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Smart antennas have received great attention for their potentials to enable communication and perception functions at the same time. However, realizing the function synthesis remains an open challenge, and most existing system solutions are limited to narrow operating bands and high complexity and cost. Here, we propose an externally perceivable leaky-wave antenna (LWA) based on spoof surface plasmon polaritons (SSPPs), which can realize adaptive real-time switching between the “radiating” and “non-radiating” states and beam tracking at different frequencies. With the assistance of computer vision, the smart SSPP-LWA is able to detect the external target user or jammer, and intelligently track the target by self-adjusting the operating frequency. The proposed scheme helps to reduce the power consumption through dynamically controlling the radiating state of the antenna, and improve spectrum utilization and avoid spectrum conflicts through intelligently deciding the radiating frequency. On the other hand, it is also helpful for the physical layer communication security through switching the antenna working state according to the presence of the target and target beam tracking in real time. In addition, the proposed smart antenna can be generalized to other metamaterial systems and could be a candidate for synaesthesia integration in future smart antenna systems.

In this study, a novel blind adaptive algorithm with discrete periodic variables is introduced. The proposed discrete periodic variable algorithm (DPVA) has been applied to the new designed 7-element antenna array. The DPVA is based on minimizing the output power to steer null (or nearly null) gain in the direction of interference. Discrete phase shift leads to the use of hybrid phase shifter in practice and thus reduction of implementation costs. In addition, the proposed algorithm has low computational complexity. Results show that DPVA has fast and reliable convergence. It is converged within less than 400 iterations and less than 1 millisecond time duration. The null depth created by this algorithm is 90 dB which is an indication of pure cancellation of the interference. Furthermore, the effect of a number of interference sources is investigated. It is shown that the null depth is decreased by the increase of interference sources. In the studied 7-element array, increasing the interference sources up to 6 decreases the null depth to 20 dB. [ABSTRACT FROM AUTHOR]

In order to improve the communication quality and transmission rate of WLAN, the key technologies of smart antenna in WLAN based on adaptive array are proposed. The adaptive array is used to suppress the WLAN interference signal, and the feedback from the output is used to adjust the weight loaded on the signal, and the optimal weight corresponding to the linear constraint minimum variance is obtained to achieve beamforming optimization. On this basis, the optimal antenna selection algorithm and the sub-optimal antenna selection algorithm are designed, and the sub-channel matrix with the maximum capacity can be obtained by repeated iterative calculation, so as to realize the smart antenna selection and further optimize the performance of WLAN. Experimental results show that the smart antenna signal has 100% coverage in WLAN compared with conventional methods. When the transmission rate is greater than 35 mb/s, the delay of the study method is not jitter compared to the contrast method. When the transmission rate is greater than 35 mb/s, the packet loss rate of the proposed method remains stable, while the packet loss rate of the two comparison methods begins to increase. When the transmission rate is greater than 12 mb/s, the throughput of the proposed method is more stable than the other methods. In conclusion, this method has the characteristics of large signal coverage, low delay, low packet loss rate and high throughput, and can be widely used in practice. The contribution of this study is that the weight of the antenna array can be dynamically adjusted through the adaptive algorithm, thus optimizing the quality of the received signal and improving the receiving sensitivity. [ABSTRACT FROM AUTHOR]

In this paper a comparative study, restricted to one-dimensional stationary case, between several Direction of Arrival (DOA) estimation algorithms of narrowband signals is presented. The informative signals are corrupted by an Additive White Gaussian Noise (AWGN), to show the performance of each method by applying directly the algorithms without pre-processing techniques such as forward-backward averaging or spatial smoothing. [ABSTRACT FROM AUTHOR]

In this paper, a beam selection method is presented for a switched beam antenna (SBA) specifically designed and built for long-range wireless communications. The proposed SBA operates with eight beams for automatic direction finding (DF) and 360-degree beam activation at 900-1400 MHz frequency range. Remarkably, this method employs a simple switch board and one sampler without synchronization requirement, avoiding the need for complex circuitry typically required for separate beam sampling. Instead, a convolutional neural network (CNN) processes the sampled signals to accurately determine the signal direction. The proposed CNN is trained by using a two-stage method with both artificial and real data to achieve fine-tuned accuracy of 99.79% with artificially generated data and 95% with real data for signal-to-noise ratios (SNR) above −10 dB. Additionally, the CNN is optimized to run on low-end hardware. A significant 16% accuracy enhancement compared to the direction estimation method based on conventional power measurement (PM) under identical conditions is demonstrated through real-time beam selection using a commercially available Raspberry Pi board. This proposed low-cost, wideband, high-accuracy, and highly efficient SBA is an excellent candidate for practical applications, showcasing the effectiveness and accuracy of data-driven methods in real world, especially in low SNR scenarios ( $\lt -10$ dB).

