Smart Antennas / Edition 1 available in Hardcover
Smart Antennas / Edition 1
- ISBN-10:
- 0471210102
- ISBN-13:
- 9780471210108
- Pub. Date:
- 05/06/2003
- Publisher:
- Wiley
Smart Antennas / Edition 1
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Overview
Product Details
| ISBN-13: | 9780471210108 |
|---|---|
| Publisher: | Wiley |
| Publication date: | 05/06/2003 |
| Series: | Wiley Series in Microwave and Optical Engineering , #143 |
| Pages: | 472 |
| Product dimensions: | 6.28(w) x 9.15(h) x 1.08(d) |
About the Author
MICHAEL C. WICKS, PhD, received undergraduate degrees from Mohawk Valley Community College and Rensselaer Polytechnic Institute, and graduate degrees from Syracuse University, all in electrical engineering. He is a Fellow of the IEEE and a member of the Association of Old Crows. Dr. Wicks is a Principal Research Engineer in the U.S. Air Force Research Laboratory in the Sensor Directorate, Radar Signal Processing Branch. He has authored over 125 papers, reports, and patents.
MAGDALENA SALAZAR-PALMA, PhD, received an Ingeniero de Telecomunicación and a PhD from the Universidad Politécnica de Madrid in Madrid, Spain, where she is a Profesor Titular in the Signals, Systems, and Radiocommunications Department at the Escuela Técnica Superiór de Ingenieros de Telecomunicación.
ROBERT J. BONNEAU, PhD, obtained his BSEE and MSEE from Cornell University and his MS and PhD from Columbia University, all in electrical engineering. He is the program manager at the Advanced Technology Office of DARPA.
Read an Excerpt
Smart Antennas
By Tapan K. Sarkar Michael C. Wicks Magdalena Salazar-Palma Robert J. Bonneau
John Wiley & Sons
ISBN: 0-471-21010-2Chapter One
INTRODUCTION
1.1 SOME REFLECTIONS ON CURRENT THOUGHTS
The fundamental bottleneck in mobile communication is that many users want to access the base station simultaneously and thereby establish the first link in the communication chain. The way the scarce resources of the base station are distributed to mobile users is through sharing. This is a technical definition of the term multiple access. Therefore, multiple accesses are implemented by sharing one or more of the four resources of the base station by the various mobile users randomly located in space and time. By time we imply that different users may start using the system at different times. This sharing can take place in any of the following four ways:
1.) Bandwidth (Frequency Division Multiple Access or in short, FDMA). Here, the frequency spectrum or the entire bandwidth is portioned off to different users and allocated for that communication duration. Hence each user communicates with the base station over an allocated narrow frequency band for the entire duration of the communication.
2.) Time (Time Division Multiple Access or in short, TDMA). Here, each mobile has the entire frequency resource of the base station for a short duration of the time (i.e., each user accesses the entire spectrum of the base station for a finite duration in an ordered sequence). With the advent of digital technology it is possible to have an intermittent connection for each mobile with the base station for a short period of time, and in this way the valuable frequency resource of the base station is shared.
3.) Code (Code Division Multiple Access or in short, CDMA). In this case, each user is assigned a unique code. In this way the user is allowed to access all the bandwidth, as in TDMA, and for the complete duration of the call, as in FDMA. All the users have access simultaneously to the entire spectrum for all the time. They are interfering with each other, and that is why this methodology was originally conceived as a covert mode of communication. There are two main types of CDMA. One is called direct sequence spread spectrum multiple access, and the other is called frequency hopped spread spectrum multiple access. In the first case, twoway communication is accomplished through spread spectrum modulation where each user's digital waveform is spread over the entire frequency spectrum that is allocated to that base station. Typically, on transmit, the actual signal is coded and spread over the entire spectrum, where on receive, the intended user first detects the signal by convolving the received signal with his/her unique code and then demodulates the convolved signal. In the second case the transmit carrier frequency changes as a function of time in an ordered fashion so that the receiver can decode each narrowband transmission. At first glance it appears that CDMA is more complex than TDMA or FDMA, but with the advent of novel digital chip design, it is easy to implement CDMA in hardware.
