Fast predictive automatic gain control for dynamic range reduction in wireless communication receiver

A method of fast predictive automatic gain control is disclosed including estimating channel gain applied to a received signal, predicting channel gain at a subsequent time by applying temporal correlation statistics to the estimated channel gain, determining a predicted receiver gain which reduces variance between the predicted channel gain and a predetermined target power level, and applying the predicted receiver gain to the received signal. The method may include applying linear minimum mean-squared error prediction to the estimated channel gain. The method may include predicting error variance at the subsequent time by applying the temporal correlation statistics to the estimated channel gain and combining the predicted channel gain and the predicted error variance. The method may include estimating channel gain of known pilot symbols, estimating a temporal correlation function using the estimated channel gain, and determining predicted channel gain using the estimated channel gain and the estimated temporal correlation function.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This disclosure relates generally to wireless communication receivers, and more specifically, to a system and method for reducing dynamic range using fast predictive automatic gain control.

2. Description of the Related Art

Automatic gain control (AGC) circuits are typically designed to compensate for the slow fading or log-normal shadowing of a received signal. Due to fast fading, such as Rayleigh fading and the like, the accuracy of the analog to digital converter (ADC) in the conventional receiver had to be relatively high to compensate for increased dynamic range. As an example, up to 5 additional bits of precision were needed to compensate for up to 30 decibels (dB) of dynamic range. Each additional bit of the ADC, however, increased power consumption of the conventional radio by a factor of approximately four. It is desired to reduce power consumption of wireless communication devices.

DETAILED DESCRIPTION

FIG. 1is a block diagram of the receiver portion of a wireless communication system100implemented according to an exemplary embodiment. The system100includes a radio101with an antenna102, a variable resolution (VR) analog to digital converter (ADC)103and a baseband processor105. A signal in a wireless channel is received by the antenna102and provided to a diplex filter107, which forwards the received signal (the RX signal) to the input of a band filter109. The diplex filter107also provides a signal generated by the radio101(TX signal) to the antenna102for transmission. Although the radio101may be implemented to transmit signals, the present disclosure primarily concerns the receiver portion of the wireless communication system100. The band filter109filters the RX signal to determine whether the received signal RX is within a selected frequency band of interest. The band filtered RX signal is provided to the input of a low noise amplifier (LNA)111, which provides an amplified signal to the input of an RX modulator113. The RX modulator113mixes the received signal with a selected frequency level to provide a received signal r(t). The r(t) signal is provided to the input of an automatic gain control (AGC) circuit115, which adjusts the gain of the received signal by a gain signal g(t) from the baseband processor105. As described further below, the gain signal g(t) is set to a predetermined initial value and is continuously updated while a signal is being received to minimize variance of the received signal power relative to a target gain level.

A gain-controlled baseband received signal y(t) at the output of the AGC circuit115is provided to the input of the ADC103. It is appreciated that additional components may be provided in the receive path between the RX modulator113and the ADC103, such as, for example, an anti-aliasing low-pass filter or the like. The ADC103samples and converts the analog received signal y(t) to a digital or discrete-time received signal y[n], which is provided to respective inputs of a dynamic range determination circuit119and a channel estimation and gain prediction circuit121within the baseband processor105. The baseband processor105includes other circuitry (not shown) for converting the y[n] signal to information symbols and to convert the symbols into a bit-stream containing the transmitted information as understood by those of ordinary skill in the art. In the illustrated embodiment, the ADC103has a variable resolution control input receiving a resolution control (RC) signal for controlling the number of resolution bits used to convert y(t) to y[n]. The dynamic range determination circuit119monitors the y[n] signal and provides the RC signal to control the resolution of the ADC103. In one embodiment, the dynamic range determination circuit119sets the initial resolution of the ADC103to a relatively high resolution (e.g., highest resolution) to ensure detection of low power signals transmitted in the channel. The channel estimation and gain prediction circuit121adjusts the g(t) signal to adjust the power level of the y(t) signal towards a target power level. In response, the dynamic range determination circuit119adjusts the RC signal to reduce the resolution of the ADC103to reduce power consumption. The channel estimation and gain prediction circuit121receives the y[n] signal and provides a discrete-time gain signal g[n]. The gain signal g[n] comprises a series of gain values, each for a corresponding signal sample of the received signal y[n]. The g[n] signal is provided to the input of a digital to analog converter (DAC)123, which converts the g[n] signal to the g(t) signal to set the gain level of the AGC circuit115.

