Smart Antenna Systems For Mobile Communications

ABSTRACT

Smart antennas are antenna arrays or group of antenna with smart processing algorithms used to identify spatial signal signature. A smart antenna takes advantage of diversity effect at the source (transmitter), the destination (receiver), or both. Diversity effect involves the transmission and/or reception of multiple radio frequency (RF) waves to increase data speed and reduce the error rate. Smart antenna technology can overcome these capacity limits as well as improve signal quality and let mobile telephones operate on less power. Smart antenna are also known as adaptive array antennas, MIMO & multiple antennas.

TABLE OF CONTENTS

COVER PAGE

TITLE PAGE

APPROVAL PAGE

DEDICATION

ACKNOWELDGEMENT

ABSTRACT

CHAPTER ONE

1.0      INTRODUCTION

1.1      BACKGROUND OF THE PROJECT

• PROBLEM STATEMENT
• AIM AND OBJECTIVE OF THE PROJECT
• SIGNIFICANCE OF THE PROJECT
• LIMITATION OF THE PROJECT
• PROBLEM OF THE PROJECT
• APPLICATION OF THE PROJECT

CHAPTER TWO

2.0      LITERATURE REVIEW

• INTRODUCTION
• EVOLUTION FROM OMNI DIRECTIONAL TO SMART ANTENNAS
• DIFFERENCE BETWEEN CONVENTIONAL AND SMART ANTENNAS
• TYPES OF SMART ANTENNAS
• CATEGORIES OF SMART ANTENNA SYSTEM

CHAPTER THREE

3.0      METHODOLOGY

3.1      SYSTEM ELEMENTS OF A SMART ANTENNA

3.2      FUNDAMENTALS OF ANTENNA ARRAYS

3.3      CHANNEL MODEL

CHAPTER FOUR

TEST AND RESULT

• SYSTEM SIMULATION
• RESULT

CHAPTER FIVE

• CONCLUSION
• REFERENCES

Acronyms

AA Adaptive Array

ADC Analog-to-Digital Converter

AoA Angle-of-Arrival

ATM Asynchronous Transfer Mode AWGN Additive White Gaussian Noise BER Bit-Error-Rate

BPSK Binary Phase-Shift Keying

CCI Co-Channel Interference

CDF Cumulative Distribution Function

CDMA Code-Division Multiple Access

CIR Carrier-to-Interference Ratio

CM Constant Modulus

DAC Digital-to-Analog Converter

D-AMPS Digital-Advanced Mobile Phone Service

DCS1800 Digital Communications System  1800

DECT Digital Enhanced Cordless Telecommunications

DMI Direct Matrix Inversion

DoA Direction-of-Arrival

DOCSIS Data Over Cable Service Interface Specification

DQPSK Differential Quadrature Phase-Shift Keying

DS-CDMA Direct Sequence CDMA

DSL Digital Subscriber Line

DSP Digital Signal Processing

ESPRIT Estimation of Signal Parameters via Rotational Invariance Techniques

ETSI European Telecommunications Standards Institute

FA Finite Alphabet

FCC Federal Communications Commission (USA)

FDD Frequency-Division Duplex

FDMA Frequency-Division Multiple Access

FIR Finite Impulse Response

GMSK Gaussian Minimum Shift Keying

GPS Global Positioning System

GSM Global System for Mobile Communications

HSR High Sensitivity Receiver

IC Integrated Circuits

IID Independently and Identically Distributed

IMT2000 International Mobile Telecommunications 2000

IRC Interference Rejection Combining

ISI Inter-Symbol Interference

LAN Local Area Network

LMS Least-Mean-Square Algorithm

LOS Line-of-Sight

MAI Multiple  Access  Interference

MIMO Multiple Input – Multiple Output

MISO Multiple  Output  –  Single  Input

ML Maximum Likelihood

MLSE Maximum Likelihood Sequence Estimation

MMDS Mulitipoint Microwave Distribution System

MMSE Minimum Mean-Square Error

MNV  Maximum  Noise  Variance

MRC Maximum Ratio Combining

MSE Mean Square Error

MU-CM Multi-User Constant Modulus

MU-MISO Multiple User with Multiple antenna composite Input at the base station and Single antenna Output at each mobile

