HK1043894B - Estimation method, receiver and decoder, of channel conditions in wireless communications - Google Patents

Description

Method for estimating channel conditions in wireless communication, receiver and decoder

Technical Field

The present invention relates to communication systems. More particularly, the present invention relates to methods and apparatus for improving the reception and decoding of turbo code encoded signals.

Background

Wireless communication signals are typically subject to more interference and noise than wired communications. In addition, there is a need to provide many channels over a given bandwidth. Accordingly, numerous coding techniques have been developed, such as Code Division Multiple Access (CDMA). CDMA techniques IN communication SYSTEMs are disclosed IN U.S. Pat. No. 4,901,307, entitled "CDMA communication System Using SATELLITE System OR TERRESTRIAL REPEATERS," and U.S. Pat. No. 5,103,459, entitled "SYSTEM AND METHOD FOR GENERATING SIGNAL WAVEFORMS IN A CDMACELLULAR TELEPHONE SYSTEM," both assigned to the assignee of the present invention.

CDMA modulation techniques can provide capacity improvements over other techniques, such as Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) based in part on the use of orthogonal functions or codes in CDMA. In addition, CDMA receivers use Viterbi decoders that use the Viterbi algorithm to perform maximum likelihood decoding of the received signal.

However, Viterbi decoding suffers from decoding errors when receiving signals over a noisy channel. A new coding scheme using a "turbo" code uses a simple encoder combination of two blocks of received K information bits that produces parity symbols from two simple recursive convolutional codes with fewer states. The uncoded K information bits are transmitted over a noisy channel along with the parity symbols. It is important that the interleaver permutes the original K information bits before inputting such bits into the second encoder. The permutation causes one encoder to produce low weight codewords and the other encoder to produce high weight codewords. The resulting code is similar to a "random" block code with K information bits. It is known that random block codes can achieve shannon-limiting performance when K is large, but true random block codes require rather expensive and complex decoding algorithms.

At the receiver, a pair of simple decoders using an iterative maximum a posteriori algorithm receives the two original K information bits and one of a set of parity symbols from one of two encoders. The decoders are each matched to a simple code, and each decoder sends a posteriori likelihood estimates of the decoded bits to the other decoders when the corresponding estimates from the other decoders are used as a priori likelihood values. When the maximum a priori decoding algorithm is used by the decoders, which require the same number of states as the Viterbi algorithm, each decoder will obtain the unusual information bits corrupted by the noisy channel to minimize the a priori likelihood values. The two component decoders perform several iterations until a satisfactory convergence is obtained, at which point the final output is a hard quantized version of either decoder likelihood estimate. More details about the turbo code can also be found, for example in the IEEE communications major 35 in month 12 1997: sklar write "A Primer On Turbo Code hubs" in B.Sklar, 12.

Academic efforts show that the turbo code has the potential to improve the amount of information transmitted in noisy channels, even very close to the shannon limit, compared to conventional Viterbi decoding. Unfortunately, this result assumes idealized conditions at the receiver end and is difficult to achieve in practice. One difficult idealized condition is to assume that the receiver knows the signal and noise power on a per symbol basis. This is particularly difficult because the signal and the information bits are multiplied such that the mean value of the signal is 0, thus not allowing the usual averaging procedure for estimating the noise power of the signal.

IEEE transactions on Communications 46 at 4 months 1998: in an article by t.summers And s.wilson entitled "SNR Mismatch And Estimation In Turbo Decoding", the authors mention that iterative Decoding of Turbo codes And other similar concatenated coding schemes require knowledge of the signal-to-noise ratio (SNR) of the channel so that a proper mix of a posteriori information of the individual decoders can be obtained. In this paper, the authors investigated the sensitivity of decoder performance to SNR error estimation and proposed a scheme to estimate the unknown SNR from each code block prior to decoding. This is suitable for Additive White Gaussian Noise (AWGN) channels. However, this method does not provide an estimate of the individual signal and noise that is required for a turbo code in a fading channel. Indeed, if there is not a good channel estimate, turbo coding is less attractive, if not worse, when compared to conventional Viterbi decoding of convolutional codes.

