查看更多>>摘要：The growing demand for high-speed,reliable wireless communication has accelerated advancements in antenna technology for5G and IoT applications. However, existing antenna designs often face challenges such as limited bandwidth, inadequate gain,and poor impedance matching, which hinder their ability to meet the stringent performance requirements of modern networks.To address these limitations, this research presents a high-gain,wideband parasitic microstrip antenna designed for 5G and IoTapplications, operating at 26 GHz within the 5G new radio Frequency Range 2 (FR2) band n258. The proposed antenna incorporatesa miniaturized parasitic patch design featuring eight microstrip patches arranged around a centrally probe-fedactive patchin a squared configuration. These parasitic patches are electromagnetically coupled via the magnetic and electric fields generatedby the active patch, achieving a compact array with a total dimension of 24 × 24 mm~2. To enhance antenna performance,a Multi-LayerAttention Graph Convolutional Network (MLAGCN) is utilized to effectively extract key features from the inputdata, whereas the Gooseneck Barnacle Optimization (GBO) algorithm iteratively fine-tunesthe design parameters. The antennaachieves a maximum gain of 12 dB and an efficiency exceeding 95% within the frequency range of 23–28 GHz. This integrateddesign and optimization approach facilitates cutting-edgeperformance in terms of bandwidth, gain, and reliability, meetingthe rigorous demands of 5G/6G, IoT, and other next-generationservices, as well as extending network coverage. This researchproposes a compact parasitic microstrip antenna optimized for 5G/IoT using an MLAGCN and GBO. The MLAGCN captures interdependentantenna parameters, whereas GBO fine-tunesdesign variables for enhanced impedance matching and bandwidth.Results show significant improvement in return loss and wideband performance compared with conventional designs.
查看更多>>摘要：Efficient resource allocation in uplink non-orthogonalmultiple access Internet of Things (NOMA-IoT)networks is crucial formanaging interferences, adapting to dynamic user activities, and optimizing performance under power and channel constraints.However, the current approaches often struggle with a large number of devices, suffer from high complexity, and fail to allocateresources effectively in dynamic and resource-constrainedenvironments. The traditional methods often exhibit high computationalcomplexity leading to performance degradation as the number of connected devices increases. To overcome this limitation,a harmonic serval optimization algorithm_deep residual network (Har-SOA_DRN) for resource allocation in uplink NOMA-IoTnetworks is developed. The Har-SOAis established by combining serval optimization algorithm (SOA) and harmonic analysis.Firstly, the NOMA-IoTnetworks are simulated, followed by user grouping based upon DeepCluster considering channel gains,received power levels, and dynamic user activity. Next, the resource allocation is done utilizing DRN trained with Har-SOA.Theexperimental outcomes of Har-SOA_DRN demonstrate a superior sum rate of 274.211 Mbps, energy efficiency of 90.172 kbit/J,achievable rate of 412.778 Mbps, and throughput of 534.846 Mbps. The devised model allows the network to efficiently allocatethe uplink resources among a large number of devices without excessive computational overhead.
查看更多>>摘要：This paper proposes an advanced deep learning framework for efficient beam training in millimeter wave (mmWave) massivemultiple-inputmultiple-output(MIMO) systems. To overcome the limitations of conventional beam training approaches such ashigh overhead, slow adaptation to dynamic environments, and poor scalability, an Improving Signal Coverage in Millimeter WaveMassive MIMO via Efficient Predefined Time Adaptive Neural Network based Beam Training (ISC-MMIMO-EPTANN-BT)model is proposed. The proposed model used deep neural network (DNN) to learn complicated nonlinearities in channel powerleakage (CPL) and used an efficient predefined time adaptive neural network (EPTANN) to provide real-timeresponsiveness andtemporal synchronism in beam training. The parameters of the model are also optimized using fire hawk optimization algorithm(FHOA) to get better convergence speed and signal coverage. The proposed technique is executed in MATLAB. The proposedapproach attains better performance under successful rate by significantly less beam training overhead and also increases signalcoverage based on simulation results. The proposed ISC-MMIMO-EPTANN-BTmethod attains 26.15%, 21.08%, and 33.75%higher successful rates and 16.32%, 28.94%, and 20.24% lower normalized mean square error compared with existing methodssuch as deep learning for beam training in millimeter wave massive MIMO schemes (BT-MMIMO-DNN),deep learning for combinedfeedback and channel prediction in large-scaleMIMO systems (CNN-JCS-MMIMO),and triple-refinedhybrid-fieldbeamtraining in mmWave extremely large-scaleMIMO (TR-FBT-MIMO),respectively. The ISC-MMIMO-EPTANN-BTtechniquereduced beam training overhead, enhanced signal coverage, and identified a promising candidate for successful beam trainingin mmWave massive MIMO schemes.
