HK1113034A - Benign interference suppression for received signal quality estimation - Google Patents

Description

Benign interference suppression for received signal quality estimation

Background

The present invention relates generally to wireless communication networks, and in particular to estimating received signal quality in such networks.

The term "link adaptation" in the context of a wireless communication network generally means the dynamic modification of one or more transmission signal parameters in response to changing network and radio conditions. For example, evolving wireless communication standards define a common packet data channel that serves multiple mobile stations, also referred to as "users," on a scheduled basis.

For example, the Wideband Code Division Multiple Access (WCDMA) standard defines a High Speed Downlink Packet Access (HSDPA) mode in which a high speed packet data shared channel (HS-PDSCH) is used on a scheduled basis to transmit packet data to a potentially large number of users. The IS-856 standard defines a similar common packet data channel service known as High Data Rate (HDR), and the cdma2000 standard, such as 1XEV-DO, defines a similar high speed common packet data channel service.

In general, the common packet data channel in all such services is rate controlled rather than power controlled, meaning that the channel signal is transmitted at the full available power, and the data rate of the channel is adjusted for that power based on the radio conditions reported for the mobile stations being served at that particular time. Then, for a given transmit power, the data rate of the channel will generally be higher if the served mobile station is in good radio conditions compared to a mobile station experiencing poor radio conditions. Of course, other parameters may have an impact on the data rate actually used, such as the type of service the mobile station is involved in, etc.

However, when serving a particular mobile station, the effective utilization of the shared channel depends in large part on the accuracy of the channel quality reported for that mobile station, since that variable represents the primary input to the data rate selection process. Briefly, if a mobile station exaggerates its channel quality, it is often served at too high a rate, resulting in a high block error rate. Conversely, if the mobile station underestimates its channel quality, it will not get adequate service. That is, it will be served at a lower data rate than it can be supported by its actual channel conditions.

Underestimation of channel quality can occur particularly when the apparent impairments (interference plus noise) at the receiver contain harmful as well as "benign" interference. As used herein, the term "benign" interference is interference that affects the calculation of the apparent signal quality, but does not actually degrade the data signal demodulation excessively. Thus, benign interference at a given power produces a much lower data error rate than harmful interference at the same power. That means that if the signal quality target is, for example, 10% frame or block error rate, the receiver may reach that target with a greater benign interference level than would be tolerable if the interference were non-benign.

As non-limiting examples, the total received signal impairment at a given communication receiver may include gaussian impairment components resulting from same-cell interference, other-cell interference, thermal noise, etc., as well as non-gaussian impairment components resulting, for example, from so-called self-interference that occurs due to imperfect derotation of received symbols. Other contributing factors to self-interference include local oscillator frequency error and fast channel fading conditions. Such interference may have a probability distribution defined by a modulation format, such as a binomial distribution associated with a Binary Phase Shift Keying (BPSK) modulation format.

Since the probability distribution of non-gaussian impairments does not contain the characteristic "tail" of a gaussian distribution, its effect on signal demodulation is generally not as severe as gaussian impairments. In practice, the effect of even a large amount of non-gaussian impairments may be relatively small. Thus, conventional methods of estimating received signal quality at a wireless communication receiver based on apparent total signal impairment, i.e., total impairment comprising gaussian and non-gaussian impairment components, may not provide a true picture of the current reception capability of the receiver, and may in fact cause the receiver to significantly underestimate its received signal quality.

Disclosure of Invention

The present invention comprises methods and apparatus for improving signal quality estimates based on suppressing or reducing the effects of "benign" interference on the calculation of signal quality estimates for received signals. In this context, interference is benign if it does not significantly impair signal demodulation. As an example, the total impairment that affects the calculation of the signal quality estimate may include non-benign interference that must be accounted for in the quality estimate as well as relatively benign interference, such as binomially distributed interference. By suppressing the effect of benign interference on the quality estimation calculations, a more realistic "picture" of the received signal quality relative to actual signal demodulation results.

Accordingly, an exemplary method of estimating signal quality of a received signal in accordance with one or more embodiments of the invention includes calculating an impairment correlation estimate for non-benign impairment of the received signal based on suppressing the effects of benign impairment. A signal-to-interference ratio (SIR) estimate may then be generated based on the impairment correlation estimate, and the SIR may then be used to report signal quality to a wireless communication network, which may use the report for signal rate adaptation. By way of non-limiting example, gaussian impairments are generally non-benign in that they significantly degrade the demodulation performance of the receiver, and non-gaussian impairments are generally benign in that they slightly degrade the demodulation performance of the receiver.

Such suppression may therefore be based, for example, on calculating a total impairment correlation estimate based on despread values comprising or received in relation to the received signal, calculating a benign non-gaussian impairment correlation estimate, and subtracting the non-gaussian impairment correlation estimate from the total impairment correlation estimate to obtain a non-benign gaussian impairment correlation estimate. Alternatively, the suppression may be based on suppressing the effects of benign impairment in the channel estimation process to obtain modified channel estimates, and calculating non-benign impairment correlation estimates from the modified channel estimates. Suppressing benign impairments from the channel estimation process may include applying an interpolation filter to despread values of the reference channel signal.

In another embodiment, a method of estimating signal quality includes: calculating an SIR estimate for a received signal that is subject to total impairment including impairment that is more detrimental and impairment that is relatively harmless to signal demodulation; and suppressing relatively harmless impairment from the calculation of the SIR estimate such that the SIR estimate is larger than that calculated based on the total impairment. This suppression may also be based on subtracting an estimate of harmless impairment from an estimate of total impairment or on obtaining modified channel estimates by filtering the effects of harmless impairment from the channel estimates used to calculate the SIR.

