Graph traversal aided detection in uplink MBM massive MIMO based on socio‐cognitive knowledge of swarm optimization

2021 ◽  
Vol 34 (5) ◽  
Author(s):  
Arijit Datta ◽  
Vimal Bhatia ◽  
Manish Mandloi ◽  
Ganapati Panda
Keyword(s):  
Massive Mimo ◽  
Swarm Optimization ◽  
Graph Traversal ◽  
Cognitive Knowledge

Particle swarm optimization: A new tool to invert geophysical data

Geophysics ◽  
2007 ◽  
Vol 72 (2) ◽  
pp. F75-F83 ◽  
Author(s):  
Ranjit Shaw ◽  
Shalivahan Srivastava
Keyword(s):  
Particle Swarm Optimization ◽  
Social Behavior ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Geophysical Data ◽  
Earth Model ◽  
Optimization Strategy ◽  
Swarm Optimization ◽  
Cognitive Knowledge ◽  
Time Required

Particle swarm optimization (PSO) is a global optimization strategy that simulates the social behavior observed in a flock (swarm) of birds searching for food. A simple search strategy in PSO guides the algorithm toward the best solution through constant updating of the cognitive knowledge and social behavior of the particles in the swarm. To evaluate the applicability of PSO to inversion of geophysical data, we inverted three noise-corrupted synthetic sounding data sets over a multilayered 1D earth model by using DC, induced polarization (IP), and magnetotelluric (MT) methods. The results show that acceptable solutions can be obtained with a swarm of about 300 particles and that convergence occurs in less than 100 iterations. The time required to execute a PSO algorithm is comparable to that of a genetic algorithm (GA). Similarly, the models estimated from PSO and GA are close to the true solutions. Whereas a ridge regression (RR) algorithm converges in four to eight iterations, it yields satisfactory results only when the initial model is very close to the true model. Models estimated from PSO explain observed, vertical electric sounding (VES) and MT data, from Bhiwani district, Haryana, India, and the Chottanagpur gneissic complex, Dhanbad, India. The results are consistent with RR and GA inversions.

scholarly journals Particle Swarm Optimization Based Beamforming in Massive MIMO Systems

2020 ◽  
Vol 14 (05) ◽  
pp. 176
Author(s):  
Thaar A. Kareem ◽  
Maab Alaa Hussain ◽  
Mays Kareem Jabbar
Keyword(s):  
Particle Swarm Optimization ◽  
Millimeter Wave ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Particle Swarm ◽  
Optimal Performance ◽  
Achievable Rate ◽  
Swarm Optimization ◽  
Transmit Precoding

<p>This research puts forth an optimization- based analog beamforming scheme for millimeter-wave (mmWave) massive MIMO systems. Main aim is to optimize the combination of analog precoder / combiner matrices for the purpose of getting near-optimal performance. Codebook-based analog beamforming with transmit precoding and receive combining serves the purpose of compensating the severe attenuation of mmWave signals. The existing and traditional beamforming schemes involve a complex search for the best pair of analog precoder / combiner matrices from predefined codebooks. In this research, we have solved this problem by using Particle Swarm Optimization (PSO) to find the best combination of precoder / combiner matrices among all possible pairs with the objective of achieving near-optimal performance with regard to maximum achievable rate. Experiments prove the robustness of the proposed approach in comparison to the benchmarks considered. <strong></strong></p><p class="IndexTerms"> </p>

scholarly journals Energy Efficiency Optimization of Massive MIMO Systems Based on the Particle Swarm Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jing Yang ◽  
Liping Zhang ◽  
Chunhua Zhu ◽  
Xinying Guo ◽  
Jiankang Zhang
Keyword(s):  
Energy Efficiency ◽  
Particle Swarm Optimization ◽  
Single Cell ◽  
Iterative Algorithm ◽  
Massive Mimo ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Base Station ◽  
Imperfect Csi ◽  
Swarm Optimization

As one of the key technologies in the fifth generation of mobile communications, massive multi-input multioutput (MIMO) can improve system throughput and transmission reliability. However, if all antennas are used to transmit data, the same number of radiofrequency chains is required, which not only increases the cost of system but also reduces the energy efficiency (EE). To solve these problems, in this paper, we propose an EE optimization based on the particle swarm optimization (PSO) algorithm. First, we consider the base station (BS) antennas and terminal users and analyze their impact on EE in the uplink and downlink of a single-cell multiuser massive MIMO system. Second, a dynamic power consumption model is used under zero-forcing processing, and it obtains the expression of EE that is used as the fitness function of the PSO algorithm under perfect and imperfect channel state information (CSI) in single-cell scenarios and imperfect CSI in multicell scenarios. Finally, the optimal EE value is obtained by updating the global optimal positions of the particles. The simulation results show that compared with the traditional iterative algorithm and artificial bee colony algorithm, the proposed algorithm not only possesses the lowest complexity but also obtains the highest optimal value of EE under the single-cell perfect CSI scenario. In the single-cell and multicell scenarios with imperfect CSI, the proposed algorithm is capable of obtaining the same or slightly lower optimal EE value than that of the traditional iterative algorithm, but the running time is at most only 1/12 of that imposed by the iterative algorithm.

scholarly journals Chicken Swarm Optimization based Optimal Channel Allocation in Massive MIMO

2021 ◽  
Author(s):  
S Nisharani ◽  
G Indumathi
Keyword(s):  
Objective Function ◽  
Power Allocation ◽  
Communication Networks ◽  
Massive Mimo ◽  
Channel Allocation ◽  
Multiple Input Multiple Output ◽  
Iteration Process ◽  
Beam Forming ◽  
Swarm Optimization ◽  
Chicken Swarm Optimization

Abstract Energy Efficiency (EE) plays a significant role in the progress towards the Fifth-Generation (5G) wireless communication networks. Due to the higher Spectral Efficiency (SE) and EE, Massive Multiple-Input Multiple-Output (MIMO) is a promising model for the 5G networks. In this work, a Channel Selection (CS) scheme is proposed by selecting the optimal channel using the Chicken Swarm Optimization (CSO) algorithm. A massive MIMO model is implemented by considering the SE, EE and Resource Efficiency (RE). The main objective is to optimize the beam-forming vectors and power allocation for all the users. The RE metric considering the multi-objective function can be defined to develop an effective and robust design with balanced SE and EE. The objective function for generating the optimal beam forming vectors is satisfying the Signal to Interference-Plus-Noise Ratio (SINR) constraints. The CSO Algorithm is applied to generate the beam-forming vectors and power allocation, based on the channel characteristics. The channel state information is predicted and a projection matrix with channel estimation framework is formed. The selection of the index sets in the iteration process provides the optimized channel. Data transmission is performed through the optimal channel. From the comparative analysis, it is observed that the proposed CS scheme provides better SE and EE than the existing CS schemes.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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