FIG. 2is a block diagram of the AGC circuit115implemented according to an exemplary embodiment. In the illustrated embodiment, the AGC circuit115includes a slow AGC circuit201having an input receiving the r(t) signal and an output coupled to the input of a fast predictive AGC circuit203. The output of the slow AGC circuit201is also fed back to its adjust input to perform conventional slow gain adjustment as understood by those of ordinary skill in the art. The AGC circuit201is typically designed to compensate for the slow fading (e.g., log-normal shadowing) of the received signal. The fast predictive AGC circuit203includes an adjust input receiving the g(t) signal to perform fast predictive gain control as described herein. In one embodiment, the fast predictive AGC circuit203multiplies the r(t) signal (gain adjusted by the slow AGC circuit201) by the gain signal g(t) to provide the y(t) signal. As described further below, the channel estimation and gain prediction circuit121uses temporal correlation statistics and estimates the channel power in the next time instance. Using the predicted power level and considering the channel prediction error statistics, the channel estimation and gain prediction circuit121controls the g(t) signal to set the gain level of the AGC circuit115to minimize the expected dynamic range of the y(t) signal provided to the ADC103. The resolution of the ADC103, therefore, is reduced as the gain is adjusted to reduce power consumption of the wireless communication system100.

The received signal r(t) is a continuous-time analog signal according to the following equation (1):
r(t)=h(t)s(t)+v(t)  (1)
where h(t) is the time-varying complex-valued narrowband wireless fading process representing the “gain” of the wireless channel, s(t) is the complex-valued transmitted signal, and v(t) is a complex Additive White Gaussian Noise (AWGN) process with mean zero and noise variance σv2. The channel estimation and gain prediction circuit121determines the appropriate value of g(t) which is applied to the received signal r(t) in order to minimize the dynamic range of the received signal y(t). The discrete-time received signal y[n], assuming ideal Nyquist sampling, is according to the following equation (2):
y[n]=g[n](h[n]s[n]+v[n])  (2)
where n is an index value (i.e., n=0, 1, 2, . . . ) such that the square brackets and index n signify the respective equivalent discrete-time sampled signal values.

FIG. 3is a block diagram of the channel estimation and gain prediction circuit121implemented according to an exemplary embodiment. The y[n] sample values are provided to an input of a channel predictor301, which determines corresponding values of the channel gain one symbol ahead, shown as ĥP[n+1], and which determines corresponding predicted error variance values, shown as σP2[n+1], for one symbol ahead. The channel predictor301is implemented according to a linear minimum mean-squared error (LMMSE), although other types of channel prediction processes are contemplated. The channel gain values ĥP[n+1] and the predicted error variance values σP2[n+1] are provided to a predictive AGC circuit303, which determines suitable corresponding gain values g[n+1] that are used to minimize the input dynamic range of the incoming received signal for the next symbol. Each gain value g[n+1] is provided to the input of a memory device305(represented as transform function Z−1), which outputs the gain values g[n]. The memory device305may be implemented as a register or one or a set of flip-flops or a set of latches or the like for temporarily storing each predicted gain value for approximately one symbol time. As shown inFIG. 1, the gain values g[n] are converted to the g(t) signal used to minimize the input dynamic range of the incoming received signal. The gain values g[n] are also provided to another input of the channel predictor301.

