查看更多>>摘要：In this paper, an intelligent reflecting surface (IRS)–assisted non-orthogonalmultiple access (NOMA) network is proposed in amulti-userscenario where the transmitter communicates with its receiver covertly by helping a friendly jammer. In this network,an adversary detects the communication existence of the users in the frequency band while the jammer sends the jamming signalsto the adversary to degrade its performance. In this case, the analytical expressions for the secrecy outage probability (SOP),false alarm probability, and the missed detection probability at adversary are obtained. Rayleigh fading channel is assumedas the channel model while the covert communication performance is improved. For this purpose, the total effective rates aremaximized by optimization of the transmission power, power allocation to multiple users, IRS reflection matrix, and also transmissionprobability adjustment with constraints on the detection performance and SOP. The problem is non-convex;therefore,we present the genetic algorithm (GA) method to find the suboptimal solution for the problem with lower complexity. Numericalresults show the performance improvement of the proposed algorithm in comparison to the benchmark algorithms.
查看更多>>摘要：Radio frequency energy harvesting (RFEH) presents a sustainable and maintenance-freesolution for powering wireless sensornetworks and Internet of Things (IoT) devices by converting ambient RF signals into usable electrical power. The previouslyleading single-bandRFEH technology was limited to one frequency only, posing a risk of unproductive operation when thatfrequency is unavailable. This paper examines and explores the different strategy of wideband RFEH system and provides anoverview of the latest advancements in wideband rectennas. The paper incorporates a comprehensive categorization of widebandrectennas that outlines the antenna type, rectifier topology, diode type, impedance matching network, and power conversion efficiencyemployed in each wideband rectenna design. The incorporation of a detailed table including various parametric study enrichesthe review with empirical data, thereby enhancing its practical significance. The discussion of design challenges, rectifiertopologies, and methods to boost power conversion efficiency addresses crucial technical aspects of wideband rectifiers, fillinggaps in current research literature. By combining a thorough examination of design challenges and various methodologies, thepaper proves to be a valuable asset for enhancing the effectiveness and adaptability of wideband rectifiers in RFEH applications.
查看更多>>摘要：The ability of orthogonal frequency division multiplexing (OFDM) to counteract frequency-selectivefading channels has made ita popular modem technology in contemporary communication systems. But maintaining dependable signaling is still difficult,especially when the signal-to-noiseratio (SNR) is low. In order to increase the dependability of OFDM systems, this study presentsan enhanced LSTM-basedautoencoder architecture. The suggested autoencoder efficiently utilizes temporal dependenciesand reduces the impacts of channel distortion by encoding and decoding OFDM signals utilizing one-hotencoding employinglong short-termmemory (LSTM) networks. The outcomes of the simulation show notable gains in performance indicators. Theaverage block error rate (BLER) of the suggested model is 0.0150, as opposed to 0.0296 for traditional autoencoders and 0.0886 forconvolutional OFDM systems. Comparably, the average packet error rate (PER) is decreased to 0.0017, surpassing convolutionalOFDM systems' 0.2260 and traditional autoencoders' 0.0070. These outcomes highlight the LSTM-basedautoencoder's efficacyin enhancing OFDM systems' dependability, especially in demanding settings. This study lays the groundwork for employingcutting-edgedeep learning methods to create reliable and effective communication systems.
查看更多>>摘要：The increasingly complex modern communication environment poses challenges for automatic modulation recognition (AMR)techniques. In AMR tasks, in order to more comprehensively capture signal features and improve recognition performance, wepropose a model named Multiscale Mobile Inverted Bottleneck Convolution and Manhattan Self-AttentionNetwork (3M-Net).In this 3M-Net,the MSMB block is designed to extract multiscale local features of the signals, and the MMG block is designedto enhance global information modeling of the model. Then, a hierarchical backbone that contains the two blocks is designed toextract multilevel features. Extensive experiments on the RML2016.10a and RML2018.01a datasets demonstrate that the 3M-Netmodel achieves superior recognition performance.
Karrar Shakir MuttairOras Ahmed ShareefHazeem Baqir Taher
e70105.1-e70105.39页
查看更多>>摘要：Artificial intelligence (AI)-aidedcommunications have gained significant traction in recent years due to the widespreadapplication of machine learning (ML) and deep learning (DL) machines with algorithms to solve math problems in wirelesscommunications. This study offers an overview of the use of ML models in antenna design and optimization. Thisincorporates DL on ML frameworks, categories, and structure to get practical and broad insights using ML techniques forhigh throughput, quick data analysis, and prediction. This article also comprehensively reviews recent research papers onantenna design via ML. This includes an analysis of several ML algorithms that have been applied to produce antenna parameterssuch as the reflection coefficient (S-parameters),efficiency and gain values, and radiation patterns of the antennas.However, the current antenna design's structure, variables, and external factors remain complex. In addition, the expenseof time and processing resources is inescapable and unacceptable to most designers. ML-basedantennas have been createdto increase antenna modeling efficiency and accuracy to solve these challenges. Techniques for modeling data may be usedto predict the performance of an antenna for a certain set of antenna factors of design. As a result, this study highlights themost sophisticated applied ML techniques that have been presented to increase antenna modeling efficiency and accuracy.The results demonstrate that AI, ML, and DL may minimize simulation needs, predict antenna behavior, and reduce timewith high accuracy.
