Satellite IoT has emerged as a promising solution to extend connectivity beyond terrestrial networks, particularly in remote and underserved regions. However, grant-free random access (GF-RA) in satellite networks faces two key challenges: severe interference from massive uncoordinated terminals and limited computational resources on satellite platforms. In contrast to terrestrial multipath channels, the dominant line-of-sight propagation in satellite-terrestrial links induces deterministic spatial correlations between terminal positions and angles-of-arrival (AoAs). To leverage this property, a hierarchical satellite-terrestrial cooperative processing architecture is proposed, where computationally intensive terminal positioning tasks are offloaded to the terrestrial center, and satellites focus on real-time joint activity detection and channel estimation (JADCE). In this framework, we propose a location-prior-aided JADCE algorithm that incorporates terminal position information into the message-passing process to prune low-probability propagation paths and reduce computational complexity. The algorithm further integrates an expectation-maximization procedure to refine AoA estimates and optimize model hyperparameters. Furthermore, a region-based location management scheme is introduced to enable efficient terminal location estimation and storage. Based on this scheme, region affiliation probabilities are computed by evaluating the expectation of the posterior distribution conditioned on AoA estimates. Simulation results demonstrate significant improvements over benchmark schemes in terms of terminal activity detection and channel estimation accuracy.

This paper investigates an uplink grant-free massive access (GF-MA) system, where a large number of IoT devices collaborate to achieve complex applications. For this scenario, device activity identification is a challenging problem due to the interference from massive devices and the limitation in the number of pilot sequences. By utilizing the inherent correlation features in multi-device collaborative scenarios, in this work, we present a detection approach that aims to enhance the accuracy of both activity detection and channel estimation. Specifically, we first propose a task-driven activity (TDA) model to capture the active probability in multi-device collaborative scenarios. Subsequently, considering the TDA model, we propose a message-passing-based algorithm named TDA-JDE for device activity detection and channel estimation. The proposed algorithm jointly processes messages containing channel impulse response (CIR), device activity, and task status information to leverage device activity correlation information. Finally, to obtain the parameters in the TDA model, we propose a parameter estimation algorithm based on the expectation-maximization framework with relaxation and reconstruction strategy (EM-RR). Extensive numerical results show that the proposed algorithm can achieve higher detection accuracy when compared with three benchmark schemes in multi-device collaborative massive access (MA) scenarios.

Low earth orbit (LEO) satellites are expected to play an important role in enhancing terrestrial Internet of Things. This paper focuses on the challenge of joint terminal activity detection (TAD) and channel estimation (CE) in grant-free non-orthogonal random access (GF-NORA)-enabled LEO communication networks. To leverage the dominant line-of-sight (LoS) path and the limited angular spread characteristic of terrestrial-satellite links, we propose a two-stage joint TAD and CE (TS-JDE) detection scheme aimed to achieve lower computational overhead. This scheme utilizes a high-accuracy algorithm for LoS path estimation and leverages the channel correlations for low-complexity NLoS paths estimation. Specifically, in LoS estimation stage, to mitigate the inaccuracies caused by Taylor expansion errors, we propose an enhanced message-passing algorithm, termed TaMP-LoS. This algorithm exploits the structured sparsity in the delay-Doppler domain and incorporates the Lagrange remainder to evaluate the expansion error, thereby enhancing estimation accuracy. In the NLoS estimation stage, we develop a sparse Bayesian learning-based algorithm, named SBL-NLoS, designed to estimate NLoS channels characterized by the limited residuals of delay and Doppler. Simulation results show that our proposed algorithm can achieve better detection performance than the existing approaches for GF-NORA-enabled LEO networks.

With the advance of various applications of unmanned aerial vehicles (UAVs), employing UAVs as mobile aerial base stations is a potential technology to improve wireless communication quality. However, due to limited load capacity, UAVs are hard to handle complex computing tasks. In this paper, a computation time minimization problem is investigated under a multi-UAV connected mobile edge computation (MEC) system, where both computation offloading and energy constrain are considered. The formulated problem is a mixed integer nonconvex optimization problem and is hard to solve. To simplify problem, we propose an efficient iterative algorithm by successive convex optimization techniques. Specific, we maximize the computation offloading under given tolerable delay. Bisection search is applied to find the optimal task completed time. For speed up algorithm convergence, a low-complexity association scheme is proposed based on greedy algorithm. Simulation result shows that the proposed algorithm can reduce time overhead and make full use of the gain brought by multiple UAVs.

Massive multiple-input-multiple-output (MIMO) and millimeter-wave (mmWave) have been adopted as the enabling technologies for the 5G and beyond 5G (B5G) systems. However, due to the large number of antennas, it is hard to obtain the channel state information (CSI) which is essential for obtaining desirable beamforming gains. Off-grid error is one of the main limiting factors of the channel estimation (CE) performance, which presents when the true angle does not lie on the discretized angle grid of mmWave channel. To address this problem, we propose a joint algorithm named off-grid approximate message passing (OG-AMP) to achieve both angular domain CE and off-grid errors eliminationin in this paper. Specially, we formulate CE issue as a Bayesian inference problem to compute the posterior of the channel coefficients and adopt the Gaussian approximation to simplify the sum-product algorithm. Our simulation results show that the proposed algorithm shows the superiority over a state-of-art benchmark method.

5G technology offers a solution to achieve huge capacity and low latency communication between vehicles and road infrastructure in vehicular networks. However, the coverage of 5G signal is significantly reduced due to high frequency band used, which means the original base stations (BSs) deployment cannot complete the task of regional seamless coverage well, especially in suburban areas with few BSs. To overcome this issue, we propose a piggyback mobile Internet of things (PMIoT) scheme to enhance 5G coverage in suburban areas, which the data is connected to 5G BSs through vehicle piggyback information. In addition, we define a new evaluation metric to measure the quality of coverage. The proposed work is simulated in real road network, and the results show that the scheme can significantly enhance 5G coverage in suburban areas with weak BSs deployment.