查看更多>>摘要：Underwater wireless optical communication systems face several challenges, such as path loss, poor signal quality, and limitedcommunication range because of environmental constraints like scattering, turbidity, and attenuation. To address these challenges,this study proposes a serial relaying underwater wireless optical communication system enhanced by a hybrid optimizationalgorithm called quantum-basedcrossover gravitational search algorithm. This algorithm incorporates a gravitationalsearch algorithm with a quantum crossover mechanism to optimize power allocation among relay nodes for increasing energyefficiency and reducing signal-to-noiseratio under various environmental constraints. This study adopts a gravitational searchalgorithm for resolving the power allocation problem that utilizes the concept of gravitational attraction among agents to explorethe search space. However, it often suffers from slow convergence and local optima trapping. To resolve this, the proposed techniquedeploys a quantum crossover mechanism to refine this process by improving solution diversity and convergence speed.By combining the significance of these approaches, the proposed quantum-basedcrossover gravitational search algorithm effectivelysolves complex optimization issues and enhances energy efficiency by distributing power optimally and reducing excessiveenergy consumption. Simulation results demonstrate that Q-CROSSGSA outperforms existing optimization methods in termsof energy efficiency, signal-to-noiseratio, and outage probability, offering a robust approach for an efficient UWOC system inchallenging underwater environments.
查看更多>>摘要：The fast evolution of 5G and next-generationnetworks requires immediate network processing combined with high reliabilityfeatures and effective resource management to enable autonomous systems along with healthcare capabilities and industrial IoTapplications. The main structural component of 5G architecture uses network slicing to create exclusive virtual networks thatmeet the precise quality-of-service(QOS) demands of different applications. Existing approaches fail to maintain precise andcomplete resource allocation with slice management in changing conditions that result in poor operational outcomes. This paperintroduces a hybrid approach using sparse spectra graph convolutional networks (SSGCN) and supervised bidirectional longshort-termmemory network (SBLSMN) to resolve current challenges in wireless sensor networks (5GWSN-SGC-SLMN).TheSSGCN optimizes spatial resource allocation by using spectral graph convolutions to understand wireless sensor network (WSN)node interactions during slice selection and the SBLSMN tracks load data through bidirectional learning for error rate predictionto implement proactive QoS management, as the spatial and temporal learning work together to optimize resource allocationand provide flexible network expansion between different network slices. Experimental results show that the proposed approachattains 19.11% better accuracy, 17.12% enhanced precision, and 18.51% reduced misclassification rate than existing methods.The proposed method attains a 32% decrease in URLLC slice latency and delivers 98% reliability because of its ability to monitorreal-timetraffic and automatic failure response before slice failures occur. These improvements highlight the effectiveness of5GWSN-SGC-SLMNin ensuring reliable and efficient wireless network slicing in 5G environments.
查看更多>>摘要：This study focused on a spatially decoupled 8-portMIMO antenna, strategically designed for point-to-point(PTP) backhaulwireless applications at designated 7-GHzfrequency band. Utilizing a conventional dipole-driven-antenna-element(DDAE), thedesign asserts a polygonal interface to assemble 8-portMIMO configuration, avoiding additional isolation network. The initializationof DDAE study shows inductively dominant and capacitively coupled nature, which are primarily analyzed with modalsynthesis on its resonance behavior and then extended to a sequential array configuration. This design topology self-annihilatethe trapped coupled signals from antenna ports without affecting its intrinsic resonance mechanism, and hence, good port-isolation(> 24 dB) is achieved. The radiation mechanism shows discrete pattern diversification with stable high peak gain of 8.02dBi at designated ports. To rationalize its counterparts, a prototype antenna has been fabricated and experimented to operatefrom 6.5 to 7.6 GHz for dedicated 7-GHzPTP microwave backhaul RF wireless applications. The correlation coefficient betweenthe antenna ports shows appreciable limits with ECC < 0.2 viable for indoor, outdoor, and isotropic environments.
查看更多>>摘要：In today's wireless communication landscape, the demand for compact MIMO antennas with low mutual coupling is critical.To meet this requirement, this paper presents two compact MIMO antenna configurations based on microstrip line principles.Initially, a compact dual-bandantenna is designed for 5G N77/N78/N79 sub-6-GHzapplications. The introduction of an invertedL-shapedslot into the broadband radiating element enables the generation of two distinct resonances. Without the slot,the antenna covers a broad frequency range from 4.30 to 5.54 GHz. Upon slot insertion, it achieves dual-bandoperation across3.3-3.78 GHz and 4.3-5.15 GHz. Subsequently, by integrating an additional inverted L-shapedslot on the opposite lateral side, atriple-bandresponse is realized, covering 3.6-3.85 GHz, 4.75-5.05 GHz, and 5.3-7.5 GHz. Both antenna designs are fabricated onan FR-4substrate (ε_r = 4.4, tanδ = 0.025) with 1.6 mm thickness and a compact footprint of 23.3 × 29.125 mm~2 (0.37λ_0 × 0.46λ_0).The radiating structure possesses an inverted T-shapedgeometry occupying (8 × 9.65) + (29.125 × 13.65) mm~2 that meets the requirementsfor modern space-limiteddevices. Furthermore, two compact two-portMIMO configurations (23.3 × 66.06 mm~2, or0.37λ_0 × 1.04λ_0) are proposed, utilizing orthogonally placed dual-bandand triple-bandmonopole elements. Electromagnetic simulationusing Ansys HFSS confirms that the designs achieve excellent MIMO performance, including envelope correlation coefficients(ECC) below 0.0019 and 0.001, channel capacity loss (CCL) under 0.26 and 0.46 bits/s/Hz, peak gains of 4.9 and 5.1 dBi,and radiation efficiencies exceeding 94% and 85%, respectively, for MIMO antennas with dual-bandand triple-bandmonopoleantenna elements. Notably, mutual coupling remains below -23 dB and -24.5 dB across the operating bands, despite maintainingan edge-to-edgespacing of less than 0.125λ.
