IET Communications最新文献

The channel in massive multiple-input multiple-output systems is fast-varying that the pilot signal needs to be sent frequently. To obtain timely channel state information with less pilot overheads, a symbol detection aided channel prediction scheme is proposed in this paper. Then, the prediction error lower bound of the proposed scheme within one interval of effective prediction is analysed. Besides, the approximate close-form post-processing signal to noise ratio is derived for zero-forcing detector with imperfect channel predictions. Numerical simulations are implemented to verify the validity of theoretical analysis. The results show that the theoretical expressions have a close match with the real simulated performance under various simulation parameter settings. In addition, the frequency of transmitting the pilot signals can be significantly reduced when adopting this proposed method. Moreover, the application of the proposed scheme can be further expanded when combining it with channel coding, thereby greatly improving the spectrum efficiency of the system.

The rapid development of the Internet of Things has exacerbated issues such as spectrum resource scarcity, poor communication quality, and high communication energy consumption. Automatic modulation recognition (AMR), a key technology in cognitive radio, has emerged as a crucial solution to these challenges. Deep neural networks have been recently applied in AMR tasks and have achieved remarkable success. However, existing deep learning-based AMR methods often need to consider the sensitivity of models to noise fully. This study proposes a masked autoencoder multi-scale attention feature fusion model (MAE-SigNet). This model integrates a MAE, multi-scale feature extraction module, bidirectional long short-term memory module, and MAM to accomplish the AMR task under low signal-to-noise ratio. Additionally, we optimize the cross-entropy loss of the MAE-SigNet model by introducing MAE decoder reconstruction error, which enhances the model's sensitivity to noise while achieving more accurate feature representation. Experimental results demonstrate that the MAE-SigNet model achieves average recognition rates of 63.77%, 65.28%, and 75.26% on the RML2016.10a, RML2016.10b, and RML2016.04c datasets. Mainly, MAE-SigNet exhibits outstanding performance at various levels of low signal-to-noise ratios from −6 to 4 dB.

To address the concerns of energy supply and spectrum scarcity for wireless devices, energy harvesting cognitive radio networks have been proposed. To improve spectrum utilization, secondary users (SUs) access the licensed spectrum in underlay mode, which may cause severe interference to primary users and SUs. The focus is on the underlay energy harvesting cognitive radio networks with multiple pairs of SUs, and formulate the long-term secondary throughput maximization problem as a mixed-integer non-linear programming problem. As traditional approaches could hardly solve the mixed-integer non-linear programming problem well, a centralized deep deterministic policy gradient (C-DDPG) approach is proposed that achieves satisfactory throughput performance. To reduce the computational complexity of C-DDPG, we further propose a clustering-based multi-agent DDPG (CMA-DDPG) approach that combines the advantages of the centralized deep reinforcement learning approach and the distributed deep reinforcement learning approach. In the CMA-DDPG, a novel interference-based clustering algorithm is proposed, which partitions the SUs that cause severe mutual interference into one cluster, and the sizes of state space and action space are smaller than those in C-DDPG. Numerical results validate the superiority of the proposed approaches in terms of the throughput and outage probability, and validate the clustering performance of the interference-based clustering algorithm in terms of the outage probability of the secondary network.

Currently, there is no unified Electronic Health Record (EHR) system connecting major healthcare organizations such as hospitals, medical centers, and specialists. Blockchain technology, with its unique features, provides an ideal platform for developing a large-scale electronic health record system. In this article, the authors introduce HealthChain, a novel blockchain-based secure EHR system that integrates advanced encryption techniques, a robust consent management system, cross-platform interoperability, and enhanced scalability. Unlike existing EHR systems, HealthChain allows patients to have comprehensive control over their health data, ensuring that access is strictly regulated according to their preferences. The experimental results demonstrate several significant improvements over traditional EHR systems. HealthChain reduces data access times by 30%, and its interoperability rate with various healthcare systems is 40% higher than that of other blockchain-based EHR solutions. Security is greatly enhanced, with HealthChain experiencing 50% fewer data breaches due to its advanced encryption and smart contract-based access controls. Moreover, patient satisfaction has increased by 35% as a result of better control and access to their health records. These findings highlight HealthChain as not only a feasible and effective solution for managing health records but also a significant advancement over existing systems.

The challenge of jointly representing both the uplink (UL) and downlink (DL) in massive multiple input multiple output (MIMO) systems have been tackled. Considering the angular reciprocity, a couple dictionary learning (CDL) support to enhance performance and address high complexity has been introduced. This approach minimizes the number of pilots and improves accuracy. Currently, accuracy analysis of UL/DL representation primarily relies on simulation. To bridge this gap, a proportion factor (PF) operator is proposed for CDL to assess accuracy. Specifically, a qualitative analysis formula is provided for accuracy and an optimal upper bound is established. Through theoretical proof, it is demonstrated that the accuracy of CDL for representation is mainly influenced by the cross-correlation between the pilot matrix and the dictionary matrix. Inspired by PF operator, an optimal couple dictionary learning (OCDL) algorithm using singular value decomposition (SVD) is introduced to obtain dictionary updating, aiming at high-precision representation. By establishing normalized mean squared error (NMSE), successful representation ratio, bit error rate (BER), and constellation performance, an OCDL algorithm that outperforms existing methods is showcased and channel representation accuracy is enhanced significantly.

