International Journal of Communication Systems最新文献

This article introduces a compact quad-port wideband MIMO antenna intended for the globally popular 5G n77/78 NR bands. The proposed MIMO antenna is dedicated for densely packed 5G smart devices and beyond. A simple printed monopole antenna with a U-shaped slot and a slit positioned at the second radiating edge is used as the antenna unit element (29.72 × 29.72 mm²) for the 2 × 2 MIMO. The proposed antenna has an overall dimension of 67.21 × 67.21 mm², fabricated using the commercial FR4 substrate of thickness 1.57 mm. The orthogonally fed radiators in MIMO demonstrate polarization-diverse feature responding to dual linear polar component (dual LP) (horizontal and vertical). The spatial diversity nature of the proposed four-port MIMO is met through the novel hash-like shaped metamaterial-inspired rotationally symmetric decoupling element (DE). The DE, through its meticulous design and positioning at the functional plane (co-plane) of the MIMO radiator, helped maintain a minimal port isolation (s_ij, where i ≠ j) of > 20 dB throughout the −10-dB impedance bandwidth of 1.66 GHz (2.58–4.24 GHz), centering the popular 5G NR band of 3.5 GHz. The DE even aided improvement in reflection coefficient (s_ij, where i = j) from −19.87 to −44.01 dB, along with improved maximum radiation gain of 4.28 dBi at 3.5 GHz, with negligible impact toward the antenna efficiency metrics. The associated MIMO metrices, such as envelope correlation coefficient (ECC) (< 0.0001) and diversity gain (DG) (> 9.99 dB), estimated from far field results also complied to the acceptable range. Above all, the measured outcomes of the proposed antenna comparably mimic the simulation results carried out using Computer Simulation Technology (CST). In addition, equivalent circuit model for the complete four-port MIMO with the DE realized in Advanced Design System (ADS) is also reported in this article. Further, the proposed MIMO antenna is also verified for its viability to real-world device integration and specific absorption rate (SAR) analysis in the CST simulator.

The evolution of 5G mmWave technology has significantly advanced wireless communication by enabling ultrafast data transmission and reduced latency. The integration of large-scale multiple-input multiple-output (MIMO) systems has improved spectral efficiency and supported high user density for more robust and scalable network infrastructures. The interference and dynamic channel fluctuations present substantial obstacles in multicellular systems. User mobility complicates effective beam forming. In this manuscript, Performance Enhancement of 5G MIMO Antennas utilizing Verifiable Convolutional Neural Network optimized with Human Evolutionary Optimization Algorithm (5G-MIMO-VCNN-HEOA) is proposed. The different antenna characteristics for the proposed antenna are analyzed by using optimization and parametric analysis through high frequency electromagnetic solver tool (Ansys HFSS). Then, the suppression of mutual coupling among MIMO antenna elements and enhancement of the isolation are achieved by using different techniques and the fabrication and verification of the proposed model by designing the prototype. Then, the Verifiable Convolutional Neural Network (VCNN) is used for design and development of MIMO Antenna in 5G applications. Human Evolutionary Optimization Algorithm (HEOA) is implemented for optimizing the VCNN hyper parameters. The proposed 5G-MIMO-VCNN-HEOA accurately displays the outcomes of the design. The proposed technique is simulated, and efficiency is examined under several performance metrics like variance score, R square, mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). The proposed 5G-MIMO-VCNN-HEOA approach attains 15.21%, 18.11%, and 16.22% lower MAE; 17.13%, 14.18%, and 14.25% lower MAPE; and 15.16%, 18.12%, and 21.23% lower MAE when compared with existing methods like broadband high gain performance MIMO antenna array in 5Gmm-wave applications (BHG-MIMO-5G), compact and highly effective four-port MIMO antenna directivity prediction for 5G mmwave applications (DP-MIMO-5G), and MIMO rectangular dielectric resonator antenna in 5G NR mmwave (MIMO-RDRA-5G), respectively.

WSNs play a pivotal role in enabling ubiquitous data collection in many areas including environmental monitoring, smart infrastructure, and industrial automation. Despite their benefits, WSNs are limited by the scarcity of energy sources and are extremely vulnerable to link failures, buffer overflow, and random SN failures. Such problems tend to cause more packet loss, transmission delays, and shorter network life. To resolve these concerns, this work proposes a smart hybrid routing framework, which is the combination of Learning Automata (LA), Genetic Algorithms (GA), and Machine Learning (ML) to achieve reliable data delivery and enhance the energy efficiency of the network. In the initial phase, LA is applied to create a context-sensitive population of candidate routes based on the SN's parameters like proximity, residual energy, link quality, and buffer occupancy. Furthermore, GA is used to optimize these paths as directed by a multiparameter fitness function. This framework is uniquely designed as it employs ML in the routing process to achieve: (i) predictive link quality estimation with supervised learning, (ii) predicting energy depletion and buffer congestion with time-series models, (iii) dynamic adaptation of fitness function weights with reinforcement learning, and (iv) anomaly detection with unsupervised learning to isolate unstable or compromised SNs. Simulation results also verify that the proposed ML-enhanced LA-GA framework is energy efficient, optimizes the delay in packet delivery, diminishes the retransmission overhead, as well as boosts the network life when compared to the traditional routing schemes based on GA.