Disclosure of Invention

The inventors of the present invention have experimentally found that conventional techniques for estimating channel conditions result in a loss of performance due to the poor performance of these estimation techniques. The inventors have also discovered a new set of techniques for signal and noise power estimation that can be applied not only to turbo codes, but also to other fields where such estimation is needed (e.g., power control in CDMA communication systems, other concatenated coding schemes, etc.). This new technique estimates the signal-to-noise ratio of the received signal and importantly it provides an estimate of the signal and noise respectively and therefore provides a significant improvement over the methods described in the Summers and Wilson articles. Furthermore, an aspect of the present invention provides for initial estimation of signal and noise, for example by using efficient curve fitting techniques. The energy of the received pilot symbols is then used to provide a better estimate of the signal and noise. Accordingly, aspects of the present invention overcome the problems in previous systems and provide additional advantages that will be apparent to those skilled in the relevant art from the following description.

The present invention provides a method for estimating channel conditions of received signals, the method being used in a communication system comprising a base station and a plurality of user stations, the user stations exchanging communication signals between the base station and each of a plurality of users, the method comprising the steps of: receiving at least one CDMA signal over a forward link traffic channel between a base station and at least one subscriber station, wherein the traffic channel has noise, the received CDMA signal has a determined amplitude, and is encoded with a turbo code; respectively estimating the determined amplitude of the CDMA received signal and the variance sigma of the noise according to the received CDMA signal; receiving a pilot signal from a base station; before the turbo decoding is carried out on the received CDMA signal, the more accurate estimation is carried out on the determined amplitude and the noise variance sigma of the received CDMA signal according to the respective estimation steps and the received pilot signal; wherein the step of separately estimating comprises fitting the received signal with a stored curve.

The present invention also provides a method for estimating channel conditions of a received signal, the method comprising (a) receiving a signal encoded with a concatenated code on a noisy channel, wherein the received signal has a determined amplitude; (b) estimating the determined amplitude value according to the received signal; and (c) separately estimating the variance σ of the noise from the received signals, wherein the separately estimating step includes fitting a stored curve to the received signals.

The present invention provides an apparatus for estimating channel conditions of received signals, the apparatus being for use in a communication system comprising a base station and a plurality of user stations, the user stations exchanging communication signals between the base station and each of a plurality of users, the apparatus comprising: means for receiving a signal encoded with a link code on a noisy channel, wherein the received signal has a determined amplitude; means for estimating the determined amplitude value from the received signal; and means, coupled to the estimating means, for individually estimating the noise variance σ from the received signals; wherein the means for separately estimating comprises means for fitting a stored curve to the received signal.

The present invention also provides an apparatus for receiving a communication signal, the apparatus comprising: at least first and second decoders for receiving at least one coded bit signal on a traffic channel, wherein the traffic channel has noise and the received signal has a determined amplitude; and an estimator coupled to the inputs of the first and second decoders, wherein the estimator receives at least one coded bit signal and estimates noise and determines amplitude, respectively, of the traffic channel based on the received signal; wherein the estimator fits the received signal with a stored curve.

Drawings

In the drawings, like numbering represents like elements. For simplicity, in discussions that identify any particular element, the leading digit(s) of the reference number refers to the figure number in which the element is first introduced (e.g., first introducing and discussing element 204 with respect to FIG. 2).

Fig. 1 is a simplified block diagram of a wireless communication system employing the present invention.

Fig. 2 is a simplified block diagram of a transceiver in the wireless communication system of fig. 1, in accordance with an embodiment of the present invention.

Fig. 3 is a simplified block diagram of a receiver in the transceiver of fig. 2, in accordance with an embodiment of the present invention.

Fig. 4A is a graph of energy versus number of symbols in a CDMA mobile demodulator using direct spreading in a conventional rayleigh fading environment moving at 3km/h using a conventional method of estimating energy per symbol.

Fig. 4B is a graph of energy versus number of symbols in a CDMA mobile demodulator using direct spreading in a conventional rayleigh fading environment with 3km/h mobile according to an embodiment of the present invention.

