Salp swarm optimization in hybrid beamforming for MIMO radar

2021 ◽  
pp. 469-480
Author(s):  
Vivek Kadam ◽  
Surendra Bhosale
Keyword(s):  
Mimo Systems ◽  
Minimum Variance ◽  
Mimo Radar ◽  
Adaptive Beamforming ◽  
Spatial Multiplexing ◽  
Practical Applications ◽  
Mimo Antenna ◽  
Salp Swarm Algorithm ◽  
Hybrid Beamforming ◽  
Swarm Algorithm

The Multi Input Multi Output (MIMO) radar waveform diversity Significantly improves parameter identifiably than phased-array radar performance. Precoding, combining and spatial multiplexing techniques improves the data throughput and reliability of the transmission in MIMO systems. But increment in transmit and receive elements in MIMO antenna array induces considerable increase in required power for hardware and computation cost. Hybrid beamforming employs fewer RF-to-baseband chains. With conscious selection of the weights for pre-coding and combining, hybrid beamforming establishes perfect trade-off between complexity, performance, cost, and power consumption in practical applications. Performance of MIMO radar system can be improved using newly developed bio inspired metaheuristic algorithms as compared to conventional and adaptive beamforming algorithms. In this work the Salp Swarm algorithm (SSA) is implemented to optimize the performance of hybrid beamforming using Raleigh channel and considering the bit error rate and normalized array power parameters. The swarming behavior of salps when navigating and foraging in oceans is the inspiration behind the SSA optimization algorithm. The obtained results are compared with the conventional phase-shift as well as adaptive linearly constrained minimum variance beamforming algorithms on simulation platform with standard considerations. It is observed that this new approach of Salp swarm algorithm is having improved and much better performance with the considered parameters.

MIMO Antenna Design for 5G Communication Systems Using Salp Swarm Algorithm

2020 ◽  
Author(s):  
Achilles D. Boursianis ◽  
Sotirios K. Goudos ◽  
Traianos V. Yioultsis ◽  
Katherine Siakavara ◽  
Paolo Rocca
Keyword(s):  
Communication Systems ◽  
Antenna Design ◽  
Mimo Antenna ◽  
Salp Swarm Algorithm ◽  
5G Communication ◽  
Swarm Algorithm

Grouped Multiuser Diversity for Spatial Multiplexing MIMO Systems

2011 ◽  
Vol 30 (1) ◽  
pp. 81-85
Author(s):  
Er-lin Zeng ◽  
Shi-hua Zhu ◽  
Xue-wen Liao ◽  
Jun Wang
Keyword(s):  
Mimo Systems ◽  
Multiuser Diversity ◽  
Spatial Multiplexing

MIMO Systems in a Composite Fading and Generalized Noise Scenario: A Review

2020 ◽  
Vol 14 ◽  
Author(s):  
Keerti Tiwari
Keyword(s):  
System Performance ◽  
Performance Enhancement ◽  
Channel Model ◽  
Mimo Systems ◽  
Mimo System ◽  
Spatial Diversity ◽  
Spatial Multiplexing ◽  
Channel Models ◽  
Efficient System ◽  
Composite Channel

: Multiple-input multiple-output (MIMO) systems have been endorsed to enable future wireless communication requirements. The efficient system designing appeals an appropriate channel model, that considers all the dominating effects of wireless environment. Therefore, some complex or less analytically acquiescent composite channel models have been proposed typically for single-input single-output (SISO) systems. These models are explicitly employed for mobile applications, though, we need a specific study of a model for MIMO system which can deal with radar clutters and different indoor/outdoor and mobile communication environments. Subsequently, the performance enhancement of MIMO system is also required in such scenario. The system performance enhancement can be examined by low error rate and high capacity using spatial diversity and spatial multiplexing respectively. Furthermore, for a more feasible and practical system modeling, we require a generalized noise model along with a composite channel model. Thus, all the patents related to MIMO channel models are revised to achieve the near optimal system performance in real world scenario. This review paper offers the methods to improve MIMO system performance in less and severe fading as well as shadowing environment and focused on a composite Weibull-gamma fading model. The development is the collective effects of selecting the appropriate channel models, spatial multiplexing/detection and spatial diversity techniques both at the transmitter and the receivers in the presence of arbitrary noise.

Salp swarm algorithm with crossover scheme and Lévy flight for global optimization

2021 ◽  
pp. 1-12
Author(s):  
Heming Jia ◽  
Chunbo Lang
Keyword(s):  
Global Optimization ◽  
Heuristic Algorithm ◽  
Experimental Results ◽  
High Dimensional ◽  
Lévy Flight ◽  
Test Functions ◽  
Levy Flight ◽  
Salp Swarm Algorithm ◽  
Swarm Algorithm ◽  
Accuracy And Stability

Salp swarm algorithm (SSA) is a meta-heuristic algorithm proposed in recent years, which shows certain advantages in solving some optimization tasks. However, with the increasing difficulty of solving the problem (e.g. multi-modal, high-dimensional), the convergence accuracy and stability of SSA algorithm decrease. In order to overcome the drawbacks, salp swarm algorithm with crossover scheme and Lévy flight (SSACL) is proposed. The crossover scheme and Lévy flight strategy are used to improve the movement patterns of salp leader and followers, respectively. Experiments have been conducted on various test functions, including unimodal, multimodal, and composite functions. The experimental results indicate that the proposed SSACL algorithm outperforms other advanced algorithms in terms of precision, stability, and efficiency. Furthermore, the Wilcoxon’s rank sum test illustrates the advantages of proposed method in a statistical and meaningful way.

scholarly journals bSSA: Binary Salp Swarm Algorithm with Hybrid Data Transformation for Feature Selection

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sayar Singh Shekhawat ◽  
Harish Sharma ◽  
Sandeep Kumar ◽  
Anand Nayyar ◽  
Basit Qureshi
Keyword(s):  
Feature Selection ◽  
Data Transformation ◽  
Salp Swarm Algorithm ◽  
Hybrid Data ◽  
Swarm Algorithm
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