 Kurdistan Journal of Applied Research (KJAR) Print-ISSN: 2411-7684 | Electronic-ISSN: 2411-7706 Website: Kjar.spu.edu.iq| Email: kjar@spu.edu.iq Design of Adaptive Planar Microstrip Patch Array Operating at 28 GHz for 5G Smart Mobile System Zhwan Mohammed Rashid Asaad M. Jassim Al-Hindawi Department of Communication Engineering Department of Communication Engineering Technical College of Engineering Technical College of Engineering Sulaimani Polytechnic University Sulaimani Polytechnic University Sulaimani, Iraq Sulaimani, Iraq Zhwan.rashid@spu.edu.iq asaad.jasim@spu.edu.iq Volume 4 - Issue 2 | December 2019 DOI: 10.24017/science.2019.2.16 Received: 26 September 2019 Accepted: 15 December 2019 Abstract Smart antenna system has been studied extensively for the fifth generation of wireless communication systems, because it has made a system better performance of higher capacity and coverage as well as of power-saving. The present paper introduces a design of planar microstrip patch antenna array for a smart mobile system operating at 28 GHz. The present smart antenna has an adaptive radiation pattern that adjusts its main beam automatically to the desired direction by following the signal environment. This is based on the processing of an algorithm called the Least Mean Square (LMS) resulting in a change in the magnitude and phase of the feeding current for each element in the antenna array. From the obtained results, the main beam can be steered 180 degrees in the phi (azimuth) plane at a constant theta (elevation) angle. The planar antenna array was designed and simulated using CST Microwave Studio and MATLAB software that is used to find the required exciting current for each element. It is found that the antenna bandwidth is greater than 1 GHz while its gain is about 21 dB. Keywords: Smart antenna, Planar array, LMS Beamforming, Adaptive radiation pattern, Microstrip patch. Kurdistan Journal of Applied Research | Volume 4 – Issue 2 – December 2019 | 159 1. INTRODUCTION Nowadays, the term communication is going to grow in the world, different applications and features have been presented day by day. Since the mobile communication request is always rising, the requirement for high data rates, better coverage, high capacity, and high quality of services are relied upon to keep on increasing quickly, thus a more efficient use of a radio spectrum. The newest technology of mobile communications 5G, has advantages of improving capacity, coverage, connectivity, energy-efficient, and has the lowest cost as contrasted to the 4G [1]. To confront those requirements of 5G communications in various application situations, it is certainly, the use of smart antenna technology is needed. At present, to improve the performance of wireless communication systems, smart antennas are the most attractive choice; proposing their applications to mobile systems. As the requirements for a large number of users are increasing, the smart antennas have been solving the problem of limited bandwidth channels [2]. Nonetheless, the traditional antenna system that uses a single antenna transmits and receives data in equal directions. The feature of this antenna is a multi-directional pattern that