EURASIP Journal on Advances in Signal Processing最新文献

In intelligent transportation systems, real-time video streaming via vehicle networks has been seen as a vital difficulty. The goal of this paper is to decrease the computational complexity of the versatile video coding (VVC) encoder for VANETs. In this paper, a low-complexity enhancement VVC encoder is designed for vehicular communication. First, a fast coding unit (CU) partitioning scheme based on CU texture features is proposed in VVC, which aims to decide the final type of CU partition by calculating CU texture complexity and gray-level co-occurrence matrix (GLCM). Second, to reduce the number of candidate prediction mode types in advance, a fast chroma intra-prediction mode optimization technique based on CU texture complexity aims to combine intra-prediction mode features. Moreover, the simulation outcomes demonstrate that the overall approach may substantially reduce encoding time, while the loss of coding efficiency is reasonably low. The encoding time can be reduced by up to 53.29% when compared to the VVC reference model, although the average BD rate is only raised by 1.26%. The suggested VVC encoder is also hardware-friendly and has a minimal level of complexity for video encoders used in connected vehicle applications.

Integrated satellite–aerial–terrestrial relay networks (ISATRNs) play a vital role in next-gen networks, particularly those with high-altitude platforms (HAP). This study introduces a new model for hybrid optical/RF-based HAP-enabled ISATRNs, incorporating reconfigurable intelligent surfaces (RIS) on unmanned aerial vehicles (UAVs) to optimize access in dense urban areas. Non-orthogonal multiple access is employed for improved spectrum efficiency. The objective is to jointly optimize UAV trajectory, RIS phase shift, and active transmit beamforming while considering energy consumption. A deep reinforcement learning approach using LSTM-DDQN framework is proposed. Numerical results show the effectiveness of our algorithm over traditional DDQN, with higher single-step exploration reward and evaluation metrics.

Integrated satellite–aerial–terrestrial relay networks (ISATRNs) play a vital role in next-gen networks, particularly those with high-altitude platforms (HAP). This study introduces a new model for hybrid optical/RF-based HAP-enabled ISATRNs, incorporating reconfigurable intelligent surfaces (RIS) on unmanned aerial vehicles (UAVs) to optimize access in dense urban areas. Non-orthogonal multiple access is employed for improved spectrum efficiency. The objective is to jointly optimize UAV trajectory, RIS phase shift, and active transmit beamforming while considering energy consumption. A deep reinforcement learning approach using LSTM-DDQN framework is proposed. Numerical results show the effectiveness of our algorithm over traditional DDQN, with higher single-step exploration reward and evaluation metrics.

FBMC is a pivotal system in 5G, serving as a cornerstone for efficient use of available bandwidth while simultaneously meeting stringent requirements for high spectral efficiency. Notably, FBMC harnesses the power of multicarrier modulation (MC), a good alternative to orthogonal frequency division multiplexing (OFDM) technology that supports fourth-generation (4G) systems. The wireless communications field is full of challenges, the most important of which are channel estimation and interference cancellation, both of which deserve comprehensive study to increase the efficiency of data transmission. In this paper, our investigation takes a deliberate step towards the convergence of two prominent modulation models: OFDM and FBMC. We specifically contrast these modulation techniques with the intricate field of joint channel estimation and interference cancellation (JCEIC). In this research study, we take advantage of recurrent neural networks' (RNNs') efficiency as a vehicular channel to perform precise channel estimation and recovery of uncorrupted transmitted signals, thereby lowering the bit error rate (BER). Our channel estimation for a dual selective channel is based on the thoughtful placement of pilots scattered over the temporal and frequency dimensions, and is further improved by the interference cancellation method of low complexity that was selected. Our JCEIC proposal aims to integrate RNNs carefully, using the output sequences of JCEIC algorithms as useful inputs to this neural architecture. By clearly demonstrating a decrease in BER as compared to traditional approaches, it is evident that the performance of the novel approach is near to that of a perfect channel. Additionally, a comparison of the performance of FBMC and OFDM systems at various signal-to-noise ratios reveals a clear performance divide that favors the former in terms of system efficiency. The BER is restricted by FBMC to a commendable threshold of less than 0.1 at a modest 5 dB, continuing the higher trend started by its improved RNN-based channel estimate. The accuracy of channel estimation is clearly improved by this paradigm shift, and the computing complexity typical of 5G networks is also clearly reduced.