MUSIC MUltiple SIgnal Classification

MU-SIMO Multi User with Single antenna Input at each mobile  and  Multiple  antenna  Output at base station

OFDM Orthogonal Frequency-Division Multiplexing

PA Phased Array

PBX Private Branch Exchange

PC Power Control

PCS Personal Communication Service

pdf Probability density  function

PHS Personal Handyphone System

QHA Quadrifilar Helix Antenna

RDBF Receive Digital Beam Former

RF Radio Frequency

RLS Recursive Least-Square

SA  Smart Antenna

SB  Switched Beam

SDMA Space-Division Multiple Access

SFIR Spatial Filtering for Interference Reduction

SIMO  Single  Input  –  Multiple  Output

SINR Signal-to-Noise-and-Interference Ratio

SISO Single Input – Single Output

SNR Signal-to-Noise Ratio

ST Space-Time (coding)

ST-MLSE Space-Time MLSE

ST-MMSE Space-Time MMSE

SU-MISO  Single User with  Multiple antenna  Input at the base station  and Single antenna Output   at the mobile

SU-SIMO Single User with Single antenna Input at mobile and Multiple antenna Output at base station

TDBF Transmit Digital Beam Former

TDD Time-Division Duplex

TDMA Time-Division Multiple Access

TRB Time Reference Beamforming (beamforming based on training signal)

ULA Uniform Linear Array

UMTS Universal Mobile Telecommunications System

WLAN Wireless LAN

WLL Wireless Local Loop

CHAPTER ONE

• INTRODUCTION

It is foreseen that in the future an enormous increase in traffic will be experienced for mobile and personal communications systems. This is due both to an increased number of users as well as new high bit rate data services being introduced. The increase in traffic will put a demand on both manufacturers and operators to provide high capacity systems in the networks.

Presently, the only mobile communication company in Rwanda is MTN Rwanda cell as at 1999. This company uses either unidirectional in the sparsely populated areas or factorized antennas in the densely populated areas – in the cities at its base stations. The basic challenge in wireless communication being the finite spectrum or bandwidth, the only technology believed to be the latest major technological innovation that has the capability of containing large increase in mobile communication systems access [G.T. Okamato, 1999] is the smart antenna.

Smart antennas is also known as adaptive array antennas, digital antenna arrays, multiple antennas and, recently, MIMO) are antenna arrays with smart signal processing algorithms used to identify spatial signal signatures such as the direction of arrival (DOA) of the signal, and use them to calculate beamforming vectors which are used to track and locate the antenna beam on the mobile/target. Smart antennas should not be confused with reconfigurable antennas, which have similar capabilities but are single element antennas and not antenna arrays.

Smart antenna techniques are used notably in acoustic signal processing, track and scan radar, radio astronomy and radio telescopes, and mostly in cellular systems like W-CDMA, UMTS, and LTE.

Smart antennas have many functions: DOA estimation, beamforming, interference nulling, and constant modulus preservation.

Smart antennas are comprised of a number of individual antennas and associated signal processors which provide the “smart” portion. Smart antennas can use either, or both, for the signal transmission and the signal reception.

The major advantages to using a smart antenna are reduction in overall system power, reduction in communication interference, and increase in system capacity and improve in power efficiency. Smart antenna at the receiver provides reduction of signal loss in multipath fading, which means more overall robust signal quality independent of the variations of the transmitted signal due to the physical environment and other electromagnetic interferences. For mobile applications, there are fewer dropped calls, reduced areas of low-signal / no-signal or dead  zones better reception, reduction of bit error rate, reduction in handoff and higher data rates.

At the end of this project a smart antenna system for the base station for mobile communication shall be designed. We show that the smart antennas can be implemented at the base station site, without requiring any changes neither at adjacent base stations nor in the mobile stations.

