Modern war relies heavily on various combat units, such as command and control units, surveillance units, and reconnaissance units. These units are usually combined organically as a combat SoS (System of Systems) to conduct given missions. To ensure that the combat SoS executes correctly, the communication systems (e.g., communication vehicles, communication UAVs) must be planned carefully to connect all constituent systems or units. However, the c ommunication a rchitecture d esign problem for combat SoS (SoS-CAD) is very challenging as the combat SoS usually resides in a dynamic and confrontational environment. In this paper, we formally formulate the SoS-CAD problem with integer programming and prove its NP-hardness. The SoS-CAD problem has high requirements for the solving speed as well as the solution quality, but traditional meta-heuristic algorithms cannot satisfy them. Thus, we propose a deep reinforcement learning-based method called CADer to solve it. Specifically, we introduce the attention mechanism and dynamic embedding mechanism into CADer with considering the characteristics of the SoS-CAD problem itself. The massive experiment results show that CADer performs well in the generalization ability and can achieve the best tradeoff between the solving speed and the solution quality against the meta-heuristic algorithm.

Localization is an enabling technique for many sensor and ad-hoc network applications. Real-world deployments demonstrate that, in practice, a network is not always entirely localizable, leaving a certain number of theoretically non-localizable nodes. Previous studies mainly focus on how to tune network settings to make a network localizable; however, they are considered to be coarse-grained, since they equally deal with localizable and non-localizable nodes. Ignoring localizability induces unnecessary adjustments and accompanying costs. In this study, we propose a fine-grained approach, Localizability-aided Localization (LAL), which basically consists of three phases: node localizability testing, component tree construction, and network adjustment. LAL triggers a single round adjustment, after which some popular localization methods can be successfully carried out. Being aware of node localizability, all adjustments made by LAL are purposefully selected. Simulation results show that LAL effectively guides the adjustment.

Due to the dynamic complexities of the multi-unmanned vessel target assignment problem at sea, especially when addressing moving targets, traditional optimization algorithms often fail to quickly find an adequate solution. To overcome this, we have developed a multi-agent reinforcement learning algorithm. This approach involves defining a state space, employing preferential experience replay, and integrating self-attention mechanisms, which are applied to a novel offshore unmanned vessel model designed for dynamic target allocation. We have conducted a thorough analysis of strike positions and times, establishing robust mathematical models. Additionally, we designed several experiments to test the effectiveness of the algorithm. The proposed algorithm improves the quality of the solution by at least 30% in larger scale scenarios compared to the genetic algorithm (GA), and the average solution speed is less than 10% of the GA, demonstrating the feasibility of the algorithm in solving the problem.

The unmanned combat system-of-systems (UCSoS) in modern warfare is comprised of various interconnected entities that work together to support mission accomplishment. The soaring number of entities makes the UCSoS fragile and susceptible to triggering cascading effects when exposed to uncertain disturbances such as attacks or failures. Reconfiguring the UCSoS to restore its effectiveness in a self-coordinated and adaptive manner based on the battlefield situation and operational requirements has attracted increasing attention. In this paper, we focus on the UCSoS reconstruction with heterogeneous costs, where the collaboration nodes may have different reconstruction costs. Specifically, we adopt the heterogeneous network to capture the interdependencies among combat entities and propose a more representative metric to evaluate the UCSoS reconstruction effectiveness. Next, we model the combat network reconstruction problem with heterogeneous costs as a nonlinear optimization problem and prove its NP-hardness. Then, we propose an approach called SoS-Restorer, which is based on deep reinforcement learning (DRL), to address the UCSoS reconstruction problem. The results show that SoS-Restorer can quickly generate reconstruction strategies and improve the operational capabilities of the UCSoS by about 20∼60% compared to the baseline algorithm. Furthermore, even when the size of the UCSoS exceeds that of the training data, SoS-Restorer exhibits robust generalization capability and can efficiently produce satisfactory results in real time.