Accordingly, an exemplary receiver circuit for estimating received signal quality comprises: a signal quality calculation circuit configured to calculate an SIR estimate for a received signal subject to total impairment including benign impairment and non-benign impairment; and an impairment suppression circuit configured to suppress benign impairment from the calculation of the SIR estimate such that the SIR estimate is larger than that calculated based on the total impairment. Exemplary receiver circuitry may be implemented in hardware, software, or any combination thereof. Additionally, it may comprise part of a baseband processor, which may be implemented as a microprocessor circuit, a Digital Signal Processor (DSP) circuit, or as some other digital logic circuit.

In one exemplary implementation, the receiver circuitry is included in a mobile terminal for use in a wireless communication network, such as a WCDMA or cdma2000 network. So configured, an exemplary terminal includes a transmitter to transmit signals to the network and a receiver to receive signals from the network. The receiver includes receiver circuitry including: a signal quality calculation circuit configured to calculate an SIR estimate for a received signal subject to total impairment including benign impairment and non-benign impairment; an impairment suppression circuit configured to suppress benign impairment from the calculation of the SIR estimate such that the SIR estimate is larger than that calculated based on total impairment.

The above features and advantages are described in more detail in the following discussion. Those skilled in the art will recognize additional features and advantages upon reading this discussion, and upon viewing the accompanying drawings, in which like elements are assigned like reference numerals.

Brief description of the drawings

FIG. 1 is a diagram of an exemplary receiver circuit in accordance with one or more embodiments of the present invention.

Figure 2 is a diagram of exemplary benign interference suppression for improved received signal quality estimation in accordance with the present invention.

Fig. 3 is a more detailed diagram of an exemplary signal quality estimate.

Fig. 4 and 5 are diagrams of alternative embodiments of benign interference suppression invoked in the exemplary process of fig. 3.

Fig. 6 and 7 are diagrams of exemplary functional implementations of the receiver circuit of fig. 1 in accordance with the processing logic of fig. 4 and 5, respectively.

Fig. 8 is a diagram of an exemplary mobile station for supporting a wireless communication network in accordance with the present invention.

Detailed Description

Although exemplary embodiments of the present invention are described in the context of CDMA-based wireless communication networks, such as WCDMA and CDMA2000, it should be understood that the present invention is applicable to a wide variety of communication systems and receiver types. In a broad sense, the present invention recognizes that the overall interference measurement at the wireless receiver may include different types of interference, and that some types of interference have less "impairment" to signal demodulation than others. By basing the received signal quality estimate on an interference estimate in which the effects of less damaging interference are suppressed from calculation, the receiver according to the present invention provides a more realistic depiction of the signal quality estimate representing its reception conditions. That more realistic depiction may be used to more efficiently control the radio link.

For example, according to the High Speed Downlink Packet Access (HSDPA) mode in WCDMA, the selection of the operational information transmission rate is determined by the radio channel conditions. When the channel conditions are good, the coding and modulation schemes corresponding to the higher data rates are employed. Conversely, in poor channel conditions, the transmission data rate is reduced to facilitate the use of more robust coding and modulation schemes. This data rate adaptation is often referred to as "link adaptation".

In the WCDMA context, the mobile provides a Channel Quality Indicator (CQI) to the supporting WCDMA network, which uses the reported CQI value to set the forward link data rate for the mobile, i.e., the better the reported channel quality, the higher the link rate, and vice versa. Such operations may also be performed in other network types. Continuing with the WCDMA example, the CQI may be estimated by estimating a symbol signal to interference plus noise ratio (SINR) on a forward common pilot channel (CPICH) received by the mobile station, converting the CPICH symbol SINR to SINR values for received symbols on a high speed packet data shared channel (HS-PDSCH) based on a reference power offset between the CPICH and HS-PDSCH and possibly on spreading factor differences, and finally determining a CQI estimate from the HS-PDSCH symbol SINR. Note that SINR is also referred to as signal-to-interference ratio (SIR).

In estimating CPICH symbol SINR, an impairment correlation matrix may be estimated, and the symbol SINR may be determined from the net response h, the RAKE receiver combining weights w, and the impairment correlation matrix R by

It is simplified into

SINR＝h^HR^-1h (2)

When generalized RAKE (G-RAKE) combining weights are used, w ═ R^-1h. Alternatively, only the impairment-related diagonal elements may be estimated, and for such receivers,

wherein h (i) is the ith element of h, and r_i，iIs the ith diagonal element of R. Such receivers are referred to herein as RAKE + and they are scaled to a complex scaleIs shown somewhere between G-RAKE and standard RAKE architecture. RAKE + can be viewed as an approximate form of G-RAKE.

If a RAKE receiver is employed, only the average impairment power is required, an

Wherein sigma_I ²Corresponding to the average impairment power that may be averaged over the fingers.

After obtaining the CQI estimate, the mobile station then sends the CQI estimate to a supporting network Base Station (BS) via uplink signaling. The BS may also adjust the reported CQI based on the instantaneous available power of the HS-PDSCH to obtain an adjusted HS-PDSCH symbol SINR. The BS selects a transmission data rate suitable for the adjusted HS-PDSCH symbol SINR and the mobile station's forward link data rate is set to the selected data rate.

Table 1 illustrates exemplary transport channel configurations for HSDPA and their corresponding SINR requirements for achieving a 10% Packet Error Rate (PER) at the mobile station.

Table 1: example of transport channel configuration for HS-PDSCH
					CQI value	Modulation	Encoding rate	Data rate	Required SINR for 10% PER
14	QPSK	0.67	1.29Mbps	9.5dB
					15	QPSK	0.69	1.66Mbps	10.5dB
16	16QAM	0.37	1.78Mbps	11.5dB
					17	16QAM	0.44	2.09Mbps	12.5dB
18	16QAM	0.49	2.33Mbps	13.5dB

The desired SINR given in table 1 is based on the gaussian assumption of all impairments (interference plus noise) at the mobile station receiver. Gaussian impairments can greatly degrade demodulation performance and are therefore considered non-benign impairments. The mobile station should determine its current SINR and then report the highest Channel Quality Indicator (CQI) value among all transport channel configurations with PER below 10%, as required by the applicable WCDMA standard.

