Physical Communication最新文献

The primary user (PU) transmission is sporadic in nature, which explains why the PU is inactive during some time slots, geographic directions or frequency bands. The frequency bands where the PU is not active are called "spectrum holes". Secondary users (SUs) periodically perform sensing to detect the spectrum holes and monitor primary spectrum. For the best possible spectrum utilization, PU signal detection is very crucial. For measuring the spectrum sensing performance, two main metrics are applied, like, probability of false alarm (PFA) and probability of detection (PD). Due to PFA and PD, the conventional sensing techniques have to face issues. These two constraints used to hinder spectrum utilization. Traditional sensing strategies are mostly based on feature extraction of received signal. Advancement of artificial intelligence (AI) has reduced the inaccuracy in detection of spectrum hole. Deep learning (DL) based approaches have shown a remarkable improvement in this aspect. Hence, the present research work was undertaken to address the problem of spectrum sensing in low SNR and improves accuracy. This research penetrates into the use of deep neural network (DNN) for sensing the vacant spectrum accurately. In this article, RadioML2016.10b dataset was used for the experiments. The results are also studied. The proposed approach shows betterment in sensing than other existing spectrum detection models. DeepSenseNet model was validated through simulation results and showed that it has achieved 98.84% prediction accuracy ($P_a$) with 97.53% precision and 97.62% recall.

Low earth orbit satellite networks (LSN) have been widely recognized as a key element in the development of next-generation wireless communication networks, offering extensive coverage and seamless connectivity across multiple domains. To achieve the minimum transmission latency for highly-dynamic LSN, this paper first establishes a low earth orbit (LEO) satellite downlink communication model while considering outdated channel state information and high mobility. Then, through the design of user association and satellite power allocation strategies, a dynamic problem of minimizing transmission latency is formulated and solved using successive convex approximation and alternating optimization methods. Simulation results clearly illustrate the substantial reduction in transmission latency achieved by the proposed algorithm, successfully meeting the quality of service demands in dynamic environments during the entire mobility cycle of the LEO satellite.

The improvement of positioning accuracy in Wireless Sensor Networks (hereafter, WSN) is crucial to develop advanced Internet of Things (IOT, for short) applications. However, the conventional distance vector-hop (DV-Hop) localization algorithm has shortcomings such as low accuracy and weak stability. To overcome these shortcomings, this paper proposes a hybrid improved compressed particle swarm optimization algorithm (HICPSO), which consists of a scheme of linearly decreasing inertia weights, compressed velocity vectors, population Gaussian variants and optimal boundary selection. Then, HICPSO is integrated with DV-Hop to gradually reduce the distance error of least squares method (LSM) estimated with the efficient search advantage of HICPSO. Our simulation results show that the HICPSO algorithm possesses better computational accuracy and search performance on the 22 benchmark test functions compared with the algorithms such as the Improved Adaptive Genetic Algorithm (IAGA) and Adaptive Weighted Particle Swarm Optimizer (AWPSO). Meanwhile, compared with IAGA and AWPSO, the positioning accuracy of HICPSO-based positioning algorithm is improved by 4.28% and 4.76% respectively, and the stability is improved by one order of magnitude.

Aiming at the problem that the existing direction of arrival (DOA) estimation algorithms are difficult to achieve high-precision estimation in environments with mixed Alpha-stable distribution noise and Gaussian-colored noise, a look ahead orthogonal matching pursuit algorithm based on Fractional Order Cumulants (FOC) is proposed for acoustic vector sensor (AVS) arrays. Firstly, the algorithm computes the FOC matrix of the observed data and exploits the semi-invariance of the FOC to separate Alpha-stable distribution noise and Gaussian-colored noise from the observed data. Furthermore, the property that FOC is insensitive to the Alpha-stable distribution processes and Gaussian processes is then exploited to suppress the Alpha-stable distribution noise and Gaussian-colored noise. Subsequently, the FOC matrix is reconstructed through the vectorization operator, and an FOC-based sparse DOA estimation model is derived. Finally, the look ahead orthogonal matching pursuit algorithm predicts the impact of each candidate atom on minimizing the residual. It selects the optimal atom to enter the support set, obtaining the DOA estimation of the target. The effectiveness of the proposed algorithm is verified through computer simulations. The simulation results show that the proposed algorithm has high estimation accuracy and success probability.

