Smart Antennas Research Papers - Academia.edu (original) (raw)

Wireless designers constantly seek to improve the spectrum efficiency/capacity, coverage of wireless networks and link reliability. Space-time wireless technology that uses multiple antennas along with appropriate signalling and receiver techniques offers a powerful tool for improving wireless
performance. More advanced MIMO techniques are planned for future mobile networks in wireless Local Area Network (LANs) and Wide Area Network (WANs). Multiple antennas when used with appropriate Space-Time Coding (STC) techniques can achieve huge performance gains in multipath fading wireless links. The Space Time Coding has evolved as a most vibrant research area in wireless communications. Recently, Space-Time Block Coding (STBC) has been trying to incorporate in the forthcoming generation of mobile communication standard which aims to deliver true multimedia capability. This paper presents the Space-Time Block Codes (STBC) for wireless networks that uses multiple numbers of antennas at both transmitter and receiver. The simulations have been done in MATLAB. The STBC which includes the Alamouti Scheme as well as an orthogonal STBC for 4 transmit antenna case has been simulated and studied.

Smart antennas enable space division multiple access, that is they manage access to a network by accounting for where users are spatially and using antenna technology to better isolate signals from noise and interference. For mobile systems, the benefits are increased capacity from existing cell sites, maximum return on infrastructure investment, and improved network efficiency and performance. In mobile systems, all the benefits for base-to-mobile and mobile-to-base paths occur as a result of smart antennas being installed at the base site. For fixed wireless systems, smart antennas are practical for both the base station and the remote station (RS). This paper discusses the increase in throughput that is realized by using smart antenna technology in a fixed wireless system. The management of packet transmission and reception, a key component of maximizing capacity, is described. Also included is a discussion of the user benefits and capacity impact of smart antennas at a remote station.

- by Doug Reudink
- •
- Smart Antenna, Wireless Systems, Space Technology, Mobile Systems
- by Burim Berisha
- •
- Smart Antennas
- by Mst. Najnin Sultana
- •
- Distributed Computing, Smart Antenna, Wireless Systems, Space Technology

The radio refractive index structure of the lower section of the atmospheric boundary layer is critical in the planning and construction of microwave communication connections. This study analyses the refractivity profile carried out in Mowe (6.8085° N, 3.4367° E) SouthWestern Nigeria. Ground measurements of air pressure, temperature, and relative humidity used in this investigation were collected from the rain gauge of the Tropospheric Observatory Data Acquisition Network (TRODAN). The radio refractivity, associated refractivity gradient and climatic factor were computed using data from January 2012 to December 2013. The vertical distributions of radio refractivity were then calculated using these parameters. Seasonal fluctuations in refractivity are visible over the location, with high values in the wet season and low values in the dry season. The findings also suggest that propagation circumstances fluctuate in frequency, with sub-refractive situations being most common between April and September. This is an indication that microwave link in Mowe will suffer higher signal loss during wet season, while the loss may be mild during the dry season. The refractivity values in this study are expected to aid in determining the necessary mitigation to be put in place to reduce loss of signal in Mowe.

Smart antenna array system is widely incorporate in wireless communication system; this paper presents a simulation based approach of smart antenna system for the performance evaluation of signal processing ability, which is optimized using different adaptive beamforming algorithms such as Least Mean Square (LMS) , Recursive List Square (RLS) and Simple Matrix Inversion (SMI).

- by Dr. Somnath Patra
- •
- Smart Antennas, Adaptive Antenna Arrays, Antenna Array Synthesis

—This paper describes the comparison between LMS and RLS. This project is concerned with analysis of different beam-forming algorithms of smart antenna like Least Mean Squares (LMS), Recursive Least Square (RLS) Algorithms. Then by comparing these algorithms with respect to different parameters of antenna such as directivity, Side lobe level, beam-width etc. we will be proving that RLS algorithm is better than LMS algorithm. Keywords—LMS algorithm, RLS algorithm, adaptive array beamforming.

- by Prakash Mainkar
- •
- Wireless Communications, Smart Antennas
- by Raed Shubair
- •
- Smart Antenna, System Design, Spectrum, Least Mean Square
- by Paulo Cardieri
- •
- Engineering, Technology, Smart Antenna, Communication System

- by Rahmad Hidayat
- •
- Smart Antennas
- by Theodore Rappaport
- •
- Distributed Computing, Smart Antenna, Microscopy, Mobile Communication

