Electrical computers: arithmetic processing and calculating – Electrical digital calculating computer – Particular function performed
Reexamination Certificate
2000-07-24
2004-03-30
Ngo, Chuong Dinh (Department: 2124)
Electrical computers: arithmetic processing and calculating
Electrical digital calculating computer
Particular function performed
Reexamination Certificate
active
06714956
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates generally to the field of adaptive systems, and, more particularly, to an efficient coefficient adaptation process for adaptive filters.
BACKGROUND OF THE INVENTION
An adaptive system is a system which searches for improved performance using a computational algorithm to adjust certain parameters or weights. An adaptive filter is a computational device that attempts to model the input-output relationship between two signals in real time in an interactive manner. Adaptive filters are used, for example, in communication systems for echo cancellation and line equalization. An adaptive filter is also suitable for use in real-time control systems for different kinds of applications related to real-time optimization. Adaptive signal processing is expanding in other fields as well, such as in radar, sonar, seismology, and biomedical electronics.
An adaptive filter is defined by four aspects: the input signal x(n) being processed by the filter; the structure that defines how the filter output y(n) is computed from its input signal x(n); the filter parameters that can be iteratively changed to alter the input-output relationship of the filter; and the adaptive algorithm that describes how parameters are adjusted from one time instant to the next.
An adaptive filter may be implemented as an open-loop filter or as a closed-loop filter with a performance feedback feature. The algorithm operates in an iterative manner to update the adjustable parameters with the arrival of new data and current signal performance feedback. With each iteration, the system learns more and more about the characteristics of the input signal x(n), and the signal processor makes adjustments to the current set of parameters based on the latest system performance through an error signal e(n). The optimal set of values of the adjustable parameters is then approached sequentially.
Adaptive filters are often realized as a set of program instructions running on a digital signal processor (DSP).
FIG. 1
shows a general adaptive filtering process. Generally, any system with a finite number of parameters affecting how the output y(n) is computed from the input x(n) could be used as the adaptive filter
10
in FIG.
1
.
The coefficient vector T(n) of the filter is defined by the equation T(n)=[t
1
(n)t
2
(n) . . . t
N
(n)]
T
. As mentioned above, the input of the filter is x(n), while the output of the filter is y(n). The desired response signal is d(n). The error signal e(n) represents the difference between the desired signal d(n) and the real output y(n). The most frequently used structure of an adaptive filter is the finite-impulse-response (FIR) filter, which is shown in FIG.
2
.
In
FIG. 2
, the unit z
−1
is called the delay unit. The filter itself is based on convolution. A complete computing step is called a “tap.”
FIG. 2
shows a suitable architecture because of its linearity. When performing the filtering process, the coefficient set is used to find the output through convolution using the following equation 1:
Y
(
n
)
=
∑
i
=
1
n
t
(
i
)
×
(
n
-
i
)
Eq
.
1
The output y(n) might not be the desired output. That is, y(n) may be very close to the desired signal d(n), but not close enough. In that case, the adaptation algorithm would be executed to correct the coefficient set so that the output y(n) will gradually approach the desired signal d(n). The desired signal d(n) is unknown and changes all the time. Therefore, the adaptive filter is a real-time closed-loop feedback system which is adapting all the time in order to follow the desired signal of d(n).
In a high quality adaptive filter, the coefficient set is constantly adapting, which requires a lot of computing power, making the adaptive filter expensive. The most popular adaptation algorithm is called the normal least-mean-square algorithm (LMS or NLMS). The LMS algorithm makes use of the so-called “steepest descent” approach, deriving an estimation of the gradient vector based on a limited number of data samples.
This adaptation algorithm includes the processes of convergence control and coefficient adaptation. Convergence control will not be performed in every tap, meaning that the computing power associated therewith is not very high. For this reason, the present invention does not deal with optimization of the convergence control process. Coefficient adaptation, however, should be performed for all taps during each sample in order to achieve a high-performance adaptive filter. Normally, then, most of the computing power is consumed when performing coefficient adaptation.
A tap of adaptive computing includes a convolution step and a coefficient adaptation step. The task involved in tap i is shown by the following equations:
y
(
i
)=
y
(
i
−1)+
x
(
i
)
t
(
n−i
) eq. 2
t
new
(
i
+1)=
t
old
(
i
+1)±(convergence factor)*
x
(
i
) eq. 3
In equation 3 above, t
new
(i+1) is the new coefficient after adaptation, and t
old
(i+1) is the old coefficient which is going to be adapted.
Because there are many taps in a sample of processing along with variable coefficients, a digital signal processor (DSP) is needed to execute the so-called LMS algorithm.
FIG. 3
shows the architecture of a typical digital signal processor.
According to the algorithm associated with a typical digital signal processor, a tap of the LMS computing process includes seven steps. First, the data is loaded from the data memory DM
20
to data register DR
28
. Next, the coefficient is loaded from the tap memory or coefficient memory TM
22
to coefficient register TR
30
. The register ACR
36
computes coefficient adaptation using the equation t
new
(i)=t
old
(i)+(convergence factor) * DR. The new coefficient is then moved to a buffer BR
26
(BR=ACR). In the next step, ACB is moved to ACR and convolution is performed such that ACR=ACR+data*BR. Next, the contents of ACR are moved to ACB, and finally t
new
(n) is moved to the coefficient memory TM
22
.
The number of executing clock cycles for one tap may vary for different architectures. For example, seven steps are necessary when using a standard central processing unit (CPU). When using advanced digital signal processors based on multiplier-accumulator hardware, the number of clock cycles for one tap of convolution and coefficient adaptation could be between two and four.
The coefficient memory has two memory accesses: one read and one write. Because both memory accesses are required for coefficient adaptation, it is difficult to execute all seven steps in one clock cycle. A dual port memory cannot be used because the memory read and write addresses could depend on each other. An asynchronous memory may be used as the coefficient memory, but problems may result including relatively slow speed and high power consumption.
In an advanced available digital processor, a special double-speed clock (compared to the process clock) is applied just to the coefficient memory, and therefore the coefficient memory can be accessed twice in one process clock cycle. However, there is an obvious drawback. The memory speed was previously slower than the logic speed; if twice the memory is used per process cycle, the system speed will not be able to be half of the memory speed. Therefore, the system clock speed cannot be high.
It would be beneficial to reduce the computing power in an adaptive system. One essential problem preventing the reduction of the computing power is that a synchronous memory cannot be physically implemented to read from and write to any position in a single clock cycle. An asynchronous memory can read from and write to any position in a single CPU clock cycle, but only when the CPU clock is much slower than the memory access time.
Therefore, when using a synchronous memory, multiple computing steps must be used to execute one tap of adaptive filtering. And, when using an asynchronous memory, although one clock cyc
Bergh Harald
Liu Dake
Skelton Nick
Stuns Stig
Cooper & Dunham LLP
Ngo Chuong Dinh
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