✅ Author Profile: A research enthusiast and MATLAB simulation developer, skilled in data processing, modeling simulation, program design, complete code acquisition, paper reproduction, and research simulation.
🍎 Previous reviews can be found on my personal homepage:MATLAB Research Studio
🍊 Personal motto: Seek knowledge through investigation; complete MATLAB code and simulation consultation available via private message.
🔥 Content Introduction
In satellite communication systems (such as High Throughput Satellites (HTS), Low Earth Orbit (LEO) satellite constellations, and satellite IoT), antennas serve as the core hub connecting ground terminals and satellites, with their performance directly determining the communication link’s “coverage area, transmission rate, and anti-interference capability.” Traditional single antennas (such as parabolic antennas) have a simple structure but face bottlenecks such as “fixed beam, limited gain, and weak anti-interference” — for example, a single parabolic antenna cannot cover multiple regional users simultaneously and is easily affected by ground interference or adjacent satellite interference. In contrast, array antennas (composed of multiple antenna elements arranged in a specific pattern) can achieve “high directional gain, dynamic beamforming, and simultaneous multi-beam coverage” through “phase/amplitude control of the element signals,” perfectly adapting to the needs of satellite communication for “long-distance transmission (36,000 km geostationary orbit GEO), wide area coverage, and complex interference environments.” This article will systematically explain the architecture design, key technologies, performance indicators, and typical applications of array antennas, providing technical references for antenna scheme design in satellite communication systems.
⛳️ Operating Results
📣 Sample Code
function delta_f = doppler_shift ( c, v_t, f, theta )
delta_f = + v_t * sind( theta ) / c * f;
end
🔗 References
[1] Duan Xiaxia, Zhang Jingang, Liu Yanming. Genetic Algorithm for Beamforming Array Antennas and MATLAB Implementation [J]. Modern Electronic Technology, 2007, 30(15):3. DOI:10.3969/j.issn.1004-373X.2007.15.018.
🎈 Some theoretical references are from online literature; please contact the author for removal if there are any copyright issues.
👇 Follow me to receive a wealth of MATLAB e-books and mathematical modeling materials.
🏆 Our team specializes in guiding customized MATLAB simulations across various research fields to support your research dreams:
🌟 Improvements and applications of various intelligent optimization algorithms
Production scheduling, economic scheduling, assembly line scheduling, charging optimization, workshop scheduling, departure optimization, reservoir scheduling, three-dimensional packing, logistics site selection, cargo location optimization, bus scheduling optimization, charging pile layout optimization, workshop layout optimization, container ship loading optimization, pump combination optimization, medical resource allocation optimization, facility layout optimization, visual field base station and drone site selection optimization, knapsack problem, wind farm layout, time slot allocation optimization, optimal distribution of distributed generation units, multi-stage pipeline maintenance, factory-center-demand point three-level site selection problem, emergency supply distribution center site selection, base station site selection, road lamp post layout, hub node deployment, transmission line typhoon monitoring devices, container scheduling, unit optimization, investment portfolio optimization, cloud server combination optimization, antenna linear array distribution optimization, CVRP problem, VRPPD problem, multi-center VRP problem, multi-layer network VRP problem, multi-center multi-vehicle VRP problem, dynamic VRP problem, two-layer vehicle routing planning (2E-VRP), electric vehicle routing planning (EVRP), hybrid vehicle routing planning, mixed flow shop problem, order splitting scheduling problem, bus scheduling optimization problem, flight shuttle vehicle scheduling problem, site selection routing planning problem, port scheduling, port bridge scheduling, parking space allocation, airport flight scheduling, leak source localization, cold chain, time windows, multi-vehicle yards, site selection optimization, port bridge scheduling optimization, traffic impedance, redistribution, parking space allocation, airport flight scheduling, communication upload and download allocation optimization.
