Adaptive channel characterization using decoded symbols

Pulse or digital communications – Receivers – Interference or noise reduction

Reexamination Certificate

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C375S342000, C375S341000

Reexamination Certificate

active

06320919

ABSTRACT:

FIELD OF THE INVENTION
The present invention relates to wireless communications and, in particular, to the characterization of channel response in digital wireless mobile radio systems.
BACKGROUND OF THE INVENTION
The radio channel over which a modulated signal propagates in mobile wireless communications may be one of the most harsh mediums in which to operate. The transmitted signals are often reflected, scattered, diffracted, delayed and attenuated by the surrounding environment. Moreover, the environment through which the signal passes from the transmitter to the receiver is not stationary due to the mobility of the user and surrounding objects. Characteristics of the channel environment also differ from one area to another. Radio propagation in such environments is characterized by multi-path fading, shadowing, and path loss. Multi-path fading may be characterized by envelope fading, Doppler spread and time-delay spread.
Multi-path waves typically combine at the receiver antenna to give a resultant signal which can vary widely in amplitude and phase. Therefore, signal strength may fluctuate rapidly over a small distance traveled or time interval, causing envelope fading. The statistical time varying nature of the received envelope of a flat fading signal, or the envelope of an individual multi-path component, is commonly characterized as a Rayleigh distribution. In satellite mobile radio and in micro-cellular radio, in addition to the many multi-path waves, a dominant signal, which may be a line-of-sight (LOS) signal, arrives at the receiver and gives rise to a Ricean distributed signal envelope. This dominant path significantly decreases the depth of fading depending on the Ricean parameter, K, which is defined as the ratio of the power in the dominant path to the power in the scattered paths.
Doppler shift is the frequency shift experienced by the radio signal when a wireless receiver, such as a wireless mobile terminal, is in motion. Doppler spread is a measure of the spectral broadening caused by the time rate of change of the mobile radio channel. Doppler spread may lead to frequency dispersion with the Doppler spread in the frequency domain being closely related to the rate of change in the observed signal. Hence, the adaptation time of the processes which are used in the receivers to track the channel variations generally should be faster than the rate of change of the channel to be able to accurately track the fluctuations in the received signal.
The dynamic characteristics of the radio channel present difficulties in tracking the channel to allow for decoding of information contained in the received signal. Often, in wireless mobile radio systems, known data sequences are inserted periodically into the transmitted information sequences. Such data sequences are commonly called synchronizing sequences or training sequences and are typically provided at the beginning of a frame of data. Channel estimation may be carried out using the synchronizing sequences and other known parameters to estimate the impact the channel has on the transmitted signal. After determining the channel response, the channel estimator enters a “decision directed” mode where the symbol estimates are used to estimate the channel.
For systems where fading changes generally occur very slowly, least square estimation may be an efficient way of estimating the channel impulse response in the presence of additive white Gaussian noise. If the fading rate is slow compared to the frame rate, the channel estimates can be updated frame by frame without significant inaccuracy. However, for many wireless mobile radio systems, the channel response changes very rapidly over a small travel distance or time interval. For example, for higher frequency bands such as those used in the Personal Communication Systems (PCS), the Doppler spread, hence, the rate of change in the observed signal, may be increased to the point that even during the reception of the synchronizing sequences, the mobile radio channel response may not be constant. Therefore, the need to track the channel parameters for fast time-varying systems provides a requirement for more robust receiver structures to enhance the receiver performance.
The most commonly used channel tracking methods are the Least Mean Square (LMS) and Recursive Least Square (RLS) based algorithms. See for example, “Optimal Tracking of Time-varying Channels: A Frequency Domain Approach for known and new algorithms,”
IEEE transactions on selected areas in communications
, Vol. 13, No. 1, January 1995, Jingdong Lin, John G. Proakis, Fuyun Ling. Stochastic based methods have recently been introduced that incorporate prior knowledge about the channel coefficient in the estimation. In contrast to the LMS and RLS, these methods provide for the extrapolation of the channel coefficients in time. More details on these approaches can be obtained in, “A wiener filtering approach to the design of tracking algorithms”,
Uppsala University Department of technology and signal processing group
, Lars Lindbom, 1995.
One difficulty with the adaptive channel tracker methods is that during the decision directed mode the estimated symbols are used for the channel response adaptation. Therefore, the effect of using potentially incorrect decisions needs to be considered for parameter selection. Tuning of design parameters may result in a trade-off between tracking capability and sensitivity to noise. For example, if the adaptation gain of the channel tracker is very large, then, the channel tracker may become very sensitive to noise and to incorrect symbol decisions. On the other hand, if the adaptation gain is chosen to have a small magnitude, the ability to track the variation of the channel parameters may be lost. Specifically, in those systems where coherent modulation and coherent demodulation schemes are used, these issues become more serious compared to systems where differential modulation is implemented.
In coherent modulation schemes like coherent Quadrature Phase Shift Keying (QPSK), even if the channel tracker tracks the magnitude of the channel response well, the channel phase may slip (i.e., the tracker can lock on a wrong phase offset) during a deep fade of the in-phase and/or quadrature phase component of the channel, resulting in a phase offset of k2&pgr;/m. In other words, the tracker actually tracks well but with an offset, which consequently causes symbol rotation and error propagation. Because the channel phase rotation and symbol rotation are in the opposite direction, a conventional tracker typically is not able to correct the problem. Thus, all the remaining information symbols may be lost because of this phase rotation until a new frame and synchronization sequence is received.
Other approaches have also been applied to improving performance of reception over communication channels subject to interference. For example, various standards have been introduced applicable to wireless digital services including the IS-136 and IS-95 standards. These and other systems are described in
The Mobile Communications Handbook
, edited by Gibson and published by CRC Press (1996). Various of these specifications provide for the use of both encoded and unencoded bit classes within a data frame. An example of the use of both encoded and unencoded bits as provided by the IS-641 specification will now be described.
An Adaptive Code Excited Linear Prediction (ACELP) source provides a data frame of 148 bits. 48 of the bits are classified as Class 1A and are processed through a CRC error detection coder to generate error detection code that is appended to the bits. An additional 48 of the bits from the 148 bit data frame are treated as Class 1B bits and are processed through a convolutional coder without error detection coding. The remaining 52 bits are treated as Class 2 bits and provided directly to an interleaver without coding. The Class 1A and 1B bits are processed through the convolutional encoder and, in turn, the resulting error correction coded output bits are punctured to provide

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