Satellite constellations are promising in enabling the global Internet. However, the increasing constellation size also complicates tracking, telemetry and command (TT&C) systems. Traditional ground-based and space-based approaches have encountered significant obstacles due to, e.g., the limited satellite visible arc and long transmission delay. Considering the fast development of intersatellite communications, synergy among multiple connected satellites can be exploited to facilitate TT&C system designs. This leads to networked TT&C, which requires much less predeployed infrastructures and even performs better than traditional TT&C systems. In this paper, we elaborate system characteristics of networked TT&C compared with traditional ground-based and space-based TT&C, and propose the unique security challenges and opportunities for networked TT&C, which includes secure routing and trust mechanisms. Furthermore, since networked TT&C is a novel scenario with few relevant researches, we first investigate the current researches on secure routing and trust mechanisms for traditional terrestrial and satellite networks, and then accordingly deliver our security perspectives considering the system characteristics and security requirements of networked TT&C.

In recent years, various maritime applications such as unmanned surface vehicles, marine environment monitoring, target tracking, and emergency response have developed rapidly in maritime communication networks (MCNs), and these applications are often accompanied by complex computation tasks and low latency requirements. However, due to the limited resources of the vessels, it is critical to design an efficient mobile edge computing (MEC) enabled network for maritime computation. Inspired by this motivation, energy harvesting space-air-sea integrated networks (EH-SASINs) for maritime computation tasks offloading are proposed in this paper. We first make the optimal deployment of tethered aerostats (TAs) with the K-means method. In addition, we study the issue of computation task offloading for vessels, focusing on minimizing the process delay of computation task based on the proposed architecture. Finally, because of the NP-hard properties of the optimization problem, we solve it in two stages and propose an improved water-filling algorithm based on queuing theory. Simulation results show that the proposed EH-SASINs and algorithms outperform the existing scenarios and can reduce about 50% of the latency compared with local computation.

The hybrid satellite-UAV-terrestrial maritime networks have shown great promise for broadband coverage at sea. The existing works focused on vessels collaboratively served by UAV-enabled aerial base station (ABSs) and terrestrial base stations (TBSs) deployed along the coast, and proved that data rate could be improved by optimizing transmit power and ABS's position. In practice, users on a vessel can be collaboratively served by an ABS and a vessel-enabled base station (VBS) in different networks. In this case, how to select the network for users on a vessel is still an open issue. In this paper, a TBS and a satellite respectively provide wireless backhaul for the ABS and the VBS. The network selection is jointly optimized with transmit power of ABS and VBS, and ABS's position for improving data rate of all users. We solve it by finding candidates for network selection and iteratively solving transmit power and ABS's position for each candidate. Simulation results demonstrate that data rate can be improved by collaborative coverage for users on a vessel.

This paper investigates an unmanned aerial vehicle (UAV)-enabled maritime secure communication network, where the UAV aims to provide the communication service to a legitimate mobile vessel in the presence of multiple eavesdroppers. In this maritime communication networks (MCNs), it is challenging for the UAV to determine its trajectory on the ocean, since it cannot land or replenish energy on the sea surface, the trajectory should be pre-designed before the UAV takes off. Furthermore, the take-off location of the UAV and the sea lane of the vessel may be random, which leads to a highly dynamic environment. To address these issues, we propose two reinforcement learning schemes, Q-learning and deep deterministic policy gradient (DDPG) algorithms, to solve the discrete and continuous UAV trajectory design problem, respectively. Simulation results are provided to validate the effectiveness and superior performance of the proposed reinforcement learning schemes versus the existing schemes in the literature. Additionally, the proposed DDPG algorithm converges faster and achieves higher utilities for the UAV, compared to the Q-learning algorithm.

The maritime communication network (MCN) plays an important role in the 6th generation (6G) system development. In MCNs, packet transport over long-distance lossy links will be ubiquitous. Transmission control protocol (TCP), the dominant transport protocol in the past decades, have had performance issues in such links. In this paper, we propose a novel transport approach which uses user datagram protocol (UDP) along with a simple yet effective bandwidth estimator for congestion control, and with a proactive packet-level forward erasure correction (FEC) code called streaming code to provide low-delay loss recovery without data retransmissions at all. We show that the approach can effectively address two issues of the state-of-the-art TCP variants in the long-distance lossy links, namely 1) the low bandwidth utilization caused by the slow increase of the congestion window (CWND) due to long round-trip time (RTT) and the frequent CWND drop due to random and congestion losses, and 2) the high end-to-end in-order delivery delay when re-transmissions are incurred to recover lost packets. In addition, we show that the scheme's goodput has good smoothness and short-term intra-protocol fairness properties, which are beneficial for multimedia streaming and interactive applications that are prominent parts of today's wireless traffic.

With the increasing maritime activities, a great demand of wide-area maritime digital data services is needed. Therefore, Narrowband Internet of Things (NB-IoT) that can provide wide coverage has been expected as an application for maritime communication networks (MCNS). In this paper, we aim to enhance the spectral efficiency in NB-IoT by reducing the cyclic prefix (CP) overhead in random access signal without causing interference. The key point of the proposed scheme is the symbols transmitted for multiple times repeatedly in NB-IoT. Specifically, all CP are removed and multi-path fading effect is eliminated by using a repeated symbol to cover the disturbed symbol to construct a circular convolution structure of the channel with the same effect as adding CP. In addition, a single-tap equalization is still appropriate. To validate the effectiveness of the proposed scheme, simulation results are carried out with respect to the bit error ratio (BER).