International Journal of Microwave Engineering (JMICRO) is a peer-reviewed, open access journal which invites high quality manuscripts that focuses on Engineering and theory associated with microwave / millimeter-wave technology, guided wave structures, electromagnetic theory and implementation. Authors are invited to submit original research works that stimulate the development of latest technology in industry and academia. Good quality review papers and short communications are also acceptable. Research areas suitable for publication include, but are not limited to the following fields: • 2D/3D printed RF and microwave components • Antenna modelling and measurements • Channel modelling and propagation • Cognitive and adaptive radio • Digital beam forming • EMI/EMC • Ferroelectric, ferrite and acoustic wave components • Hybrid and monolithic active components (amplifiers, mixers, oscillators, etc) • Methods of maintaining signal integrity • Micro-machined transmission lines and waveguides • Microwave and millimetre wave systems • Microwave device modelling • Microwave photonics • MIMO components • Novel waveguides and new phenomenon in

- by GEORGE TSOULOS
- •
- Distributed Computing, Technology, Smart Antenna, Beamforming

When several independent terminals need to access a limited resource, a Medium Access Control (MAC) protocol is needed in order to manage and assign the resource. If no type of protocol is considered, conflicts could occur if more than one terminal wants to access the resource at the same time. Therefore, this work reports the analysis of MAC protocols. In addition to, we propose a protocol that handles transmission rates to maximize the transmission success rate. This proposed protocol is based on CSMA (Carrier Sense Multiple Access)-CDMA (Code Division Multiple Access). Simulations show that the protocol improves traffic transmission in the network, as compared with fixed rate mechanisms used to access the wireless medium.

- by Aldo Mendoza
- •
- Engineering, Distributed Computing, Smart Antenna, Spine
- by Bart Nauwelaers
- •
- Textiles, Antenna arrays, Patient Monitoring, GNSS receivers
- by Elyas Palantei
- •
- Smart Antenna, Radio Frequency, Smart Antennas, Diodes

International Journal of Antennas is a peer-reviewed, open access journal that publishes original research as well as review articles in the field of antennas and its allied domain. This journal aims to bring together leading academic scientists, researchers, engineers and research scholars to exchange and share their experiences and research results in their specialized arena in antenna engineering. The journal invites good quality research and review papers for publications.

- by Björn Ottersten
- •
- Distributed Computing, Statistics, Smart Antenna, Congestion Control
- by Reinaldo Valenzuela
- •
- Distributed Computing, Smart Antenna, Wireless Systems, Directional Antenna

This paper deals with the design of beam-forming networks (BFN) for scannable multibeam antenna arrays using Coherently Radiating Periodic Structures (CORPS). This design of CORPS-BFN considers the optimization of the complex inputs of the feeding network by using the Differential Evolution (DE) algorithm. Simulation results for different configurations of CORPS-BFN for a scannable multibeam linear array are presented. The results shown in this paper present certain interesting characteristics in the array factor response for the scannable multibeam linear array and the feeding network simplification for the design of BFN based on CORPS.

- by Carlos del Río Bocio
- •
- Engineering, Differential Evolution, Optimization, Antenna arrays
- by Douglas Reudink and +1
- •
- Smart Antenna, Wireless Systems, Space Technology, Mobile Systems

We study the problem of multiuser downlink beamforming under the assumption that only an erroneous channel state information (CSI) is available at the transmitter. The optimization criterion is to minimize the total transmit power subject to the constraints that the user worst-case signal-to-interference-plus-noise ratios (SINRs) are above a pre-defined quality of service (QoS) threshold. In contrast to the earlier approaches to this problem, we take into account positive semi-definiteness of the mismatched channel covariance matrices. Two approaches to solve the resulting robust downlink beamforming problem are proposed using semidefinite programming (SDP). Simulation results validate the robustness of the proposed techniques.

- by Vimal Sharma
- •
- Quality of Service, Smart Antennas, Radio Transmitters, Covariance analysis
- by Emmanuel Van Lil and +1
- •
- Engineering, Technology, Smart Antenna, Signal Processing

The number of supportable users in the TDD-CDMA downlink is usually limited by multiple-access interference and by the limited number of available orthogonal spreading codes. By using multiuser transmission (MUT) methods in the base station in conjunction with smart antennas, the interference can be mitigated and the downlink cell capacity can be substantially increased. The number of supported users and

- by Ralf Irmer
- •
- Smart Antenna, Bit Error Rate, BER, Second Order
- by ahmed magdy
- •
- Engineering, Physical sciences, Smart Antennas

- by Hadi Aliakbarian
- •
- Textiles, Antenna arrays, Patient Monitoring, GNSS receivers

- by Zahir M Hussain
- •
- Smart Antenna, Signal Processing, Comparative Study, Space
- by Constantinos T. Angelis
- •
- GSM, Patch Antennas, Smart Antennas

An adaptive multi-target space-time downlink receiver for user terminals of WCDMA systems under soft handoff is presented. The receiver consists of M antennas and K array processors, each one followed by a chip-rate equalizer and a despreader. The resulting K signals are combined in an "adaptive ratio combiner" which is updated by pilot common channel information in UTRA-FDD systems. All the structure in the FDD WCDMA downlink signal is exploited for improved performance. Simulation results using COST-259 channel models for vehicle speed of 50 km/h are presented.