In this paper, for the first time we present the complete design of a dynamic metasurface antenna (DMA) array at the 60 GHz millimeter-wave (mmWave) industrial, scientific, and medical (ISM) band. First, a novel complementary electric inductive-capacitive (CELC) metamaterial element (unlike conventional rectangular CELC) is designed to resonate around 60.5 GHz. The proposed CELC meta-element in its resonance state manifests dispersive characteristics and exhibits significant left-handed metamaterial properties such as negative group refractive index, negative effective permittivity, and negative group velocity, which are thoroughly elucidated. A low-loss V-band planar substrate-integrated waveguide (SIW) structure is designed at dominant $TE_{10}$ to excite the CELC meta-element by an in-plane magnetic field. Two PIN diodes are loaded in the small capacitive gap between the CELC meta-element and the SIW structure. The switching state of the PIN diodes readily renders the meta-element either radiating or non-radiating. The difference between radiating and non-radiating states is more than 11 dB. Consequently, a fully addressable digital tunable DMA element is formed. Then, a one-dimensional DMA is designed by embedding 16 such meta-elements into the upper conducting wall of the edge-fed SIW structure for electronic steering with high gain, high radiation efficiency, and low side lobe levels. The radiation state of each CELC meta-element is dynamically controlled through a high-speed field programmable gate array (FPGA). The DC biasing network for PIN diodes at such high frequency is meticulously designed and integrated using 4-layer standard printed circuit board (PCB) technology. The parallelized biasing network of PIN diodes through a high-speed FPGA enables agile dynamic control over the radiation pattern of the entire digitally coded metasurface aperture. Versatile beam synthesis (such as narrow beams, wide beams, and multiple beams) is achieved based on different digital coding combinations. The prototypes of the single DMA element and 16-element 1-dimensional DMA array are fabricated and verified through practical measurements. Simulated and measured results show good agreement. The beam-switching agility is quantified and observed to be within 5 ns, indicating significant promise for mmWave applications with ultra-low latency. The proposed DMA is a potential enabler to unfold a diverse range of next-generation mmWave wireless applications such as agile electronic beam-steering, adaptive beamforming and beam-shaping, holographic computational imaging, mmWave industrial wireless communication, cognitive radars, as well as integrated sensing and communication (ISAC).

The paper proposes a four-directional beamforming switched antenna system based on Butler matrix for 28 GHz mmWave. It is made up of a 90-degree coupler, a crossover, and a 45-degree phase shifter. A $4\times 4$ Butler matrix is combined with a 28 GHz patch antenna to form a Butler network antenna. Four directions of the radiation beam can be changed when the input position is changed. From the simulation and measurement results, the measured reflection coefficients cover 27.9-29.3 GHz at all four ports with a standard reflection coefficient of -10 dB and four different angles of 15°, -46°, +44° and -13° for switching beam direction. The antenna’s compact size of $39.1\times 45.6\,\,{\mathrm {mm}}^{2}$ ( $3.65\lambda _{0}\times 4.26\lambda _{0}$ ) makes it ideal for use in millimeter wave bands for 5G NR n258, and n261 applications, while also providing a high gain of 11.5 dBi.

To improve radio access capability, sky connections relying on satellites or unmanned aerial vehicles (UAV), as well as high-altitude platforms (HAP) will be exploited in 6G wireless communication systems, complementing terrestrial networks. For long-distance communication, a large smart antenna will be used that is characterized by high amounts of power consumed by digital beamformers. This paper focuses on reducing power consumption by relying on a thinned smart antenna (TSA). The performance of TSA is investigated in the sub-6 GHz band. The differential evolution (DE) algorithm is used to optimize excitation weights of the individual dipoles in the antenna array and these excitation weights are then used in TSA for beamforming, with signal processing algorithms deployed. The DE technique is used with the least mean square, recursive least square and sample matrix inversion algorithms. The proposed method offers almost the same directivity, simultaneously ensuring lower side lobes (SLL) and reduced power consumption. For a TSA of 20, 31, and 64 dipoles, the power savings are 20%, 19.4%, and 17.2%, respectively. SLL reductions achieved, in turn, vary from 5.2 dB to 8.1 dB. [ABSTRACT FROM AUTHOR]

