Pulse or digital communications – Spread spectrum – Direct sequence
Reexamination Certificate
1999-05-11
2003-12-09
Deppe, Betsy Lee (Department: 2634)
Pulse or digital communications
Spread spectrum
Direct sequence
C375S148000, C375S227000, C370S342000
Reexamination Certificate
active
06661832
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of Invention:
This invention relates to communications systems. Specifically, the present invention relates to systems for estimating the interference spectral density of a received signal in wireless code division multiple access (CDMA) communications systems for aiding in rate and power control and signal decoding.
2. Description of the Related Art:
Wireless communications systems are used in a variety of demanding applications including search and rescue and business applications. Such applications require efficient and reliable communications that can effectively operate in noisy environments.
Wireless communications systems are characterized by a plurality of mobile stations in communication with one or more base stations. Signals are transmitted between a base station and one or more mobile stations over a channel. Receivers in the mobile stations and base stations must estimate noise introduced to the transmitted signal by the channel to effectively decode the transmitted signal.
In a code division multiple access (CDMA) communications system, signals are spread over a wide bandwidth via the use of a pseudo noise (PN) spreading sequence. When the spread signals are transmitted over a channel, the signals take multiple paths from the base station to the mobile station. The signals are received from the various paths at the mobile station, decoded, and constructively recombined via path-combining circuitry such as a Rake receiver. The path-combining circuitry applies gain factors, called weights, to each decoded path to maximize throughput and compensate for path delays and fading.
Often, a communications system transmission includes pilot interval, a power control interval, and a data interval. During the pilot interval, the base station transmits a pre-established reference signal to the mobile station. The mobile station combines information from the received reference signal, i.e., the pilot signal, and the transmitted pilot signal to extract information about the channel, such as channel interference and signal-to-noise (SNR) ratio. The mobile station analyzes the characteristics of the channel and subsequently transmits a power control signal to the base station in response thereto during a subsequent power control interval. For example, if the base station is currently transmitting with excess power, given the current channel characteristics, the mobile station sends a control signal to the base station requesting that transmitted power level be reduced.
Digital communications systems often require accurate log-likelihood ratios (LLRs) to accurately decode a received signal. An accurate signal-to-noise ratio (SNR) measurement or estimate is typically required to accurately calculate the LLR for a received signal. Accurate SNR estimates require precise knowledge of the noise characteristics of the channel, which may be estimated via the use of a pilot signal.
The rate or power at which a base station or mobile station broadcasts a signal is dependant on the noise characteristics of the channel. For maximum capacity, transceivers in the base stations and mobile stations control the power of transmitted signals in accordance with an estimate of the noise introduced by the channel. If the estimate of the noise, i.e., the interference spectral density of different multipath components of the transmitted signal is inaccurate, the transceivers may broadcast with too much or too little power. Broadcasting with too much power may result in inefficient use of network resources, resulting in a reduction of network capacity and a possible reduction in mobile station battery life. Broadcasting with too little power may result in reduced throughput, dropped calls, reduced service quality, and disgruntled customers.
Accurate estimates of the noise introduced by the channel are also required to determine optimal path-combining weights. Currently, many CDMA telecommunications systems calculate SNR ratios as a function of the carrier signal energy to the total spectral density of the received signal. This calculation is suitable at small SNRs, but becomes inaccurate at larger SNRs, resulting in degraded communications system performance.
In addition, many wireless CDMA communications systems fail to accurately account for the fact that some base stations that broadcast during the pilot interval do not broadcast during the data interval. As a result, noise measurements based on the pilot signal may become inaccurate during the data interval, thereby reducing system performance.
Hence, a need exists in the art for a system and method for accurately determining the interference spectral density of a received signal, calculating an accurate SNR or carrier signal-to-interference ratio, and determining optimal path-combining weights. There is a further need for a system that accounts for base stations that broadcast pilot signals during the pilot interval, but that do not broadcast during the data interval.
SUMMARY OF THE INVENTION
The need in the art for the system for providing an accurate interference value for a signal received over a channel and transmitted by an external transceiver of the present invention is now addressed. In the illustrative embodiment, the inventive system is adapted for use with a wireless code division multiple access (CDMA) communications system and includes a first receiver section for receiving the signal, which has a desired signal component and an interference and/or noise component. A signal-extracting circuit extracts an estimate of the desired signal component from the received signal. A noise estimation circuit provides the accurate interference value based on the estimate of the desired signal component and the received signal. A look-up table transforms the accurate noise and/or interference value to a normalization factor. A carrier signal-to-interference ratio circuit employs the normalization factor and the received signal to compute an accurate carrier signal-to-interference ratio estimate. Path-combining circuitry generates optimal path-combining weights based on the received signal and the normalization factor.
In the illustrative embodiment, the system further includes a circuit for employing the accurate interference value to compute a carrier signal-to-interference ratio (C/I). The system further includes a circuit for computing optimal path-combining weights for multiple signal paths comprising the signal using the accurate interference value and providing optimally combined signal paths in response thereto. The system also includes a circuit for computing a log-likelihood value based on the carrier signal-to-interference ratio and the optimally combined signal paths. The system also includes a circuit for decoding the received signal using the log-likelihood value. An additional circuit generates a rate and/or power control message and transmits the rate and/or power control message to the external transceiver.
In a specific embodiment, the first receiver section includes downconversion and mixing circuitry for providing in-phase and quadrature signal samples from the received signal. The signal extracting circuit includes a pseudo noise despreader that provides despread in-phase and quadrature signal samples from the in-phase and quadrature signal samples. The signal extracting circuit further includes a decovering circuit that separates data signals and a pilot signal from the despread in-phase and quadrature signal samples and provides a data channel output and a pilot channel output in response thereto. The signal extracting circuit further includes an averaging circuit for reducing noise in the pilot channel output and providing the estimate of the desired signal component as output in response thereto. The noise estimation circuit includes a circuit for computing a desired signal energy value associated with the estimate, multiplying the desired signal energy value by a predetermined constant to yield a scaled desired signal energy value, and subtracting the scaled desired signal energy value fr
Esteves Eduardo A. S.
Sindhushayana Nagabhushana T.
Baker Kent D.
Deppe Betsy Lee
Greenhaus Bruce W.
Qualcomm Incorporated
Wadsworth Philip
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