Radio frequency fingerprint identification (RFFI) is a widely used technique for authenticating equipment. It identifies transmitters by extracting hardware defects found in the RF front end. Recent research has focused on the impact of transmitters and wireless channels on radio frequency fingerprint (RFF). Most work is based on the same receiver assumption, while the influence of the receiver on RFF remains unresolved. This paper focuses on the impact of receiver hardware characteristics on RFF and proposes a few-shot cross-receiver RFFI method based on feature separation. Data augmentation with noise addition and simulated channels addresses sparse sample issues and enhances the model's robustness to channel variations. Simultaneously, feature separation is realized by reducing the correlation between transmitter and receiver features through classification loss and similarity loss. We evaluate the proposed approaches using a large-scale WiFi dataset. It is shown that when a trained transmitter classifier is deployed on new receivers with only 30 samples per trained transmitter, the average identification accuracy of the proposed method is 83.6%. This accuracy is 9.45% higher than the baseline method without considering transmitter hardware influence. After fine-tuning, the average identification accuracy can reach 98.25%.

To reduce the impact of residual Doppler frequency offset (RDFO), a joint DFO estimation and CE scheme is proposed for the OFDM systems under the high-speed mobile environment. In the paper, the expression of interference power caused by the RDFO is first derived, and the effect of RDFO on time-varying characteristic of channel is analysed. Then, a joint DFO and channel estimation scheme is presented. Specifically, a high-precision DFO estimator based on the convolutional neural network with anti-noise is firstly designed. Due to its ability to use fewer samples to adapt well to the new environments, the meta learning is adopted to estimate the time-varying channel. Moreover, to improve the practicality of the algorithm, the non-ideal values rather than ideal values are used as the training targets in the two neural networks. Additionally, the proposed method is only based on the received signal and does not require any pilots or training sequences, which has higher transmission efficiency compared to the existing algorithms. The research results indicate that the proposed method has good estimation performance and good practicality, and it is suitable for high-speed mobile scenarios.

Coherent multi-band splicing is an optimal solution for extending existing band-limited communication systems to support high-precision sensing applications. Conceptually, the communication system performs narrow-band measurements at different centre frequencies, which are then concatenated to increase the effective bandwidth without altering the sampling rate. This can be done in parallel for multiple non-contiguous subbands or by hopping across the different bands. However, multi-band splicing poses significant challenges, particularly in compensating for phase offsets and hardware distortions before stitching the acquired samples, which can be distributed in contiguous or non-contiguous manners. This survey paper studies the state of the art in coherent multi-band splicing and identify open research questions. For beginners in the field, this review serves as a guide to the most relevant literature, enabling them to quickly catch up with the current achievements. For experts, open research questions that require further investigation are highlighted.

The radio map serves as a vital tool in assessing wireless communication networks and monitoring radio coverage, providing a visual representation of electromagnetic spatial characteristics. To address the limitation of low accuracy in current radio map construction method, this article presents a novel method based on Generative Adversarial Network (GAN), called ACT-GAN. This method incorporates the aggregated contextual-transformation block, the convolutional block attention module, and the transposed convolutional block into the generator, significantly enhancing the construction accuracy of radio map. The performance of ACT-GAN is validated in three distinct scenarios. The results indicate that, in scenario 1, where the transmitter locations are known, the average reduction in Root Mean Square Error (RMSE) is 14.6%. In scenario 2, where the transmitter locations are known and supplemented with sparse measurement maps, the average reduction in RMSE is 13.3%. Finally, in scenario 3, where the transmitter locations are unknown, the average reduction in RMSE is 7.1%. Moreover, the proposed model exhibits clearer predictive results and can accurately capture multi-scale shadow fading.

With the rapid expansion of the Internet of Things (IoT), cloud storage has emerged as one of the cornerstones of data management, facilitating ubiquitous access and seamless sharing of information. However, with the involvement of a third party, traditional cloud-based storage systems are plagued by security and availability concerns, stemming from centralized control and management architectures. A novel blockchain-IoT model that leverages blockchain technology and decentralized storage mechanisms to address these challenges is presented. The model combines the Ethereum blockchain, interplanetary file system, and attribute-based encryption to ensure secure and resilient storage and sharing of IoT data. Through an in-depth exploration of the system architecture and underlying mechanisms, it is demonstrated how the framework decouples storage functionality from resource-constrained IoT devices, mitigating security risks associated with on-device storage. In addition, data owners and users can easily exchange data with one another through the use of Ethereum smart contracts, fostering a collaborative environment and providing incentives for data sharing. Moreover, an incentive mechanism powered by the FileCoin cryptocurrency is introduced, which motivates and ensures data sharing transparency and integrity between stakeholders. Furthermore, in the proposed blockchain-IoT model, the proof-of-authority system consensus algorithm has been replaced by a delegated proof-of-capacity system, which reduces transaction costs and energy consumption. Using the Rinkby Ethereum official testing network, the proposed model has been demonstrated to be feasible and economical, emphasizing its potential to redefine IoT data management.