FBMC is a pivotal system in 5G, serving as a cornerstone for efficient use of available bandwidth while simultaneously meeting stringent requirements for high spectral efficiency. Notably, FBMC harnesses the power of multicarrier modulation (MC), a good alternative to orthogonal frequency division multiplexing (OFDM) technology that supports fourth-generation (4G) systems. The wireless communications field is full of challenges, the most important of which are channel estimation and interference cancellation, both of which deserve comprehensive study to increase the efficiency of data transmission. In this paper, our investigation takes a deliberate step towards the convergence of two prominent modulation models: OFDM and FBMC. We specifically contrast these modulation techniques with the intricate field of joint channel estimation and interference cancellation (JCEIC). In this research study, we take advantage of recurrent neural networks' (RNNs') efficiency as a vehicular channel to perform precise channel estimation and recovery of uncorrupted transmitted signals, thereby lowering the bit error rate (BER). Our channel estimation for a dual selective channel is based on the thoughtful placement of pilots scattered over the temporal and frequency dimensions, and is further improved by the interference cancellation method of low complexity that was selected. Our JCEIC proposal aims to integrate RNNs carefully, using the output sequences of JCEIC algorithms as useful inputs to this neural architecture. By clearly demonstrating a decrease in BER as compared to traditional approaches, it is evident that the performance of the novel approach is near to that of a perfect channel. Additionally, a comparison of the performance of FBMC and OFDM systems at various signal-to-noise ratios reveals a clear performance divide that favors the former in terms of system efficiency. The BER is restricted by FBMC to a commendable threshold of less than 0.1 at a modest 5 dB, continuing the higher trend started by its improved RNN-based channel estimate. The accuracy of channel estimation is clearly improved by this paradigm shift, and the computing complexity typical of 5G networks is also clearly reduced.

Due to the advantages of rapid deployment, flexible response and strong invulnerability, unmanned aerial vehicle (UAV) swarm has been widely applied in collaborative warfare and emergency communication. However, UAV swarm in complex environments is prone to chaotic collapse due to obstructions. A UAV swarm obstacle avoidance system model for multi-narrow type obstacles is established. Due to the fact that only one UAV is allowed to pass through each small hole at any given moment, addressing the issue of congestion caused by swarming effects becomes crucial in addition to managing the competitive allocation of multiple UAVs to multiple holes. Aiming at this problem, a dual-game real-time obstacle avoidance scheme is proposed for UAV swarm with multi-narrow type obstacle scenarios, which divides the flight process of the UAV swarm into two stages: maintaining the flight state of the UAV swarm unchanged when no obstacles are encountered, and implementing matching separation and motion state switching by means of dual-game strategy when facing multi-narrow type obstacles, ultimately achieving orderly passage after multiple rounds of games. For the proposed scheme, a dual-game based Flocking (DGF) obstacle avoidance algorithm is proposed. Specifically, the motion state of each UAV obtained from the game is parameterized and integrated with the Flocking algorithm to calculate the motion control input for each UAV. The solution is iteratively obtained until the UAV swarm completes the obstacle avoidance. Simulation results demonstrate that the proposed DGF algorithm not only enables smooth obstacle avoidance for the UAV swarm in multi-narrow type obstacle scenarios, but also effectively resolves the internal chaos problem in the UAV swarm, thereby preventing rigid collisions.

Due to the advantages of rapid deployment, flexible response and strong invulnerability, unmanned aerial vehicle (UAV) swarm has been widely applied in collaborative warfare and emergency communication. However, UAV swarm in complex environments is prone to chaotic collapse due to obstructions. A UAV swarm obstacle avoidance system model for multi-narrow type obstacles is established. Due to the fact that only one UAV is allowed to pass through each small hole at any given moment, addressing the issue of congestion caused by swarming effects becomes crucial in addition to managing the competitive allocation of multiple UAVs to multiple holes. Aiming at this problem, a dual-game real-time obstacle avoidance scheme is proposed for UAV swarm with multi-narrow type obstacle scenarios, which divides the flight process of the UAV swarm into two stages: maintaining the flight state of the UAV swarm unchanged when no obstacles are encountered, and implementing matching separation and motion state switching by means of dual-game strategy when facing multi-narrow type obstacles, ultimately achieving orderly passage after multiple rounds of games. For the proposed scheme, a dual-game based Flocking (DGF) obstacle avoidance algorithm is proposed. Specifically, the motion state of each UAV obtained from the game is parameterized and integrated with the Flocking algorithm to calculate the motion control input for each UAV. The solution is iteratively obtained until the UAV swarm completes the obstacle avoidance. Simulation results demonstrate that the proposed DGF algorithm not only enables smooth obstacle avoidance for the UAV swarm in multi-narrow type obstacle scenarios, but also effectively resolves the internal chaos problem in the UAV swarm, thereby preventing rigid collisions.

A single type of signal processing means that it is difficult to analyze vibration signals comprehensively and effectively. By comprehensively using wavelet analysis techniques, a comprehensive and in-depth study of aero-engine vibration conditions is realized as a way to carry out health management. By introducing various types of wavelet analysis techniques and using Labview2022 programming, corresponding signal processing tools are developed for the analysis of the collected vibration signals. The comprehensive analysis of aero-engine vibration signals based on the wavelet transform method is realized, and the corresponding products are successfully applied in engineering practice.

A single type of signal processing means that it is difficult to analyze vibration signals comprehensively and effectively. By comprehensively using wavelet analysis techniques, a comprehensive and in-depth study of aero-engine vibration conditions is realized as a way to carry out health management. By introducing various types of wavelet analysis techniques and using Labview2022 programming, corresponding signal processing tools are developed for the analysis of the collected vibration signals. The comprehensive analysis of aero-engine vibration signals based on the wavelet transform method is realized, and the corresponding products are successfully applied in engineering practice.