In the wireless sensor network, different spatially distributed sensors are used to sense, integrate, and transfer data for further evaluations. Several traditional approaches struggle with a few major difficulties like inefficient data routing and static cluster head selection. Therefore, this paper proposes a novel optimization approach termed the Lévy flight–boosted starfish optimization model for an improved low-energy adaptive clustering protocol. This model integrated the Lévy flight mechanism and the starfish optimization algorithm for effective global search ability and cluster head selection. The output of experiments conducted revealed that the proposed model attained robust performance compared to existing models with a throughput of 4 Mbps and a packet delivery rate of 98.6%. Overall, the proposed model exhibits significant efficiency in enhancing the performance of wireless sensor networks, making a model optimal for long-term and large-scale applications.

The lack of unified medical health record systems necessitates the development of large-scale electronic health record (EHR) systems. Blockchain-based frameworks are efficient when it comes to processing massive sensitive data and reliable data-sharing mechanisms. This paper presents a novel proof-of-trust (PoT) consensus algorithm for a blockchain-based healthcare framework (Health Chain) to offer secure and trustworthy data sharing. The consensus mechanism is formulated with fine-grained access control and different encryption techniques (post-quantum verifiable random function (PQVRF) algorithm and walrus-based sidechaining model). The distributed data storage from blockchain utilizes the consortium chain-based Hyperledger framework integrated with the interplanetary file system. The paper presents a PQVRF algorithm that can withstand quantum attacks and modify the consensus algorithm based on random functions to result in rapid and reliable consensus. The access and writing delays for the consensus algorithm associated with different EHRs are controlled via the walrus-based sidechaining algorithm. The proposed framework validates the EHRs and blocks with minimal computational time. The proposed consensus algorithm is designed based on different objectives. The first objective is to offer scalability to support millions of users. The second objective is to overcome collusions and adversary attacks by designing Byzantine and unfaithful fault tolerance. The third objective is to offer comprehensive control to the user over their health data to ensure that the user's access is maintained as per their preferences. When compared with the existing techniques such as PoTE, IB, HBZKP, and MrBlock, the proposed model offers an improvement of up to 35% in data access times, 42% in interoperability, and 2% in data breaches; as per the results, we can infer that the proposed model offers authorized access to the user data, improved data scalability, data integrity, and data privacy. Data security is achieved by storing encrypted hashes of the EHR while sharing and retrieving them among different end-users in the healthcare network. Although the proposed framework adopts post-quantum cryptographic primitives for consensus formation, trust evaluation, and leader election, SHA-2 is retained exclusively for lightweight EHR data hashing and integrity verification. This design choice does not compromise post-quantum security, as SHA-2 remains resilient under known quantum attack models when used for hashing.

In Internet of Things (IoT)–based wireless sensor network (WSN), mobile data collectors (MDCs), which move over various geographic regions to transport data from sensors to access points, are thought to be a more effective way than the traditional data collection techniques employing static sinks. The direct transfer of data from all sensors to the base station would be inefficient given the energy constraints on the sensor node. This is a result of the data redundancy brought about by nearby sensors' relatively strong correlation. Moreover, base stations are unable to handle the enormous volumes of data produced by a larger sensor network. In order to integrate data and generate meaningful information at sensors or intermediate nodes, specific networks are therefore needed. In this paper, tour path scheduling using optimized deep reinforcement learning (DRL) for MDCs in IoT-WSN. The DRL algorithm schedules the visiting pattern of the MDCs based on the type of IoT sensors and their data generation rates. To accelerate the convergence speed of DRL, sunflower optimization (SFO) algorithm is used. Then, optimum tour paths are determined using Capuchin Search Algorithm (CapSA) based on path stability and data collection latency. Simulation results have shown that DRL–SFO–CapSA minimizes the data collection delay and packet drop while maximizing the packet delivery ratio and residual energy.

Cognitive Radio—Wireless Sensor Network (CR-WSN) plays a vital role in spectrum utilization by allowing secondary users (SUs) to utilize the under-used licensed bands in an opportunistic manner. However, spectrum sensing accuracy is often affected by various channel perturbations such as multipath fading channel, noise, and interference. In this paper, we propose an Adaptive Spectrum Sensing Switching (ASSS) technique where the SU Cluster Head (SU-CH) in each cluster adaptively switches between Energy Detection (ED) and Matched Filter (MF) sensing methods to improve the detection accuracy of SU nodes. The proposed ASSS method uses Received Signal Strength Indicator (RSSI) as a metric where the SU nodes report their sensing results to their cluster heads, and then the SU-CHs take an appropriate decision based on the channel conditions. During poor channel conditions, the SU-CH nodes employ MF-based detection for its robustness against fading and noise, leading to optimal PU node detection. On the other hand, during good channel conditions, ED is employed, resulting in reduced energy consumption and computational complexity. The simulation results prove that at low SNR, the proposed ASSS method without Bayesian threshold significantly improves detection probability on average by about 78% for ED and inferior by slightly about 14.28% for MF and 40% for the proposed ASSS method with Bayesian threshold, thereby overcoming the trade-off in energy consumption by achieving an energy efficiency of 60.79% lesser than ED, 35.95%, and 18.87% more energy efficient on average compared to MF and proposed ASSS method with Bayesian threshold based approaches.

