To satisfy the explosive access demands of Internet-of-Things (IoT) devices, various kinds of multiple access techniques have received much attention. In this article, we investigate the multicast communication of a satellite and aerial-integrated network (SAIN) with rate-splitting multiple access (RSMA), where both satellite and unmanned aerial vehicle (UAV) components are controlled by network management center and operate in the same frequency band. Considering a content delivery scenario, the UAV subnetwork adopts the RSMA to support massive access of IoT devices (IoTDs) and achieve desired performances of interference suppression, spectral efficiency, and hardware complexity. We first formulate an optimization problem to maximize the sum rate of the considered system subject to the signal-interference-plus-noise-ratio requirements of IoTDs and per-antenna power constraints at the UAV and satellite. To solve this nonconvex optimization problem, we exploit the sequential convex approximation and the first-order Taylor expansion to convert the original optimization problem into a solvable one with the rank-one constraint, and then propose an iterative penalty function-based algorithm to solve it. Finally, simulation results verify that the proposed method can effectively suppress the mutual interference and improve the system sum rate compared to the benchmark schemes.

Supporting IoT With Rate-Splitting Multiple Access in Satellite and Aerial-Integrated Networks

The traditional ground industrial Internet of Things (IIoT) cannot supply wireless interconnections anywhere due to its small-scale communication coverage. In this article, a multibeam satellite IIoT in Ka-band is proposed to realize wide-area coverage and long-distance transmissions, which uses nonorthogonal multiple access (NOMA) for each beam to improve transmission rate. To guarantee Quality of Service (QoS) for the satellite IIoT, the beam power is optimized to match the theoretical transmission rate with the service rate. The NOMA transmission rate for each beam is maximized by optimizing the power allocation proportion of each node subject to the constraints of the total power for the beam and the minimal transmission rate for each node within the beam. Satellite-ground integrated IIoT is proposed to use the ground cellular network to supplement the satellite coverage in the blocked areas. The power allocation and network selection for the integrated IIoT are proposed to decrease the transmission cost. Simulation results are provided to validate the superiority of employing NOMA in the satellite IIoT and show higher transmission performance for the QoS-guarantee resource allocation.

QoS-Guarantee Resource Allocation for Multibeam Satellite Industrial Internet of Things With NOMA

The development of the Internet-of-Things (IoT) security is comparatively slower than the pace of the IoT innovations. The seamless IoT network operates in an untrusted environment and is exposed to many malicious active attacks. As the process of identifying the modulation format of signals is corrupted by noise and fading, automatic modulation classification (AMC) can be viewed as an effective approach to counter physical-layer threats for IoT as it can detect and identify the pilot jamming, deceptive jamming, and Sybil attacks. Nowadays, data-driven deep learning (DL) techniques, which are capable of extracting discriminative features and perform better robustness to channel and noise conditions, have drawn widespread attention. The deep residual network (ResNet) has a strong representative ability, which can learn latent information repeatedly from the received signals and improve the classification accuracy. Meanwhile, the gated recurrent unit (GRU), which is capable of exploiting temporal information of the received signal can expand the dimension of the signal features for satisfactory classification performance. Considering the advantages of the above networks, this article proposes a novel gated recurrent residual neural network (GrrNet) for feature-based AMC, where the amplitude and phase of the received signal are utilized as the inputs of GrrNet. In GrrNet, a ResNet extractor module is first designed to extract the highly representative features and then temporal information is obtained by the subsequent GRU module which is capable of processing the representative features with the arbitrary length for modulation classification. Moreover, extensive simulations are conducted to verify the classification performance and robustness of the proposed GrrNet and it is shown that GrrNet outperforms other recent DL-based AMC methods. Moreover, the influence of the network parameters, symbol length, and frequency offset on performance is also explored.