Fig. 4C is a graph of reference energy showing the energy per symbol in the fading environment of fig. 4A and 4B.

FIG. 5 is a function g (E (x)²) Graph of/E (| x |).

Fig. 6 is a simplified flow chart of the process performed by the receiver of fig. 3 to estimate channel performance.

Detailed Description

Communication systems, and more particularly, apparatus and methods for controlling signal interference in a system are described in detail herein. In the following description, numerous specific details are provided to provide a thorough understanding of the invention. However, it will be apparent to one skilled in the relevant art that the present invention may be practiced without these specific details or with alternate units or steps. In other instances, well-known structures and methods have not been shown in detail to avoid obscuring the invention.

Fig. 1 illustrates a typical cellular subscriber communication system 100 that uses multiple access techniques, such as CDMA, to effect communication between subscribers at a subscriber site (e.g., a mobile telephone) and cell sites or base stations. In fig. 1, a mobile subscriber station 102 communicates with a base station controller 104 through one or more base stations 106a, 106b, etc. Similarly, the fixed subscriber site 108 communicates with the base station controller 104 only through one or more predetermined nearest base stations, such as base stations 106a and 106 b.

The base station controller 104 is coupled to and typically includes interface and processing circuitry to provide system control for the base stations 106a and 106 b. The base station controller 104 may also be coupled to and in communication with other base stations 106a and 106b, and possibly even other base station controllers. The base station controller 104 is coupled to a mobile switching center 110, which in turn is coupled to a home location register 112. During registration of each subscriber station at the start of each call, the base station controller 104 and the mobile switching center 110 compare the registration signals received from the subscriber station 102 or 108 with the data contained in the home location register 112, as is well known in the art. As known to those skilled in the art, soft handoffs may occur between the base station controller 104 and other base station controllers, and even between the mobile switching center 110 and other mobile switching centers.

When the system 100 processes a telephone or data traffic call, the base station controllers 104 establish, maintain, and terminate wireless links with the mobile station 102 and the fixed subscriber stations 108, while the mobile switching center 110 establishes, maintains, and terminates communications with the Public Switched Telephone Network (PSTN). Although the following discussion focuses on signals transmitted between the base station 106a and the mobile station 102, those skilled in the art will appreciate that the discussion is equally applicable to other base stations and fixed user stations 108.

Fig. 2 is a simplified block diagram of a portion of a transceiver using either base station 106a or 106b, or either user station 102 or 108, in the wireless communication system of fig. 1, in accordance with an embodiment of the present invention. In the example of fig. 2, the transceiver 200 includes a transmitter 202 and a receiver 204 that share an antenna 210 that transmits and receives signals to and from other transceivers. The antenna converter 212 separates the received signal from the signal transmitted by the transmitter and transmits the received signal to the receiver 204. The receiver 204 shifts the frequency, demodulates, and decodes the received signal. For example, receiver 204 converts the received signal to baseband or intermediate frequency and performs Walsh code demodulation and also performs power and signal quality measurements.

Control processor 216 provides most of the received signal processing, as described below. The memory 218 permanently stores programs that control the execution of the processor 216 and provides temporary storage of data, such as received frames. The transmitter 202 encodes, modulates, amplifies, and upconverts a transmit signal.

In one embodiment, the transmitter 202 forms a forward traffic link data signal for retransmission of the received signal from the base station 106a or 106b to the user station 102 or 108. In another embodiment, the transmitter 202 forms a reverse link traffic data signal for reverse transmission from the user station 102 or 108 to the base station 106 a. In mobile station 102, receiver system 204 provides decoded received signals to users and receives information from users for transmission by a transmitter system through an input/output (I/O) module 222 coupled to control processor 216.

Referring to fig. 3, a turbo decoder 300 is shown which forms part of the receiver system 204. Alternatively, the decoder 300 may form a portion of the control processor 216, or the control processor may perform the operations of the decoder 300 described below. Decoder 300 includes a signal and noise estimator 302 that receives an input channel with information and two parity signals or channels. Signal and noise estimator 302 estimates the power of the signal and noise, respectively, in the received input channel, as will be described more fully below.