🌟 Time series, regression, classification, clustering, and dimensionality reduction in machine learning and deep learning
2.1 BP time series, regression prediction, and classification
2.2 ENS voice neural network time series, regression prediction, and classification
2.3 SVM/CNN-SVM/LSSVM/RVM support vector machine series time series, regression prediction, and classification
2.4 CNN|TCN|GCN convolutional neural network series time series, regression prediction, and classification
2.5 ELM/KELM/RELM/DELM extreme learning machine series time series, regression prediction, and classification
2.6 GRU/Bi-GRU/CNN-GRU/CNN-BiGRU gated neural network time series, regression prediction, and classification
2.7 Elman recurrent neural network time series, regression prediction, and classification
2.8 LSTM/BiLSTM/CNN-LSTM/CNN-BiLSTM long short-term memory neural network series time series, regression prediction, and classification
2.9 RBF radial basis function neural network time series, regression prediction, and classification
2.10 DBN deep belief network time series, regression prediction, and classification
2.11 FNN fuzzy neural network time series, regression prediction
2.12 RF random forest time series, regression prediction, and classification
2.13 BLS broad learning system time series, regression prediction, and classification
2.14 PNN pulse neural network classification
2.15 Fuzzy wavelet neural network prediction and classification
2.16 Time series, regression prediction, and classification
2.17 Time series, regression prediction, and classification
2.18 XGBOOST ensemble learning time series, regression prediction, and classification
2.19 Transform various combinations of time series, regression prediction, and classification
Covering wind power prediction, photovoltaic prediction, battery life prediction, radiation source identification, traffic flow prediction, load forecasting, stock price prediction, PM2.5 concentration prediction, battery health status prediction, electricity consumption prediction, water body optical parameter inversion, NLOS signal identification, subway stop precision prediction, transformer fault diagnosis.
🌟 In image processing
Image recognition, image segmentation, image detection, image hiding, image registration, image stitching, image fusion, image enhancement, image compressed sensing.
🌟 In path planning
Traveling salesman problem (TSP), vehicle routing problem (VRP, MVRP, CVRP, VRPTW, etc.), three-dimensional path planning for drones, drone collaboration, drone formation, robot path planning, grid map path planning, multimodal transport problems, electric vehicle routing planning (EVRP), two-layer vehicle routing planning (2E-VRP), hybrid vehicle routing planning, ship trajectory planning, full path planning, warehouse patrol, bus time scheduling, reservoir scheduling optimization, multimodal optimization.
🌟 In drone applications
Drone path planning, drone control, drone formation, drone collaboration, drone task allocation, online optimization of safe communication trajectories for drones, vehicle collaborative drone path planning.
🌟 In communication
Sensor deployment optimization, communication protocol optimization, routing optimization, target localization optimization, Dv-Hop localization optimization, Leach protocol optimization, WSN coverage optimization, multicast optimization, RSSI localization optimization, underwater communication, communication upload and download allocation.
🌟 In signal processing
Signal recognition, signal encryption, signal denoising, signal enhancement, radar signal processing, signal watermark embedding and extraction, EMG signals, EEG signals, signal timing optimization, ECG signals, DOA estimation, encoding and decoding, variational mode decomposition, pipeline leakage, filters, digital signal processing + transmission + analysis + denoising, digital signal modulation, bit error rate, signal estimation, DTMF, signal detection.
🌟 In power systems
Microgrid optimization, reactive power optimization, distribution network reconstruction, energy storage configuration, orderly charging, MPPT optimization, household electricity, electric/cold/heat load forecasting, power equipment fault diagnosis, battery management system (BMS) SOC/SOH estimation (particle filter/Kalman filter), multi-objective optimization in power system scheduling, photovoltaic MPPT control algorithm improvement (perturb and observe method/incremental conductance method), electric vehicle charging and discharging optimization, microgrid day-ahead and intra-day optimization, energy storage optimization, household electricity optimization, supply chain optimization.
🌟 In cellular automata
Traffic flow, crowd evacuation, virus spread, crystal growth, metal corrosion.
🌟 In radar
Kalman filter tracking, trajectory association, trajectory fusion, SOC estimation, array optimization, NLOS identification.
🌟 In workshop scheduling
Zero-wait flow shop scheduling problem (NWFSP), permutation flow shop scheduling problem (PFSP), hybrid flow shop scheduling problem (HFSP), zero idle flow shop scheduling problem (NIFSP), distributed permutation flow shop scheduling problem (DPFSP), blocking flow shop scheduling problem (BFSP).
👇