Now a days, huge amount of data is communicated through channels in wireless network. It requires an efficient parallel operation for the optimal utilization of frequency, time allocation and coding model for signal subspace factorization in smart antenna. In view of this requirement, an efficient reconfigurable optimal source detection and beam allocation algorithm (RoSDBA) is proposed. The proposed algorithm is able to allocate desired signal to the user space to reduce the noise and also for efficient allocation of subspace to remove disturbance in all directions. The proposed method efficiently utilizes the antenna array elements by accurate identification and allocation of antenna array elements such as individual radiators, radiation beam, signal strength, and disturbance factor. With respect to simulation analysis, the proposed method shows better performance for the resolution, radiation beam allocations, identification bias, distribution factor and time taken for the detection of various array arrangements and source numbers. [ABSTRACT FROM AUTHOR]

To improve radio access capability, sky connections relying on satellites or unmanned aerial vehicles (UAV), as well as high-altitude platforms (HAP) will be exploited in 6G wireless communication systems, complementing terrestrial networks. For long-distance communication, a large smart antenna will be used that is characterized by high amounts of power consumed by digital beamformers. This paper focuses on reducing power consumption by relying on a thinned smart antenna (TSA). The performance of TSA is investigated in the sub-6 GHz band. The differential evolution (DE) algorithm is used to optimize excitation weights of the individual dipoles in the antenna array and these excitation weights are then used in TSA for beamforming, with signal processing algorithms deployed. The DE technique is used with the least mean square, recursive least square and sample matrix inversion algorithms. The proposed method offers almost the same directivity, simultaneously ensuring lower side lobes (SLL) and reduced power consumption. For a TSA of 20, 31, and 64 dipoles, the power savings are 20%, 19.4%, and 17.2%, respectively. SLL reductions achieved, in turn, vary from 5.2 dB to 8.1 dB.

In this article, an optimal beamforming technique using a low cost intelligent Rotman lens antenna is proposed. The proposed technique enjoys full diversity with high coding gain, has a very low encoding and decoding complexity, and does not require channel knowledge at any transmitting or receiving antenna. From our simulation results, the proposed technique outperforms the best known non-coherent multi-antenna techniques. Furthermore, we prove that the BER expressions obtained from the theoretical analysis enjoy full diversity gain and match our conducted simulations.

Massive MIMO (multiple-input multiple-output) is a multi-user MIMO technology that can provide high-speed multimedia services in 5G wireless networks using sub-6 GHz and millimeter wave bands. The massive MIMO (MMIMO) installs array antennas in the base stations, using hundreds of transceivers with other RF modules. One of the drawbacks of the MMIMO system is its huge power consumption, and the beamforming network with RF modules for a large number of antennas is the main contributor to the power consumption. In this paper, a novel beamforming method is proposed for the low power consumption of an MMIMO system. The proposed thinned smart antenna (TSA) of a semi-circular array produces a secure beam toward the user’s terminal with reduced interference. By thinning the antenna array, some of the antenna elements are kept off, resulting in less power consumption, while the array pattern remains the same as a fully populated array with a reduced side lobe level (SLL). The sub-6 GHz band of 5 GHz is used for the design of thinned array antennas. The genetic algorithm (GA) is used to determine the array sequence in thinning, and the adaptive signal processing algorithms least mean square (LMS), recursive least square (RLS), and sample matrix inversion (SMI) are used for the beamforming of the TSA, and the corresponding algorithms are GA-LMS, GA-RLS, and GA-SMI. The power saving of 40% to 55% is achieved using TSA. The maximum SLL reductions of 13 dB, 12 dB, and 14 dB are achieved for TSA using GA-LMS, GA-RLS, and GA-SMI algorithms, respectively.

