Communications: directive radio wave systems and devices (e.g. – Directive – Including a steerable array
Reexamination Certificate
2002-03-26
2003-07-15
Phan, Dao (Department: 3662)
Communications: directive radio wave systems and devices (e.g.,
Directive
Including a steerable array
C342S378000
Reexamination Certificate
active
06593882
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates generally to an antenna device and a control method thereof, and in particular, to a smart antenna device and a control method thereof.
2. Description of the Related Art
In general, an antenna device receives a signal at a predetermined frequency. Antenna devices have been developed for long distance communications and are currently being used for base stations and terminals in mobile communication systems. Along with the rapid development of antennas, the concept of a smart antenna has been introduced. A smart antenna is an intelligent antenna that forms beams after signal processing for a particular purpose in a base station or a mobile terminal. It is expected that smart antennas will be adopted for IMT-2000 (International Mobile Telecommunicaiton-2000). The use of a smart antenna increases system performance.
The smart antenna has emerged as a solution to the problem of limited available frequency resources. Especially with low power, the smart antenna can exert the same performance as or higher performance than the existing systems. The smart antenna is operated on the principle that only a desired signal is extracted from interfering signals, that is, a high gain is given to a desired signal's direction, and a low gain to the directions of the other signals to thereby enable a transmitting/receiving end to obtain more power with respect to the same transmission power. The smart antenna incurs constructive interference in the desired location and destructive interference elsewhere, which is called beamforming.
The smart antenna has three advantages on the whole.
First, signals are gathered to a desired location without distribution, thereby increasing gain. Therefore, a coverage area per base station becomes wider and the increase of gain reduces the power consumption of a mobile terminal, that is, the life of its battery is increased.
Secondly, since signals in undesired directions are effectively removed, interference can be cancelled. In particular, the interference canceling effect becomes great in a system susceptible to interference like CDMA (Code Division Multiple Access). A CDMA system then accommodates more subscribers in the case of voice communication and provides high rate data communication in the case of data communication.
Thirdly, the smart antenna also implements spatial filtering. Accordingly, multipath effects can be remarkably reduced.
There are two types of smart antennas depending on their beamformation methods: switched beam smart antennas and adaptive beam smart antennas. The former uses a fixed beam pattern and so may result in performance decrease if a user is disposed between antenna patterns. On the other hand, the latter uses an antenna pattern that varies with time or according to ambient environment, thus operating more adaptively to the environment than the former and can form a beam direct to a user.
The aim of a switched beam smart antenna is to detect the direction of a strong signal and select the signal from the direction.
Most of adaptive beamforming algorithms may be categorized into the following three classes or combinations of them.
Algorithms based on estimation of DOAs (Directions Of Arrival) of received signals.
In the DOA-estimation-based algorithms, the DOAs of received signals are first estimated and beams are then formed in the estimated directions. The techniques for DOA estimation include MUSIC (Multiple Signal Classification), Pisarenko, ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques), and ML (Maximum Likelihood). Beamformers operate by conventional beamforming and LCMV (Linear Constraint Minimum Variance).
Algorithms based on training sequence.
In these algorithms including SMI (Sample Matrix Inversion), LMS (Least Mean Square) and RLS (Recursive Least Square), a beam pattern is determined using a training sequence which is known to both the transmitter and the receiver. The training sequence based algorithms are easy to implement though limited, due to the use of a training sequence.
Blind adaptive algorithms.
Blind adaptive algorithms do not require a training sequence. Instead, they exploit some known properties of a desired received signal in determining a beam pattern. These blind adaptive algorithms include CMA (Constant Modulus Algorithm) and FA (Finite Alphabet) utilizing signal constellation, and a cyclo-stationary method and a high order statistic method based on oversampling characteristics. A disadvantage of these algorithms is complexity though they are free of overhead such as the use of a training sequence and constraints.
Many blind adaptive algorithms have been studied and suggested. Blind adaptive beamformation is done by spectral estimation or parameter estimation.
Major spectrum estimation methods are power maximization and LS-SCORE (Least Square-Spectral Self Coherence Restoral). Most of those methods are based on eigen decomposition. Especially, MCGM (Modified Conjugate Gradient Method) allows real time processing and has relatively good performance.
Major parameter estimation methods are ML (Maximum Likelihood) and ILSP (Iterative Least Square Projection). Despite its excellent performance, ML requires a considerable volume of computation. Meanwhile, ILSP markedly reduces the computation requirements inherent to the ML by iterating the least square solutions of ML, but it is not suitable for real time processing. The volume of required computation can be remarkably reduced by ILSP-CMA that employs ILSP along with CMA utilizing the constant envelope of a signal.
ILSP-CMA iteratively calculates solutions using the constant envelope of a signal. While it stably operates and has good performance, ILSP-CMA is not available to a signal that does not have a constant envelope. ILSP-CMA is the process of generating an M×N input signal matrix for the input of N snapshots and processing the matrix as a block. This method causes latency and requires a great volume of instantaneous computation and large instantaneous memory capacity.
ILSP-CMA will be described below in more detail in connection with the structure of an adaptive array antenna.
FIG. 1
is a block diagram of a typical adaptive array antenna. Referring to
FIG. 1
, an array antenna
101
includes a set of M antennas, each antenna having the same characteristics. The antennas are uniformly spaced from each other by a distance d. &thgr;
k
is the incident angle of a signal impinging on an antenna from a k
th
signal source Signals received at the array antenna
101
are fed to a pre-beamforming block
102
. The pre-beamforming block
102
, which is an optional block, performs coarse beamforming using the result of post-beamforming or preliminarily acquired information. A despreader
103
despreads the coarsely beamformed signals to reduce interference from the other signal sources and thus to facilitate signal processing. The despreader
103
is available only to a CDMA system. The despreader
103
may be disposed as shown in
FIG. 1
or at the rear end of an adaptive array processing unit
105
. M despread signals, X
□
to X
M
are applied to the input of the adaptive array processing unit
105
.
The adaptive array processing unit
105
includes weight factor operators
108
to
110
for assigning weight factors to the M input signals, an adder
111
for summing the outputs of the weight factor operators
108
to
110
, an error generator
107
, and an adaptive algorithm processor
106
. In operation, the output signal Ŝ
k
of the adaptive array processing unit
105
and a reference signal S
k
are fed to the error generator
107
. The error generator
107
generates an error signal using the two input signals. The adaptive algorithm processor
106
calculates weight factors W
1
to W
M
from the error signal by a predetermined algorithm. The weight factor operators
108
to
110
calculate the input signals X
1
to X
M
with the weight factors W
1
to W
M
. The adder
111
sums the calculated signals received f
Chang Tae-Ryun
Kim Je-Woo
Park Jong-Hyeon
Roh Sang-Hoon
Shim Bok-Tae
Grossman Tucker Perreault & Pfleger PLLC
Phan Dao
TeleCIS Technologies, Inc.
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