For example, if the measured channel quality is 13dB, the mobile station should report the CQI as 17 since that is the highest CQI value among all configurations where the 13dB SINR has a PER below 10%. In practice, only the CQI value column and SINR column of table 1 are stored in the mobile station. Such a table is commonly referred to as an "MCS switching table".

PER decreases rapidly for each transport channel configuration. Within 1dB, the PER can be turned from 100% to less than 1%. This means that the accuracy of the CQI estimation is essential for correct link adaptation. If the mobile station estimates the CQI too high beyond 1dB, the throughput is significantly degraded due to the very high block error rate. On the other hand, if the mobile underestimates the CQI, it does not operate at the highest possible data rate allowed by the channel conditions, resulting in underutilization of the channel. Therefore, CQI estimation plays an important role in designing a high-speed data terminal.

It is common practice to estimate the SINR from a received reference channel signal, e.g., a pilot signal or training sequence received in conjunction with a received signal of interest, such as a traffic channel or control channel signal. For example, SINR calculations are typically based on received pilot symbols. In that context, let y (k) be a vector that collects despread values of CPICH signal samples from all RAKE fingers in the k-th symbol period. If the noise is additive only, y (k), y_k(i) Can be represented as

y_k(i)＝s(k)h_k(i)+n_k(i) (5)

Where s (k) is the modulation symbol value, h_k(i) Is the net response of the ith finger delay position, and n_k(i) Is y_k(i) Additive damage component of (1). Note that the additive impairment component accounts for own-cell interference, other-cell interference, thermal noise, and degradation caused by typical receiver impairments such as DC offset and non-ideal filtering, and is generally modeled as gaussian.

It can be assumed that the symbol values have normalized average power (E [ | s (k))²]1) and the damage samples are i.i.d. and each have a zero average value (E n)_k(i)]＝0，E[n_k(i)n^* _k+1(i)]0). In conventional SINR estimation, impairment correlations are estimated as

WhereinIs an estimate of the net response of the kth symbol period, anThe ith component ofIs h_k(i) An estimate of (a). For RAKE + and possibly for RAKE, only estimates are madeThe diagonal elements of (a). The G-RAKE, RAKE + and RAKE receiver architectures all implement some form of equation (6) in the SINR estimation.

If the noise is ergodic, the expected value in equation (6) can be obtained by averaging over time. When in usei， Wherein R ═ E [ n (k)^H]And n (k) is a vector that collects impairment components across all RAKE fingers.

As described above, it is common practice to use CPICH symbols for generating a CQI estimate for the received traffic channel signal. Thus, y_k(i)、 Andobtained from the CPICH despread value and the SINR of the CPICH channel can be converted to the SINR of the HS-PDSCH (or another receive channel of interest) by adjusting for the difference in power and spreading factor.

Generally, the above-described method of signal quality estimation works perfectly well in the case where the dominant component of received signal impairment is gaussian. However, the accuracy of this approach degrades significantly in cases where non-gaussian impairments constitute an appreciable component of the total impairment. Non-gaussian impairments include impairments that cannot be modeled well by a gaussian approximation. This non-Gaussian lossImpairments may result from multiplicative impairments arising from phase noise, residual frequency error, and/or fast time-varying fading. Such damage not only causes an additive (gaussian) damage term n_k(i) And also introduces an additional multiplicative term in the signal model, denoted as

y_k(i)＝s(k)h(i)m_k(i)+n_k(i)

In which a multiplicative term m is added_k(i) To illustrate the multiplicative effect caused by, for example, phase noise and residual frequency error.

Note that in the CDMA downlink, n is caused by phase noise and/or frequency error_k(i) Is a result of the loss of orthogonality. In that context, the majority of the degradation caused by phase noise and residual frequency error is at the additive impairment term n_k(i) Is captured in the increase in power of. Multiplicative term m_k(i) Without actually causing significant degradation of the demodulation. That is, the multiplicative impairment term is less detrimental to signal demodulation than the additive impairment term.

It can be shown that without the inventive work, the multiplicative term m in the received signal model_k(i) Causes severe underestimation of CQI. Underestimation of CQI results in lower achievable user throughput when link adaptation is employed.

This problem may be illustrated by assuming that the net (channel) response is constant over the symbol interval of interest-omitted for the sake of brevitySubscripts of (a). Additive impairment component of RAKE finger i (i.e., without taking into account multiplicative impairment term)The power of (i, i) element(s) of (a) can be estimated by

It can be shown that the estimate of equation (7) gives in case of multiplicative impairmentWherein v is_k(i) Is a new impairment signal which is a signal of interest,in downlink CDMA, v_k(i) Indicating CPICH self-interference caused by multiplicative impairments. It can be seen that the multiplicative term m_k(i) The resulting instantaneous rotation rotates the desired signal away from the estimated channel coefficients. This imperfect alignment produces additional damage that appears to come from orthogonal directionsThus, in this context, v_k(i) May be referred to as the CPICH Quadrature Phase Interference (QPI). Note, v_k(i) Is non-gaussian. In other words, imperfect derotation of CPICH symbols based on channel estimation errors produces non-gaussian multiplicative interference components that cause underestimation of true received signal quality unless reduced during SINR/CQI estimation.

Similarly, the impairment correlation estimate according to equation (6) is based on the presence of multiplicative impairment m_k(i) Is generated underOff diagonal elements of (1), as shown below

Andlike, elementWith a component due to gaussian impairments and another component due to non-gaussian impairments. Thus, impairments can be correlated with a matrixWriting

WhereinIs an estimate of the impairment correlation for Gaussian impairment n (k), anImpairment correlation estimates for non-Gaussian impairment v (k), where v (k) is the acquisition of v from all fingers of a RAKE receiver providing despread values of a reference signal_k(i) The vector of (2).