distributes energy in all directions, this energy is regarded as the source of lost power for interference between different users or different base stations in different cells [3]. The block diagram of smart antenna systems was illustrates in figure 1 which consisting of an antenna array unit, with a signal processing unite which is the process of estimating the Direction of Arrival (DOA) and beamforming algorithm to automatically adjust the main beam direction in the response to signal environment, so that the antenna system essentially increases the required signal strength and prevents the interference signals by beamforming the required signal according to the Direction of Arrival (DOA) and nullifying strength against the unrequired signals [4]. Smart antenna systems are two classifications: beam switch system and adaptive antenna arrays [5]. A switched-beam antenna is a type of smart antenna in its simplest form, where it consists of several static beams in prearranged a direction that is used to serve the users. On the other hand, the smart antenna technology, that is the most advance type, is known as adaptive beamforming, which is consist of antenna arrays with smart signal processing capability to automatically adjust the beam pattern according to the changing signal environment. In addition to direct maximum radiation according to the direction of the desired mobile user, it makes nulls at interfering directions at the same time. Figure 1: Smart antenna system [6]. Kurdistan Journal of Applied Research | Volume 4 – Issue 2 – December 2019 | 160 Adaptive array antennas are regarded as one of the most common types of smart antenna arrays which are also known as digital antenna arrays and most newly, MIMO antenna arrays. With the algorithms of digital signal processing utilized to determine the signal direction of arrival (DOA), and for every component of the array, use them to compute the amplitude and phase of the feeding current which are important to guide the main beam of the antenna to the mobile goal [7]. The adaptive antenna system finds the signal direction of arrival based on various methods like: Multiple Signal Classification (MUSIC) and speculation of signal parameters via an algorithm called rotational invariance techniques (ESPRIT) [8],[9]. They include determining the array spatial spectrum, and from the peaks of that spectrum computing the DOA. The radiation pattern of the array is creating by beamforming techniques, by changing the amplitude and phase of the exciting signal to every array components to guide the main beam to the required target and nulling the pattern of the undesired goals. This can be achieved using a simple Finite Impulse Response (FIR) tapped delay line filter. To get the optimum beamformer the FIR filter could be changing the weights adaptively and reduced the Mean Square Error (MSE) between the required and existing signals [10]. The common algorithm that is used in the present work is the Least Mean Square (LMS) method. The first development of this algorithm was in 1960 by Widrow and Hoff [11],[12]. Using the set of Wiener Hopf equations with the stochastic gradient method, this developed procedure was later known as the