A pair of simple decoders 304 and 306 each receive an information signal from an input channel. In addition, first decoder 304 (decoder 1) receives the first parity symbols while second decoder 306 (decoder 2) receives the second parity symbols. For example, the decoder 300 (and transceiver 200) transceiver may reside in the mobile station 102 and receive information and parity symbols from the base station 106 a. As described above, a typical turbo encoder (not shown) in base station 106a provides a pair of simple encoders that produce a parity signal from two simple recursive convolutional codes, which have fewer states. Thus, the first decoder 304 receives parity symbols generated by the first encoder, while the second decoder 306 receives parity symbols generated by the second encoder.

The decoders 304 and 306 are to match the codes of their respective encoders. Importantly, the first decoder 304 provides a posteriori likelihood estimates on the decoded bits of the information channel to the second decoder 306 via line 308. The second decoder 306 also performs the same operations, providing its corresponding estimate via line 310. The a posteriori estimates are used as a priori estimates for each decoder. Several iterations are performed until a satisfactory convergence is obtained, at which point the final output of the likelihood estimates is provided.

To greatly improve the performance of decoders 304 and 306, these decoders require efficient estimation of the signal and noise power of the received input channel. Thus, signal and noise estimator 302 analyzes the signal and noise of the received input channel, respectively, to produce an appropriate estimate.

Two embodiments of the estimator 302 operation will be described below. The first method is discussed first, followed by a discussion of a preferred and preferred method. Mathematically, the input signal x of the decoder 300_iCan be expressed as:

x_i＝b_iA_i+n_i， (1)

wherein b is_iIs a binary information signal (e.g. + -. 1), A_iIs the amplitude of the signal, Gaussian noise n_iCan be expressed as: n is_i＝N(0，σ²) Where the noise has a mean 0 and a variance σ². Amplitude A_iIs the square root of the symbol energy, which is related to the energy per bit, and has the relation E_BR, where R is the code rate, typically 1/2, E_BIs the energy per bit. The square root of the energy per symbol (i.e., the square root of the energy per symbol) can be used) Alternative amplitude A_iTo overwrite equation (1).

The estimator 302 must be the signal amplitude or energySum noise variance σ²The estimates are provided separately. If signal b_iAlways equal to 1, the estimator will conveniently measure the sample mean and the sample variance. However, since b_iEqual to ± 1, the effective mean is equal to 0.

In one embodiment, the estimator 302 may utilize x_iThe sample mean of's size, according to the following equation:

and E (x)²)＝E_s+σ²， (3)

Wherein E_sIt can be estimated that:

E_s＝E(x²)-σ²， (4)

therefore, the system can be selected from E (| x |) and E (x)²) In the estimation of (2), Es (or A) and σ are estimated separately. In short, the above equation can be written in the form of a fixed point:

the stationary point is then solved for with iteration.

To simplify the iteration, the upper half of equation (5) may use a simple curve fit. Due to the fact that

Curve fitting of negative quadratic polynomials can be used for E_s/N_tThe function f of (c).

As an example, combining equations (5) and (6), we can derive:

E(x²)＝E_s+σ²and

and assume that for the first step of the iterative process,it represents an SNR of infinity. Thus, we have:

and

unfortunately, it is difficult to determine the true noise of the channel at a certain point. Considering the first extreme, the iterative process has only one step, where the traffic symbol energy is much larger than the noise, i.e., E_s＞＞σ²Then, thenAnd isThus E (x)²)-E(|x|)²＜＜E_sAnd isThe ratio of (a) is close to infinity. However, consider the opposite extreme case, where E_s＜＜σ²Then, thenAs a result of which,and isTherefore, it is not only easy to useSNR (in dB) is greater than 0 and not equal to minus infinity.

