Pattern Synthesis of Dual-band Shared Aperture Interleaved Linear Antenna Arrays

This paper presents an approach to improve the efficiency of an array aperture by interleaving two different arrays in the same aperture area. Two sub-arrays working at different frequencies are interleaved in the same linear aperture area. The available aperture area is efficiently used. The element positions of antenna array are optimized by using Invasive Weed Optimization (IWO) to reduce the peak side lobe level (PSLL) of the radiation pattern. To overcome the shortness of traditional methods which can only fulfill the design of shared aperture antenna array working at the same frequency, this method can achieve the design of dual-band antenna array with wide working frequency range. Simulation results show that the proposed method is feasible and efficient in the synthesis of dual-band shared aperture antenna array

Reduce side lobes using linear Antenna Arrays by comparing PSO, GA, and FPA algorithms

ان مصفوفات الهوائي الخطي هي نظام كهرومغناطيسي يستخدم على نطاق واسع في الاتصالات اللاسلكية الحديثة، وقد تم استخدام خوارزميات الميتاهوريستس لتقليل مستوى الفص الجانبي والوصول إلى الحل الأمثل. يستخدم هذه البحث ثلاث خوارزميات: الأولى، تحسين سرب الجسيمات، والثانية، الخوارزمية الجينية، والثالثة، خوارزمية تلقيح الزهور. يتكون كل اختبار من عدد العناصر 8و16و32و64و128و256. عنصرًا من مجموعة عناصر الهوائي. لتقليل مستوى الفص الجانبي وتركيز الطاقة المشعة في الفص الرئيسي، تقارن كل خوارزمية نمط الحزمة بنمط الحزمة النظرية. بالإضافة إلى ذلك، تمت مقارنة الخوارزميات بوجود نمط الحزمة النظرية، وتم اكتشاف وجود خوارزمية فائقة لكل عدد من عناصر الهوائي؛ في ن= 8 عند مقارنة خوارزمية التلقيح بالخوارزميات الأخرى، تم اكتشاف أنها قللت مستوى الفص الجانبي بقيمة 20.8492- ديسبل، والتي كانت متفوقة على الخوارزميات الأخرى. انخفض مستوى الفص الجانبي بمقدار 27.2992- ديسبل، عند مقارنة خوارزمية تحسين سرب الجسيمات مع الخوارزميات الأخرى عند ن=16, عندما ن = 32,64 يمثل خوارزمية تلقيح الجسيمات بشكل أكثر دقة من الخوارزميات الأخرى حيث انخفض الفص الجانبي إلى 28.3071-ديسبل و 28.0148- ديسبل، على التوالي. ان الخوارزمية الجينية متفوقة على الخوارزميات الأخرى عندما ن= 128و256، مما يقلل الفصوص الجانبية بنسبة 28.5568- ديسبل -28.6204- ديسبل، على التوالي.Linear Antenna Arrays (LAAs) are widely used electromagnetic systems in modern wireless communication, and Metaheuristics algorithms have been utilized to reduce side lobe level SLL and reach the optimal solution. This paper employs three algorithms: the first, Particle Swarm Optimization PSO, the second, Genetic Algorithm GA, and the third, Flower Pollination Algorithm FPA. Each test consists of N = 8, 16, 32, 64, 128, and 256 antenna array elements. To reduce SLL and the concentration of radioactive energy in the main lobe, each algorithm compares the beam pattern to the theoretical beam pattern. In addition, the algorithms were compared with the existence of the theoretical beam pattern, and it was discovered that there is a superior algorithm for each number of antenna elements; in N = 8, when comparing FPA to other algorithms, it was discovered that FPA reduced SLL by a value of -20.8492dB, which was superior to the other algorithms. SLL decreased by -27.2992dB when comparing PSO with other algorithms at N = 16. When N = 32,64 represents FPA more accurately than other algorithms where the SLL plummeted to -28.3071dB and -28.0148dB, respectively. GA is superior to other algorithms when N = 128,256, reducing SLL by -28.5568 dB and -28.6204 dB, respectively