... Dirk Hampicke, Markus Landmann, Christian Schneider, Gerd Sommerkorn, Reiner Thomä Technische Universität Ilmenau Dept. ... the analyses contained in this paper, data has been se-lected from a measurement drive in a street canyon... more

- by M. Landmann
- •
- Smart Antenna, Wireless Systems, Parameter estimation, Antenna arrays

Smart antennas are considered to be promising technology for increasing the performance of wireless communication systems. A smart antenna consists of several antenna elements, whose signals are processed adaptively in order to exploit the spatial domain of the mobile radio channel (1). Usually, the signals received at the different antenna elements are multiplied with complex weights w, and then summed up; the weights are chosen adaptively. Not the antenna itself, but the whole antenna system including the signal processing is called "adaptive" (2). Only the antenna is not smart, the antenna system which contains also antenna elements is called smart.

- by Adnan Kavak
- •
- Computer Science, Smart Antenna, Signal Processing, Mobile Radio Channel Estimation
- by Emad Zieur
- •
- Smart Antennas, AOA
- by Peter Russer
- •
- Error Correction, Optical physics, Smart Antennas, Antenna Array
- by Daniele Piazza
- •
- Smart Antenna, Metamaterials, Communication System, Performance Improvement

An adaptive beam former is a device, which is able
to steer and modifies an array's beam pattern in or
der
to enhance the reception of a desired signal, while
simultaneously suppressing interfering signals thr
ough
complex weight selection. However, the weight selec
tion is a critical task to get the low Side Lobe Le
vel
(SLL) and Low Beam Width. One needs to have a low S
LL and low beam width to reduce the antenna's
energy radiation/reception ability in unintended di
rections. The weights can be chosen to minimize the
SLL and to place nulls at certain angles. The conve
rgence of the array output towards desired signal i
s
also very important for a good signal processing to
ol of an adaptive beam former. A vast number of
possible window functions are available to calculat
e the weights for Smart Antennas. From the analysis
of many of these algorithms, it is observed that th
ere is a compromise between HPBW and SLL. But in
case of smart antennas, both of these parameters mu
st have low values to get good performance. In our
earlier work it is proposed that Complex Least Mean
Square (CLMS) and Augmented Complex Least
Mean Square ( ACLMS) algorithms gives low beam widt
h and side lobe level in noisy environment.
Another neural algorithm Adaptive Amplitude Non Lin
ear Gradient Decent algorithm (AANGD) has the
advantage of more number of control parameters over
CLMS and ACLMS algorithms. In this paper the
hybrid of CLMS and AANGD is presented and this nove
l hybrid algorithm has outperformed the hybrid
algorithm of CLMS and ACLMS in the aspect of conver
gence towards the desired signal.

A statistical channel model for fading channels is presented that is applicable for the simulation and design of communication systems with multiple (smart) antennas: Firstly, the continuous time vector channel model is developed starting from the fundamental wave equation in free space. This continous time channel model is then used to derive a T/2-spaced discrete vector channel model that is suitable for simulation. A detailed description of how to generate the short-term (fast) fading random processes follows. Finally, the power-, direction-of-arrival- and delay-profiles that are needed to model directional multipath propagation are discussed.

- by Jens Baltersee
- •
- Partial Differential Equations, Smart Antenna, Signal Processing, Fading Channel

Spatial antenna diversity has been important in improving the radio link between wireless users. Historically, microscopic antenna diversity has been used to reduce the fading seen by a radio receiver, whereas macroscopic diversity provides multiple listening posts to ensure that mobile communication links remain intact over a wide geographic area. In later years, the concepts of spatial diversity have been expanded to build foundations for emerging technologies, such as smart (adaptive) antennas and position location systems. Smart antennas hold great promise for increasing the capacity of wireless communications because they radiate and receive energy only in the intended directions, thereby greatly reducing interference. To properly design, analyze, and implement smart antennas and to exploit spatial processing in emerging wireless systems, accurate radio channel models that incorporate spatial characteristics are necessary. In this tutorial, we review the key concepts in spatial...

- by Theodore Rappaport
- •
- Distributed Computing, Smart Antenna, Microscopy, Mobile Communication
- by Jemal H Abawajy
- •
- Engineering, Computer Science, Algorithms, Analytical Chemistry

In order to fulfil the increasing demands on capacity and coverage of mobile communication systems smart or intelligent antennas have been suggested. These antennas increase the spectral efficiency of a wireless system by using arrays of antenna elements to shape RF signals in particular directions. Generally, smart antennas can be divided into two approaches: switched multi-beam and adaptive. Adaptive antennas

- by Claes Beckman
- •
- Smart Antenna, Signal Processing, Wireless Systems, Array Signal Processing
- by Josef Blanz
- •
- Engineering, Smart Antenna, Monte Carlo Simulation, Mobile Radio Channel Estimation