When a low SNR signal is first estimated, a portion of the traffic symbol energy may be misinterpreted as noise. Enabling system slave functionsIn the finding of_s/σ²The function of (c) is bijective, so the method does not converge if there is no stopping condition. Thus, one option is to end the iteration using a stop condition, such as:

in the above embodiment, the system controls the power based on an energy value estimate for each symbol currently received. The power control attempts to keep the received signal-to-noise ratio constant and thereforeThe ratio of (a) to (b) varies little. Furthermore, for what is encounteredTypical range of (a), (b), (c), (d), (5) The lower half remains relatively constant. Thus, the system may use equation (5) without iterative estimation. In addition to this, CDMA demodulation techniques typically combine symbols from various multi-finger receivers, either in carriers or by weighting of pilot signals (explained in more detail below).

The results of experimental simulation using the above method in equation (5) produce fig. 4A to 4C by using simulated measurements of the signal or traffic channel. In particular, FIG. 4C shows a reference curve of amplitude versus Quadrature Phase Shift Keying (QPSK) symbols 1-5000 in a Rayleigh fading environment where the decoder is moving at 3 km/h. The demodulator in this example demodulates in a direct spread mode under CDMA technology ("CDMA 2000"). Fig. 4A shows an exemplary method for estimating amplitude and noise using a conventional method for estimating the mean of a received signal. FIG. 4B shows the relationship between amplitude and QPSK symbols in which the noise σ is estimated separately using the above method²And a signal amplitude a. The curve of fig. 4B closely follows the reference curve of fig. 4C, whereas the curve of fig. 4A does not track the reference curve well. In fact, as shown in fig. 4B, the fading deeper between symbols 4500 and 4700, approximately, in the reference curve can be well tracked using the methods described above. However, as shown in fig. 4A, this deep fading results in reverse in the conventional method.

As described above, the above embodiment suffers from subtracting two noise amounts. Furthermore, the above solves E_s/σ²The fixed point operator equation has certain disadvantages in practical application, that is, the iterative process may converge very slowly, and each step in the iteration needs to calculate each sample. Thus, the direct method may be more convenient and may be faster in computation time. Another problem with the above embodiment is that it is difficult to find a satisfactory iteration start point.

A second preferred embodiment is partly derived from the method of Summers and Wilson, which avoids the subtraction in equation (5) and provides E_sAnd σ²A better estimate of. The second embodimentThe examples begin with the following equation:

wherein the two mean values of the left part can be calculated using the above-described motionless point method; the ratio is E_s/σ²As a function of (c). In equation (10), the function can be inverted using a simple curve fit. The complexity of this calculation is equivalent to the complexity of the motionless point method described above, but only one step.

To ensure that the advantages provided by the second embodiment exceed the first embodiment, two extreme cases applied to the first embodiment are considered. Taking into account the first extremeIn a situation where the traffic symbol energy is much greater than the noise E_s＞＞σ²Then, thenTherefore, the temperature of the molten metal is controlled,consider the opposite extreme case, where the traffic symbol energy is much less than the noise E_s＜＜σ²Then, thenTherefore, the temperature of the molten metal is controlled,

these two extremes provide the upper and lower bounds of the ratio of equation (10), and also show that the ratio of the two extremes is approximately equal to 1. Intuitively, the more noise, the larger the ratio of equation (10). The complexity of the equation prevents the determination of E from the statistics_s/σ²Closed-form solution of (1). By first taking the E of interest_s/σ²Estimating the equation point by point over the's range, and then approximating the statistical ratio and E using a simple polynomial function_s/σ²The difficulty can be reduced.

By using a very simple quadratic curve fit, usingThe complexity of equation 11 can be reduced. An example of a simple quadratic curve fit is described in the above Summers and Wilson articles. The curve fit is provided below:

the function g of equation (11) is not bijective, it means that for each z found under this equation there is one and only one ratio E_s/σ². FIG. 5 shows E (x)²) Function g of/E (| x |) ratio and E_s/σ²The relation of the ratio. Variances can be found using known methods, for example as described in the article entitled "A Novel Variance Estimator for turbocoded decoding" written by M.Reed and J.Asenstorfer, in 4 months 1997, page 173-178 of Int' l Conf.Telecommunications, Melbourne, Australia.