Smart antenna technologies improve spectral efficiency, security, energy efficiency, and overall service quality in cellular networks by utilizing signal processing algorithms that provide radiation beams to users while producing nulls for interferers. In this paper, the performance of such ML solutions as the support vector machine (SVM) algorithm, the artificial neural network (ANN), the ensemble algorithm (EA), and the decision tree (DT) algorithm used for forming the beam of smart antennas are compared. A smart antenna array made up of 10 half-wave dipoles is considered. The ANN method is better than the remaining approaches when it comes to achieving beam and null directions, whereas EA offers better performance in terms of reducing the side lobe level (SLL). The maximum SLL is achieved using EA for all the user directions. The performance of the ANN algorithm in terms of forming the beam of a smart antenna is also compared with that of the variable-step size adaptive algorithm. [ABSTRACT FROM AUTHOR]

Antenna design has significantly advanced as a result of the widespread need for wireless communications and data substitution through wireless devices. The research article's goal is to provide a conceptual framework, difficulties, and opportunities for a source as well as a general overview of the antenna used in wireless communications applications. In this proposed research, we will go over a variety of topics related to mobile communication and fifth generation (5G) technologies, including its pros and benefits. A thorough comparison between the expected properties of the antennas and each generation, from 1st generation (1G) to 5G, is also included. This article also provides an overview of the investigated 5G technologies and various antenna designs. [ABSTRACT FROM AUTHOR]

Smart antenna technologies improve spectral efficiency, security, energy efficiency, and overall service quality in cellular networks by utilizing signal processing algorithms that provide radiation beams to users while producing nulls for interferers. In this paper, the performance of such ML solutions as the support vector machine (SVM) algorithm, the artificial neural network (ANN), the ensemble algorithm (EA), and the decision tree (DT) algorithm used for forming the beam of smart antennas are compared. A smart antenna array made up of 10 half-wave dipoles is considered. The ANN method is better than the remaining approaches when it comes to achieving beam and null directions, whereas EA offers better performance in terms of reducing the side lobe level (SLL). The maximum SLL is achieved using EA for all the user directions. The performance of the ANN algorithm in terms of forming the beam of a smart antenna is also compared with that of the variable-step size adaptive algorithm.

5G antenna arrays are being designed to generate a dedicated stream of data for every single user. This results in more capacity and speed over the network along with the least possible latency rate. Depending upon the direction of the user demanding internet access, the beam can be steered in that direction. Meanwhile, the occurrence of side lobes in such systems can be menacing. The smart antenna is a pivotal technology for modern cellular communication. It helps the user's signal to determine the direction of arrival It also estimates antenna arrays using an adaptive signal processing algorithm that produces a radiation beam for communication. This work presents beamforming for uniform linear smart antenna array using leaky least mean square algorithm and side lobe level reduction using array synthesis methods like Tchebycheff and Taylor distribution. Multiple interferers are considered for the smart antenna. Here, one of the aims is to reduce side lobe levels nearer to the main beam which can enhance more efficient frequency reuse in a cellular network. Side lobe level is reduced up to about 16 dB that enacted for signal-to-noise ratio to 20 dB. [ABSTRACT FROM AUTHOR]

As the capabilities of individual nodes in wireless sensor networks increase, so does the opportunity to perform more complicated tasks, such as cooperative distributed beamforming to improve the range of communications and save precious battery power during the transmission. This work presents a review of the current literature focused on implementing distributed beamformers; covering the calculation of ideal beamforming weights, practical considerations such as carrier alignment, smart antennas based on distributed beamformers, and open research problems in the field of distributed beamforming.

The rapid development of broadband satellite internet has brought satellite communication into a new era.The establishment of large-scale low-orbit satellite constellations has gradually saturated spectrum resources, and the high overlap in frequency use has led to serious interference problems between satellite communication systems.Satellite payloads typically communicate using phased-array antennas, which reduce interference by beamforming at the receiving and transmitting ends.The adaptive beamforming algorithm was used at the receiving end to suppressed interference by adaptive zeroing; The low sidelobe beamforming algorithm was used at the transmitting end, which reduced the interference by imposing specifi c constraints on the sidelobes to suppressed the sidelobe level.Three anti-jamming beamforming algorithms were introduced for the ground receiving end and the satellite sending end respectively, which provided a beamlevel method for reduced the interference between satellite communication systems.