least-mean-square (LMS) method. In Kong et al (2011) [13] The LMS algorithm is used to design the uniform linear array and rectangular planner array and then to prefer the method of designing heterotypic antenna. In this way, the size of the antenna is much smaller and the performance of the antenna is good in terms of system gaining and Signal to Interference Ratio (SIR). The experimental result proved that the 4x4 antenna array was further suitable for the environment that has a high interference. In Fadl et al (2013) [14] the C-band (4-8)GHz circular microstrip patch antenna array is studied with the designing beamforming algorithm which is used in the smart antenna system. The DOA of desired and interference signal was precisely assessed by Matrix Pencil (MP) method, and by applying the LMS method the main beam is directed to the desired signal while the nulls is directed to the interference signal. Nadu (2015) [15] presented two parts, the first demonstration was designing various rectangular microstrip patch components which were appropriate for beamforming techniques at the operating frequency of (1.8-2.4) GHz. The designed module was consist of eight linear arrays which can achieve a 15dB gain and directivity of greater than 58% as constructed to the conventional patches. In the second part of the beamforming procedure, the author suggested an NLVFF-RLS algorithm for concentration of the user power direction and negation of the interferer power direction. Mercy (2017) [16] presented the design of a smart antenna for cellular networks using a microstrip single patch antenna and a linear microstrip patch antenna array of (1x3) to meet triple ISM frequency band of 2.45GHz, 4.5GHz, and 7.1GHz. Thus due to these multiband frequencies, the smart antenna designed can be used for multiple applications Supratha & Robinson (2018) [17] analyzed the design of four elements of the microstrip patch array antenna to use it in mobile phone applications at the frequency of 28GHz. The implementation of the designed antenna was using economical FR-4 substrate material which has a good performance with the gain and efficiency. Furthermore, the result of this study demonstrated that in the frequency range of 22-34GHz the S11 has a response bellow -10dB. The present paper aims to introduce a design of a planar array consisting of 64 microstrip patches operating at frequency of 28.7 GHz. This array antenna has an adaptive radiation pattern based on the LMS beamforming algorithm. The proposed antenna can be used on the top of the BTS tower for the 5G system. Kurdistan Journal of Applied Research | Volume 4 – Issue 2 – December 2019 | 161 2. ANTENNA DESIGN 2.1 Single Element Design In this section, a rectangular microstrip patch element as a basic element of the proposed array is designed and analyzed for operating frequency of 28.7 GHz. The antenna element is designed on a Rogers CLTE-MW substrate material. For design purposes, 10 mils substrate of a dielectric constant (𝜖𝜖𝑟𝑟 = 3.1) and 0.0015 of a delta tangential are selected. This selection is more adequate for 5G applications [18]. For preliminary design, the following calculations for the single element dimensions are considered [19].The resonance length can be determined by: 𝐿𝐿𝑝𝑝 = 1 2𝑓𝑓𝑓𝑓�𝜀𝜀𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 �𝜇𝜇𝑜𝑜𝜀𝜀𝑜𝑜 − 2∆𝐿𝐿 (1) Where fr is the resonant frequency, c is the free-space velocity of light (3x108 m/s) and 𝜀𝜀𝑟𝑟 is the dielectric constant of substrate while: Δ𝐿𝐿 = 0.412ℎ (𝜀𝜀𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 + 0.3)( 𝑊𝑊𝑝𝑝 ℎ + 0.264) (𝜀𝜀𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 − 0.258)( 𝑊𝑊𝑝𝑝 ℎ + 0.8) (2) Where h is the substrate thickness and 𝜀𝜀𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 = 𝜀𝜀𝑟𝑟 + 1 2 + 𝜀𝜀𝑟𝑟 − 1 2�1 + 12ℎ 𝑊𝑊𝑝𝑝 (3) Now, to determine the patch width (Wp), the following equation can be used: 𝑊𝑊𝑝𝑝 = 1 2𝑓𝑓𝑟𝑟�𝜇𝜇𝑜𝑜𝜀𝜀𝑜𝑜 � 2 𝜀𝜀𝑟𝑟 + 1 = 𝑐𝑐 2𝑓𝑓𝑟𝑟 � 2 𝜀𝜀𝑟𝑟 + 1 (4) At a certain resonance frequency fr, the priliminary values of length Lp and width Wp can be found and these valuse are considerd to analyise the input and output characteristics of this antenna using CST Microwave Studio prgramm. Referred to figure 2, optimization of antenna characteristics is performed to find the optimum dimensions of the single element. These dimensions are illustrated in Table 1 for single element. These dimensions are then used for the array design with optimum inter-element spacing. Figure 2: The structure of the patch element. Kurdistan Journal of Applied Research | Volume 4 – Issue 2 – December 2019 | 162 Table 1: Final dimensions of the patch element Parameters Value(mm) Patch Width (Wp) 3.3 Patch length (Lp) 2.8 Substrate Width (Wsub) 5.5116 Substrate Length (Lsub) 5.0116 Feeding Width (Wf) 0.13 Feeding Length (Lf) 2 Insertion feeding (yo) 0.832 Patch thickness (t) 0.035 2.2 Array Design In a planar array, M × N elements are placed in a planar or rectangular grid where M represents No. of rows in the x-direction while N is No. of columns in the y-direction as shown in Figure 3 [19]. Figure 3: Geometry of the uniform planar array[19]. The principle of multiplication pattern is used to determine the field pattern of the entire array. The array factor of planar array is given by [19]: 𝐴𝐴𝐴𝐴(𝜃𝜃, 𝜑𝜑)𝑀𝑀×𝑁𝑁 = 𝐴𝐴𝐴𝐴𝑥𝑥 𝐴𝐴𝐴𝐴𝑦𝑦 𝐴𝐴𝐴𝐴(𝜃𝜃, 𝜑𝜑)𝑀𝑀×𝑁𝑁 = � � 𝑤𝑤𝑚𝑚𝑚𝑚 𝑒𝑒𝑗𝑗� (𝑚𝑚−1)(𝜓𝜓𝑥𝑥+𝛽𝛽𝑥𝑥)+(𝑚𝑚−1)�𝜓𝜓𝑦𝑦+𝛽𝛽𝑦𝑦�� 𝑀𝑀 𝑚𝑚=1 𝑁𝑁 𝑚𝑚=1 (5) Where: 𝜓𝜓𝑥𝑥 = 𝑘𝑘𝑑𝑑𝑥𝑥 sin 𝜃𝜃 cos 𝜑𝜑 and 𝜓𝜓𝑦𝑦 = 𝑘𝑘𝑑𝑑𝑦𝑦 sin 𝜃𝜃 sin 𝜑𝜑. 𝛽𝛽𝑥𝑥 = −𝑘𝑘𝑑𝑑𝑥𝑥 𝑠𝑠𝑠𝑠𝑠𝑠𝜃𝜃𝑑𝑑 𝑐𝑐𝑐𝑐𝑠𝑠𝜑𝜑𝑑𝑑 and 𝛽𝛽𝑦𝑦 = −𝑘𝑘𝑑𝑑𝑦𝑦 𝑠𝑠𝑠𝑠𝑠𝑠𝜃𝜃𝑑𝑑 𝑠𝑠𝑠𝑠𝑠𝑠𝜑𝜑𝑑𝑑 are phase delays which are used to steer the main beam to desired angle (𝜃𝜃𝑑𝑑, 𝜑𝜑𝑑𝑑), 𝑑𝑑𝑥𝑥, 𝑑𝑑𝑦𝑦 are the distances between elements in x & y directions, respectively. 𝑘𝑘 = 2𝜋𝜋 𝜆𝜆� is the propagation constant in free space, and 𝑤𝑤𝑚𝑚𝑚𝑚 is the complex weights of excitation current for the individual element. Kurdistan Journal of Applied Research | Volume 4 – Issue 2 – December 2019 | 163 The total electric field for the actual planar microstrip patch antenna array can be obtained by multiplying the electric field of single patch element by the array factor as follow [20]: 𝐸𝐸𝑡𝑡𝑜𝑜𝑡𝑡𝑡𝑡𝑡𝑡(𝜃𝜃, 𝜑𝜑) = 𝐴𝐴𝐴𝐴(𝜃𝜃, 𝜑𝜑) ∗ 𝐸𝐸(𝜃𝜃, 𝜑𝜑) (6) Where 𝐸𝐸(𝜃𝜃, 𝜑𝜑) = 𝐸𝐸𝜃𝜃(𝜃𝜃, 𝜑𝜑) + 𝐸𝐸𝜑𝜑(𝜃𝜃, 𝜑𝜑) (7) And � 𝐸𝐸𝜃𝜃 𝐸𝐸𝜑𝜑 � = � 𝑒𝑒𝜃𝜃𝑅𝑅 sin𝜓𝜓𝑎𝑎 𝜓𝜓𝑎𝑎 cos 𝜓𝜓𝑏𝑏 cos(kt cos 𝜃𝜃) sin 𝜑𝜑 𝑒𝑒𝜑𝜑𝑅𝑅 sin𝜓𝜓𝑎𝑎 𝜓𝜓𝑎𝑎 cos 𝜓𝜓𝑏𝑏 cos(kt cos 𝜃𝜃) cos 𝜑𝜑 cos 𝜃𝜃 � (8) � 𝜓𝜓𝑡𝑡 𝜓𝜓𝑏𝑏 � = � 𝑘𝑘𝑜𝑜 𝐿𝐿𝑝𝑝 2 𝑐𝑐𝑐𝑐𝑠𝑠𝜑𝜑𝑠𝑠𝑠𝑠𝑠𝑠𝜃𝜃 𝑘𝑘𝑜𝑜 𝑊𝑊𝑝𝑝 2 𝑠𝑠𝑠𝑠𝑠𝑠𝜑𝜑𝑠𝑠𝑠𝑠𝑠𝑠𝜃𝜃 � (9) 𝑅𝑅 = 𝑗𝑗� 𝐸𝐸𝑧𝑧 𝜋𝜋𝜋𝜋 �� 𝑒𝑒−𝑗𝑗𝑘𝑘𝑜𝑜𝑟𝑟 𝑓𝑓 � (10) The designed array was analyzed and simulated using CST Microwave studio, and it consists of 8x8 patch elements (M=8, N=8) placed in a rectangular grid. Figure 4 shows the studied array antenna with an inter-element distance of 0.5λ in both x & y directions. Figure 4: Geometry of 8x8 microstrip patch antenna array. 