settings that include different system loads, iteration numbers, code lengths, fast/block fading, and imperfect channel estimation.

To resolve intra-cell pilot collision in crowded massive multiple-input multiple-output systems, a new pilot random access protocol, called strongest-user collision resolution combined graph-based pilots access (SUCR-GBPA), is proposed. This protocol allows all failed user equipments (UEs) to randomly select a pilot from the pilots that are not selected by any UE in the initial step. By exploiting the characteristic that the channel responses between UEs and the base station are invariant within the coherence time, a bipartite graph is established where active UEs and selected pilots are considered variable nodes and factor nodes, respectively. Based on this bipartite graph, the successive interference cancellation algorithm is employed to estimate the channel response of each UE. Finally, utilizing the and-or tree principle, we analyze the performance of the proposed SUCR-GBPA protocol, including the maximum number of the tolerable active UEs, the minimum number of the required pilots, uplink throughput, and the mean-square-error performance of the SUCR-GBPA channel estimation. Simulation results demonstrate that, compared with the SUCR protocol, the proposed SUCR-GBPA protocol significantly improves the uplink throughput and provides accurate estimation on the channel response at the same time.