4.) Space (Space Division Multiple Access or in short, SDMA). If a base station has to cover a large geographical area, the region is split into cells where the same carrier frequency can be reused in each cell. Therefore, for a large number of cells there is a high level of frequency reuse, which increases the capacity. In this primitive form the transmitted power of the base station limits the number of cells that may be associated with a base station since the level of interference at a base station is determined by the spatial separation between cells, as the mobiles are using the same frequency. This is one of the reasons that microcells and picocells have been proposed for personal communication systems. However, it was soon realized that the capacity of the base station could be increased further by spatially focusing the transmitted energy along the direction of the intended users. In this way, transmission can be achieved at the same carrier frequency simultaneously with different users. This can be accomplished by using an array of antennas at the base station and either a switched beam array or a tracking beam array can be used to direct the electromagnetic energy to the intended users.
In current times it appears that further enhancement in the capacity of a communication system can be achieved primarily in the implementation of SDMA. This is generally carried out using an adaptive process where we have a collection of antennas called phased arrays. One now dynamically combines the output from each antenna element using different weights. The weights modify the amplitude and phase of the voltages received at each antenna element. Through an appropriate combination of the voltages that are induced in them by the incident electromagnetic fields, one forms an antenna beam. This antenna beam can either be steered continuously or the beam can be switched along certain prefixed directions by selecting a set of a priori weights. This can be achieved in either of two ways.
The first way is to design an antenna with a narrow main beam. This is generally implemented by using a physically large antenna, as the width of the main lobe of the antenna is inversely proportional to the physical dimensions. Hence, an electrically large antenna structure will have a very narrow beam and may also possess very low sidelobe levels. Creation of very low sidelobe levels may require extremely high tolerances in the variability of the actual physical dimensions of the radiating structures. This requires accurate design of the antenna elements in the phased array. Now one can mechanically steer this highgain antenna to scan the entire geographical region of interest. This is actually done in developing the rotating antenna arrays in AW ACS (Airborne Early Warning and Control System) radars. Such a design makes the cost of the antennas very high. The other alternative is to use simple antenna elements such as dipoles, and then form the antenna beam by combining the received signals from a number of them by using a signal-processing methodology. This usually requires a receiver with an analog-to-digital converter (ADC) at the output of every antenna element, which also increases the cost. The signals from the antenna elements are now downconverted and sampled using an ADC, and then a digital beam-forming algorithm is used to form the main beam along the desired direction and to place nulls in the sidelobe regions along the direction of the interferers. The advantage of digital beam forming is that one can form any arbitrary low level of sidelobes with any width of the main lobe along the look direction.
Historically, analog beam forming has been going on for a long time. Also, application of the Butler matrix to combine the outputs of the antenna elements is similar in principle to application of the fast Fourier transform (FFT) to the output voltages available at the antenna elements to form a beam. This is because the far field is simply the Fourier transform of the induced current distribution on the radiating structure. Even though there is a one-to-one correspondence between the Butler matrix and the fast Fourier transform, there is an important fundamental difference. The Butler matrix processes the signals in the analog domain, whereas an FIT carries out similar processing in the digital domain. By processing signals in the analog domain, one is limited by the Rayleigh resolution criterion, which states that in order to resolve two closely spaced signals in space (i.e., their directions of arrival at the antenna array are very close to each other), one needs an antenna whose physical size is inversely proportional to the difference in the spatial angles of arrival at the array. Therefore, the closer two signals are located in space, the greater should be the physical size of the antenna in order to separate the two incoming signals. Therefore, the physical length of the array determines the angular resolution of a phased array performing analog processing. On the other hand, digital beam forming allows us to go beyond the curse of the Rayleigh limit if there is adequate signal strength and enough effective bits in the measured voltages (dynamic range) at each of the antenna elements to carry out beam forming.