In the past few years, due to the massive growth of IoT-related devices in an interconnected ecosystem, serious attacks like distributed denial of service (DDoS), spoofing, sinkhole, and ransomware attacks have been observed. These extend from data breaches and privacy violations to several other types of cyber-attacks. Therefore, this paper proposed a novel type of clustering-based Tree Hierarchical Deep Convolutional Neural Network (TH-DCNN) model with Upgraded Human Evolutionary Optimization Algorithm (UHEOA) as an additional dimension for safeguarding the IoT from such attacks. It utilizes an Improved Soft-K-Means (IS-K-Means) algorithm to effectively cluster the IoT nodes in order to optimize resource utilization. The TH-DCNN guarantees efficient security by way of effective malicious attack recognition, whereas UHEOA adapts model parameters to operate at its best. The proposed TH-DCNN-UHEOA framework is tested in a simulation environment implemented using Python with 500 IoT nodes on a 4000 × 3600 m terrain area for 7 h under random mobility, with broadcast transmission and node restriction. The proposed framework achieves outstanding improvements compared with the state-of-the-art progress, including DNN-CL-IoT, Co-FitDNN-IoT, and CNN-TSODE-IoT. The proposed TH-DCNN-UHEOA achieves a packet delivery ratio (PDR) of up to 25.04%, a network lifetime (NLT) of up to 19.56%, and a detection accuracy of up to 26.76% higher compared with these baselines. All the parameters such as energy consumption, communication cost, throughput, PDR, NLT, energy consumption (EC), number of alive sensor nodes (NAN), accuracy, and number of dead sensor nodes (NDN) determine its efficiency, certifying the framework can repel malicious attacks like DDoS, spoofing, and sinkhole attacks, providing strong security to IoT systems.

A broadband dual-beam dual-polarized antenna array having low sidelobe and controllable beam pointing is proposed. A crossed-dipole antenna element with link lines is introduced to achieve excellent impedance matching, symmetrical and stable radiation pattern, low cross-polarization level, as well as very steady gain at 1.7–2.7 GHz. Based on the element, a new staggered 2 × 2 subarray is proposed to achieve excellent sidelobe suppression over the wide operating band. Then a dual-beam subarray, which consists of two staggered 2 × 2 subarrays and controllable metal fixtures, is designed to introduce dual-beam performance with low sidelobe. The dual-beam pointing can be easily controlled by tuning the metal fixtures. Finally, a dual-beam dual-polarized array is proposed to obtain high gain for practical application. It obtains a wide bandwidth of 45.5% (1.7–2.7 GHz) for reflection coefficient < −14 dB, and good isolation between all the ports larger than 21 dB. The array also has good dual-beam performance, with sidelobe levels below −20 dB and beam-pointing angles that can be varied to ±18°, ±28°, and ±38°.

Millimeter-wave (mmWave) communication plays a crucial role in wireless systems due to its high data rate capabilities and suitability for 5th generation (5G) networks. However, mmWave signals confront significant propagation issues, which include greater path loss, major attenuation from blockages, and sparse multipath propagation, constraining coverage and consistency. Accurate path loss validation is a serious and composite task for successful network planning, optimization, and resource allocations. To overcome these limitations, effective deep learning–based path loss estimation in mmWave communication systems is developed in this research work. Initially, the required data are collected from the standard datasets and given to the preprocessing phase. Once the data are preprocessed, they are given into the deep feature extraction phase, and it is done by applying the Pyramid Multihead Convolutional Cross Attention Network (PMC-CANet). The ability to ensure the efficiency of next-generation wireless networks is what makes it effective in feature extraction tasks. Finally, the path loss estimation process is performed on the extracted deep features through Adaptive Residual Bidirectional Gated Recurrent Unit (AR-BiGRU), where several parameters are tuned using the Updated Random Attribute–based Sculptor Optimization (URA-SO). One of the primary advantages of using AR-BiGRU with USOA for path loss estimation is its ability to process large, high-dimensional datasets, which can include not only geographical and environmental information but also temporal data, such as time-of-day or seasonal variations in path loss. The optimal solution outcome can be achieved by using the developed model. Then, its effectiveness is validated by comparing it with other existing models. This proposed system provides a consistent and best solution for tackling the problems of mmWave signal attenuation, thus enhancing the effectiveness and performance of next-generation wireless networks. The outcomes reveal that the proposed URA-SO-AR-BiGRU obtained an accuracy of 97.12% when taking the batch size as 15, leading to highly reliable and precise path loss estimations.