With the continuous evolution of communication technologies such as 5G/6G and the continuous development of artificial intelligence (AI), rail transit wireless communication systems have seen unprecedented growth opportunities. However, this is accompanied by a series of challenges, including the accuracy of channel estimation in high-speed mobile environment, the complexity of resource management, and edge collaborative optimization. The aim of this paper is to explore these issues in depth and propose corresponding solutions. Firstly, we integrate AI with rail transit wireless communication to build relevant architectures and summarize the solutions to the rail transit wireless communication problems based on AI algorithms and the related research progress. Secondly, we apply AI algorithms to improving the stability of channel estimation in complex and changing channel environments, so as to enhance the communication quality. Finally, to meet the demands of rail transit wireless communication, we introduce a resource management and edge collaborative optimization model, and explore the prospects of the wide application of multiple AI algorithms in these fields. In this paper, significant progress has been made in channel estimation, resource management and edge collaborative optimization through in-depth research and innovation combined with AI algorithms. This lays the foundation for introducing more efficient and reliable communication solutions for intelligent rail transit systems.

The advent of 6G wireless networks has the potential to unlock diverse applications of scalable autonomy. By advantageously coupling the individual and aggregated attributes of diverse multi-UAV fleets, a range of high-value applications such as logistics, enhanced disaster response, urban navigation, and surveillance can be significantly improved. However, enabling effective communication for knowledge fusion necessitates the intrinsic optimization of performance metrics like energy consumption, resource allocation, latency, and computational overheads to enhance autonomous efficiency. Furthermore, designing robust security features is essential to safeguarding privacy, control, and operational integrity. This paper explores a novel collaborative knowledge-sharing (KS) framework that leverages 6G and edge-computing capabilities to facilitate the cooperative training of decentralized machine learning models among multiple UAVs, without the need to transmit raw data. This framework aims to enhance the learning experience and operational efficiency of autonomous vehicles. The DECKS (distributed edge-based collaborative knowledge-sharing) architecture enables Federated Learning (FL) within UAV networks, allowing local models to be trained and shared among neighboring UAVs for creating global models. This promotes intelligent knowledge aggregation without a central entity, enhancing collaborative capabilities among autonomous vehicles. The DECKS architecture efficiently extracts and distributes collaborative shared experience to ground vehicles through edge and direct inference, reducing energy consumption, latency, and computational overhead. Our simulation analysis demonstrates that the DECKS architecture has the potential to reduce energy consumption by 70% in sensorless vehicles and improve autonomous vehicle learning performance by 15% compared to centralized approaches in a distributed environment. This improvement is achieved by comparing the efficiency of systems with and without aggregated knowledge, as well as with a centralized system.

In mobile opportunistic networks, messages are transmitted through opportunistic contacts between nodes. Hence, the successful delivery of messages heavily relies on the mutual cooperation among nodes in the network. However, due to limited network resources such as node energy and cache space, nodes tend to be selfish, and they are unwilling to actively participate in message forwarding. In response to this challenge, lots of incentive mechanisms have been proposed. However, most of them rely on single incentives, there are issues such as inadequate handling of selfish nodes and vulnerability to malicious attacks, which ultimately lead to poor incentive effects. Therefore, in this paper, a Dual Incentive mechanism based on Graph attention neural network and Contract (DIGC) is introduced to encourage active participation of network nodes in data transmission. This incentive mechanism is divided into two steps. In the first step, the graph attention neural network is used to evaluate the reputation of nodes to achieve the goal of reputation-based incentive, and blockchain is employed to store and manage node reputation to ensure security and transparency. In the second step, an incentive based on contract theory is introduced, where personalized contracts were designed based on the different resources owned by nodes, thereby establishing a reward mechanism to encourage collaborative transmission. Extensive simulations based on two real-life mobility traces have been done to evaluate the performance of our DIGC compared with other existing incentive mechanisms. The results show that, our proposed mechanism can greatly improve throughput and reduce average delay while ensuring the overall delivery performance of the network.

In massive multi-input multi-output (MIMO) systems, it is necessary for user equipment (UE) to transmit downlink channel state information (CSI) back to the base station (BS). As the number of antennas increases, the feedback overhead of CSI consumes a significant amount of uplink bandwidth resources. To minimize the bandwidth overhead, we propose an efficient parallel attention transformer, called EPAformer, a lightweight network that utilizes the transformer architecture and efficient parallel self-attention (EPSA) for CSI feedback tasks. The EPSA expands the attention area of each token within the transformer block effectively by dividing multiple heads into parallel groups and conducting self-attention in horizontal and vertical stripes. The proposed EPSA achieves better feature compression and reconstruction. The simulation results display that the EPAformer surpasses previous deep learning-based approaches in terms of reconstruction performance and complexity.