Telecommunications – Receiver or analog modulated signal frequency converter – With wave collector
Reexamination Certificate
2000-09-13
2002-08-13
Bost, Dwayne (Department: 2681)
Telecommunications
Receiver or analog modulated signal frequency converter
With wave collector
C455S562100, C455S062000, C455S272000, C455S273000, C455S278100, C375S144000, C375S350000, C370S342000, C370S479000
Reexamination Certificate
active
06434375
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to wireless telecommunications. More particularly, the present invention relates to a design of an inexpensive and efficient smart antenna processor for a code division multiple access wireless communications system. In general, a conventional smart antenna requires phase calibration due to different characteristics at the radio frequency (RF) mixers at a receiver front end. Phase calibration is an expensive component since it is built With analog device in general. The present invention describes a smart antenna processor, which does not require phase calibration.
2. Description of the Related Art
A smart antenna is a blind adaptive antenna array intended to use spatial diversity properties by placing multiple antenna elements in a linear array or other shape. It can enhance the desired signal reception by suppressing the interference signal with a direction of arrival angle (DOA) different from that of the desired signal. The general techniques employed in smart antennas have been developed from adaptive filter theory.
A smart antenna algorithm discussed in S. Tanaka, M. Sawahashi, and F. Adachi, “Pilot Symbol-Assisted Decision-Directed Coherent Adaptive Array Diversity for DS-CDMA Mobile Radio Reverse Link,” IEICE Trans. Fundamentals, Vol. E80-A, pp. 2445-2454, December 1997, “Tanaka I”); S. Tanaka, A. Harada, M. Sawahashi, and F. Adachi, “Transmit Diversity Based on Adaptive Antenna Array for W-CDMA Forward Link,” The 4
th
CDMA International Conference and Exhibition Proceedings, pp. 282-286, 1999, “Tanaka II”); and F. Adachi, M. Sawahashi, and H. Suda, “Wideband DS-CDMA for Next-Generation Mobile Communications Systems,” IEEE Communications Magazine, Vol. 36, No. 9, pp. 56-69, September 1998, “Adachi”) was tested in a field experiment for a 3
rd
generation (3G) wideband (W)-CDMA wireless communications system. Known pilot symbol patterns are inserted into a common control channel in a W-CDMA system, as discussed for example in 3rd Generation Partnership Project, “Physical Channels and Mapping of Transport Channels onto Physical Channels (FDD),” 3GPP Technical Specification, TS25.211, v3.2.0, March, 2000; 3rd Generation Partnership Project, “Spreading and Modulation (FDD),” 3GPP Technical Specification, TS25.213, v3.2.0, March., 2000; and 3rd Generation Partnership Project, “FDD: Physical Layer Procedures,” 3Gpp Technical Specification, TS25.214, v3.2.0, March, 2000 (collectively “3GPP”). On the other hand, a pilot channel is used in a 3G CDMA2000 system, such as discussed in TIA, Interim V&V Text for cdma2000 Physical Layer (Revision 8.3), Mar. 16, 1999 “TIA”). A smart antenna processor generates a weight vector
w
(k) at the k-th snapshot (i.e., iteration). The smart antenna algorithms such as those proposed by Tanaka I, Tanaka II and Adachi try to let the weight vector converge to the array response vector
a
(&thgr;)=[1, e
−j&pgr; sin &thgr;
, . . . , e
−j(M−1)&pgr; sin &thgr;
] rather than the total input phase vector including the fading phase, different mixer phase distortion and array phase difference, where &thgr; is the DOA from the desired signal, M is the number of antenna array elements, e is the exponential operator, and &pgr; is 3.14159. Also, the updated weight vector in Tanaka I, Tanaka II and Adachi is used in a channel estimation block to estimate and cancel the fading phase. Furthermore, the phase and amplitude of each array element in the smart antenna-parallel radio frequency (RF) base station receiver circuitry are different from those of other receiver unit, and vary as the received signal power changes, see Tanaka II. Fortunately, the measured data indicate that phase difference between RF receiver units is almost constant, and amplitude difference is almost zero even the received signal power changes. Therefore, phase calibration was suggested before the adaptation processing, in Tanaka II. Phase calibration is an expensive component.
The least mean square (LMS) adaptive algorithm, which is an art related to the present invention, has been known for its simplicity because the LMS does not require any calculations of correlation functions or matrix inversion. For example, the weight vector in Simon Haykin, “Adaptive Filter Theory,” pp. 437, Summary of The NLMS Algorithm, Prentice Hall, 1996 (“Haykin”) was updated for a general adaptive filter application by using the normalized least mean square (N-LMS) algorithm. And, it has been shown that the N-LMS algorithm in Haykin not only shows a faster convergence than the LMS algorithm but also overcomes the gradient noise amplification problem existing in the LMS algorithm. The N-LMS algorithm lets the output converge to the desired adaptation processing output. The N-LMS algorithm minimizes the mean square estimation error between the desired output and the adaptation processing output.
BRIEF SUMMARY OF THE INVENTION
It is an object of the present invention to provide an inexpensive and efficient smart antenna processor useful in a wireless communications system, such as a code division multiple access (CDMA) wireless communications system, e.g., a 3
rd
generation (3G) CDMA2000 or W-CDMA system. Separate channel estimation is not required in the present invention. In addition, the phase distortion due to the radio frequency (RF) mixer in each antenna element can be compensated automatically by the present invention. Thus, the phase calibration is not necessary for a smart antenna processor in the present invention if the reverse link demodulation is concerned. One embodiment of the present invention is obtained by modifying the normalized least mean square (MN-LMS) adaptive filter. This requires only (5M+2) complex multiplication and (4M+1) complex additions per snapshot. Finally, bit error rate (BER) performance of a CDMA system with the MN-LMS algorithm in the present invention is better than that with the conventional N-LMS algorithm.
The present invention is a modified and normalized (MN)-LMS adaptive filter, which can track the individual total input phase at each element. The individual total input phase consists of the DOA, fading phase, and the phase distortion due to the mixer. The smart antenna in the presentation can track the individual total input phase at each element. In addition, the smart antenna algorithm in the present invention can be applied for both W-CDMA and CDMA2000 systems while the smart antenna in Tanaka I, Tanaka II and Adachi was tested for only a W-CDMA system. Furthermore, the present invention presents an inexpensive smart antenna because the W-CDMA or CDMA2000 system with the MN-LMS algorithm in the present invention does not require either any phase calibration or any channel estimation for data demodulation purpose.
In accordance with one aspect of the invention, there is provided a method and system for receiving a signal for use in combination with wireless communications. A signal is received in a plurality of antennas. The received signal is processed utilizing an updated weight vector, wherein the updated weight vector compensates substantially for a phase distortion of the signal.
According to one alternative aspect of the invention, the received signal is processed according to an MN-LMS algorithm. According to a more specific alternative aspect of the invention, the received signal is processed according to
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Chulajata Tatcha
Kwon Hyuck M.
Min Kyung Y.
Bost Dwayne
Heintz James M.
Kelber Steven B.
NeoReach, Inc.
Persino Ray
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