IET Communications最新文献

With the rapid development of communication technology and the increasing demand for coverage refinement in wireless communication networks, the optimization of wireless communication networks is faced with unprecedented challenges. Obtaining the signal distribution map of wireless communication networks efficiently has become a popular area of study in this field. This paper considers a distributed sensing network architecture, a radial basis function neural network is used to process electromagnetic data and optimize the parameters of the random forest model. Then, interpolation processing of incomplete electromagnetic data is achieved by the improved random forest model, based on which a signal distribution map of the wireless communication network is reconstructed. The results indicate that the proposed algorithm yields high interpolation accuracy. The average error between the real signal distribution and the reconstructed signal distribution is 2.7973 dBm when the proportion of sampled nodes is 1%, and the similarity of the reconstructed signal distribution map to the original signal distribution map is good, demonstrating certain application prospects.

A novel frequency regime of the spectrum from 0.1 to 10 THz enables an emerging paradigm of modern wireless communication referred as 6G communication networks. However, to assure the success of 6G communication networks, ever-growing development and deployment of nano-machines, leading to ultra-dense nano-network, is witnessed. Nevertheless, the potential feature analysis of such ultra-dense nano-network in the presence of interference limited scenarios, by virtue of ultra-dense deployment of nano-machines, under the performance improvement techniques is missing from the reported literatures. Therefore, in this article, several performance metrics for an ultra-dense nano-network under the selection combining (SC) diversity technique is presented. The analytical expressions for error rates of different modulation schemes such as BPSK/BFSK, DPSK/ NFSK, Q-PSK and M-QAM under SC diversity technique for the considered nano-network in the presence of interference limited ecosystems are presented. In addition, analytical expressions of capacity under constant power with optimal rate adaptation (C_ora) and capacity under channel inversion with fixed rate (C_cifr) are explored. It is worthy to mention that the proposed analytical expressions are generic in nature for the considered scenarios, in which severity and shaping parameters in both the multipath fading and shadowing are included. Numerically simulated results support mathematical formulation.

The original online fountain codes discard a large number of symbols that do not meet the requirements at the decoder. To improve channel utilization, this article proposes a new online fountain code. In the completion phase, the proposed code improves the receiving rules of encoded symbols, that is, the encoded symbols discarded in the original online fountain codes are selectively cached. Moreover, an optimal degree selection strategy of encoded symbols is obtained in the proposed scheme. The valid degree range of the proposed strategy is also analyzed, leading to an upper bound of cached events which eventually limits the number of feedbacks. The theoretical analysis and simulation results reveal that the proposed scheme outperforms two state-of-the-art online fountain codes in terms of overhead factors, number of feedback transmissions, and encoding/decoding efficiency.

Smart device manufacturers rely on insights from smart home (SH) data to update their devices, and similarly, service providers use it for predictive maintenance. In terms of data security and privacy, combining distributed federated learning (FL) with blockchain technology is being considered to prevent single point failure and model poising attacks. However, adding blockchain to a FL environment can worsen blockchain's scaling issues and create regular service interruptions at SH. This article presents a scalable Blockchain-based Privacy-preserving Federated Learning (BPFL) architecture for an SH ecosystem that integrates blockchain and FL. BPFL can automate SHs' services and distribute machine learning (ML) operations to update IoT manufacturer models and scale service provider services. The architecture uses a local peer as a gateway to connect SHs to the blockchain network and safeguard user data, transactions, and ML operations. Blockchain facilitates ecosystem access management and learning. The Stanford Cars and an IoT dataset have been used as test bed experiments, taking into account the nature of data (i.e. images and numeric). The experiments show that ledger optimisation can boost scalability by 40–60% in BCN by reducing transaction overhead by 60%. Simultaneously, it increases learning capacity by 10% compared to baseline FL techniques.

To ensure the reliable transmission with increased spectral efficiency of the small information block, this article proposes the novel distributed generalized Reed-Solomon coded differential spatial modulation (DGRSC-DSM) scheme in the relay cooperation manner. Two different generalized Reed-Solomon (GRS) codes with identical lengths are deployed at the source and relay, respectively, where the relay utilizes the subcode of the GRS code, which can construct an ultimate code having a unique algebraic structure at the destination. In addition, the Global search algorithm (Algorithm 1) is developed to obtain an optimal selection mode at the relay, resulting in the final code with an optimal code weight enumeration. Since the complexity of this algorithm is extremely high, the low-complexity Parity-position local search algorithm (Algorithm 2) is then proposed to get the suboptimal mode. Monte-Carlo results indicate that there is a minimal disparity in performance between the two proposed algorithms that both effectively enhance the system performance. Moreover, based on the optimized final code and the special construction of GRS code, the novel Smart-joint decoding algorithm is first presented at the destination which can fully exploit two-way information data and further lower the bit error rate (BER) of the DGRSC-DSM scheme. Also, the analytical union bound of the proposed scheme is formulated over the quasi-static Rayleigh fading channel, which illustrates a tight fit at the high signal-to-noise ratio (SNR) regions.