3. ADAPTIVE BEAMFORMING ALGORITHM 3.1 Beamforming Assumption Let the proposed array be composed of 𝑀𝑀 × 𝑁𝑁 elements, and let it receives D narrowband source signals sd(t) from desired users arriving at directions ((θ1, φ1), (θ2, φ2),…..,(θD, φD)) as shown in Figure 1. The array also receives I narrowband source signals si(t) from undesired (or interference) users arriving at directions ((θ1,φ1),(θ2,φ2),.…, (θI,φI)). The desired users signal vector XD(t) can be represented as [21]: Kurdistan Journal of Applied Research | Volume 4 – Issue 2 – December 2019 | 164 𝑋𝑋𝐷𝐷(𝜋𝜋) = � 𝑎𝑎(𝜃𝜃𝑑𝑑, 𝜑𝜑𝑑𝑑) 𝐷𝐷 𝑑𝑑=1 𝑠𝑠𝑑𝑑(𝜋𝜋) (11) Where 𝑎𝑎(𝜃𝜃𝑑𝑑, 𝜑𝜑𝑑𝑑) is the M × N array steering vector which represents the array response at direction (𝜃𝜃𝑑𝑑, 𝜑𝜑𝑑𝑑) which is given by: 𝑎𝑎(𝜃𝜃𝑑𝑑, 𝜑𝜑𝑑𝑑) = �𝑒𝑒−𝑗𝑗( (𝑚𝑚−1)𝛽𝛽𝑥𝑥+(𝑚𝑚−1)𝛽𝛽𝑦𝑦)� 1 ≤ 𝑚𝑚 ≤ 𝑀𝑀, 1 ≤ 𝑠𝑠 ≤ 𝑁𝑁 (12) Where 𝛽𝛽𝑥𝑥 and 𝛽𝛽𝑦𝑦 were indicated in equation (5) .The desired users signal vector XD(t) of equation (10) can be rewritten as: 𝑋𝑋𝐷𝐷(𝜋𝜋) = 𝐴𝐴𝐷𝐷 𝑆𝑆(𝜋𝜋) (13) Where AD is three dimensional matrixes (M × N × D) of the desired users signal direction vectors and is given by: 𝐴𝐴𝐷𝐷 = [𝑎𝑎(𝜃𝜃1, 𝜑𝜑1), 𝑎𝑎(𝜃𝜃2, 𝜑𝜑2), … … , 𝑎𝑎(𝜃𝜃𝐷𝐷, 𝜑𝜑𝐷𝐷)] (14) And 𝑆𝑆(𝜋𝜋) is the D × 1 matrix of desired users source waveform vector defined as: 𝑆𝑆(𝜋𝜋) = [𝑠𝑠1(𝜋𝜋) 𝑠𝑠2(𝜋𝜋) … 𝑠𝑠𝐷𝐷(𝜋𝜋)]𝑇𝑇 (15) We also let to define the undesired (or interference) users signal vector 𝑋𝑋𝐼𝐼(𝜋𝜋) as: 𝑋𝑋𝐼𝐼(𝜋𝜋) = 𝐴𝐴𝐼𝐼 𝐼𝐼(𝜋𝜋) (16) Where 𝐴𝐴𝐼𝐼 is the three dimensional matrixes (M × N × I) of the undesired users signal direction vectors and is given by: 𝐴𝐴𝐼𝐼 = [𝑎𝑎(𝜃𝜃1, 𝜑𝜑1), 𝑎𝑎(𝜃𝜃2, 𝜑𝜑2), … … , 𝑎𝑎(𝜃𝜃𝐼𝐼, 𝜑𝜑𝐼𝐼)] (17) And 𝐼𝐼(𝜋𝜋)is the I × 1 matrix of undesired (or interference) user’s source waveform vector defined as: 𝐼𝐼(𝜋𝜋) = [𝑠𝑠1(𝜋𝜋) 𝑠𝑠2(𝜋𝜋) … 𝑠𝑠𝐼𝐼(𝜋𝜋)]𝑇𝑇 (18) The overall received signal vector X(t) is given by the superposition of the desired users signal vector XD(t), undesired (or interference) users signal vector XI(t), and an M×N vector n(t) which represents the white Gaussian noise for each element of the designed array. Hence, 𝑋𝑋(𝜋𝜋) can be written as: 𝑋𝑋(𝜋𝜋) = 𝑋𝑋𝐷𝐷(𝜋𝜋) + 𝑋𝑋𝐼𝐼(𝜋𝜋) + 𝑠𝑠(𝜋𝜋) (19) Where 𝑠𝑠(𝜋𝜋) can be represented by matrix vector as: 𝑠𝑠(𝜋𝜋) = ⎣ ⎢ ⎢ ⎢ ⎡ 𝑠𝑠11 𝑠𝑠12 … 𝑠𝑠1𝑁𝑁 𝑠𝑠21 𝑠𝑠22 ⋯ 𝑠𝑠2𝑁𝑁 𝑠𝑠31 𝑠𝑠32 ⋯ 𝑠𝑠3𝑁𝑁 ⋮ ⋮ ⋯ ⋮ 𝑠𝑠𝑀𝑀1 𝑠𝑠𝑀𝑀2 ⋯ 𝑠𝑠𝑀𝑀𝑁𝑁⎦ ⎥ ⎥ ⎥ ⎤ 𝑇𝑇 (20) Kurdistan Journal of Applied Research | Volume 4 – Issue 2 – December 2019 | 165 In the case that we consider all sources at the same time, the signal at the mnth element will be [5]: 𝑥𝑥𝑚𝑚𝑚𝑚(𝜋𝜋) = � 𝑎𝑎(𝜃𝜃𝑑𝑑, 𝜑𝜑𝑑𝑑) 𝐷𝐷 𝑑𝑑=1 𝑠𝑠𝑑𝑑(𝜋𝜋) + �𝑎𝑎(𝜃𝜃𝑖𝑖, 𝜑𝜑𝑖𝑖) 𝐼𝐼 𝑖𝑖=1 𝑠𝑠𝑖𝑖(𝜋𝜋) + 𝑠𝑠𝑚𝑚𝑚𝑚(𝜋𝜋) (21) Then the total array output will be: 𝑦𝑦(𝜋𝜋) = � � 𝑤𝑤𝑚𝑚𝑚𝑚 𝑀𝑀 𝑚𝑚=1 𝑥𝑥𝑚𝑚𝑚𝑚(𝜋𝜋) 𝑁𝑁 𝑚𝑚=1 (22) 3.2 LMS Algorithm The LMS algorithm is a method of adaptive weighted beamforming of radiant weights. The weight vector for the array is started with such that it is updated frequently for optimal weight. The