An intelligent security model for the big data environment is presented in this paper. The proposed security framework is data sensitive in nature and the level of security offered is defined on the basis of the data secrecy standard. The application area preferred in this work is the healthcare sector where the amount of data generated through the digitization and aggregation of medical equipment’s readings and reports is huge. The handling and processing of this great amount of data has posed a serious challenge to the researchers. The analytical outcomes of the study of this data are further used for the advancement of the medical prognostics and diagnostics. Security and privacy of this data is also a very important aspect in healthcare sector and has been incorporated in the healthcare act of many countries. However, the security level implemented conventionally is of same level to the complete data which not a smart strategy considering the varying level of sensitivity of data. It is inefficient for the data of high sensitivity and redundant for the data of low sensitivity. An intelligent data sensitive security framework is therefore proposed in this paper which provides the security level best suited for the data of given sensitivity. Fuzzy logic decision making technique is used in this work to determine the security level for a respective sensitivity level. Various patient attributes are used to take the intelligent decision about the security level through fuzzy inference system. The effectiveness and the efficacy of the proposed work is verified through the experimental study.

This paper presents the design of a parabolic reflector fed through a patch antenna array feed to enhance its directivity and radiation properties. Adaptive beam formers steer and alter an array's beam pattern to increase signal reception and minimize interference. Weight selection is a critical difficulty in achieving low SLL and beam width. Low Side Lobe Level [SLL]and narrow beam reduce antenna radiation and reception. Adjusting the weights reduces SLL and tilts the nulls. Adaptive beam formers are successful signal processors if their array output converges to the required signal. Smart antenna weights can be determined using any window function. Half Power Beam Width and SLL could be used to explore different algorithms. Both must be low for excellent smart antenna performance. In noisy settings, ACLMS and CLMS create narrow beams and side lobes. AANGD offers more control than CLMS and ACLMS. The blend of CLMS and ACLMS is more effective at signal convergence than CLMS and AANGD. It presents an alternative to the conventionally used horn-based feed network for C-band applications such as satellite communication. Broadside radiation patterns and 4x4 circular patch antenna arrays are used in the proposed design. 1400 aperture illumination is provided by the array's feed parabolic reflector, whose F/D ratio is 0.36. The proposed design's efficacy is assessed using simulation analysis. [ABSTRACT FROM AUTHOR]

5G applications such as the Internet of Things, high-resolution video streaming, robotic cars, smart cities, and telehealth care have a universal presence nowadays. These applications mandate higher data rates, large bandwidth, increased capacity, low latency and high throughput. The key element to meet this demand is to apply efficient frequency reuse and scheduling strategies based on adaptive beamforming. Employing efficient frequency reuse and scheduling techniques based on adaptive beamforming will significantly increase the cellular system capacity by eighteen times and substantially reduce the consumption power levels and interference. In cellular networks, the performance of the adaptive beamforming algorithms is severely degraded by the presence of interfering signals. In this paper, we introduce a beamforming-based algorithm for 5G applications named Direction Finding for Beamforming and Synthesizing. This algorithm combines the Direction of Arrival (DOA), adaptive beamforming, and radiation pattern synthesizing. The proposed algorithm uses the DOA technique to feed the adaptive beamforming algorithms with estimations of the desired user direction, desired user signal, and the interfering signals with their directions as initial values. In addition, we use the adaptive beamforming process to supply the radiation pattern synthesizing algorithms with an initial radiation pattern, and the required positions of nulls. At the beamformer output, we evaluate our proposed mechanism in terms of error convergence, tracking capabilities, and the obtained radiation pattern characteristics. At the synthesizer output, we carry out analysis in terms of the convergence speed and the resultant radiation pattern attributes to investigate the efficiency of the proposed algorithm. The simulation results show that our proposed algorithm has significantly fast convergence, reliable tracking capabilities, and radiation patterns with very low Side Lobe Levels. [ABSTRACT FROM AUTHOR]

The number of sources estimation is one of the vital key technologies in smart antenna. The current paper adopts a new system that employs a hybrid algorithm of artificial bee colony (ABC) and complex generalized Hebbian (CGHA) neural network to Bayesian information criterion (BIC) technique, aiming to enhance the accuracy of number of sources estimation. The advantage of the new system is that no need to compute the covariance matrix, since its principal eigenvalues are computed using the CGHA neural network for the received signals. Moreover, the proposed system can optimize the training condition of the CGHA neural network, therefore it can overcome the random selection of initial weights and learning rate, which evades network oscillation and trapping into local solution. Simulation results of the offered system show good responses through reducing the required time to train the CGHA neural network, fast converge speed, effectiveness, in addition to achieving the correct number of sources. [ABSTRACT FROM AUTHOR]

