Adaptive smart antennas in 3G networks and beyond

Concept of wireless communication systems which use smart antennas is based on digital signal processing algorithms. Thus, the smart antennas systems become capable to locate and track signals by the both: users and interferers and dynamically adapts the antenna pattern to enhance the reception in Signal-Of-Interest direction and minimizing interference in Signal-Of-Not-Interest direction. Hence, Space Division Multiple Access system, which uses smart antennas, is being used more often in wireless communications, because it shows improvement in channel capacity and co-channel interference. However, performance of smart antenna system greatly depends on efficiency of digital signal processing algorithms. The algorithm uses the Direction of Arrival (DOA) algorithms to estimate the number of incidents plane waves on the antenna array and their angle of incidence. This paper investigates performance of the DOA algorithms like MUSIC, ESPRIT and ROOT MUSIC on the uniform linear array in the presence of white noise. The simulation results show that MUSIC algorithm is the best. The resolution of the DOA techniques improves as number of snapshots, number of array elements and signal-to-noise ratio increases.

Many significant sources of error take place in the smart antenna system like mismatching between the supposed steering vectors and the real vectors, insufficient calibration of array antenna, etc. These errors correspond to adding spatially white noise to each element of the array antenna, therefore the performance of the smart antenna falls and the desired output signal is destroyed. This paper presents a performance study of a smart antenna system at different noise levels using five adaptive beamforming algorithms and compares between them. The investigated algorithms are Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Sample Matrix Inversion (SMI), Recursive Least Square (RLS) and Hybrid Least Mean Square / Sample Matrix Inversion (LMS/SMI). MATLAB simulation results are illustrated to investigate the performance of these algorithms.

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.

This paper presents a new study in an attempt to apply Fast Euclidean Direction Search (FEDS) algorithm on adaptive smart antennas. The novelty in this work is the use of FEDS algorithm in a smart antenna system for mobile communications because up to our knowledge this algorithm is not applied on smart antenna so far. In order to properly evaluate the performance of smart antenna for mobile communication systems, an Additive White Gaussian Noise (AWGN) channel model is required that each received signal at each element in the array includes only an additive, zero mean, Gaussian noise . The ability of the FEDS algorithm to operate in a fast changing signal environment is examined by subjecting the input signal to Rayleigh fading with a Jakes power spectral density which is a popular choice for land mobile communications systems. Through simulation results of smart antennas for AWGN channel, the RLS and FEDS start to converge from the initial iteration and 10 respectively, whereas, the LMS and NLMS start to converge after 50 and 10 iterations respectively. In the presence of the Rayleigh fading channel, the LMS, and NLMS algorithms converge after 60 and 25 iterations respectively, while the RLS convergence from the initial iteration and FEDS converge from the iteration number 25. On the other hands, the FEDS algorithm shows better tracking and estimation of the desired input signal compared with both LMS and NLMS algorithms and comparable with RLS algorithm. The comparison of linear plot of array factor using FEDS and RLS , both algorithms generates the same deeper null for undesired signals of about í30 dB towards the interferer using an AWGN channel, while it generates deeper null of about -23 dB and -30 dB respectively using the Rayleigh fading channel.

Emerging trends of mobile communication and ever growing demand of mobile users with high speed data requirements and migrating towards 4G, Co-Channel interference and multipath in urban/dense urban environment will limit the capacity of the network. Smart antenna is now recognized by its higher user capacity in 4G wireless networks by efficiently reducing multipath and co-channel interference. Smart Antenna use adaptive beam forming algorithms in a dynamic environment continuously adjusting the weight of antenna arrays for creating a beam to track desired users automatically, and minimize interference from other users by placing nulls in their directions. This research work provides comparative analysis to results of different blind and non blind adaptive algorithms for one desired angle and three undesired angles. These algorithms have been simulated in a GUI based application's interface develop in MATLAB. Results of simulation will help in beam forming and antenna design

Throughput enhancement by exploiting spatial reuse opportunities with smart antenna systems in wireless ad hoc networks

With an ever-increasing need to remotely monitor human health and activities, there has been an increase in the number of activity and health tracking devices. In this scenario, Internet-of-things technology plays a very important role by uploading the data to the cloud and enabling analysis of the data. As these devices need to connect to a wireless base station for Internet access, the smart antenna plays an indispensable role. These systems use antenna arrays to enhance the gain and processors running adaptive algorithms for beamforming and beam-steering. The learning and estimating capabilities of artificial neural networks have proven to be unparalleled in modeling systems that would have otherwise required lengthy calculations. Thus, in this paper, we propose a neuro-computational model based on a multilayer perceptron model to model and predict how the gain and the beamwidth depend upon the number of antenna elements in a smart antenna system and the distance between them. We found that the neuro-computational model was able to achieve the objective to a very high degree of accuracy. The gains predicted for 50 elements with 0.5λ and 0.6λ spacing were 16.86 dB and 16.5 dB, respectively, and the beamwidths were 10 degrees and 6.7 degrees, respectively. The predicted values were found to be very close to that of the computed values.KeywordsBeamformingBeamwidthGainMultilayer perceptronNeuro-computational modelSmart antenna system

Direction of arrival (DOA) estimation has become a major factor in the performance of smart antenna systems. Through DOA estimation techniques, it is possible to determine the angular position of signal sources and interferers in the cell environment. Nevertheless, in a multipath fading channel, electromagnetic waves interact with the environment in a very complex way, thus significantly degrading the performance of DOA estimation algorithms. This paper provides insight into how Root MUSIC enhances resolution on DOA estimation and has better performance than spectral MUSIC in the context of a linear equispaced array in a multipath macrocell environment. This improvement and its better precision result in a higher channel capacity, given that it allows to spatially separate closely spaced sources.

