Nowadays, Long-Term Evolution Advanced (LTE-A) network is the primary innovation in 4G networks. The LTE-A networks convey exceptional data rates and low latency for few sorts of application. Sometimes, in LTE-A, the multiobjective uplink resource allocation is the perplexing optimization problem, and this problem is considered as 0-1 multiobjective knapsack trouble. In order to overcome this trouble, a multiobjective cooperative swarm intelligence algorithm for resource allocation is proposed in this paper. In our proposed work, hybrid firefly algorithm (FFA) and particle swarm optimization (PSO) algorithm are utilized to solve 0-1 multiobjective knapsack problem. Initially, a priority and urgency factor (urgency of packets)-based user ranking and quantifying scheme is designed for scheduling process. After the scheduling process, the resource allocations employ three objective functions, such as maximization of resource utilization, maximization of quality of service (QoS), and interference minimization. The optimization problem is overcome by using the hybrid FFA and PSO algorithm. The experimental outcomes demonstrate that it has the best QoS and less interference in the resource allocation in LTE-A network than state-of-art methods in the proposed strategy.Trans Emerging Tel Tech. 2019;30:e3748.wileyonlinelibrary.com/journal/ett

The core requirements are generated for sixth generation (6G) wireless communication with low‐latency and ultra‐high speeds to increase the count of ultra scale intelligent factors, like smart cars, mobile root users. The advancement of 6G communication can lead the interference exploitation. To manage the exploitation of uplink multiuser massive (UMM), the multiple‐input and multiple‐output (MIMO) is very difficult to detect the mechanisms, particularly, quadrature amplitude modulation (QAM) signals. To overcome these issues, a novel deep graph neural network optimized with fertile field algorithm based detection model (DGNNO‐FFA) is proposed in this article for uplink multiuser massive MIMO System. The proposed DGNNO‐FFA approach minimizes the channel estimation errors under low signal to noise ratio (SNR) with better bit error rate (BER). Finally, the proposed DGNNO‐FFA approach attains 11.02%, 12.22%, and 25.27% lower BER value, 14.55%, 18.66%, and 29.49% higher energy efficiency, 15.59%, 19.06%, and 29.59% lower NMSE, and 15.59%, 19.06%, and 29.59% lower computational complexity compared with other existing approaches, like deep neural network based semi definite relaxation (DNN‐SDR), QR based zero forcing algorithms (QR‐ZF), and QAM based 2‐dimensional double successive projection model (QAM‐2D‐DSP).

Recently, the fundamental problem with Hybrid Mobile Ad-hoc Networks (H-MANETs) is to find a suitable and secure way of balancing the load through Internet gateways. Moreover, the selection of the gateway and overload of the network results in packet loss and Delay (DL). For optimal performance, it is important to load balance between different gateways. As a result, a stable load balancing procedure is implemented, which selects gateways based on Fuzzy Logic (FL) and increases the efficiency of the network. In this case, since gateways are selected based on the number of nodes, the Energy Consumption (EC) was high. This paper presents a novel Node Quality-based Clustering Algorithm (NQCA) based on Fuzzy-Genetic for Cluster Head and Gateway Selection (FGCHGS). This algorithm combines NQCA with the Improved Weighted Clustering Algorithm (IWCA). The NQCA algorithm divides the network into clusters based upon node priority, transmission range, and neighbour fidelity. In addition, the simulation results tend to evaluate the performance effectiveness of the FFFCHGS algorithm in terms of EC, packet loss rate (PLR), etc.

Albeit the government buoy up the penetration of renewable energy sources (RES) particularly solar photovoltaic (PV) system, the dependency on fossil fuels is still growing. The power generation using solar PV system may enhance when the enactment of solar PV system is improved. The faults occurred in the system is an important performance degradation factor. Incessant studies have been performed to identify and mitigate the faults. Currently, several smart techniques are utilized to identify the faults rapidly. In this study, Back Propagation Neural Network (BPNN) has been implemented to identify the faults. The output power get degraded when the faults happened in source side, Maximum Power Point Tracking (MPPT), DC-DC converter, rectifier and grid. The investigations has performed on 100 kW solar PV system using Matlab. The outcomes imply that the proposed method has detected the faults quickly, economically and effectively.

An inventive innovation for access of underutilized spectrum and unused range is cognitive radio (CR). Spectrum sensing is the capacity of a gadget to effectively detect the environment and to extricate data from utilizing its radio. A CR framework utilizing hypothesis testing channel banks and the enhancement of the range detecting limit for the predefined requirements on probabilities of missed discovery and of the false alert are proposed to optimize the energy in the system. The limit is additionally upgraded to limit the range detecting mistake subject to the predetermined limitations. The regions of convergence for different estimations of clients are found, and furthermore, the absolute error rate has been broken down for different number of clients going from 1 to 10 and has supported the upgraded estimation of range detecting with least vitality misfortune. The exhibition of a helpful range detecting by different voting standard like OR, AND and M out of N voting rule are applied. From the outcomes, it is seen that the proposed conspire displays exceptionally least impedance for the essential clients. Wastage of energy is limited in the system which characterizes the term green sensing.