查看更多>>摘要：This paper proposed a compact four-portfractal-loadedmultiple input multiple output (MIMO) antenna for 5G applications. Theproposed antenna is fabricated on an FR4 substrate and operates at dual-frequencybands of 29.8133 and 34.2933 GHz, coveringa wide bandwidth from 28 to 36 GHz. The design incorporates fractal loading and slots to enhance the bandwidth, which coversthe frequency range from 28 to 36 GHz. The orthogonal placement of MIMO antenna elements minimizes mutual coupling,while the coplanar waveguide (CPW) feed efficiently transmits signals. The proposed fractal geometry allows significant sizereduction while maintaining optimal performance at 5G frequencies, making the antenna suitable for compact wireless systems.The fractal geometry significantly improves isolation between the antenna elements, achieving an isolation level of -30 dB. Theantenna exhibits peak gains of 6.4 dBi at 29.8133 GHz and 5.37 dBi at 34.2933 GHz, demonstrating its suitability for high-speed,low-latencycommunication. The design process begins with a single-portantenna, transfers to a nonfractal MIMO configuration,and finally develops into the fractal-loadedMIMO antenna. The results show that the proposed fractal-loadedMIMOantenna offers better bandwidth, isolation, and compactness compared to traditional designs, making it ideal for next-generation5G wireless communication systems.
K. V. VineethaM. Siva KumarP. DurgaprasadaraoSatti Sudha Mohan Reddy...
e70196.1-e70196.18页
查看更多>>摘要：THz antennas, which function at high speeds, frequencies, and data rates, were developed in response to the increased need forhigh-speedcommunication equipment. High data transfer is necessary for wireless communication in modern technologies.After 5G, the next generation of wireless technology is called 6G (sixth-generationwireless). Because 6G networks may run atgreater frequencies than 5G networks, their capacity and latency will be significantly increased. Allowing communications witha latency of 1 μs is one of the objectives of the 6G internet. This paper introduces a compact and highly efficient sunshine-slottedMIMO antenna designed for 6G and wireless applications. The antenna features a sun-shapedpatch and a partial ground layerto improve its overall performance. The proposed antenna operates across 3.6, 4.5, 5.2, and 6.2 THz, respectively, which are usedfor wireless communication systems. This work presents a MIMO antenna constructed with a low reflection coefficient valueof -34, -38, -18, and -23 dB, with an insertion loss of less than -30 dB over the operational frequency. In addition to this, theantenna has a gain value of 8.5-9.2 dBi. The proposed MIMO antenna also has a minimum ECC value of < 0.01; similarly, acrossthe operating frequency, the antenna has a DG value range of 10-11 dB respectively. The proposed antenna has dimensions of 33× 33 × 100 μm~3 using graphene as a conducting layer and silicon as a substrate layer. The suggested antenna can be utilized forhigh-speedcommunications because of its high gain and operating frequency applicability.
查看更多>>摘要：The Internet of Things (IoT) is an advanced technology that has seen significant growth over the past years. Because of theenergy limitations of IoT devices, implementing effective managing practices to develop IoT applications remains a complicatedtask. One of the most critical IoT challenges that needs to be considered is routing, because of its significant impact on the consumptionof energy. Software-definednetworking (SDN) is a method that decouples the control plane from the data plane, whichenables the network administrators to program effectively. This work focuses on an energy-awarecommunication protocol inSDN for a heterogeneous IoT model based on the proposed fractional poor rich optimization (FPRO). Initially, SDN is simulatedand the SDN controller helps to map devices to particular applications for performing the counting process. The SDN poses triplelayers, like the request analysis layer, routing layer, and communication layer. In the request analysis layer, the user requests aretaken by the network devices. In the routing layer, clustering and routing are performed. Here, clustering is done by Bayesianfuzzy clustering (BFC) and routing is performed with FPRO. Here, FPRO is formed by combining fractional calculus (FC) withpoor and rich optimization (PRO). Fitness is developed by considering the attributes that involve distance, delay, energy, and linkcost. At last, the communication layer is used for transmitting data among nodes using optimal paths. The proposed FPRO-basedSDN showed superior performance with the least delay of 0.166 s, higher energy of 0.935 J, and higher throughput of 0.935 Mbpsfor 200 nodes.
NETL
NSTL
Wiley