signals received by these array elements are multiplied with the weight vector as demonstrated in figure 1. These weighted signals are added to obtain the beamformer output [22]. For the beamformer, the comparison between the output from the array antenna Y(n) and the desired signal Sd(n) that must be in similar to the reference signal, is strongly taken into consideration for the error minimization between the desired signal and the reference signal. At time n, where n (bold letter) is the overall number of snapshots occupied, the output Y(n), is determined by a summation of the signals at the M × N antenna which is presented as [23]: 𝑌𝑌(𝐧𝐧) = 𝑊𝑊𝐻𝐻𝑋𝑋(𝐧𝐧) (23) Where W represents the complex weights vector while 𝑋𝑋(𝐧𝐧) represents the received signal vector given in (19). Therefore the error signal is expressed as: 𝑒𝑒(𝐧𝐧) = 𝑆𝑆𝑑𝑑(𝐧𝐧) − 𝑊𝑊𝐻𝐻𝑋𝑋(𝐧𝐧) (24) The beamformer normally uses the error signal 𝑒𝑒(𝐧𝐧) to adjust the complex weights vector W in an adaptive way to reduce the Mean Squared Error (MSE). Using the steepest descent method the LMS algorithm calculates and updates recursively the weights vector W. Successive corrections to the weights vector W are achieved resulting in minimum mean square error. The weights vector W may be started arbitrarily and updated based on the given LMS equation: 𝑊𝑊(𝐧𝐧 + 1) = 𝑊𝑊(𝐧𝐧) + 𝜇𝜇𝑋𝑋(𝐧𝐧) 𝑒𝑒∗(𝐧𝐧) (25) Where 𝑊𝑊(𝐧𝐧 + 1) defined as the weights vector to be calculated at iteration n+1 while μ represents the size of the LMS step that relates to the convergence rate, where describes how quickly the LMS reaches a steady state. The size of an adaptive step must be within the range defined as [23]: 0 < 𝜇𝜇 < � 1 𝜆𝜆𝑚𝑚𝑡𝑡𝑥𝑥 � (26) Where 𝜆𝜆𝑚𝑚𝑡𝑡𝑥𝑥 represents the maximum Eigenvalues of the correlation matrix Rxx which is given by following equation [23]: Kurdistan Journal of Applied Research | Volume 4 – Issue 2 – December 2019 | 166 𝑅𝑅𝑥𝑥𝑥𝑥 = 𝐸𝐸{𝑋𝑋(𝑠𝑠) 𝑋𝑋𝐻𝐻(𝑠𝑠)} (27) Where E{.} is defined as ensemble average and (.)H represents the Hermitian transposition operator. 4. RESULTS AND DISCUSSION 4.1. Simulation of Single Element and Array The proposed antenna of dimensions given in Table 1 has been constructed in CST Microwave Studio. The simulated results for the reflection coefficient are shown in figure 5. It can be seen that the antenna element can achieve the bandwidth from 28.209 GHz to 29.237 GHz for |S11|<-10dB, for the resonant frequency of 28.7GHz. For the whole array of 8x8 elements, the same input characteristics (S11) are obtained. Figure 4: The reflection coefficient of the single patch element. The radiation pattern of an antenna is important for determining the output characteristics which include beamdwidth, beam shape, directivity and radiated power. So in figure 6, 3D radiation pattern of single patch antenna element is plotted. Figure 7, shows the radiation pattern for 8x8 array elements with a uniform amplitude and phase. The main lobe is more directive than single element pattern. Figure 6: Far-field Radiation Pattern for single element. Kurdistan Journal of Applied Research | Volume 4 – Issue 2 – December 2019 | 167 Figure 7: Far-Field Radiation Pattern for the uniform array. 