Adaptive beamforming plays an important role in smart antenna systems. Nonblind adaptive algorithm utilizes the known training sequence to update the weights of antenna array at the cost of low transmission efficiency. Without sending training sequences, blind adaptive algorithm is a solution to implement beamforming and increase the transmission efficiency. Two classic blind algorithms are studied, namely constant modulus (CM) algorithm and Least squares-constant modulus (LS-CM) algorithm. Considering the advantages of the nonblind and blind algorithms, a semi-blind adaptive beamforming based on CM algorithm is proposed in this paper. Simulation results indicate it has comparable performance compared with nonblind adaptive algorithm, while it only needs the training sequence in the first iteration.

The smart antenna is believed to be the solution for a high capacity system. It improves both C/I ratio and trunk efficiency. In this paper, we use a computer simulation to compare the capacity among omni, 3-sector, Lee's micro cell, 6 sector and a 12-antenna switched beam smart antenna. First, the C/I ratios under different pathless exponent and reuse factor are compared within these systems. Then, the trunking efficiencies among these systems are also compared. As the result shows, the smart antenna does support a reasonably good C/I ratio, and the highest trunking efficiency. This simulator is also enhanced to calculate the path loss exponent for specific design areas based on the collected measured data. This path loss exponent then can be used as input to the simulator to simulate the C/I and frequency reuse factor for the area. Measured data were collected in LA. Path loss exponents were calculated using Lee's propagation model. Based on the calculated path loss exponent and the simulator, in a dense urban area, the smart antenna system can support a k=3 reuse scheme.

The capacity of wireless sensor network (WSN) can be severely limited due to interference constraints. The integration of smart antenna system (SAS) in wireless sensor networks is a challenging and very attractive technical solution to improve the system capacity, the quality of service, and the power control. Smart antenna system has the advantage over traditional omnidirectional antennas system (OAS), of being able to orientate signals into the desired direction in either transmission node or reception node. In this paper, we create a view of ground with nodes by using MATLAB, firstly nodes composed with OAS and secondly nodes composed of SAS, then we compare active communication using SAS and active communication using OAS. The energy efficiency to bring by smart antenna system is described.

The development of a smart low-profile helmet-mounted antenna with pattern diversity has been previously reported by the authors. This smart antenna array virtually ensures stable reception despite problems such as the continually varying skeletal position associated with the movements of the individual, as well as propagation interferences, even for locations which are dead spots for conventional antennas. However, with the extremely limited space and battery power available on the helmet, it is necessary that the antenna be multifunction and the power consumption be minimal. We present a design for an advanced smart helmet-mounted antenna which employs low-power CMOS control devices and innovative antenna technology for size reduction. This smart array uses pattern diversity to mitigate the effects of multipath fading and the soldier's changing skeletal orientation. In the past, smart antenna techniques have generally been applied to base-station antennas only. By taking advantage of a unique low-cost, low-power, pattern diversity switching mechanism, as well as the rapid decline in component and device costs and size, we believe we have made one of the first practical portable smart antenna systems.

This paper evaluates the impact of the newest micro & nanotechnologies on smart antenna systems. After analyzing the key features of the main mobile wireless systems of the present - UMTS, CDMA2000, WiMax 802.16e, and MBWA 802.20, we have identified some common issues, requirements and limitations. Dwelling on the SDC -Software-Defined Component perspectives we have analyzed the impact of novel RF MEMS on reconfigurable circuits, namely on smart antenna systems. This lead us to imagine a software-defined autonomous antenna - SDAA, which includes the RF module. This in turn, integrated on a SDR platform, will prototype the idea of extending the SDR concept over the RF module. This paper introduces our proposed SDR & SDAA architecture and its benefits.

This paper describes the modelling and analysis of a new smart antenna concept. The paper outlines the rational for the development of a dynamically reconfigurable smart antenna system without greatly increasing the complexity of the system. The subject smart antenna system is developed to offer good coverage and capacity using multiple beams throughout a cell sector and to be able to reconfigure the beam to provide enhanced coverage and maintain capacity in certain areas at reduced, but acceptable coverage throughout the rest of the cell sector of a mobile telecommunication network. The motivation behind the architecture and the methodology used is discussed and then modelling and analysis of the antenna system is presented.

Adaptive type smart antennas have not been implemented yet on the deployed UMTS systems, although UTRA-UMTS preview their operation and they also could improve capacity especially in a multiservice environment. This paper describes a set of novel measurement techniques that must be performed to evaluate the correct operation of a smart antenna system in an anechoic chamber. The validity of these new techniques are supported by the measurement results obtained from a real smart antenna prototype for WCDMA.