FIG. 4is a figurative diagram of the r(t) signal according to one embodiment. The r(t) signal is depicted as a sequence of information symbols “S” with pilot symbols SPinserted between every “P” set of symbols, where P is a positive integer greater than zero. Pilot symbols are known reference or periodic symbols in the transmitted signal. The baseband processor105attempts to identify and resolve the information symbols into a bit-stream as previously described. The collective set of pilot symbols, denoted as sp[nP], are known periodic symbols incorporated within the transmitted signal which may be used to estimate channel gain among other parameters. In one embodiment, the pilot symbols are sent at unit energy. The known pilot symbols, together with the known gain level, enables a least-squares (LS) estimation of the wireless channel at the pilot locations according to the following equation (3):

FIG. 5is a more detailed block diagram of the channel predictor301implemented according to an exemplary embodiment. The y[n] signal samples are provided to a down sampler501, which selects and provides signal samples y[nP] corresponding to the pilot symbols. In a similar manner, the g[n] gain values are provided to another down sampler503, which selects and provides gain values g[nP] corresponding to the pilot symbols. The y[nP] and g[nP] sample values are provided to respective inputs of a channel gain estimator505. A pilot symbol generator507provides corresponding known pilot symbols sp[nP] to another input of the channel gain estimator505. The channel gain estimator505operates according to equation (3) and provides least-squares channel gain estimates ĥLS[nP] at the pilot locations to respective inputs of a channel gain predictor509and a channel statistics estimator511. The channel statistics estimator511receives the channel gain estimates and estimates a temporal correlation function r[δ] and provides estimated values for a temporal autocorrelation matrix R and estimated values for a temporal cross-correlation vector rΔ to another input of the channel gain predictor509. As used herein, a signal descriptor in bold text represents multiple signal values, such as a vector or a matrix or the like. The channel gain predictor509receives the channel power estimates ĥLS[nP] and estimated temporal correlation statistics values and provides least-squares estimated predicted values of the channel gain ĥp[n+1] and predicted error variance values σp2[n+1] to the predictive AGC circuit303.

In the illustrated embodiment, the gain level of the channel one symbol ahead of the current symbol, or ĥp[n+1], is predicted using LMMSE by performing a linear combination of the available current and previous L least-squares estimates of the channel according to the following equation (4):

h^p⁡[n+1]=h^p⁡[m⁢⁢P+Δ]=∑l=0L-1⁢wΔ⁡[l]⁢h^LS⁡[(m-l)⁢P](4)
where m=└n/P┘ (in which the brackets “└ ┘” denote the floor operation, e.g., contents rounded down to nearest integer), Δ=n+1−mP, wΔ[l] defines the filter coefficients according to a weighting function, and L is a positive integer determining the filter length, i.e., the number of filter coefficients. The parameter “Δ” represents an index to the intermediate symbols between the pilot symbols. The LMMSE predictor coefficients are determined according to the following equation (5):
wΔ=(R+σv2I)−1rΔ(5)
in which wΔis an L-length prediction coefficients vector, R is an L×L temporal autocorrelation matrix as further described below, I is the L×L identity matrix as known to those skilled in the art (in which main diagonal values of matrix are unity values and remaining matrix values are zero), the power notation “−1” denotes matrix inversion, and rΔis an L-length temporal cross-correlation vector as further described below. The prediction coefficients vector wΔmay be written according to the following equation (6):
wΔ=[wΔ[0], . . . ,wΔ[L−1]]T(6)
in which the power notation “T” denotes the transpose function as understood by those skilled in the art. The temporal autocorrelation matrix R may be written according to the following equation (7):

The temporal correlation function r[δ] is defined according to the following equation (9):
r[δ]≡E[h[n]h*[n+δ]](9)
in which “E” denotes the expectation function. In the illustrated embodiment, the temporal correlation function r[δ] is estimated using a block of N least-squares channel estimates to determine temporal correlation estimated values at the pilot location spacing P, or r[0], r[P], r[2P], . . . , r[LP] according to the following equation (10):

The predictive AGC circuit303uses the predicted channel gain values ĥp[n+1] and the predicted error variance values σp2[n+1] to provide the predicted gain values g[n+1]. The predicted gain values g[n+1] are provided through the memory device305to provide the g[n] gain values, which are converted to the g(t) signal used to minimize the dynamic gain range of the ADC103. In one embodiment, the variance of the received signal power is minimized with respect to a target power level for the wireless communication system100, denoted as γ, according to the following equation (12):

ming⁢E⁡[(γ-y2)2❘h^p]=ming⁢E⁡[(γ-g2⁢h⁢⁢s+v2)2❘hp^]=minα⁢E⁡[(γ-α⁢h⁢⁢s+v2)2❘hp^](12)
in which the index “n+1” is dropped for brevity, the variable g is changed according to α g2, and “min” denotes the minimum function, and