Basically, the mean estimate of the SNR is less than the true SNR. Some offset is generated due to the deviation between the fitting line of the quadratic polynomial estimate and the actual sample mean function. Curve fitting is not suitable for very small SNRs nor for very large SNRs. However, if the SNR is large, then the decoders 304 and 306 can easily decode the frame. On the other hand, if the SNR is small, the frame is lost anyway. If the SNR is in between, a better SNR estimate will help decoders 304 and 306; the curve estimates the SNR in the range of about-4 dB to 8 dB.

To achieve a one percent Frame Error Rate (FER), the traffic channel does not have to have a high signal-to-noise ratio; however, it is difficult to obtain an estimate of the better traffic symbol energy and associated noise with only the incoming uncoded bits:

this is more difficult when the signal-to-noise ratio is low.

In existing CDMA systems, the energy of the traffic channel transmitted by base station 106 over the forward link is varied by adding or subtracting one-half dB after each power control command. The TIA/EIA/IS-95-a CDMA standard requires that during each frame (50 frames per second), there be sixteen power control commands at a rate of 9600kb/s, where the energy of the traffic channel transmitted in one power control bit group IS to be kept constant. The IS-95 standard, as well as other modulation schemes, uses a pilot channel to transmit pilot symbols from each base station. The energy of the pilot symbols is constant for a given base station 106. Thus, the ratio of pilot energy to traffic energy is constant within a power control bit group. Further details regarding Pilot Symbols may also be found, for example, in U.S. patent application No. 09/144,402, entitled "Method and Apparatus for Reducing amplitude variations and interference and Communications Signals," filed on 8, 31, 1998, by the inventor of Holtzman' z.

From the energy estimates of the pilot symbols, a better approximation of the traffic energy can be obtained, which is more accurate than estimating the varying energy in the traffic channel. Thus, the pilot channel can be used to provide an independent estimate of the noise variance σ. Thus, the E of the upper half of equation (10) can be estimated by first determining an independent σ estimate and then using the E | x | estimate in the signal or traffic channel_sOr by E (x) in the signal or traffic channel²) Estimate to estimate E of the lower half of equation (10)_sThereby simplifying equation (10). By finding the pilot symbol energy E_pAnd E_sThe correct ratio between the first estimates, E can be achieved_sThe second estimation. Dividing the approximation of the ratio by E_pTo find out E_s。E_pRatio E_sStrong (typically one fifth of the base station power is allocated to E_p) And therefore also more accurate.

In summary, the second embodiment described above uses first the approximation of equation (10). Second, the received pilot symbol energy and the ratio between the pilot and traffic channel energy provide a better estimate.

Referring to fig. 6, a routine 600 performed by estimator 300 is shown. Source code or program logic arrays may be developed by those skilled in the relevant art based on the detailed description provided herein. In step 602, the estimator 302 receives incoming traffic signal samples. Such a sample is greater than or equal to one symbol and may be a frame or a power control group.

In step 604, the estimator 302 fits the stored curve to the traffic channel samples. For example, the stored curve may be the curve of fig. 5. Such a profile may be stored in the memory 218 and may be accessed by the receiver system 204 or the control processor 216 for the appropriate profileAnd (6) fitting. In step 606, the estimator 302 determines the energy E per symbol in the traffic channel_sAnd an estimate of the noise variance σ is determined.

In step 608, the estimator 302 estimates the ratio of pilot energy to symbol energy using the received pilot channel samples. The ratio in step 608 can be estimated using the method described above, wherein the current ratio is calculated as follows: if the current ratio is higher or lower than the previous ratio, then the current ratio is equal to one-half of the + -previous ratio, respectively. In step 610, the estimator 302 uses an improved method to derive the ratio E_p/E_sIn determining E_sAnd sigma. The process 600 then returns to step 602 where another sample is received and processed.

