Aiming at the problems of traditional dynamic weapon-target assignment algorithms in command decision-making, such as large computational amount, slow solution speed, and low calculation accuracy, combined with deep reinforcement learning theory, an improved Deep Deterministic Policy Gradient algorithm with dual noise and prioritized experience replay is proposed, which uses a double noise mechanism to expand the search range of the action, and introduces a priority experience playback mechanism to effectively achieve data utilization. Finally, the algorithm is simulated and validated on the ground-to-air countermeasures digital battlefield. The results of the experiment show that, under the framework of the deep neural network for intelligent weapon-target assignment proposed in this paper, compared to the traditional RELU algorithm, the agent trained with reinforcement learning algorithms, such as Deep Deterministic Policy Gradient algorithm, Asynchronous Advantage Actor-Critic algorithm, Deep Q Network algorithm performs better. It shows that the use of deep reinforcement learning algorithms to solve the weapon-target assignment problem in the field of air defense operations is scientific. In contrast to other reinforcement learning algorithms, the agent trained by the improved Deep Deterministic Policy Gradient algorithm has a higher win rate and reward in confrontation, and the use of weapon resources is more efficient. It shows that the model and algorithm have certain superiority and rationality. The results of this paper provide new ideas for solving the problem of weapon-target assignment in air defense combat command decisions.

Increasing demand for higher-speed and large-capacity data communications has driven the development of constellation shaping technology. This paper proposes a hybrid constellation shaping scheme for 64-quadrature amplitude modulation (64QAM) based on hexagonal lattice of a constellation subset. The proposed scheme aims to enhance the nonlinear tolerance of higher-order modulated signals and further improve the constellation shaping gain. The initial quantitative characterization of the constellation is firstly performed based on the hexagonal lattice structure. Then, the objective function of maximizing constellation figure of merits (CFM) is utilized to determine the position distribution of constellation points, resulting in the generation of the geometric shaping-64QAM (GS-64QAM) signal. Finally, according to concentric hexagonal layers, all constellation points are divided into multiple subsets where points within the same subset are assigned the same probability, and the hybrid shaping-64QAM (HS-64QAM) signal is generated. To validate the effectiveness of the proposed scheme, the experimental verification was demonstrated in a 120 Gbit/s multi-span coherent optical communication system. Experimental results indicate that, at the soft-decision forward error correction threshold, HS-64QAM achieves an optical signal-to-noise ratio (OSNR) gain of 1.9 dB and 4.1 dB over uniform GS-64QAM in back-to-back and 375 km transmission scenarios, respectively. Furthermore, HS-64QAM achieves an OSNR gain of 2.7 dB and 7.6 dB over uniform Square-64QAM in back-to-back and 375 km transmission scenarios, respectively.

Orthogonal time–frequency space (OTFS) modulation combined with massive multiple-input multiple-output (MIMO) can simultaneously address the problems caused by multipath delay, the Doppler effect, and channel fading. To mitigate inter-subcarrier and inter-symbol interference in satellite–terrestrial MIMO-OTFS systems, an iterative maximum ratio combining detection algorithm based on hard-decision interference cancelation (ICH-IMRC) is proposed. The signal detection is iterated by performing MRC on the interference-canceled received symbols. To mitigate the error spread in the interference cancelation process, iterative maximum ratio combining detection based on soft symbol interference cancelation (S-IMRC) is proposed, which is improved based on ICH-IMRC. The interference cancelation is updated by the expectation of other symbols, and the expectation and variance of symbols are updated by soft judgment with the posterior probability of symbols. To improve the detection convergence speed, optimal relaxation parameters are obtained based on the Sparrow Search Algorithm (SSA). Simulation results show that the proposed S-IMRC has superior error rate performance compared to the conventional algorithms for satellite–terrestrial MIMO-OTFS systems. Furthermore, the proposed algorithm is applicable to various satellite channel models and achieves excellent BER for different orders of orthogonal amplitude-modulated signals and different antenna array sizes.