E⁡[·❘hp^]
is the conditional expected value of the argument given the predicted channel value ĥp. Hence, equation (12) seeks to find the optimal value for α such that the conditional variance of the received signal power α|hs+v|2given the predicted channel value ĥpis minimized. The square value within the expectation function E is expanded, the derivative is taken with respect to α, and the result is set to zero to determine an optimal value for α, denoted αOPT, according to the following equation (13):

αOPT=γ⁣E⁡[h⁢⁢s+v2❘hp^]E⁡[h⁢⁢s+v4❘hp^](13)
If it is assumed that the transmitted symbols have normalized transmit energy (E[s2]=1), and that the random variables of the channel gain h, the transmitted symbols s, and the noise v are independent of each other, equation (13) is simplified according to the following equation (14):

αOPT=γ⁣E⁡[h2❘hp^]+σv2E⁡[h4❘hp^]+2⁢σv2⁢E⁡[h2❘hp^]+σv4(14)
If it is further assumed that each realization of the channel gain h is modeled as the predicted value ĥpperturbed by a zero-mean complex Gaussian prediction error term with vpwith variance given by the prediction error σp2according to the following equation (15):
h=ĥp+vp(15)
then equation (14) may further be simplified such that the random variable h|ĥpis complex Gaussian with non-zero mean given by the predicted channel value ĥpand variance σp2. These assumptions make h2given ĥpa non-central Chi-squared random variable with two degrees of freedom and non-centrality parameter ĥp2whose first two raw moments are given according to the following equations (16) and (17):

E⁡[h2❘hp^]=hp^2+σp2(16)E⁡[h4❘hp^]=hp^4+4⁢σp2⁢hp^2+2⁢σp4(17)
The results of equations (16) and (17) are substituted into corresponding parameters of equation (14) to obtain a simplified expression for ΔOPTaccording to the following equation (18):

In a practical system, a maximum allowable gain value gMAXis imposed on the fast predictive AGC circuit115due to amplifier limitations, so that the g[n+1] values are determined according to the following equation (19):

FIG. 6is a flowchart diagram illustrating operation of the wireless communication system100according to an exemplary embodiment. Various blocks and corresponding functions may be re-ordered or reorganized depending upon the particular implementation. At a first block601, initialization is performed including initialization of parameters for receiving a signal. For example, the g(t) signal may be set to an initial gain value or otherwise adjusted based on previously received signals. Also, the RC signal may be set to an initial value for setting the ADC103to an initial resolution. At next block602, the wireless communication system100receives and processes a signal s(t) transmitted through the wireless medium and received by the radio101into the received signal r(t). As previously described, such processing includes various filtering, amplifying and modulation functions to provide the received signal r(t). At next block603, the gain signal g(t) is applied to the received signal r(t) to generate the received signal y(t). In the illustrated embodiment, the fast predictive AGC circuit203amplifies the received signal r(t) by the gain signal g(t) to provide the received signal y(t). In one embodiment, the initial value of g(t) is set to an initial gain level to ensure detection of the transmitted signal by the radio101. In another embodiment, the initial value of g(t) is adjusted based on the last signal received by the radio101. As the received signal is processed through the wireless communication system100, the predicted gain signal g(t) converges towards a suitable gain level to properly compensate for the channel gain in order to reduce or otherwise minimize the variance of the power level of the received signal as compared to a target power level.

At next block605, the received signal y(t) is sampled to provide received signal samples y[n]. In the illustrated embodiment, the ADC103samples according to Nyquist criterion to convert the analog signal y(t) to the received signal samples y[n] for processing by the baseband processor105. At next block606, the RC signal is adjusted based on the signal samples y[n]. It is noted that the dynamic range determination circuit119may operate independently with respect to the channel estimation and gain prediction circuit121. In general, as the g(t) signal converges to the target gain, the RC signal is adjusted to reduce resolution of the ADC103thereby reducing power consumption. At next block607, the channel gain h is estimated using the signal samples y[n]. In the illustrated embodiment, channel estimation and prediction is performed within the channel predictor301. As shown inFIG. 4, the received signal r(t) includes known pilot symbols at known pilot locations (e.g., spacing of every P symbols) to enable an estimation of the channel gain. In the illustrated embodiment, the channel gain estimator505uses the signal samples y[nP] and corresponding gain values g[nP] at the pilot locations along with the known pilot symbols sp[nP] and employs a least-squares estimate to determine estimated channel gain values ĥLS[nP] at the pilot locations in accordance with equation (3) as previously described.