Against the above background, the present invention suppresses benign interference from the calculation of received signal quality, such that the quality estimate is based primarily on harmful interference rather than on total apparent impairment including both benign and harmful interference. That is, in general, the present invention suppresses the effects of benign impairment from the calculation of received signal quality so that whether reported as SIR, CQI, etc., the received signal quality estimate depends primarily on the effects of non-benign impairment and is therefore generally higher than calculated considering the total impairment (i.e., benign plus non-benign impairment). In the following description of exemplary embodiments of the invention, the terms "gaussian" and "non-gaussian" are used as non-limiting examples of non-benign and benign interferences, respectively.

Fig. 1 illustrates an exemplary receiver circuit 10 configured to provide improved signal quality estimation in the context of the present invention. Although not illustrated as such for clarity, those skilled in the art will appreciate that receiver circuit 10 is operatively associated with other receiver circuits such as a receiver front end, a RAKE receiver, and the like. Such other structures are disclosed later herein.

In the described embodiment, the receiver circuit 10 includes an interference suppression circuit 12 and a signal quality calculation circuit 14. The terms "comprising", "including" and "comprises", as used herein, are to be construed as open-ended terms that are not exclusive of what is claimed.

Broadly speaking, the receiver circuit 10 receives signal samples, such as reference signal samples, e.g., pilot/training despread values, from which an improved signal quality estimate is calculated based on the effect of suppressing benign (non-gaussian) interference from that estimate. To this end, the interference suppression circuit 12 operates on the signal samples for received signal quality calculations to obtain an estimate of the gaussian impairment component of the signal samples, and the signal quality calculation circuit 14 calculates therefrom a signal quality estimate, e.g. it calculates a SINR value as a function of the estimated gaussian impairment. Note that the SINR may be estimated for a traffic channel signal (or similar data signal) received in conjunction with a reference signal, which as previously described may be scaled or adjusted to account for differences in transmit power and/or CDMA spreading factor between the reference signal and the received signal of interest for which the SINR estimate was generated.

Fig. 2 illustrates the basic operation of the receiver circuit 10, wherein processing begins with suppression of benign interference from SINR calculations (step 100). By estimating the received signal quality as a function of the harmful interference in this manner, the receiver circuitry 10 maps the improved SINR value to a channel quality indicator table, e.g., a CQI look-up table, which may be stored in the associated receiver circuitry (step 102). The associated receiver then reports the CQI value indexed by the improved SINR estimate and reports that CQI value to the supporting network for link adaptation (step 104).

Exemplary details of this general process are provided in fig. 3, in which the receiver circuit 10 calculates a gaussian impairment correlation estimate for the received signal based on suppressing the effects of non-gaussian impairments from that calculation (step 106). A signal-to-interference ratio estimate, e.g., SINR value, is generated from the gaussian impairment correlation estimate (step 108), meaning that the estimate of signal quality reduces the effect of any non-gaussian impairment that may be present at the receiver. Accordingly, the signal-to-interference ratio estimate and/or corresponding CQI value is reported to the supporting network (step 110).

Suppressing non-gaussian impairments from the signal quality estimate can be done in a number of ways. In one exemplary embodiment shown in fig. 4, the receiver circuit 10 performs an overall impairment estimation and then removes the estimated non-gaussian components from the overall estimate to derive an estimated gaussian impairment for signal quality estimation. Fig. 6 illustrates an exemplary functional configuration of the receiver circuit 10 in this context, wherein the interference suppression circuit 12 produces a total impairment correlation estimate, a non-gaussian impairment correlation estimate and a gaussian impairment correlation estimate. The suppression circuit 12 may include a correction term calculator 20 to "scale" or compensate for the gaussian impairment component to increase its accuracy, such operation being described in more detail later herein.

Supplementing the illustrated suppression circuit 12, the signal quality calculation circuit 14 includes an SINR estimator 22 and a CQI mapper 24. In one exemplary embodiment, the signal quality calculation circuit 14 is configured to calculate the SINR estimate based on the gaussian impairment correlation estimate from the suppression circuit 12. The CQI mapper circuit 24, which may comprise a lookup circuit that accesses a memory stored CQI table or may comprise logic circuitry that calculates a functional CQI value from the SINR estimate, in turn produces a CQI value from the SINR estimate. CQI values may be reported to the supporting communication network for the purpose of ongoing link adaptation.

Returning to the exemplary logic of fig. 4, processing thus begins with the calculation of a total impairment correlation estimate for the received signal (step 112), which may likewise be based on received pilot/training signal samples. The receiver circuit 10 then calculates a non-gaussian impairment correlation estimate for the received signal (step 114) and then "cancels" that non-gaussian impairment correlation estimate from the total impairment correlation estimate to obtain a gaussian impairment correlation estimate (step 116). The receiver circuit 10 generates a signal quality estimate from the gaussian impairment correlation estimate (step 118).

A simple method of this process is to estimate the total impairment correlation matrix, estimate the non-gaussian impairment correlation matrix and then subtract the latter from the former to obtain the gaussian impairment correlation matrix. In more detail, the total impairment correlation matrices are first estimated separatelyAnd a correlation matrix composed of non-Gaussian impairmentsCorrelation matrix of gaussian impairmentIt is obtained by taking the difference between the two estimated correlation matrices.

The diagonal term of (c) can be estimated by averaging the products of the impairment components in successive symbol intervals, e.g.

Wherein

The off-diagonal term of (c) can be estimated by averaging the products of the impairment components in successive symbol intervals, e.g.

Wherein

It can be shown that, in case the multiplicative noise changes slowly with respect to the symbol duration,

when the multiplicative noise changes more rapidly with respect to the symbol duration, the power and correlation caused by the non-gaussian self-interference term generally cannot be completely resolved because of the fact thatIs less thanFor example, where the interfering phase noise has a large effective bandwidth relative to the symbol rate of the reference signal on which the SINR estimation is based, this occursSuch a situation. As an example, the non-gaussian impairment component may have a large bandwidth, on the order of 15kHz, relative to the symbol rate of the CPICH in WCDMA.