Location Optimization for Square Array Antennas Using Differential Evolution Algorithm

In recent works thinned arrays giving minimum peak sidelobe levels for planar square antenna arrays are obtained using Hadamard difference sets. In the current work thinned array configurations giving lower peak sidelobe levels than those given in the literature are obtained for square arrays of 6×6, 8×8, 12×12, and 16×16 elements. Differential evolution algorithm is used in the determination of the antenna locations

Pattern Nulling of Linear Antenna Arrays Using Backtracking Search Optimization Algorithm

An evolutionary method based on backtracking search optimization algorithm (BSA) is proposed for linear antenna array pattern synthesis with prescribed nulls at interference directions. Pattern nulling is obtained by controlling only the amplitude, position, and phase of the antenna array elements. BSA is an innovative metaheuristic technique based on an iterative process. Various numerical examples of linear array patterns with the prescribed single, multiple, and wide nulls are given to illustrate the performance and flexibility of BSA. The results obtained by BSA are compared with the results of the following seventeen algorithms: particle swarm optimization (PSO), genetic algorithm (GA), modified touring ant colony algorithm (MTACO), quadratic programming method (QPM), bacterial foraging algorithm (BFA), bees algorithm (BA), clonal selection algorithm (CLONALG), plant growth simulation algorithm (PGSA), tabu search algorithm (TSA), memetic algorithm (MA), nondominated sorting GA-2 (NSGA-2), multiobjective differential evolution (MODE), decomposition with differential evolution (MOEA/D-DE), comprehensive learning PSO (CLPSO), harmony search algorithm (HSA), seeker optimization algorithm (SOA), and mean variance mapping optimization (MVMO). The simulation results show that the linear antenna array synthesis using BSA provides low side-lobe levels and deep null levels

On Development of Some Soft Computing Based Multiuser Detection Techniques for SDMA–OFDM Wireless Communication System

Space Division Multiple Access(SDMA) based technique as a subclass of Multiple Input Multiple Output (MIMO) systems achieves high spectral efficiency through bandwidth reuse by multiple users. On the other hand, Orthogonal Frequency Division Multiplexing (OFDM) mitigates the impairments of the propagation channel. The combination of SDMA and OFDM has emerged as a most competitive technology for future wireless communication system. In the SDMA uplink, multiple users communicate simultaneously with a multiple antenna Base Station (BS) sharing the same frequency band by exploring their unique user specific-special spatial signature. Different Multiuser Detection (MUD) schemes have been proposed at the BS receiver to identify users correctly by mitigating the multiuser interference. However, most of the classical MUDs fail to separate the users signals in the over load scenario, where the number of users exceed the number of receiving antennas. On the other hand, due to exhaustive search mechanism, the optimal Maximum Likelihood (ML) detector is limited by high computational complexity, which increases exponentially with increasing number of simultaneous users. Hence, cost function minimization based Minimum Error Rate (MER) detectors are preferred, which basically minimize the probability of error by iteratively updating receiver’s weights using adaptive algorithms such as Steepest Descent (SD), Conjugate Gradient (CG) etc. The first part of research proposes Optimization Techniques (OTs) aided MER detectors to overcome the shortfalls of the CG based MER detectors. Popular metaheuristic search algorithms like Adaptive Genetic Algorithm (AGA), Adaptive Differential Evolution Algorithm (ADEA) and Invasive Weed Optimization (IWO), which rely on an intelligent search of a large but finite solution space using statistical methods, have been applied for finding the optimal weight vectors for MER MUD. Further, it is observed in an overload SDMA–OFDM system that the channel output phasor constellation often becomes linearly non-separable. With increasing the number of users, the receiver weight optimization task turns out to be more difficult due to the exponentially increased number of dimensions of the weight matrix. As a result, MUD becomes a challenging multidimensional optimization problem. Therefore, signal classification requires a nonlinear solution. Considering this, the second part of research work suggests Artificial Neural Network (ANN) based MUDs on thestandard Multilayer Perceptron (MLP) and Radial Basis Function (RBF) frameworks fo