Rotman lens, which is a radio frequency beam-former that consists of multiple input and multiple output beam ports, can be used in industrial, scientific, and medical applications as a beam steering device. The input ports collect the signals to be propagated through the lens cavity toward the output ports before being transmitted by the antenna arrays to the destination in order to enhance the error performance by optimizing the overall signal to noise ratio (SNR). In this article, a low-cost Rotman lens antenna is designed and deployed to enhance the overall performance of the conventional cooperative communication systems without needing any additional power, extra time or frequency slots. In the suggested system, the smart Rotman lens antennas generate a beam steering in the direction of the destination to maximize the received SNR at the destination by applying the proposed optimal beamforming technique. The suggested optimal beamforming technique enjoys high diversity, as well as, low encoding and decoding complexity. Furthermore, we proved the advantages of our suggested strategy through both theoretical results and simulations using Monte Carlo runs. The Monte Carlo simulations show that the suggested strategy enjoys better error performance compared to the current state-of-the-art distributed multiantenna strategies. In addition, the bit error rate (BER) curves drawn from the analytical results are closely matching to those drawn from our conducted Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Code division multiple access (CDMA) is a class of techniques allowing multiple users to transmit simultaneously to meet the demand of the recently unprecedented revolution in wireless services. In CDMA systems, a unique pseudo-random (PN) code is assigned to each user to spread the signal at the transmitter side and facilitate signal’s recovery at the receiver side. In particular, a CDMA receiver initially seeks synchronization or acquisition of PN code. This paper focuses on the development and analysis of a new adaptive serial search acquisition method for direct sequence (DS)-CDMA systems in Rayleigh slowly fading channels. The proposed method makes use of smart antenna and switched constant false alarm (S-CFAR) processor for PN code acquisition. The smart antenna and S-CFAR processor are employed to improve the PN acquisition performance by adjusting a decision threshold according to the environment. The performance of proposed acquisition system has been evaluated in terms of probability of false alarm, probability of detection, and mean acquisition time. Closed form expressions for the probability of false alarm and probability of detection have been derived. Further, numerical and simulation results have been presented to show the performance of proposed PN acquisition system in comparison with other existing CFAR-based PN acquisition schemes.

A beam-changeable concentric annular Leaky-Wave Antenna (LWA) array is proposed, which contains two annular LWAs with constant-interval slots. By changing the excitations of the two LWAs, the radiation beam can be changed to different shapes and in different directions i.e. single beam to normal direction, conical beam to 40° and a synthetic beam. A theoretical model for analyzing the radiation properties of the LWA array is put forward, and a prototype of the array with the radius of 42.6 mm and the height of 4 mm is fabricated. By comparing simulation and the measurement results, it is found that the proposed LWA array exhibits a very well expected performance. The proposed antenna array is then applied to construct a self-sensing smart antenna which can provide flexible radio wave coverage according to the positions of users. Image-aware function is integrated into the smart antenna, and a series of tests including received power and Error Vector Magnitude (EVM) measurements are carried out in indoor scenarios. The results verify the effectiveness of the design.

A novel low-profile vertically polarized planar endfire smart antenna that can achieve 360° beam-steering and high beam-shaping capability is proposed. The microstrip antenna is printed on a single thin substrate with a vertically polarized omnidirectional driven source and 36 tunable slot-integrated microstrip vertically polarized resonators (SMVR) for shaping the endfire beams. To verify the design approach, a prototype operating at the 5 GHz WLAN band is fabricated and tested. The results show that the antenna operates from 5.55 to 5.75 GHz and is capable of scanning a single endfire beam from 0° to 360° in the full azimuth plane. The scanning resolution and the maximum realized gain are 15° and 8.2 dBi, respectively. Moreover, multiple radiation pattern shapes including omnidirectional beam, dual-beams, and tri-beams can also be generated by the antenna. The overall antenna height is only $0.06~\lambda $ at 5.6 GHz. This design provides a new antenna configuration for low-profile vertically polarized endfire beam-steering and beam-shaping antenna and is promising for future wireless communication systems with lost-cost beamforming property. [ABSTRACT FROM AUTHOR]