查看更多>>摘要：This article presents an overview of the utilization of substrate-integratedwaveguide (SIW) technology in the design of variousmicrowave circuits and other components. SIW is one of the promising and emerging technologies for designing various high-frequencycomponents because of its high performance and cost-effectivesolutions. The proposed article provides insights intothe very basic structure and working of the SIW and its use cases in the design of various components for various applications.The very significant use of SIW is in the antenna design, which includes the cavity-backedslotted antennas, antenna arrays, andphased array antennas. Moreover, this article also discusses other applications of the SIW that include power dividers, diplexingantennas, the use of empty SIW, and multilayer organic antenna in package technologies. The article provides more valuableinsights in terms of presenting an exhaustive analysis of all the key performance indicators of all the components. At last, the surveyalso proposed the challenges and future direction regarding SIW technologies in the realization of microwave components.
查看更多>>摘要：Due to the massive usage of smartphones, frequent usage of the IoT, and wireless visual streaming services, data traffic in thewireless network and data explosion has increased over the next years. System modeling and channel estimation are the twomain challenges while designing the wireless 5G MIMO communication system. A 2 × 2 MIMO-SFBCsystem is proposed toenhance the spectral efficiency and capacity of wireless communication systems by exploiting spatial diversity and frequencydiversity. The SFBC coding technique gives a low bit error rate (BER) and high signal-to-noiseratio (SNR). Channel modelingand channel estimation are very difficult tasks in the complex propagation characteristics of highly dynamic channels. Thispaper proposes an improved ERNN-LSTMnetwork to enhance the accuracy and efficiency of channel modeling and estimationin wireless communication systems. Initially, a least squares estimator is employed to obtain an initial estimate of the historicalchannel responses of a pilot block. These initial estimates are subsequently utilized to train an Elman recurrent neural network(ERNN). The weights of the ERNN's channel parameters are optimized using the Adaptive Crocodile Algorithm. Simulation resultsshow that the proposed ACO-DERNNmethod achieves a BER of 10~(−5) at 30 dB SNR, outperforming conventional methods.
查看更多>>摘要：Wireless body area networks (WBAN) containing wearable sensing medical devices have enticed huge attention by providinghigh-qualitymedical services to people without restraint in their day-to-dayactivities. WBAN monitors elderly people or patientssuffering from any long-lastingdiseases from their place without being hospitalized, saving critical time transportation delaysand admission costs. Wireless medical devices are attached or implanted in the human body to sense medical-relateddata andfurther transmit it for medical services in an unsecured wireless medium. These sensing medical devices are miniature-sizedwith a limited battery source, so energy should be exploited carefully. The sensing devices deplete their energy more during datatransmission. Efficient energy exploitation and assuring data security and privacy in WBAN are highly recommended. Data tobe transmitted from the sensing device are compressed before transmission to reduce the number of data transmissions and saveenergy. In this paper, to attain efficient energy exploitation, Lightweight Fixed Binary Golomb (LFBG) data compression is performedat each sensor before transmission, and further to guarantee the privacy and security of the data, Lightweight SymmetricMutual Authentication (LSMA) protocol is implemented. The LFBG compression with the least computation saves up to 84% ofenergy, and LSMA with the least computation and transmission authenticates and also shares the session key securely.
查看更多>>摘要：The Spectral Energy Efficiency (SEE) is the concrete feature of future generations of wireless systems. It is in turn dependentupon the System User-Achievable-DataRate (SAR). The SAR of the current generation systems can be enhanced by use of LargeIntelligent Surfaces (LIS). They implement a pane of reflecting antennas made up of meta-materials.These panels are mountedon any architectural structure like apartments, schools/colleges etc. The beauty of LIS is that they can be trained by means ofmachine learning models to reflect the incoming electro-magneticsignal towards the required direction that can increase the receivedsignal strength at the receiver. This increased signal strength at the receiver further boosts the Signal to Noise ratio (SNR)and SAR. This paper implements a Reinforcement Learning (RiL) based customized loss model in a Recurrent Neural Network(RNN) model to enhance the SEE of the LIS based systems. The dataset required for training and validation of DL model isproduced from the publicly available ray tracing based DeepMIMO generator. The simulation findings demonstrate that thesuggested RNN-RiLmodel exhibits an enhancement of 1.14 bps/Hz in SAR, and an improvement of 2.75 Mbits/J enhancementin the SEE when compared to the baseline technique. This rise in the SEE can be useful in inculcating more number of users persec while maintaining the Quality of Service (QoS) thus enabling energy harvesting in LIS.
查看更多>>摘要：This article explains the layout and examination of two-portprinted radiator with a metasurface (MS) absorber wall in betweenthe port and a suspended MS over the designed radiator. The unique characteristics of designed printed multi-inputmulti-output(MIMO) radiator are as follows: (i) Absorbing wall in between the port advances the separation level among the closely spacedantennas by 25 dB and (ii) suspended MS made up with double negative metamaterial unit cell, which adds two importantfeatures, that is, high gain (up to 8.0 dBi) and converts the linear wave into the circularly polarized waves (5.31–5.65 GHz) in betweenworking frequency range. Experimental outcomes have decent promise with optimized simulated findings. The plannedaerial works effectively in between 5.2 and 5.95 GHz with above 25 dB separation. Decent assessment of MIMO parameter andstable radiation features confirms its applicability for sub-6.0GHz-based5G communication system.
NETL
NSTL
Wiley