4.2. Simulation of Adaptive Beamforming To evaluate the performance of adaptive beamforming that is using the LMS algorithms to form the pattern when the smart antenna system uses a planar antenna array in its input, we consider a planar antenna array with 8×8 elements and half-wavelength element spacing in x and y directions (dx=dy=λ/2). The impinging desired signal on the array is supposed from the one direction 𝜃𝜃𝑑𝑑1 = 45 𝑜𝑜, 𝜑𝜑𝑑𝑑1 = 30 𝑜𝑜. Two interferers’ signals are supposed to impinge the array from the directions 𝜃𝜃𝑖𝑖1 = 10 𝑜𝑜, 𝜑𝜑𝑖𝑖1 = 20 𝑜𝑜, and 𝜃𝜃𝑖𝑖2 = 30 𝑜𝑜, 𝜑𝜑𝑖𝑖2 = 10 𝑜𝑜, in the white Gaussian noise channel. The iteration number has been set to 500 iterations and the step size of the LMS algorithm is chosen as 𝜇𝜇 = 0.001. The simulation based on MATLAB program showed the possibility of forming the radiation pattern in the angle of the desired direction and suppressing the angles of undesired directions as follows. Figure 8 represents the beamforming pattern at the phi-plane for a constant theta- angle (𝜃𝜃𝑑𝑑1 = 45 𝑜𝑜). The x-axis shows the phi-plane angle (in degrees), while, the y-axis shows the total normalized radiation pattern electric field. The figure also shows that the main lobe of radiation is shaped towards the desired angle 𝜑𝜑𝑑𝑑1 = 30 𝑜𝑜, while the lower side lobes appear in other directions. From the result, the HPBW (φaz) is approximately equal to 20o. Figure 8: Radiation pattern for 8X8 antenna array in phi (Azimuth)-plane. Kurdistan Journal of Applied Research | Volume 4 – Issue 2 – December 2019 | 168 Figure 9 shows the radiation pattern at the theta-plane for a constant phi-angle (𝜑𝜑𝑑𝑑1 = 30 𝑜𝑜), where the main lobe of radiation is at the desired theta-angle (𝜃𝜃𝑑𝑑1 = 45 𝑜𝑜). The x-axis shows the theta-plane angle (in degrees), while the y-axis shows the total normalized electric field. The HPBW (θel) is approximately equal to 18o, and the array directivity (D) is approximately equal to 21.7dB. Figure 10 illustrates the progression of amplitudes and phases of the feeding currents that are used to excite the planar array elements. Figure 9: Radiation pattern for 8X8 antenna array in theta (Elevation) - plane. Figure 10: (a) The distribution of the amplitude. (b) The distribution of the phase of the excitation currents for planar array elements. Using the weights of exciting current to each array element, that obtained by the LMS algorithm, the antenna is capable to direct narrow beams towards the desired user. The complex weights consist of amplitude and phase which are manually fed to the CST simulator. The array is therefore capable of steering narrow beams towards desired users at the exact resonant frequency as shown in figure 11. It is found that the antenna gain is 20.9 dB. Kurdistan Journal of Applied Research | Volume 4 – Issue 2 – December 2019 | 169 Figure 11: 3D Radiation Pattern of the 8x8 antenna array of different weights with steered main beam at angles. Figure 12 shows the mean square error (MSE) related to the iteration number, it is observed that the value of the MSE decreases while the amount of iterations increases. It is noted that the LMS algorithm needs 20 iterations to reach a minimum error value between the reference and actual outputs, and then the algorithm stabilizes. Furthermore, Figure 