Pulse or digital communications – Spread spectrum
Reexamination Certificate
2000-05-25
2004-08-24
Chin, Stephen (Department: 2634)
Pulse or digital communications
Spread spectrum
C375S130000
Reexamination Certificate
active
06782036
ABSTRACT:
TECHNICAL FIELD OF THE INVENTION
The present invention relates, in general, to the field of processing of signals received from a sensor array and, in particular, to a smart antenna system for separating and reconstructing a symbol stream generated by an individual user in a code division multiple access (CDMA) communication system.
BACKGROUND OF THE INVENTION
Without limiting the scope of the invention, its background is described in connection with code division multiple access telecommunication devices and systems, as an example.
Heretofore, in this field, a user using a wireless handset transmits information from the handset to a base station. In commercial applications the wireless handset is most commonly a cellular phone or a personal communication system (PCS) phone. In military and other applications involving wireless transmission, the wireless handset may be generally be any mobile radio apparatus such as a spread spectrum transceiver or a mobile satellite terminal. A communication protocol used to transmit and receive wireless signals between the wireless handset and the base station is called an air interface. The air interface is typically agreed upon by an international standards committee.
Common air interface standards include analog service, time division multiple access (TDMA) and code division multiple access (CDMA). The most common CDMA air interface is called IS-95, based on the ITU-T IS-95 standard. CDMA technology and related spread spectrum coding techniques are discussed in detail in the ITU-T IS-95 standard, and in a variety of communications technology references. These references are useful in providing a systems level understanding of CDMA and emerging wideband-CDMA (W-CDMA) technologies.
Within CDMA and WCDMA applications, certain spread spectrum processing occurs at an inner spreading layer within the spread spectrum coding architecture. For example, a reverse channel (uplink) of one type of W-CDMA system includes a pair of in-phase(I) and quadrature(Q) outer-layer long codes and an inner layer orthogonal code. The orthogonal code has 64 chips per symbol. Depending on a particular system architecture, an inner-layer orthogonal code is typically 64 chips long and repeats every symbol. An outer layer long code and/or short code in a given CDMA system are de-spread, prior to de-spreading the orthogonal code. Hereafter, reference to a user's spreading code indicates the user's orthogonal code as found at an inner layer of a layered CDMA coding architecture.
A wireless communications antenna is commonly sectorized; each sector utilizing a portion of the operational frequency spectrum. Sectorization provides various functionalities to system operators and mobile units; frequency re-use amongst different sectors being a primary concern of system operators. Technologies such as CDMA allow “sharing” of a sector's frequency spectrum by multiple mobile units.
While CDMA is effective at increasing the capacity of wireless systems when compared to analog and TDMA (time division multiple access) technologies, there is increasing demand to further increase capacity. Increased capacity means more users can be serviced using with same amount of frequency spectrum resources in a given geographical area.
One method of increasing capacity is to use a smart antenna system. In general, a smart antenna system may rely on a time-varying beam pattern instead of sectorization. Conventional techniques generally use spatial filtering to separate communication signals by exploiting diversity in the spatial coordinates of their sources. Some methods, such as those based upon beamformer algorithms, make use of a signal propagation model which directly incorporates direction of arrival information. However, current beamformer approaches are limited in the number of beams which can be formed by an antenna array with a fixed number of antennas. Similarly, matrix based signal copy algorithms such as those derived from eigenstructure-oriented direction finding techniques are limited in the number of signals they can resolve for a fixed number of antennas, are sensitive to modeling errors, and have difficulty in effectively dealing with multi-path signals.
One class of conventional antenna system utilizes a front end spatial beam-steering processor coupled to a standard receiver, such as a “rake” receiver (RAKE). Some systems of this class are generally categorized as switched beam systems. Switched beam systems (SBS) are similar to antennas having fixed and sectorized fields of view, but fields of view in a SBS are typically more directive and may be electronically “steered” in response to system loading conditions. Other systems of this class are generally categorized as adaptive arrays.
Adaptive arrays form individual beams to isolate a signal due to a particular user. Conventional adaptive arrays generally perform adaptive spatial filtering to isolate a user's signal, and then pass this spatially-filtered signal to a down-stream processor for de-spreading and equalization. Such a system may use an architecture which requires a bank of correlators for each multi-path of each user's signal received from each antenna. Outputs of the correlator banks are processed by RAKE beam-formers. Such architecture makes modeling assumptions about the array pattern, which can lead to performance degradations due to modeling error sensitivity.
Recently, a diversity-reception antenna system has integrated CDMA interference suppression, noise suppression, and multipath interference suppression into a signal optimized structure. The system uses an orthogonalizing adaptive filtering approach which performs decision-directed MMSE (minimum mean squared error) updating. This system is applied with a diversity combiner which is used to add together outputs of various diversity paths to form a decision statistic. Individual paths are adapted either individually or collectively based upon the most reliable path, depending on the embodiment.
While such a system provides a diversity-reception device based upon orthogonalizing filters, characteristics of the approach are undesirable. Decision-directed adaptation often becomes unreliable and fails and under severe interference conditions. Also, these systems involve sub-optimal diversity combining and do not perform jointly optimized space-time processing. All adaptive optimization is performed in the time domain to orthogonalize user signals, but no joint-spatial adaptive processing is employed which also orthogonalizes a desired signal from interference in the spatial domain.
Such approaches can be improved upon using various block-adaptive algorithms together with fully blind cost functions. Block-adaptive algorithms may involve, for example, a block-gradient descent algorithm, a block conjugate-gradient algorithm, a block-Gauss-Newton algorithm, or a block-Shanno algorithm. In general, a block-nonlinear optimization algorithm may be used to cause a CDMA user signal to be demodulated, so as to minimize a nonlinear objective function, such as a constant-modulus error function. Thus, reduced bit errors rates may be achieved across a broad range of signal and interference scenarios with a lower computational complexity as compared with the previous approach.
Conventional antenna array signal processors, based on a constant modulus algorithm (CMA), typically involve a spatial-domain set of parameters which are adapted to cause a demodulated signal to have a constant modulus. Some algorithms of the CMA array type involve a space-time beam-former structure having both space domain and temporal domain taps. However, these methods generally serve as front end processors and do not provide orthogonalizing structures for isolating CDMA signals in both a chip-domain and a spatial-domain.
Another conventional approach involves “subtractive CDMA”. In subtractive CDMA, a user's signal power is deduced. When a symbol decision is made based on a user's signal having strong power, the symbol decision is re-spread, weighted, time-aligned
Dowling Eric M.
Golden Richard M.
Jani Umesh
Wang Zifei
Board of Regents , The University of Texas System
Chin Stephen
Gardere Wynne & Sewell LLP
Wang Ted
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