In urban settings, the highly complex communication environment leads to a frequent occurrence blockage in the link between ground users and unmanned aerial vehicle (UAV), which renders the quality of communication poor. To address this issue, an UAV edge computing system based on reconfigurable intelligent surface (RIS) is proposed in this paper. The goal of this system is to minimize energy consumption by considering various factors such as user transmission power, intelligent reflector phase shift matrix, UAV trajectory, computational resource allocation and the stability of task queues. By applying the Lyapunov optimization theory, the original non-convex problem is decomposed into a series of optimization-related sub-problems. Additionally, it is possible to obtain the closed solution to the original problem through Lagrange dual, Karush-Kuhn-Tucker (KKT) conditions, and mixed differential genetic evolution algorithm (MDGEA). As indicated by the simulation results, the proposed RIS-assisted UAV edge computing system is more effective in reducing energy consumption for the system than traditional UAV mobile edge computing on the basis of maintaining queue stability.

The compensation methods for range and Doppler migration in digital television (DTV) based passive radar are studied in this paper. Firstly, the problems of the compensation method based on Keystone transform (KT) and Chirp-Fourier transform (CFT) when applied to passive radar are analyzed, and then a comparison is made between this method and the compensation method based on envelope shift and multirate signal processing. The simulation analysis and real data verification imply that both of these methods can achieve effective compensation results. However, in DTV based passive radar, the compensation performance of the latter is distinctly superior to the former.

With the widespread application of UAV technology, various countries are facing new threats in both military and civilian domains. To effectively counter these threats, UAV communication jamming has become a reliable countermeasure. Traditional communication jamming methods face several challenges. These include low efficiency, difficulties in managing dynamic and complex electromagnetic environments, and a heavy reliance on prior knowledge about the signals. This study presents intelligent jamming techniques for UAV signal processing to overcome the limitations found in current communication jamming technologies. The aim is to achieve precise jamming of individual target signals using intelligent jamming methods. This paper introduces an enhanced jamming signal generation algorithm built on the traditional convolutional autoencoder. Without relying on prior signal knowledge, the algorithm introduces complex convolutional networks and residual modules. The model leverages advanced convolutional networks to integrate the real and imaginary components of the signal for feature extraction, while the residual modules further refine signal processing and enhance feature learning. The model generates jamming signals by altering hidden layer features. These signals closely resemble the original communication waveforms.Simulation results show that, across different communication systems, the generated jamming signal waveforms exhibit strong similarity to the original signal waveforms. Moreover, under various signal-to-interference ratios (SIR), the jamming performance of the generated signals surpasses that of conventional jamming signals such as Gaussian noise and amplitude modulation noise.

This paper investigates the generalized pattern of Poisson summation formulae from the special affine Fourier transform (SAFT) and offset Hilbert transform (OHT) points of view. Several novel summation formulae are derived accordingly. Firstly, the relationship between SAFT (or OHT) and Fourier transform (FT) is obtained. Then, the generalized Poisson sum formulae are obtained based on above relationships. The novel results can be regarded as the generalizations of the classical results in several transform domains such as FT, fractional Fourier transform, and the linear canonical transform.

Network slicing is a key technology in sixth-generation (6G) communication networks. However, the performance of virtualized nodes sharing cache resources in 6G network slicing is degraded by the inappropriate allocation of resources. This paper proposes a cache resource allocation strategy for 6G network slicing based on game dynamics (RASGD). A 6G slicing network model is developed that describes the relationship between virtualized node mapping and resource sharing, and an evolutionary public goods game cache resource allocation (PGGRA) method is proposed to improve data transmission performance. This strategy defines the cache resource gap and selects the input cost according to the cache resource gap and evolutionary game strategy. When the cache resources are sufficient, the input is carried out, and when the cache resources are insufficient or the demand is large, unnecessary input is reduced to avoid the waste of resources, so as to achieve the intelligent allocation of cache resource. Simulation results demonstrate that the RASGD strategy improves the successful arrival rate of slice packets by at least 4.79% and improves the network congestion by at least 22.9% compared with the resource allocation strategy based on number of nodes (RANN) and resource allocation strategy based on node degree (RAND).

In watermarking system designing, there are always encounter two conflicting objectives: robustness and invisibility. It is difficult to satisfy them at same time. Traditional watermarking algorithm often solve this problem by choosing parameters via experience, it always be inefficient. We resolve the problem by using genetic algorithm (GA) to obtain the optimized solution. With a proper fitness function in the watermarking system, GA search for the optimal parameters to improve the performance of watermarking algorithm. The simulation results show that both watermark robustness and invisibility can be achieved under different attack schemes.