13 shows the obtained array output and the desired signal tracks after 20 iterations. Figure 12: Mean square error Vs No. of iteration. Figure 13: Comparison of desired signal and actual array output Kurdistan Journal of Applied Research | Volume 4 – Issue 2 – December 2019 | 170 Finally, to obtain the steering radiation pattern, figure 14 shown that the total field radiation pattern capable of steering in the phi (Azimuth)-plane from 0o to 180o at the constant theta- angle (θd=40º). Therefore, it could be needed two sectors at the base station to cover 360o. Figure 15 also shows the total field radiation pattern coverage at the theta (Elevation)-plane for a constant phi-angle (φd=40o). It is observed that there is a covering angle of 60o in the elevation plan without beam interference. Figure 14: Radiation pattern for phi (Azimuth)-plan. Figure 15: Radiation pattern for theta (Elevation)-plan. . Kurdistan Journal of Applied Research | Volume 4 – Issue 2 – December 2019 | 171 5. CONCLUSION The present paper has discussed the possibility of designing a planar microstrip patch antenna array based on adaptive beamforming of LMS algorithm and it can be used for the smart antenna system. An array of 8×8 microstrip antenna elements has been designed to operate at 28.7GHz for the 5G networks with an inter-element spacing of 0.5λ. A considerable bandwidth of this antenna was over 1.2 GHz and a gain of about 21 dB. The main beam of the studied antenna can be steered towards the required user in the desired direction of (θd, φd) using the beamforming of LMS algorithm. The program of CST Microwave Studio has been utilized to optimizing the design of antenna while MATLAB software program has been used to obtain array patterns and weights factors of amplitude and phases for the exciting currents for each array element. The designed antenna array can scan an angle from 0o to 180o in phi- plane at constant theta-angle which is suitable for one sector base station; therefore, two sectors of this array at the base station could satisfy the smart mobile system. REFERENCES [1] T. Varum, A. Ramos, and J. N. Matos, “Planar microstrip series-fed array for 5G applications with beamforming capabilities,” 2018 IEEE MTT-S Int. Microw. Work. Ser. 5G Hardw. Syst. Technol. IMWS-5G 2018. [2] M. Jain and R. P. Agarwal, “Capacity & coverage enhancement of wireless communication using smart antenna system,” Proceeding IEEE - 2nd Int. Conf. Adv. Electr. Electron. Information, Commun. Bio- Informatics, IEEE - AEEICB 2016, USA, pp. 310–313, 2016. [3] C. A. Balanis and P. I. Ioannides, Introduction to Smart Antennas, A Publication in the Morgan &Claypool Publishers’ series, vol. 2, no. 1. 2007. [4] I. A. 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Vigneshwari, “Design and Simulation of Smart Antenna Array Using Adaptive Beam forming Method,” Certif. Int. J. Eng. Sci. Innov. Technol., vol. 9001, no. 6, pp. 2319–5967, 2008. 1. INTRODUCTION 2. ANTENNA DESIGN 2.1 Single Element Design 2.2 Array Design 3. ADAPTIVE BEAMFORMING ALGORITHM 3.1 Beamforming Assumption 3.2 LMS Algorithm 4. RESULTS AND DISCUSSION 4.1. Simulation of Single Element and Array 4.2. Simulation of Adaptive Beamforming