Pulse or digital communications – Spread spectrum – Direct sequence
Reexamination Certificate
1998-09-18
2001-03-27
Vo, Don N. (Department: 2734)
Pulse or digital communications
Spread spectrum
Direct sequence
C375S148000
Reexamination Certificate
active
06208684
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates to methods and systems for receiving direct-sequence code division multiple access signals, in general and to methods and systems for adaptively receiving such signals, in particular.
BACKGROUND OF THE INVENTION
In recent years, direct-sequence (DS) code division multiple access (CDMA) spread spectrum communication systems and methods experience growing attention worldwide. The IS-95 cellular communication standard is one example for application of DS-CDMA communications, which are described in TIA/EIA/IS-95-A, “Mobile Station-Base Station Compatibility Standard for Dual-Mode Wideband Spread Spectrum Cellular System,” Feb. 27, 1996.
Other implementations of CDMA can be found in third generation cellular systems, wireless multimedia systems, personal satellite mobile systems, and more. The basic principle of direct sequence code division multiple access communications, is that each user is assigned with a distinct spreading code, which is often referred to as a pseudo noise (PN) sequence. The spreading code bits (also called chips), are used to modulate the user data. The number of chips used to modulate one data symbol is known as the spreading factor (processing gain) of the system, and it is related to the spreading in bandwidth between the (unmodulated) user data and the CDMA signal.
In its simplest form, the base-band equivalent of the transmitted CDMA signal is,
T
[
n
]
=
∑
i
=
1
K
a
i
[
⌊
n
/
SF
⌋
]
·
PN
i
[
n
]
Equation
1
where SF is the spreading factor, └/SF┘ denotes the integer part of n/SF, a
i
[└n/SF┘] and PN
i
[n] are the data symbol and spreading code of the i-th user, respectively, and K is the number of active users. Note that by the definition of └n/SF┘, a
i
[└n/SF┘] is fixed for SF consecutive chips, in accordance with the definition above that each data symbol is modulated by SF chips.
If T
S
and T
C
denote the symbol and chip intervals in seconds, respectively, then T
S
=SF·T
C
. The chip rate is defined as 1/T
C
, and the symbol rate is defined as 1/T
S
. Accordingly, the chip rate is SF times greater than the symbol rate.
In a DS-CDMA system, all of the users are continuously transmitting over the same frequency band. Thus, at the receiver end, each user is distinguishable from all other users, only through his spreading code. The spreading codes are therefore designed to minimize cross-talk effects between the different users. Conventional systems often use orthogonal spreading sequences.
In practice, however, channel distortions and asynchronicity modify the transmitted signals, and as a consequence, cross-talks between the users exist even when orthogonal spreading codes are utilized by the transmitter.
A plurality of receiver structures are known in the art for DS-CDMA signals, including single-user (SU) and multi-user (MU) receivers, interference cancellation (IC) receivers, and more.
A conventional single-user receiver correlates the received signal with the spreading code of the desired user (user no.
1
), as follows
y
1
[
m
]
=
1
2
·
SF
∑
n
=
1
SF
R
[
m
·
SF
+
n
]
·
PN
1
[
m
·
SF
+
n
]
*
Equation
2
where R[n] denotes the received signal after down conversion and sampling and “*” denotes the complex conjugation. For simplicity we assume QPSK signaling in Equation 2. A simplistic example is provided, by setting K=2 (i.e. a system which includes two users) and discarding channel degradation (i.e. R[n]=T[n]). Hence, the following expression is obtained by substituting Equation 1 into Equation 2,
y
1
[m]=a
1
[m
]+CrossCorr
1,2
[m]·a
2
[m]
Equation 3
where
CrossCorr
i
,
j
[
m
]
=
1
2
·
SF
·
∑
l
=
1
SF
PN
i
[
m
·
SF
+
l
]
*
·
PN
j
[
m
·
SF
+
l
]
Equation
4
The term CrossCorr
1,2
[m]·a
2
[m] in Equation 3 denotes the interference caused to user
1
by user
2
. This simple example reveals a well known weakness of the SU receiver, namely, its performance is governed by the noise level induced by the cross-talk from all other channel users (see for example, A. J. Viterbi, “
CDMA Principals of Spread Spectrum Communication
”, Addison-Wesley Publishing Company, 1995). A more advanced SU receiver includes some means of interference cancellation, which are aimed at reducing these cross-talks, and improving the receiver's performance. For example, see the following references:
Yoshida, “
CDMA
-
AIC highly spectrum Efficient CDMA cellular system based on adaptive interference cancellation
”, IEICE transactions on communication v e79-b n Mar. 3, 1996, p. 353-360,
A. Yoon, “
A Spread spectrum multi
-
access system with co
-
channel interference cancellation
”, IEEE journal of selected areas in communications, September 1993,
U.S. Pat. No. 5,105,435 to Stilwell, entitled “Method And Apparatus For Canceling Spread Spectrum Noise”, and
Y. Li, “
Serial interference cancellation method for CDMA
” electronics letters, September 1994.
Multi-user (MU) receivers jointly demodulate several or all of the received signals associated with the currently active users. The structure of MU receivers is much more complicated than that of SU receivers, but their performance is significantly better since these receivers are less sensitive to cross-talks between the users. (see for example, S. Verdu “
Multi
-
user Detection
” Cambridge University Press, 1998, and the references therein).
In practice, the communication link between the transmitter and the receiver is often time varying. Therefore, the CDMA receiver, which can be an SU, MU or IC receiver, is required to be adaptive, thereby being capable of tracking the time variations of the communication channel. See for example U.S. Pat. No. 5,572,552 to Dent et. al, entitled “Method and system for demodulation of down-link CDMA signals”. See also, G. Woodward and B. S. Vucetic, “Adaptive Detection for DS-CDMA,” Proceedings of the IEEE, Vol 86, No. 7 July 1998.
Adaptive algorithms, like those available for DS-CDMA applications, are designed to minimize the expectation of a predetermined cost function (preferably a convex one) with respect to the receiver's parameters. For example, S. Verdu, “Adaptive Multi-User Detection”, Proc. IEEE Int. Symp. On Spread Spectrum Theory and Applications, (Oulu Finland, July 1994), is directed to an adaptive least-mean-squares (LMS) MU algorithm which minimizes the mean squared error between the transmitted and reconstructed symbols, i.e.
MSE
i
≡E
{(
â ;
i
[n]−a
i
[n]
)
2
} Equation 5
where â ;
i
[n] are the MU receiver output samples at the i-th terminal, and a
i
[n] are the transmitted symbols of the i-th user. The cost function in Equation 5 requires training sequences. In other words, the receiver must know the exact value of at least some of the transmitted symbols (the a
i
[n]'s) in order to minimize this cost.
Other methods, which are known in the art, do not require training data. S. Verdu, “Adaptive Multi-User Detection”, Proc. IEEE Int. Symp. On Spread Spectrum Theory and Applications, (Oulu Finland, July 1994), is also directed to such a method. This method encompasses a decision directed approach, which replaces the unknown a
i
[n]'s by estimation values thereof.
In the binary case, for example, a
i
[n] accepts only two levels: “1” and “−1”. Thus, an estimate of a
i
[n] can be obtained from the sign of the corresponding receiver outputs. In this case, the cost in Equation 5 reduces to
E
{(
â ;
i
[n]
−Sign{
â ;
i
[n]}
)
2
} Equation 6
Another method known in the art is described in M. Ho
Shamai Shlomo
Yellin Daniel
Darby & Darby
DSPC Technologies Ltd.
Vo Don N.
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