Automatic Modulation Classification Using Gated Recurrent Residual Network

Recently, wearable devices have got increasing popularity in wide applications in medical and disaster rescue efforts to ensure the health and safety of users, which facilitates the development of the Internet of Medical Things (IoMT). Due to the posture alteration and mobility of users, the topology of the IoMT changes frequently, which increases the difficulty for resource allocation and routing strategy. In this paper, we respectively probe into the health monitoring architectures of the IoMT for both individual and group, allowing the monitored users to move at will. Furtherly, combined with the diversity of disaster rescuers, we build an IoMT-based disaster rescuer health monitoring system with searchers, doctors and porters. For each application, we point out the enabling technologies and demonstrate the existing researches. It is worth noting that the complexity of environment and high mobility of rescuers increase the probability of route breakage. Thus, this paper creatively addresses effective routing repair solutions for route breakage in IoMT-based disaster rescuer health monitoring system by exploiting the mobility of rescuers. Finally, we forecast three most likely directions in the field of IoMTs.

Health Monitoring Based on Internet of Medical Things: Architecture, Enabling Technologies, and Applications

Information dissemination in mobile networks turns out to be a problem when the network is sparse Mobile networks begin to establish a separate cluster attributable to the limited communication range of terminals The multi-beam satellite communication systems can play a significant role in providing direct-to-user satellite mobile services and connecting the separated clusters This paper focuses on how to efficiently schedule limited satellite-based radio resources to enhance transmission efficiency and meet the requested traffic with low complexity Taking the inter-beam interference and resource utilization variance into consideration, we build a game-theoretic based model for bandwidth allocation in the forward link As the size of satellite beams increases, the size of the action space for deep reinforcement learning based on a single agent becomes large, resulting in high time complexity Thus, we extend the single-agent deep reinforcement learning to the multi-agent context and then propose a cooperative multi-agent deep reinforcement learning method to achieve the optimal bandwidth allocation strategy Each beam works as a player who is willing to satisfy the request traffic with flexible payloads We built a multi-beam satellite platform using real historical data The experimental results show that this approach is capable of enhancing transmission efficiency and can be flexible to achieve the desired goal with low complexity

Multi-Agent Deep Reinforcement Learning-Based Flexible Satellite Payload for Mobile Terminals

Future Internet of Things should contain space segment and terrestrial segment. In addition, the multibeam satellite communication systems, especially working in S shared band, have gained more attention, which plays a significant role in providing direct-to-user satellite mobile services. Besides, due to the limited on-board resources, it is increasingly urgent to improve resource utilization. Taking the interbeam interference, channel conditions, delay factor, capacity, bandwidth utilization variance into consideration, a novel joint resource allocation algorithm is proposed in this paper. Interbeam interference coefficient matrix derived from frequency reuse is established to measure the level of co-channel interference. Moreover, the proposed algorithm can allocate resources flexibly according to specific traffic requirements and channel conditions. A novel joint power and bandwidth allocation algorithm is proposed by optimizing throughput and approximation problem of actual requirement. The optimal solution to this optimization problem can be obtained by golden section theory and subgradient iteration. The evaluation results demonstrate that the proposed algorithm can maximize the capacity, minimize the bandwidth utilization variance, and it can also allocate resource intelligently adapting to the user requirements and channel conditions.

Interbeam Interference Constrained Resource Allocation for Shared Spectrum Multibeam Satellite Communication Systems

The invention relates to the technical field of information and communication, specifically to an optimal forbidden zone width method combining spectrum effectiveness and interference suppression in a cognition satellite and ground integrated system, and aims to solve the problem of same frequency interference of ground components and satellite components. A system model built by the method comprises a multi-beam satellite, a plurality of complementary ground components (CGCs) and an earth station, and mainly directs at analysis of a scene where an earth station within a spot beam causes interference to an uplink of a ground user within an adjacent spot beam. To reduce interference to which a CGC of a spot beam edge is subjected, the method adopts a forbidden zone principle, utilizes an improved frequency division multiplexing scheme to perform theoretical analysis and simulation verification on same frequency interference and spectral effectiveness in a cognition satellite and ground integrated system, and at the same time, the method considers spectrum effectiveness and interference suppression in a combined manner, and proposes an optical EA strategy capable of enabling a system to realize maximum spectrum effectiveness under an acceptable interference level.

Optimal forbidden zone width method combining spectrum effectiveness and interference suppression in cognition satellite and ground integrated system

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Interbeam Interference Constrained Resource Allocation for Shared Spectrum Multibeam Satellite Communication Systems

Optimal forbidden zone width method combining spectrum effectiveness and interference suppression in cognition satellite and ground integrated system