Mingyang Cui

When a fixed-wing Unmanned Aerial Vehicle (UAV) conducts All-Weather Post-Disaster Coverage Path Planning (PDCPP), the commonly used Sequential Path Coverage (SPC) method tends to generate redundant flight distance during turning transitions between adjacent coverage paths, which in turn increases the UAV’s flight energy consumption and thereby compromises the timeliness of rescue information acquisition. To address these challenges, this paper proposes a Multi-Selector Genetic Algorithm with Reinforcement Learning (MSGA-RL). It enhances population diversity through a distance-priority heuristic greedy initialization strategy, employs a multi-selector crossover operator to improve both solution diversity and convergence speed, and integrates a reinforcement learning-based individual retention mechanism with an elite pool protection strategy to prevent premature convergence. To simulate post-disaster scenarios, the disaster-affected area is modeled as a convex polygonal region with obstacles, while the flight energy consumption and stability of MSGA-RL are evaluated under different numbers of coverage paths. Simulation results indicate that, across all coverage path settings, MSGA-RL consistently achieves lower flight energy consumption than SPC, the Genetic Algorithm (GA), and the Dubins-based Enhanced Genetic Algorithm (DEGA), while exhibiting superior stability. In particular, in the convex quadrilateral scenario with 50 coverage paths, the flight energy consumption of MSGA-RL is reduced by 52.80%, 32.06%, and 15.96% compared with SPC, GA, and DEGA, respectively.

To reduce beam search complexity without decreasing feedback efficiency for millimeter-wave communication in the LoS environment, this paper proposes a fast beam search scheme based on joint judgment, which requires a masterly design of the training beams and let their mainlobe overlap according to certain rules. As a consequence, its state utilization efficiency has been improved to 100% while keeping feedback efficiency is still 100%. In this paper, through theoretical analysis, we find that to encode the index of every subinterval A _q using the Gray mapping can decrease the possibility and the impact of misestimating, compared with using a binary index, which is adopted. The transceiver emits the training beam in turn and then jointly determines optimal communication beam pair according to the relationship between the receiving power and the threshold. The simulation results show that our proposed scheme is more efficient and its search complexity has been further decreased while its FE remains 100%. Especially, this method has more obvious advantages in multi-user simultaneously beam search scenarios.

Millimeter wave (mmWave) communications have been considered as a promising candidate for next generation network since it offers orders-of-magnitude wider underutilized spectrum than classical microwave bands. To combat the severe signal attenuation of the mmWave channel, hybrid precoding has recently received extensive attention. In this paper, we transform the hybrid precoding problem and develop a low complexity joint hybrid precoding algorithm. First, the analog precoder and combiner are obtained by iterative updating using the coordinate descent method. Then, the joint design of the digital precoder and combiner are computed based on the obtained analog precoder and combiner. Simulation results demonstrate that the proposed algorithm approaches optimal unconstrained percoding performance with low complexity. Finally, we investigate the performance of the proposed algorithm for the practical setting, in which only the quantized analog components are available, and find that it is still effective even with coarse quantization.

Hybrid precoding has been proposed as a promising technology for millimeter wave (mmWave) systems recently. However, the complexity of hybrid precoding for mm Wave multiple-input multiple-output systems is still high. In this paper, we propose a low-complexity joint hybrid precoding algorithm. By introducing the concept of equivalent channel, the hybrid precoding problem can be formulated as the problem of maximizing the gain of the equivalent channel. To solve this problem, we jointly design the radio-frequency precoder and combiner by selecting the appropriate codewords in an orthogonal codebook. Then, we complete the baseband precoder and combiner jointly through the singular value decomposition of the equivalent channel. The simulation results show that comparing with classical orthogonal matching pursuit algorithm, the proposed algorithm could achieve similar performance with much lower complexity. Furthermore, the proposed algorithm will be more efficient with the increase of antennas.