A high-data-rate solution for M-ary differential chaos shift keying (MDCSK) based on carrier index modulation is proposed in this paper. At the transmitter, the proposed system employs index selectors, Hilbert transform, and MDCSK encoders. Walsh codes are used for separating different data-bearing wavelets. At the receiver, the proposed system adopts energy comparators and MDCSK detectors. The transmitted chaotic signals are duplicated for several times, and the averaging operation is performed on received signals to reduce noise. Theoretical bit error rate expressions are obtained over the AWGN and the multipath Rayleigh fading channels, respectively. Simulations and comparisons are performed to verify the effectiveness of the proposed scheme.

In recent days, the Internet of Vehicles (IoV) and its network of connected automobiles have revealed several new security risks. Classical intrusion detection systems face challenges in identifying intrusions due to the growing number of vehicles, the dynamic nature of IoV, and limited resources. A hierarchical clustering method allows dividing the IoV network into clusters. The elements that determine the outcome are the geographical proximity and the traffic density. It is called the Dynamic Hierarchical Intrusion Detection Framework (DHIDF) for the IoV. To protect infrastructure and passengers, an IoV-specific DHIDF using edge computing has been proposed. Because of this, anomaly detection and localised assessment of danger will become less required. The application of DHIDF on a large scale inside the ecosystem of IoV is not entirely out of the question. The term encompasses several subfields, including intelligent transportation networks (ITNs), smart city infrastructure, fleet management, transportation, and autonomous vehicle systems. The efficacy of DHIDF is assessed through simulations that replicate current and potential future threats, including those related to the Internet of Things. Analysis of key performance parameters, including response time, detection accuracy, asset utilization, and scalability, has been conducted to assess the system's feasibility and durability.

Reconfigurable intelligent surface (RIS)-assisted physical layer key generation plays an important role in the eavesdropping problem on a typical multi-user scenario of wireless networks. Existing RIS-assisted physical key generation technology mainly solved the security issues in the single-antenna and multi-user scenario, which is based on the idealistic assumption of a spatially independent channel model at the RIS. It limits the degrees of freedom and neglects the existence of channel spatial correlation, which would possibly degrade the sum key generation rate (SKGR). To solve the above problem, this paper proposes a secret key generation scheme for RIS-assisted multiple-input single-output multi-user system. This scheme establishes a spatial correlation channel model, which comprehensively considers the influence of spatial correlation among antennas of the base station (BS), multi-user, and RIS elements on SKGR. Based on this, a key generation framework is designed to generate a more secure secret key with the maximum SKGR. Specifically, a closed-form SKGR expression and an optimization problem formulation are obtained to obtain the joint optimal coefficient of the BS and RIS. Then, the difference convex-successive convex approximation based alternating optimization algorithm is proposed to solve this optimization problem. The simulation results show that the proposed scheme has improved 10-bit/channel use compared with the randomization scheme of BS precoding and RIS reflection coefficient. It also has an improvement of 12-bit/channel use compared with the spatially independent scheme.

Traffic prediction serves as a critical foundation for traffic balancing and resource management in Low Earth Orbit (LEO) satellite networks, ultimately enhancing the efficiency of data transmission. The self-similarity of traffic sequences stands as a key indicator for accurate traffic prediction. In this article, the self-similarity of satellite traffic data was first analyzed, followed by the construction of a satellite traffic prediction model based on an improved Long Short-Term Memory (LSTM). An early stopping mechanism was incorporated to prevent overfitting during the model training process. Subsequently, the Diebold-Mariano (DM) test method was applied to assess the significance of the prediction effect between the proposed model and the comparison model. The experimental results demonstrated that the improved LSTM satellite traffic prediction model achieved the best prediction performance, with Root Mean Squared Error values of 18.351 and 8.828 on the two traffic datasets, respectively. Furthermore, a significant difference was observed in the DM test compared to the other models, providing a solid basis for subsequent satellite traffic planning.

The accuracy analysis of dictionary sparse representation for channels in massive MIMO systems is a relatively unexplored field. Existing research has primarily focused on investigating the accuracy of dictionary sparse representation using simulation in massive MIMO systems, but has not provided quantitative accuracy analysis. To address this gap, the correlation numerical proportional factor is proposed to represent the accuracy performance of non-zero elements in the coefficient matrix. Additionally, a qualitative analytical formula for dictionary sparse representation accuracy is provided and an optimal upper bound for the correlation numerical proportional factor is established. Furthermore, the innovation indicates that the accuracy of dictionary sparse representation is mainly influenced by the cross-correlation between the pilots matrix and the dictionary matrix, as well as sparsity. The author has also developed a method for minimizing the correlation numerical proportional factor. In order to obtain an optimal sparse representation coefficient matrix, a cross-correlation matrix is constructed and an analytical expression is derived for it as well as its use as an optimal hard decision threshold is determined. Finally, a sparse representation coefficient optimization algorithm is proposed using this optimal threshold. Simulation results demonstrate that this algorithm can significantly improve channel sparse dictionary representation accuracy.