1.1                              BACKGROUND OF THE STUDY

Global demand for voice, data and video related services continues to grow faster than the required infrastructure can be deployed. Despite huge amount of money that has been spent in attempts to meet the need of the world market, the vast majority of people on Earth still do not have access to quality communication facilities. The greatest challenge faced by governments and service providers is the “last-mile” connection, which is the final link between the individual home or business users and worldwide network. Copper wires, traditional means of providing this “last-mile” connection is both costly and inadequate to meet the needs of the bandwidth intensive applications. Coaxial cable and power line communications all have technical limitations. And fiber optics, while technically superior and widely used in backbone applications, is extremely expensive to install to every home or business user. This is why more and more the wireless connection is being seen as an alternative  to quickly  and cost effectively meeting the need for flexible broadband links [1].

The universal and spread use of mobile phone service is a testament to the public’s acceptance of wireless technology. Many of previously non-covered parts of the world now boast of quality voice service thanks in part to the PCS (Personal Communications Service) or cellular type wireless systems. Over the last few years the demand for service provision via the wireless communication bearer has risen beyond all expectations. At the end of the last century more than 20 million users in the United States only utilized this technology [2]. At  present the number of cellular  users is growing rapidly all over the world.

The proliferation of wireless networks and an increase in the bandwidth required has led to shortages in the scarcest resource of all,  the finite number of radio frequencies that these devices use.   This has increased the cost to obtain the few remaining licenses to use these frequencies and the related infrastructure costs required to provide these services.

In a majority of currently deployed wireless communication systems, the objective is to sell a product at a fair price (the product being information transmission) [3]. From a technical point of view, information transmission requires resources in the form of power and bandwidth. Generally, increased transmission rates require increased power and bandwidth independently of medium. While, on the one hand, transmission over wired segments of the links can generally be performed independently  for each  link (if  we  ignore the cross-talk  in land lines) and,  on the other hand,  fibers are excellent    at confining most of the useful information (energy) to a small region in space, wireless transmission is much less efficient. Reliable transmission over relatively short distances in space requires a large amount of transmitted energy, spread over  large regions of space, only a very  small portion of which  is actually received by the intended user. Most of the wasted energy is considered as interference to other potential users of the system.

Somewhat simplistically, the maximum range of such systems is determined by the amount of power that can be transmitted and the capacity is determined by the amount of spectrum available. For a given amount of power and a fixed amount of bandwidth (the amount one can afford to buy) there is a finite (small) amount of capacity that operators can sell to their customers, and a limited range over which customers can be served from any given location. Thus, the two basic problems that arise in such systems are:

1. How to acquire more capacity so that a larger number of customers can be served at lower costs maintaining the quality at the same time, in areas where demand is large (spectral efficiency).
2. How to obtain greater coverage areas so as to reduce infrastructure and maintenance costs in areas where demand is relatively small (coverage).

In areas where demand for service exceeds the supply operators have to offer, the real game being played is the quest for capacity. Unfortunately, to date a universal definition of capacity has  not evolved. Free to make their own definitions, operators and consumers have done so.  To  the consumer, it is quite clear that capacity  is measured in the quality of each  link he gets and the number of times  he can successfully get such a link when he wants one. Consumers want the highest possible quality links at the lowest possible cost. Operators, on the other hand, have their own  definitions of capacity  in which great importance is placed on the number of links that can simultaneously be established. Since the quality and number of simultaneous links are inversely related in a resource-constrained environment, operators lean towards providing the lowest possible quality links to the largest possible number of users. The war wages on: consumers are wanting better links at lower costs, and operators are continually trying to maximize profitability providing an increasing number of lower quality links at the highest acceptable cost to the consumer. Until the quest for real capacity is successful,  the  battle between operators and their consumers over capacity, the precious commodity that operators sell to consumers, will continue.

There are many situations where coverage, not capacity, is a more important issue.  Consider the rollout of any new service. Prior to initiating the service, capacity is certainly not a problem  –  operators have no customers. Until a significant percentage of the service area is covered, service cannot begin. Clearly, coverage is an important issue during the initial phases of system deployment. Consider also that in many instances only an extremely small percentage of the area to be served is heavily populated. The ability to cover the service area with a minimum amount of infrastructure investment is clearly an important factor in keeping costs down.