Typically, adaptive beam forming is supposed to be synonymous with digital beam forming and smart antennas. The term smart antennas implies that the antenna array can operate in any environment and has the capability to extract the signal of interest in the presence of interference and clutter and thus to adapt to the signal environment. However, a very important factor has been overlooked in the design process of adaptive systems. For example, if one observes a typical cellular phone, the chip and the signal processors that have been used in the system were probably developed within the last year, but the key ingredient (i.e., the antenna) currently used in many systems was developed about 100 years ago by Hertz, as it is a modification of a simple dipole. Nowadays, the dipole is being replaced by some form of helix (bifilar or quadrifilar), which had been used in AM radios for almost 75 years. The same disparity in technology can also be observed in television sets. Even though a modern television set may have advanced components both for video displays and for processing the video and audio signals, the very high frequency (VHF) antenna is still the "rabbit ear"-a dipole, and the ultra high frequency (UHF) antenna is a loop which was developed in the early nineteen hundreds. The principle behind such wide disparities in component technologies of modern communication systems lies primarily in the assumption that an antenna captures a spatial-temporal signal propagating through space and transforms it into a pure temporal signal without any distortion. This assumes that the antennas are essentially isotropic omnidirectional point radiators. That is the reason why in contemporary literature the antenna is often referred to as a sensor of a temporal channel. In electromagnetics, the smallest source is an infinitesimally small dipole and it does not have an isotropic pattern, even though it is omnidirectional in certain planes.
An antenna to a spatial signal is equivalent to what an ADC is to a temporal signal. The purpose of an ADC is to produce high-fidelity temporal samples through the sample-and-hold mechanism of a temporal signal. For an ADC to be of good quality, it is essential that the sample time be much smaller than the hold period so that the sampled values provide a true representation of the analog signal. However, the quality of the ADC becomes questionable if the hold time is comparable to the sample time. In that case the temporal sample obtained from the ADC is not going to be representative of the true signal, as the ADC averages the output over the sample period, during which the signal of interest may have wide variations in amplitude. Under this scenario, where the hold time is comparable to the sample period, unless the effects of the ADC are removed through deconvolution, additional signal and data processing may not produce meaningful results.
This same problem arises in the practical application of antennas. An antenna is a spatial sampler of the electromagnetic fields propagating through space. A receiving antenna generally samples the electric field over its length and produces a voltage at the antenna terminals by integrating vectorially the electromagnetic fields incident upon it. When dealing with narrowband electromagnetic signals, a high-quality receiving antenna is often composed of an array of half-wavelength dipoles, typically spaced a half-wavelength apart. So in an adaptive antenna environment, we are assuming the integrated value of the electrical field over a half wavelength to be equal to the actual value of the electric field at a point in space which corresponds to the feed point of the antenna. In other words, we are replacing the value of the incident electromagnetic field at the feed point of the antenna by a quantity that is the integral of the electromagnetic fields over a half wavelength in space. Thus by comparing the performance of a finite-sized antenna in spatial sampling of electromagnetic fields to that of temporal sampling of a signal by an ADC, it is quite clear that unless the effects of the antenna are removed from the measured data, signal and data processing may not result in the desired output. This is due to the basic premise that the spatial integral value of the electric field along the half-wavelength antenna is representative of the actual value of the electric field at the antenna feed point. This is not correct. Hence, one of the objectives of this book is to merge the electromagnetic analysis with the signal processing. Now one can implement adaptive processing using realistic antenna elements operating in close proximity and incorporate mutual coupling effects. Moreover, there may be coupling between the antenna elements and the platform on which it is mounted. In addition, there may be near-field scatterers, including other antennas, buildings, trees, and so on, near the array that may again distort the beam. In this book we present and illustrate methods for adaptive processing incorporating near-field electromagnetic effects.