One skilled in the relevant art will appreciate that the above-described routine 600, as well as other functions and methods, may be performed by the estimator 302 and/or the control processor 216, wherein the estimator 302 may be implemented using a custom ASIC, or a digital signal processing integrated circuit, or by conventional logic circuit elements, or software programming of a general purpose computer or microprocessor, such as the control processor 218.

While specific embodiments of, and examples for, the invention are described herein for illustrative purposes, various equivalent modifications can be made without departing from the scope of the invention, as will be recognized by those skilled in the relevant art. For example, while many of the embodiments shown and described above are implemented in hardware (e.g., one or more integrated circuits specifically designed for a certain task), these embodiments could equally be implemented in software and executed by one or more processors. Such software may be stored in any suitable computer readable medium, such as microcode stored in a semiconductor chip, as a computer readable disk, or downloaded and stored by a server. The various embodiments described above can be combined to provide further embodiments. In general, the estimation techniques described in detail above are only examples, and those skilled in the relevant art can create similar techniques under the explanation and concept of the present invention.

The teachings of the present invention provided herein are applicable to other communication systems and are not limited to the exemplary communication systems described above. For example, although the case where the present invention is applied to the CDMA communication system 100 is generally described above, the present invention is applicable to a digital or analog communication system, particularly, a concatenated coding scheme. The present invention can also be modified, if necessary, to employ the systems, circuits and concepts of the various patents described above, all of which are incorporated by reference.

These and other changes can be made to the invention in light of the above detailed description. In general, in the following claims, the terms used should not be construed to limit the invention to the specific embodiments disclosed in the specification and the claims, but should be construed to include any communication system that operates under the claims for signal-to-noise power estimation of a transmitted signal. Accordingly, the invention is not limited by the disclosure, but instead its scope is to be determined entirely by the following claims.

Claims (22)

1. A method for estimating channel conditions of a received signal, the method for use in a communication system including a base station and a plurality of user stations that exchange communication signals between the base station and each of a plurality of users, the method comprising the steps of:

receiving at least one CDMA signal over a forward link traffic channel between a base station and at least one subscriber station, wherein the traffic channel has noise, the received CDMA signal has a determined amplitude, and is encoded with a turbo code;

respectively estimating the determined amplitude of the CDMA received signal and the variance sigma of the noise according to the received CDMA signal;

receiving a pilot signal from a base station; and

before the turbo decoding of the received CDMA signal, the determined amplitude and the noise variance sigma of the received CDMA signal are more accurately estimated according to the respective estimation steps and the received pilot signal;

wherein the step of separately estimating comprises fitting the received signal with a stored curve.

2. The method of claim 1, wherein the separately estimating step comprises fitting a stored curve to the CDMA received signal to estimate the energy per symbol E of the received CDMA signal if the determined magnitude is between-4 and 8dB_sOr the noise variance σ.

3. The method of claim 1, wherein the separately estimating step comprises fitting a stored curve to the received CDMA signal to estimate a determined amplitude of the received CDMA signal, or a noise variance σ.

4. The method of claim 1 wherein the separately estimating step comprises estimating the energy per symbol E in the received CDMA signal according to the following equation_sAnd noise variance σ:

5. a method for estimating channel conditions of a received signal, the method comprising the steps of:

receiving a signal encoded with a concatenated code on a noisy channel, wherein the received signal has a determined amplitude;

estimating the determined amplitude value according to the received signal; and

respectively estimating the variance sigma of the noise according to the received signals;

wherein the step of separately estimating comprises fitting the received signal with a stored curve.

6. The method of claim 5, further comprising the steps of:

receiving a pilot signal; and

more accurate estimation of the determined amplitude and noise variance σ from the received pilot signal, an

Wherein the step of estimating the determined amplitude or respectively the estimate comprises fitting a stored curve to the received signal to estimate the energy E of each symbol in the received signal_sOr a noise variance σ, where the memory curve has the following function:

whereinIn dB.