As it is often painfully obvious to operators, the two requirements, increased capacity and increased range, conflict in most instances.  While up to recently used technology can provide for increased  range in some cases and up to a limit increased capacity in other cases, it rarely can provide both simultaneously.

The International Mobile Telecommunications-2000 (IMT2000) and the European Universal Mobile.

Telecommunications System (UMTS) are two systems among the others that have been proposed to take wireless communications into this century [2]. The core objective of both systems is to take the “personal communications user” into new information society where mass-market low-cost telecom- munications services will be provided. In order to be universally accepted, these new networks have to offer mobile access to voice, data and multimedia facilities in an extensive range of operational envi- ronments, as well as economically supporting service provision in environments conventionally served by other wired systems.

1.2                                                  PROBLEM STATEMENT

Traditional antennas have problems such as low radiation security system, low range of operation and few number of users at a time. It was because of the mentioned problem that led to the invention of a smart antenna. Smart antenna overcomes the disadvantages in that smart antenna focuses gain on the communicating device, the range of operation increases. This allows the area serviced by a smart antenna to increase. smart antenna naturally provide increased security, as the signals are not radiated in all directions as in a traditional Omni-directional antenna, smart antenna can accommodate numerous users, and also, smart antenna has low interference and low band width.

1.3                                                   AIM OF THE PROJECT

Therefore, the aim of this project is to analyse and design a smart antenna system for a mobile communications, at the end of this study how the system can increase capacity in mobile communications shall also be discussed.

1.4                                         SIGNIFICANCE OF THE PROJECT

The advantages of smart antenna are as below:

i ) Increased number of users: Due to the targeted nature of smart antennas frequencies can be reused allowing an increased number of users. More users on the same frequency space means that the network provider has lower operating costs in terms of purchasing frequency space.

1. ii) Increased Range: As the smart antenna focuses gain on the communicating device, the range of operation increases. This allows the area serviced by a smart antenna to increase. This can provide a cost saving to network providers as they will not require as many antennas /base stations to provide coverage.

iii) Security: Smart antennas naturally provide increased security, as the signals are not radiated in all directions as in a traditional Omni-directional antenna. This means that if someone wished to intercept transmissions they would need to be at the same location or between the two communicating devices.

1. v) Reduced Interference: Interference which is usually caused by transmissions which radiate in all directions is less likely to occur due to the directionality introduced by the smart antenna. This aids both the ability to reuse frequencies and achieve greater range.
2. iv) Increased bandwidth: The bandwidth available increases form the reuse of frequencies and also in adaptive arrays as they can utilize the many paths which a signal may follow to reach a device.

1.5                                           LIMITATION OF THE PROJECT

This project is limited to the design of a smart antenna system for the base station for mobile communication

1.6                                              PROBLEM OF THE PROJECT

1. i) Complex: A disadvantage of smart antennas is that they are far more complicated than a traditional antennas. This means that faults or problems may be harder to diagnose and more likely to occur.
2. ii) More Expensive: As smart antennas are extremely complex, utilizing the latest in processing technology they are far more expensive than traditional antennas. However this cost must be weighed against the cost of frequency space.

iii) Larger Size: Due to the antenna arrays which are utilized by smart antenna systems, they are much larger in size than traditional/conventional antenna systems. This can be a problem in a social context, as antennas can be seen as ugly or unsightly.

1. iv) Location: The location of smart antennas needs to be considered for optimal operation. Due to the directional beam that ‘swings’ from a smart antenna locations which are optimal for a traditional antenna is not for a smart antenna. For example in a road context, smart antennas are better situated away from the road, unlike normal antennas which are best situated along the road.

1.7                                         APPLICATIONS OF THE PROJECT

A space-time processor (‟smart „antenna‟) is capable of forming transmit/receive beams towards the mobile of interest. At the same time it is possible to place spatial nulls in the direction of unwanted interferences. This capability can be used to improve the performance of a mobile communication system.

Smart antenna technologies can be used to improve most wireless applications, including:

• Wi-Fi a/b/g access points and clients
• In-vehicle DBS entertainment systems, such as:
• Mobile video
• Mobile broadband/gaming
• Satellite/digital radio
• GPS
• 3G Wireless
• WiMax