Smart Antennas are important to provide mobility support for many enhanced 5G and future wireless applications and services, such as energy harvesting, virtual reality, Voice over 5G (Vo5G), connected vehicles, Machine-to-Machine Communication (M2M), and Internet of Things (IoT). Smart antenna technology enables us to reduce interference and multipath problems and increase the quality in communication signals. This paper presents a number of nonlinear configurations of dipole arrays for forming a single beam in any desired direction. We propose three, four, six, and eight-element array structures to perform this single beam-steering functionality. The proposed array configurations with multiple axes of symmetry (in the azimuthal plane) decrease the computational repetitions in optimizing respective weight factors for beam-steering. The optimized weight factors are obtained through the Least Mean Square (LMS) method. MATLABTM is used to calculate optimized weight factors as well as to determine the resulting radiation patterns. Since antennas are bidirectional elements, beamforming in one direction means that the antenna will also have high receiving gain in that direction. Performances of differently configured models are compared in terms of their directivity, sidelobe reduction, and computational complexities for beam-steering. [ABSTRACT FROM AUTHOR]

Future wireless communication networks will be largely characterized by small cell deployments, typically on the order of 200 meters of radius/cell, at most. Meanwhile, recent studies show that base stations (BS) account for about 80 to 95 % of the total network power. This simply implies that more energy will be consumed in the future wireless network since small cell means massive deployment of BS. This phenomenon makes energy-efficient (EE) control a central issue of critical consideration in the design of future wireless networks. This paper proposes and investigates (the performance of) two different energy-saving approaches namely, adaptive-sleep sectorization (AS), adaptive hybrid partitioning schemes (AH) for small cellular networks using smart antenna technique. We formulated a generic base-model for the above-mentioned schemes and applied the spatial Poisson process to reduce the system complexity and to improve flexibility in the beam angle reconfiguration of the adaptive antenna, also known as a smart antenna (SA). The SA uses the scalable algorithms to track active users in different segments/sectors of the microcell, making the proposed schemes capable of targeting specific users or groups of users in periods of sparse traffic, and capable of performing optimally when the network is highly congested. The capabilities of the proposed smart/adaptive antenna approaches can be easily adapted and integrated into the massive MIMO for future deployment. Rigorous numerical analysis at different orders of sectorization shows that among the proposed schemes, the AH strategy outperforms the AS in terms of energy saving by about 52 %. Generally, the proposed schemes have demonstrated the ability to significantly increase the power consumption efficiency of micro base stations for future generation cellular systems, over the traditional design methodologies.

Digitalization is becoming more and more important. Building smart houses and industries to offer humans longer lives is one of the main goals of the digitization movement. The main aim of this work is to make those verbal exchange modules more powerful with the aid of either improving the antennas to have a higher layout or changing it with new varieties of antennas that are better able to facilitate powerful verbal exchange. This paper proposes an antenna that can be applied to a verbal exchange module to be able to perform inside the Industrial, Scientific, and Medical (ISM) band at 2.4 GHz. After designing and simulating a complete Printed Circuit Board (PCB) antenna, it found that a few designs produced advanced simulation effects in terms of gain, reflection coefficient, voltage standing wave ratio (VSWR), and band width. Always-pointing antennae are protected by the layout of the antenna, allowing it to protect features inside the Global Positioning System (GPS) and ISM (Industrial, Scientific, and Medical) bands for Wi-Fi devices. [ABSTRACT FROM AUTHOR]

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Maintaining a good trajectory and the transmission of signal towards the target, is a very delicate subject in the field of transmission. In particular, wireless transmission which is permitted on a sensible physical medium to guide the trajectory of the wave, is the greatest concern of radio transmission. Following this challenge, we propose approaches to the applicability of the Beamforming (BF) techniques and Convolutional Neural Network (CNN) on a signal received from an antenna array. The approaches consist of monitoring and driving the signal trajectory from the antenna output to the device target detecting and removing all interferences which may deflect the signal from the desired direction by pivoting it in another one (around 45 degrees). The work is based on experimentation with BF approaches such as the MVDR (minimum-variance distortionless-response), and the LCMV (linear-constraint minimum-variance) to optimize the transmission phase. We chose this BF technique above others because; the LCMV technique detects and reduces interference signals efficiently by utilizing calculated weight vectors to direct the powerful beam-based signal towards its intended target. The LCMV technique also ensure that each sensor outcomes are filtered with a finite impulse response (FIR) filter to satisfy narrow-band signal restrictions. For accurate signal transmission and reception with noise and interference suppression, LCMV seems promising. We use LCMV to cancel out interference and maintain the desired signal. When noise is not separable from the data (signal), an estimate of the sample covariance matrix is obtained from the data. Some approaches are taken to specify constraints, such as amplitude and derivative constraints. For example, specifying weights that suppress spurious signals from a particular direction while transmitting signals from a different direction without distortion. To avoid self-cancellation of signals, the LCMV permits multiple constraints to be placed along the target direction (directional vector). The last developed (marginal) approach is about the application of a 24-layer CNN model exploiting in depth all features and components of the Device-To-Device (D2D) signal. Three different sizes of convolution kernels are using (32, 64 and 128) to exploit the original signal which is noisy to remove the noise on the signal and to recover the desired signal. The scientific merit and novelty value of this paper is to allow us to analyse and understand how MVDR and LCMV filters identify and reduce interference in new expressions for their output signal-to-interference ratio (SIR) and signal-to-noise ratio (SNR). We showed the trade-off between noise reduction and interference rejection. [ABSTRACT FROM AUTHOR]