In such cases, the correction term calculator 20 may be configured to calculate the available expansionThe correction term F of (1). This correction term may be determined based on the rate at which the multiplicative impairment changes between symbol periods. That is, it may be configured to be larger as a function of more rapid changes, and smaller as a function of less rapid changes. In this way, the degree to which the non-gaussian impairment correlation component is underestimated due to its larger bandwidth is reduced.

For example, the correction term F can be calculated as

It is the ratio between the autocorrelation of the multiplicative impairment and the temporal cross-correlation of the multiplicative impairment. In the design stage, multiplicative damage may be based onThe receiver characteristics are simulated and F can be found during such simulation. An exemplary F is approximately 1.2 for a residual frequency of 10-50Hz and for a 4kHz loop bandwidth for phase noise. The receiver circuit 10 may be configured to use one or more predetermined values for F or may be configured to calculate a correction term. The correction term F can also be used at R_gIn which contains R_aA fraction of (a). For example, when the non-gaussian interference is not completely benign, F ═ 0.2 may be used.

For example, the total impairment correlation matrix may be estimated by a method according to equation (6). Therefore, forAndthe impairment correlation matrix formed by the Gaussian components is

The symbol SINR may then be calculated for the G-RAKE receiver based only on the contribution of the Gaussian impairment components, as shown below

For a RAKE + receiver, the appropriate calculation is

WhereinIs thatThe ith diagonal element of (1). Finally, if a RAKE receiver is employed, the calculation is denoted as

Wherein sigma² _ICan be based on as a function of the Gaussian impairment componentTo calculate.

With any of the above SINR calculations, the corresponding CQI may be generated from an indexed SINR to CQI look-up table or from performing SINR to CQI calculations.

Turning to another exemplary method for suppressing or reducing the effects of benign non-gaussian impairments from the calculation of received signal quality, fig. 5 illustrates an embodiment in which such impairments are suppressed during channel estimation. By suppressing non-gaussian impairment components from the channel estimation process, the impairment correlation estimates based on those channel estimates depend primarily on gaussian impairment components.

Broadly, the method includes suppressing non-gaussian impairment from the calculation of the signal-to-interference ratio estimate by filtering in a channel estimation process to obtain a modified channel estimate for the received signal for which multiplicative impairment is compensated. The modified channel estimates are tuned to track rapid changes in the multiplicative impairments and are generally different from the channel estimates obtained for demodulation. A gaussian impairment correlation estimate is in turn calculated from the modified channel estimates and a corresponding signal-to-interference ratio estimate is calculated from the gaussian impairment correlation estimate.

Filtering in the channel estimation process to obtain modified channel estimates for the received signal may include: calculating a filter coefficient of an interpolation filter; and calculating a modified channel estimate based on applying an interpolation filter to despread values of a pilot signal received in conjunction with the received signal. Further, the method may include configuring the interpolation filter to have a filter bandwidth that is high enough to track the multiplicative impairment, but less than a noise power bandwidth of the despread values.

Then, according to fig. 5, the exemplary process begins with the generation of interpolated filter coefficients (step 120). Alternatively, these interpolation filter coefficients may be pre-calculated and stored in memory. The filter coefficients are used in conjunction with the received reference signal samples (e.g., despread pilot values) to obtain modified channel estimates such that non-gaussian impairments are suppressed when estimating impairment correlations (step 122). A gaussian impairment correlation component is estimated from the modified channel response estimate (step 124) and a signal quality estimate is generated from the estimate of gaussian impairment correlation (step 126) as previously described.

Fig. 7 illustrates an exemplary functional embodiment of receiver circuit 10 in this filter-based suppression context, where suppression circuit 12 includes channel estimator/filter 26 and correction term calculator 20 as previously described, but in a filter-based embodiment where the tracking of the multiplicative impairments may be sufficiently accurate over its full bandwidth, correction term calculator 20 may not be used.

Thus, by a filter-based approach to obtaining modified channel estimates, the multiplicative impairment term is incorporated into the computation of the net response of the channel in the channel coefficient estimation process. By this approach, there is little or no mismatch between the instantaneous CPICH despread values and the modified net channel response, which prevents QPI from causing multiplicative impairment terms. For satisfactory performance, the channel coefficient estimation process should be fast enough to keep up with the variation of the multiplicative impairment term, which tends to have a bandwidth of about 1kHz due to the effective bandwidth of the phase noise.

In more detail, the filter-based approach treats the multiplicative impairment as a part of the net response, h ', of the modified channel'_k(i)＝h(i)m_k(i) And thus each despread value is represented as

y_k(i)＝s(k)h_k′(i)+n_k(i) (19)

H 'since the multiplicative impairment may change rapidly'_k(i) Possibly changing from symbol to symbol.

H is taken to be equal to H'_k(i) Of (i, k) th element of (a). The estimate of H may be applied to the despread value y by using an interpolation filter configured to have a bandwidth on the same order of magnitude as the bandwidth of the multiplicative impairment term_k(i) Interpolation is performed to obtain. In this way, the multiplicative impairment term m can be better tracked_k(i) A change in (c). Or, multiplicative impairment term m_k(i) Phase locked loops may be employed for tracking and it should be understood that such variations are contemplated by the present invention and fall within its scopeAnd (4) the following steps.

Continuing with the filter-based embodiment, let A be the matrix representing the interpolation filter. The (i, j) th element of a is represented as:

wherein f is_wIs the bandwidth of the interpolation filter, and_sis to obtain y_k(i) The sampling rate of (c). A sinc function is defined as sinc (x) sin (x)/x. If CPICH in WCDMA is used, then f_s15 kHz. Those skilled in the art will appreciate that such specifics may vary for other systems of interest, such as cdma2000, etc.

