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首页> 外文期刊>Indian Journal of Science and Technology >Performance Comparison of Nature-inspired Optimization Algorithms Applied to MVDR Technique for Canceling Multiple Access Interference Signals
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Performance Comparison of Nature-inspired Optimization Algorithms Applied to MVDR Technique for Canceling Multiple Access Interference Signals

机译:MVDR技术消除多址干扰信号的自然启发式优化算法的性能比较

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Background/Objectives: Minimum Variance Distortionless Response (MVDR) beam forming technique is among the most widely used in antenna array field. The conventional MVDR has poor performance, and low Signal to Interference plus Noise Ratio (SINR) gain in the condition of limited snapshots or Multiple Access Interference (MAI) signals existing. Heuristic optimization algorithms are broadly used to solve many engineering problems. Methods/Analysis: In this work, two nature-inspired optimization methods, namely Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) are applied to enhancing the conventional MVDR performance. In particular, the complex weight coefficients of the conventional MVDR solution are improved using both approaches. First, SINR calculated from MVDR basing linear antenna array configuration then the PSO and GSA implemented to minimizes the power of noise and interference in the constraint condition. The performance of the proposed methods is assessed based on various QoS criteria such as beampattern accuracy for azimuth and elevation scanning angles and SINR output. Findings: In comparison to conventional MVDR, the proposed algorithms have indicated that MVDR GSA providesfavorable agreement of synthesizing a maximum gain toward the desired real user angle while introducing deep null-forming in the undesired user directions. As a result, average SINR is evaluated over 20 runs in all simulation scenarios, the performance of MVDR GSA is better than the performance of MVDR PSO . Moreover, a good control over the null-forming level can be achieved by MVDR GSA for iteration number < 100 whereas MVDR PSO is simple and easy to implement but required more convergence time to get high SINR. Application/Improvements: In general, it was observed that MVDR GSA out performs the MVDR PSO with respect to solution quality, stability and convergence speed.
机译:背景/目的:最小方差无失真响应(MVDR)波束形成技术是天线阵列领域中使用最广泛的技术之一。传统的MVDR具有较差的性能,并且在快照有限或存在多路访问干扰(MAI)信号的情况下,信噪比(SINR)增益较低。启发式优化算法广泛用于解决许多工程问题。方法/分析:在这项工作中,应用了两种自然启发式的优化方法,即粒子群优化(PSO)和引力搜索算法(GSA)来增强常规MVDR性能。特别是,使用这两种方法都可以改善常规MVDR解决方案的复数权重系数。首先,根据基于线性天线阵列配置的MVDR计算SINR,然后实施PSO和GSA,以在约束条件下将噪声和干扰的功率降至最低。基于各种QoS标准(例如,方位角和仰角扫描角的波束图精度和SINR输出)评估所提出方法的性能。发现:与常规MVDR相比,提出的算法表明MVDR GSA提供了在朝着期望的实际用户角度合成最大增益的有利协议,同时在不需要的用户方向上引入了深空值形成。结果,在所有模拟方案中,均通过20次运行评估了平均SINR,MVDR GSA的性能优于MVDR PSO的性能。此外,对于迭代次数<100,MVDR GSA可以实现对空值形成水平的良好控制,而MVDR PSO简单易实现,但需要更多的收敛时间才能获得较高的SINR。应用/改进:通常,在解决方案质量,稳定性和收敛速度方面,已经观察到MVDR GSA执行MVDR PSO。

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