Optimizing the current distribution of an evenly spaced antenna array has shown to be an efficient approach for reducing side lobe levels. In this article, the Tchebyscheff distribution-based antenna array synthesis approach is combined with an adaptive signal processing algorithm for beamforming and side lobe level reduction in smart antennas in various fading situations. The performance of smart antennas in uniformly spaced linear, planar, circular, and semi-circular arrays are evaluated. The presence of Rayleigh and Rician channels is examined in the network. The least mean square (LMS) and normalised least mean square (NLMS) algorithms are applied as adaptive algorithms. In fading environments, the NLMS algorithm with Tchebyscheff distribution outperforms than the LMS algorithm with Tchebyscheff distribution, with a side lobe level decrease of 11.23 dB. The lowest side lobe achieved with the NLMS algorithm with Tchebyscheff distribution is − 45.59 dB for uniform planar array. [ABSTRACT FROM AUTHOR]

According to several research circles, it is predicted that future wireless systems that employ smart antenna techniques would be more effective at using available spectrum and at building new networks at lower cost, while also improving service quality and allowing for cross-technology operation. These systems require constant monitoring in order to function properly, allowing users to apply machine learning algorithms to analyse large amounts of data from various antenna settings. Machine learning is a technique in which a machine learns and improves on its own, based on past data. These techniques enable the smart antenna target to be learned in an efficient, reliable, and adaptive manner. In this paper, the antenna array and antenna developed for the Internet of Things applications were highlighted. In this paper, we review how machine learning techniques can handle these applications effectively and what the concept of adaptive antenna is and when it can be used in this day and age characterized by the rapid development of information technology. [ABSTRACT FROM AUTHOR]

Blind beamforming algorithms are the first choice for radar applications as they won't use any training signal. But the major downside of constant modulus algorithm (CMA) is very low convergence rate. To overcome this problem and to make this algorithm more suitable for real-time applications, we firstly improved the convergence rate of CMA by making its step size adaptive. Then, we applied Kaiser window to improved CMA and conventional least-square CMA (LS-CMA) to suppress the sidelobe levels. These proposed algorithms are named Kaiser window-constant modulus algorithm and Kaiser window least square-constant modulus algorithm, respectively. Computer simulations validate the effectiveness of the proposed beamformers. [ABSTRACT FROM AUTHOR]

An intelligent security model for the big data environment is presented in this paper. The proposed security framework is data sensitive in nature and the level of security offered is defined on the basis of the data secrecy standard. The application area preferred in this work is the healthcare sector where the amount of data generated through the digitization and aggregation of medical equipment's readings and reports is huge. The handling and processing of this great amount of data has posed a serious challenge to the researchers. The analytical outcomes of the study of this data are further used for the advancement of the medical prognostics and diagnostics. Security and privacy of this data is also a very important aspect in healthcare sector and has been incorporated in the healthcare act of many countries. However, the security level implemented conventionally is of same level to the complete data which not a smart strategy considering the varying level of sensitivity of data. It is inefficient for the data of high sensitivity and redundant for the data of low sensitivity. An intelligent data sensitive security framework is therefore proposed in this paper which provides the security level best suited for the data of given sensitivity. Fuzzy logic decision making technique is used in this work to determine the security level for a respective sensitivity level. Various patient attributes are used to take the intelligent decision about the security level through fuzzy inference system. The effectiveness and the efficacy of the proposed work is verified through the experimental study. [ABSTRACT FROM AUTHOR]