In any case, the estimate of H is

Wherein the (i, k) th element of the matrix Y is Y_k(i)。

By interpolation, the estimation noise can be reduced without compromising the ability to track the fast changes of the multiplicative impairment term, as long as the bandwidth of the interpolation filter is sufficient.

In this case, the implementation of additive Gaussian impairment can be obtained by

ByThe induced impairment correlations can be estimated as

WhereinIs to collect signals on all RAKE fingersA vector of all elements of (a). It should be noted, however, that during interpolation, the despread values are due to the low frequency components of the additive gaussian impairmentsThe variation is eliminated, therebyOnly having high frequency components. Therefore, the temperature of the molten metal is controlled,can be adjusted to

It will be appreciated from the above exemplary details that the present invention is applicable to many receiver implementations. However, fig. 8 illustrates an exemplary application of the receiver circuit 10, wherein the mobile station 40 includes an embodiment of the receiver circuit 10 such that it generates (and reports) an improved signal quality estimate to a supporting wireless communication network. The term "mobile station" as used herein should be given its broadest construction. Thus, the mobile station 40 may be a cellular radiotelephone, Portable Digital Assistant (PDA), palm top/laptop computer, wireless pager, or other type of portable communication device.

In the depicted embodiment, mobile station 40 includes a transmit/receive antenna assembly 42, a switch/duplexer 44, a receiver 46, a transmitter 48, a system controller 50, and a user interface 52, which may include a keypad, display, speaker, and microphone. System controller 50 typically provides overall system control and may include microprocessor/microcontroller circuitry that may or may not be integrated with other processing logic in mobile station 40.

The exemplary receiver 46 includes a receiver front-end circuit 54 that may include one or more filtering and amplification stages and typically one or more analog-to-digital conversion circuits to provide an incoming received signal as sampled data to a receiver processor 56. Accordingly, the receiver processor 56 may receive signal samples corresponding to a combination of received signals, such as traffic, control, and pilot signals.

The receiver processor circuit 56 may include all or a portion of a baseband digital signal processor implemented in hardware, software, or any combination thereof. However, in addition to the receiver circuit 10, the exemplary receiver processor circuit 56 includes a RAKE receiver circuit 60 that includes (or is associated with) a despreader/combiner circuit 62 and also includes an impairment correlation estimator 64, a channel estimator 66 and a buffer (storage circuit) 68. Note that in one or more embodiments, the impairment correlation estimator 64 and/or the channel estimator 66 may be implemented as part of the RAKE receiver circuit 60, in which case the receiver circuit 10 is configured to receive outputs therefrom. In other embodiments, the receiver circuit 10 may be configured to include these elements, in which case the appropriate impairment and channel estimation information is provided to the despreader/combiner 62 for RAKE despreading and combining operations. As previously disclosed herein, the RAKE receiver circuit 60 may include a RAKE, RAKE + or G-RAKE circuit.

In any event, despreader/combiner 62 includes a plurality of correlators, also referred to herein as RAKE fingers, that provide despread values of selected received signal components. In an exemplary embodiment, RAKE processor circuit 60 provides despread pilot values to receiver circuit 10 for use in gaussian impairment correlation estimation and a corresponding improved estimate of received signal quality. Note that the receiver circuit 10 may use the buffered despread values stored in the buffer 68 for the signal quality estimation process.

For example, the baseband received signal output by the receiver front-end circuit 54 is despread by the RAKE receiver circuit 60 according to a reference channel (e.g., CPICH) to produce despread values. These despread values are collected over a predetermined duration (e.g., a WCDMA transmission time interval) and stored in a buffer 68. The buffered despread values may be processed to produce channel coefficient estimates. The despread values and channel coefficient estimates may be passed to the receiver circuit 10 for calculation of an impairment realization and corresponding gaussian impairment correlation estimate. The receiver circuit 10 then employs the gaussian impairment correlation estimate to produce a signal quality estimate, e.g., a SINR value. That SINR value is then mapped to a CQI value that is provided to system controller 50 for reporting back to the supporting network via transmission of control signaling by transmitter 48.

So configured, the mobile station 40 implements an exemplary channel quality estimation method based on receiving incoming signals from a supporting wireless communication network. Exemplary received signals include: traffic or control channels for which the network should receive periodic signal/channel quality reports from the mobile station 40; and a reference signal, such as a pilot signal, used by the mobile station to calculate the received signal quality. The mobile station 40 may be configured as a WCDMA terminal or may be configured in accordance with one or more other wireless standards as needed or desired.

Indeed, while the above discussion sets forth exemplary details in the context of WCDMA, the present invention is not limited to such applications. Broadly speaking, the present invention provides an improved signal quality estimate by suppressing or reducing the effects of benign non-gaussian impairments from the computation of received signal quality. Accordingly, the invention is not to be limited by the foregoing discussion, but is only limited by the following claims and their appropriate equivalents.

Claims (45)

1. A method of estimating signal quality, comprising:

calculating a signal-to-interference ratio estimate for a received signal that is subject to total impairment comprising a first type of impairment that is relatively detrimental to demodulation of the received signal and a second type of impairment that is relatively benign to demodulation of the received signal; and

the effect of the second type of impairment is suppressed from the calculation of the signal-to-interference ratio estimate such that the signal-to-interference ratio estimate depends primarily on the first type of impairment.

2. The method of claim 1, wherein the first type of impairment comprises gaussian type interference and the second type of impairment comprises non-gaussian type interference.

3. The method of claim 1, wherein suppressing the effect of the second type of impairment from the calculation of the signal-to-interference ratio estimate such that the signal-to-interference ratio estimate depends primarily on the first type of impairment comprises: subtracting the estimate of the second type of impairment correlation from the estimate of the total impairment correlation to obtain a correlation estimate for the first type of impairment; and calculating a signal-to-interference ratio estimate based on the correlation estimate for the first type of impairment.