7. The method of claim 5, wherein the steps of estimating the determined magnitude and separately estimating comprise separately estimating the energy E of each symbol in the received signal according to the following equation_sAnd noise variance σ:

8. the method of claim 5, further comprising the steps of:

receiving a pilot signal; and

a more accurate estimation of the determined amplitude and noise variance σ from the received pilot signal comprises altering the ratio of the energy per symbol of the current pilot based on the ratio of the energy per symbol of at least one previous pilot.

9. The method of claim 5, further comprising the steps of:

receiving a pilot signal; and

from the received pilot signal, a more accurate estimate of the determined amplitude and noise variance σ is made.

10. The method of claim 5, wherein the steps of estimating the determined magnitude and separately estimating comprise separately estimating the energy of each symbol in the received signal, and the noise variance σ, according to the following equation:

and iteratively solve for the stationary point, wherein E_sIs the energy of each symbol in the received signal and x represents the product of the received binary signal and the signal amplitude plus noise.

11. The method of claim 5, further comprising the steps of:

iteratively estimating to determine the amplitude and separately estimating the variance; and

and when the stop condition is met, stopping iteration.

12. An apparatus for estimating channel conditions of received signals for use in a communication system including a base station and a plurality of subscriber stations that exchange communication signals between the base station and each of a plurality of users, the apparatus comprising:

means for receiving a signal encoded with a link code on a noisy channel, wherein the received signal has a determined amplitude;

means for estimating the determined amplitude value from the received signal; and

means, coupled to the estimating means, for individually estimating the noise variance σ from the received signals;

wherein the means for separately estimating the noise variance σ comprises means for fitting a stored curve to the received signal.

13. The apparatus of claim 12, further comprising:

means for receiving a pilot signal; and

means for more accurately estimating the determined amplitude and noise variance σ from the received pilot signal, an

Wherein the means for estimating the determined magnitude or the means for separately estimating the noise variance σ comprises means for fitting a stored curve to the received CDMA signal to estimate the energy E per symbol in the received signal_sOr noise variance σ.

14. The apparatus of claim 12, wherein the means for estimating the determined magnitude and the means for separately estimating the noise variance σ comprise means for separately estimating the energy E per symbol in the received signal according to the following equation_sAnd noise variance σ:

15. the apparatus of claim 12, further comprising:

means for receiving a pilot signal; and

means for more accurately estimating the determined amplitude and noise variance σ from the received pilot signal, including means for altering the ratio of the energy per symbol of the current pilot based on the ratio of the energy per symbol of at least one previous pilot.

16. The apparatus of claim 12, further comprising:

means for iteratively estimating the determined amplitudes and estimating the variances, respectively; and

means for stopping the iteration when a stop condition is met.

17. An apparatus for receiving a communication signal, the apparatus comprising:

at least first and second decoders for receiving at least one coded bit signal on a traffic channel, wherein the traffic channel has noise and the received signal has a determined amplitude; and

an estimator coupled to the inputs of the first and second decoders, wherein the estimator receives at least one coded bit signal and estimates noise and determines an amplitude of the traffic channel based on the received signal;

wherein the estimator fits the received signal with a stored curve.

18. The apparatus of claim 17 wherein the estimator fits a stored curve to the received signal to estimate the energy E of each symbol in the received signal_sOr the noise variance σ; and the estimator also receives the pilot signal and makes a more accurate estimate of the determined amplitude and noise based on the received pilot signal.

19. The apparatus of claim 17 wherein the estimator separately estimates the energy E of each symbol in the received signal according to the following equation_sAnd noise variance σ:

20. the apparatus of claim 17 wherein the estimator receives a pilot signal and provides a more accurate estimate of the determined amplitude and noise based on the received pilot signal, and wherein the means for altering the ratio of energy per symbol of the current pilot based on the ratio of energy per symbol of at least one previous pilot.

21. The apparatus of claim 17 wherein the estimator receives a pilot signal and makes a more accurate estimate of the determined amplitude and noise based on the received pilot signal.

22. The apparatus of claim 17, wherein the estimator separately estimates the energy E of each symbol in the received signal according to the following equation_sAnd noise variance σ:

and iteratively solving for the stationary point, wherein x represents the product of the received binary signal and the signal amplitude plus noise.