When dealing with broadband signals, we often assume that the omnidirectional isotropic point radiators have no effects on the signal. Such a simplistic assumption is seriously flawed. An antenna is not only a spatial sampler of the propagating electromagnetic field, it has a temporal response as well. It has a unique transfer function. For example, the far-field response from even an electrically small antenna is the result of a temporal differentiation of the driving time domain waveform. In addition, the radiated waveforms will have different signal shapes along different spatial directions. Moreover, an antenna of finite size will not mimic an omnidirectional point radiator in performance. On receive, an antenna vectorially integrates the spatial-temporal waveform that is incident on the structure. Therefore, unless the transfer function of the antenna is removed from the measured data, carrying out additional signal processing may not lead one to the correct solution to the problem at hand. This is not a simple problem, as the impulse response of both a transmitting and a receiving antenna of finite size is dependent simultaneously on both azimuth and elevation look directions. In a practical situation it is difficult to characterize the impulse response of either a transmitting or a receiving antenna, as it is difficult to know a priori at what azimuth and elevation angles the coupling is taking place. In broadband applications, the antenna responses must be accounted for. The easy way out in most practical systems in theory and in practice is to deal only with narrowband signals. One of the objectives of this book is to initiate a dialogue so that adaptive processing of the data collected through an antenna is performed in the correct fashion. Thus by combining electromagnetic analysis with signal processing, one can build toward a much more effective solution to the problem at hand.
A related problem in adaptive processing is that one often uses antenna elements that are very close to omnidirectional in nature. Even a dipole may have some directivity in elevation, but in azimuth it is still omnidirectional. This may not be an intelligent choice for cellular telephony, since a mobile will radiate most of the power in azimuth directions away from the intended user. The efficiency of a mobile communications system can be improved by using directive elements in a phased array. However, the problem with using directive elements is that it is not clear how to apply classical adaptive processing. Beamspace solutions offer one answer, but there may be others. One of the objectives of this book is to suggest adaptive systems with directive antenna elements and to illustrate how the measured outputs from directive elements can be combined when the directive element patterns of the antenna elements are properly oriented. Equivalently, the antenna elements in an array can be distributed nonuniformly to cover the physical structure and thereby further increase the radiation/receive efficiency. This is particularly useful in mobile systems, where the electromagnetic environment is not predictable nor may it be characterized in an accurate fashion. We address these issues and more in the following chapters.
(Continues...)
Excerpted from Smart Antennas by Tapan K. Sarkar Michael C. Wicks Magdalena Salazar-Palma Robert J. Bonneau Excerpted by permission.
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Table of Contents
Preface.Acknowledgments.
Introduction.
What is an Antenna and How it Works.
Anatomy of an Adaptive Algorithm.
Direct Data Domain Least Squares Approaches to Adaptive Processing Based on Single Snapshots of Data.
Elimination of the Effects of Mutual Coupling on Adaptive Antennas.
Direction of Arrival Estimation and Adaptive Processing Using A Nonuniformly Spaced Array from a Single Snapshot.
Estimating Direction of Arrivals by Exploiting Cyclostationarity Using a Real Antenna Array.
A Survey of Various Propagation Models for Mobile Communication.
Methods for Optimizing the Location of Base Stations for Indoor Wireless Communication.
Identification and Elimination of Multipath Effects Without Spatial Diersity.
Signal Enhancement In Multiuser Communication through Adaptivity on Transmit.
Direct Data Domain Lease Squares Space-Time Adaptive.
Appendix A: The Concept of a Random Process and its Philosophical Implications in Analyzing Communication Systems.
Appendix B: A Brief Survey of the Conjugate Gradient Method.
Appendix C: Estimation of the Direction of Arrival in One and Two Dimensions Using the Matrix Pencil Method.
Index.