4. The method of claim 1, wherein suppressing the effect of the second type of impairment from the calculation of the signal-to-interference ratio estimate such that the signal-to-interference ratio estimate depends primarily on the first type of impairment comprises: suppressing the effects of the second type of impairments in the channel estimation process to obtain modified channel estimates; and calculating a signal-to-interference ratio estimate based on the modified channel estimate.

5. A method of estimating signal quality, comprising:

calculating a signal-to-interference ratio estimate for a received signal subject to total impairment comprising a first impairment and a second impairment;

the second impairment is suppressed from the calculation of the signal-to-interference ratio estimate such that the signal-to-interference ratio estimate is larger than a value calculated based on the total impairment.

6. The method of claim 5, wherein the first impairment corresponds to interference that is well modeled as Gaussian interference and the second impairment corresponds to interference that is not well modeled as Gaussian interference.

7. The method of claim 5, further comprising mapping the signal-to-interference ratio estimates to channel quality indicators and reporting the channel quality indicators to a supporting wireless communication network.

8. The method of claim 7, wherein the received signal includes at least a reference signal, and reporting the channel quality indicator to a supporting wireless communication network comprises reporting the channel quality indicator to the supporting wireless communication network for data rate adaptation of a CDMA packet data channel signal.

9. The method of claim 8, wherein calculating the signal-to-interference ratio estimate for the received signal subject to the total impairment comprising the first impairment and the second impairment comprises calculating the signal-to-interference ratio estimate from a reference channel signal.

10. The method of claim 9, further comprising adjusting the signal-to-interference ratio estimate for differences in power and spreading factor between the CDMA packet data channel signal and the reference channel signal.

11. The method of claim 5, wherein calculating a signal-to-interference ratio estimate for the received signal subject to total impairment comprising the first impairment and the second impairment comprises calculating a signal-to-interference-plus-noise ratio estimate for the received signal.

12. The method of claim 5, wherein calculating a signal-to-interference ratio estimate for a received signal subject to total impairment comprising the first impairment and the second impairment comprises: calculating a total impairment correlation estimate; obtaining a correlation estimate for the first impairment by subtracting a correlation estimate for the second impairment from the total impairment correlation estimate; and calculating a signal-to-interference ratio estimate based on the correlation estimate of the first impairment.

13. The method of claim 12, further comprising calculating a correction term as a function of a rate of change of the multiplicative impairment, and scaling the correlation estimate for the second impairment by the correction term.

14. The method of claim 12, wherein the first impairment comprises gaussian impairment and the second impairment comprises non-gaussian impairment, and wherein obtaining the correlation estimate for the first impairment by subtracting the correlation estimate for the second impairment from the total impairment correlation estimate comprises subtracting the non-gaussian impairment correlation estimate from the total impairment correlation estimate to obtain a gaussian impairment correlation estimate used for calculating the signal-to-interference ratio estimate.

15. The method of claim 5, wherein suppressing the second impairment from the calculation of the signal-to-interference ratio estimate comprises: filtering the effects of the second impairments in the channel estimation process to obtain modified channel estimates; calculating a correlation estimate for the first impairment based on the modified channel estimates; and calculating a signal-to-interference ratio estimate based on the correlation estimate of the first impairment.

16. The method of claim 15, wherein the first impairment comprises gaussian impairment and the second impairment comprises non-gaussian impairment, and wherein filtering effects of the second impairment in the channel estimation process to obtain modified channel estimates comprises suppressing effects of the non-gaussian impairment from the channel estimation process, and wherein calculating the correlation estimate for the first impairment based on the modified channel estimates comprises calculating gaussian impairment correlation estimates from the modified channel estimates.

17. The method of claim 15, wherein filtering the effects of the second impairment in the channel estimation process to obtain the modified channel estimates for the received signal comprises: calculating a filter coefficient of an interpolation filter; and calculating a modified channel estimate based on applying an interpolation filter to despread values of a reference channel signal received as part of the received signal.

18. The method of claim 17, further comprising configuring the interpolation filter to have a filter bandwidth large enough to track a multiplicative impairment term corresponding to the second impairment.

19. A receiver circuit for estimating received signal quality, comprising:

a signal quality calculation circuit configured to calculate a signal-to-interference ratio estimate for a received signal subject to total impairment comprising a first impairment and a second impairment;

an impairment suppression circuit configured to suppress the second impairment from the calculation of the signal-to-interference ratio estimate such that the signal-to-interference ratio estimate is primarily dependent on the first impairment.

20. The receiver circuit of claim 19, wherein the calculation circuit is configured to map the signal-to-interference ratio estimate to a channel quality indicator for reporting to a supporting wireless communication network.

21. The receiver circuit of claim 20, wherein the received signal comprises at least a reference channel signal, and wherein reporting the channel quality indicator to the supporting wireless communication network comprises reporting the channel quality indicator to the supporting wireless communication network for data rate adaptation of the CDMA packet data channel signal.

22. The receiver circuit of claim 21, wherein the calculation circuit is configured to calculate the signal-to-interference ratio estimate for the received signal based on calculating the signal-to-interference ratio estimate from a reference channel signal.

23. The receiver circuit of claim 22, wherein the calculation circuit is configured to adjust the signal-to-interference ratio estimate for differences in power and spreading factor between the CDMA packet data channel signal and the reference channel signal.

24. The receiver circuit of claim 19, wherein the calculation circuit is configured to calculate the signal-to-interference ratio estimate as a signal-to-interference-plus-noise ratio of the received signal.

25. The receiver circuit of claim 19, wherein the suppression circuit is configured to calculate a total impairment correlation estimate, to obtain a correlation estimate for the first impairment by subtracting a correlation estimate for the second impairment from the total impairment correlation estimate, and to calculate the signal-to-interference ratio estimate based on the correlation estimate for the first impairment.

26. The receiver circuit of claim 25, wherein the suppression circuit includes a correction term calculation circuit configured to calculate the correction term as a function of a multiplicative impairment term generated from the second impairment, and wherein the suppression circuit is configured to scale the correlation estimate for the second impairment by the correction term.

27. The receiver circuit of claim 19, wherein the suppression circuit comprises a channel estimation and filtering circuit, and wherein the suppression circuit is configured to suppress the second impairment from the calculation of the signal-to-interference ratio estimate by filtering an effect of the second impairment in the channel estimation process to obtain modified channel estimates for the received signal and calculating a first impairment correlation estimate based on the modified channel estimates.

28. The receiver circuit of claim 27, wherein the calculation circuit is configured to calculate the signal-to-interference ratio estimate based on the modified channel estimates.

29. The receiver circuit of claim 27, wherein the channel estimation and filtering circuit comprises an interpolation filter, and wherein the suppression circuit is configured to calculate filter coefficients of the interpolation filter and to calculate the modified channel estimates based on applying the interpolation filter to despread values of a reference channel signal received as part of or in association with the received signal.

30. The receiver circuit of claim 29, wherein the interpolation filter is configured to have a filter bandwidth sufficient to track a multiplicative impairment term associated with the second impairment.

31. A mobile terminal for use in a wireless communication network, comprising:

a transmitter to transmit a signal to a network;

a receiver that receives a signal from a network;

the receiver includes a receiver circuit comprising:

a signal quality calculation circuit configured to calculate a signal-to-interference ratio estimate for a received signal subject to total impairment comprising a first impairment and a second impairment;

an impairment suppression circuit configured to suppress the second impairment from the calculation of the signal-to-interference ratio estimate such that the signal-to-interference ratio estimate is based primarily on the first impairment.

32. The mobile terminal of claim 31, wherein the calculation circuit is configured to map the signal-to-interference ratio estimate to a channel quality indicator, and wherein the mobile terminal is configured to send the channel quality indicator to the network.

33. The mobile terminal of claim 32, wherein the received signal comprises at least a reference channel signal, and wherein the mobile terminal is configured to periodically transmit an updated channel quality indicator for data rate adaptation of the CDMA packet data channel signal by the wireless communication network.

34. The mobile terminal of claim 33, wherein the calculation circuit is configured to calculate the signal-to-interference ratio estimate for the received signal based on calculating a signal-to-interference ratio estimate from the reference channel signal, and to adjust the signal-to-interference ratio estimate for differences in power and spreading factor between the reference channel signal and the CDMA packet data channel signal.

35. The mobile terminal of claim 31, wherein the suppression circuit is configured to calculate a total impairment correlation estimate, to obtain a correlation estimate for the first impairment by subtracting a correlation estimate for the second impairment from the total impairment correlation estimate, and wherein the calculation circuit is configured to calculate the signal-to-interference ratio estimate from the correlation estimate for the first impairment.

36. The mobile terminal of claim 35, wherein the first impairment is gaussian impairment and the second impairment is non-gaussian impairment, and wherein the suppression circuit is configured to obtain a gaussian impairment correlation estimate as the correlation estimate for the first impairment by subtracting the non-gaussian impairment correlation estimate from the total impairment correlation estimate.

37. The mobile terminal of claim 35, wherein the suppression circuit includes a correction term calculation circuit configured to calculate a correction term as a function of a multiplicative impairment term produced from the second impairment, and further configured to scale the correlation estimate for the second impairment by the correction term.

38. The mobile terminal of claim 31, wherein the suppression circuit comprises a channel estimation and filtering circuit, and wherein the suppression circuit is configured to suppress the second impairment from the calculation of the signal-to-interference ratio estimate by filtering an effect of the second impairment in the channel estimation process to thereby obtain modified channel estimates for the received signal, and calculating a first impairment correlation estimate based on the modified channel estimates.

39. The mobile terminal of claim 38, wherein the calculation circuit is configured to calculate the signal-to-interference ratio estimate based on the modified channel estimates.

40. The mobile terminal of claim 38, wherein the channel estimation and filtering circuit includes an interpolation filter, and wherein the suppression circuit is configured to calculate filter coefficients of the interpolation filter and to calculate the modified channel estimates based on applying the interpolation filter to despread values of a reference channel signal received as part of or in conjunction with the received signal.

41. A method of estimating signal quality, comprising:

suppressing benign interference from the calculation of signal-to-interference ratio estimates for received signals experiencing benign and non-benign interference; and

the signal quality is reported to the wireless communication network as a function of the signal-to-interference ratio estimate.

42. The method of claim 41, wherein the received signal is subject to benign non-Gaussian interference and to non-benign Gaussian interference, and wherein suppressing benign interference from the calculation of the signal-to-interference ratio estimate for the received signal comprises suppressing the effects of non-Gaussian interference from the calculation of the signal-to-interference ratio estimate.

43. The method of claim 42, wherein suppressing the effects of non-Gaussian interference from the calculation of the signal-to-interference ratio estimate comprises: filtering non-gaussian interference in the channel estimation process to obtain a modified channel estimate; and calculating a signal-to-interference ratio based on the modified channel estimates.

44. The method of claim 42, wherein suppressing the effects of non-Gaussian interference from the calculation of the signal-to-interference ratio estimate comprises: calculating a total impairment correlation estimate for gaussian and non-gaussian interference; calculating a non-Gaussian impairment correlation estimate of the non-Gaussian interference; subtracting the non-gaussian impairment correlation estimate from the total impairment correlation estimate to obtain a gaussian impairment correlation estimate for the gaussian interference; and calculating a signal-to-interference ratio estimate based on the gaussian impairment correlation estimate.

45. A computer readable medium storing a computer program, comprising:

program instructions for calculating a signal-to-interference ratio estimate for a received signal that is subject to total impairment comprising a first type of impairment that is relatively detrimental to demodulation of the received signal and a second type of impairment that is relatively benign to demodulation of the received signal; and

program instructions for suppressing the effect of the second type of impairment from the calculation of the signal-to-interference ratio estimate such that the signal-to-interference ratio estimate depends primarily on the first type of impairment.