At following blocks609,611,613,615and617, temporal correlation statistics are applied to the estimated channel gain to predict channel gain at a subsequent time instance. In the illustrated embodiment, LMMSE prediction is applied to the estimated channel gain values to perform the prediction. At blocks609and611, a temporal correlation function is estimated using the estimated channel gain values. At block609, temporal autocorrelation values are estimated first, and at block611, estimated cross-correlation values are determined by interpolating between the temporal autocorrelation values. In the illustrated embodiment, the channel statistics estimator511operates according to equation (10) to determine the estimated temporal autocorrelation values (block609) and further performs the interpolation to determine the estimated cross-correlation values (block611). At next block613, predictor coefficients are determined using the estimated temporal autocorrelation values and the estimated cross-correlation values. At next block615, the estimated channel gain values are linearly combined with the predictor coefficients to determine the predicted channel gain values. At next block617, the estimated temporal autocorrelation values and the estimated cross-correlation values are combined to determine the predicted error variance values. In the illustrated embodiment, the channel gain predictor509receives the temporal correlation values from the channel statistics estimator511and operates according to equations (5)-(8) to determine the predictor coefficients wΔ. The channel gain predictor509then operates according to equations (4) and (6) to linearly combine the predictor coefficients with the estimated channel gain values to provide the predicted channel gain values ĥp[n+1]. The channel gain predictor509further operates in accordance with equation (11) to determine the predicted error variance values σp2[n+1] by combining the estimated temporal autocorrelation values and the estimated cross-correlation values.

At next block619, the predicted channel gain values and the predicted error variance values are used to determine the predicted gain values g[n+1]. In the illustrated embodiment, the predictive AGC circuit303operates in accordance with equation (19) using the predicted channel gain values ĥp[n+1], the predicted error variance values σp2[n+1], a predetermined target gain value γ and the maximum gain value gMAXto determine predicted gain values g[n+1]. The predicted gain values g[n+1] are calculated to minimize the difference between the estimated power of the received signal (as attenuated through the channel based on channel gain) and the target gain. The target value γ and maximum gain value gMAXare determined based on the particular implementation of the wireless communication system100including the implementations of the AGC circuit115, the ADC103, and baseband processor105. At next block621, the predicted gain values g[n+1] are stored for a signal sample time (used by the ADC103) to provide the “current” gain values g[n], such as illustrated by the memory device305. In one embodiment, the memory device305is synchronized with the ADC103and the DAC123to synchronize between the predicted values, the current values, and the applied gain values. At next block623, the gain values g[n] are converted to the gain signal g(t), such as illustrated by the DAC123. The determined gain signal g(t) is applied to the received signal r(t) to convert to the received signal y(t) at block603. At next block625, it is queried whether the signal r(t) currently being received is completed. If not, operation loops back to block602and operation continues to loop between blocks602-625while the signal is being received. In general, each loop iteration corresponds to each received signal sample y[n]. When the current signal r(t) is completed as determined at block625, operation returns to block601to prepare for the next signal.

The channel estimation and gain prediction circuit121has been described according to a single carrier communication system. The concepts described herein may be applied in similar manner to a multi-carrier communication system. In an orthogonal frequency-division multiplexing (OFDM) system operating in a frequency selective multi-path fading channel, the received signal within the kth OFDM symbol may be modeled according to the following equation (20):

y⁡[n]=g⁡[k]⁢(∑l=1L⁢h⁡[l,k]⁢s⁡[n-Nl]+v⁡[n])(20)
in which Nsymbk n Nsymb(k+1) and where h[l,k], l=1, . . . , L are the complex channel taps that are assumed statistically independent across taps l and constant across the kth OFDM symbol, g[k] is the gain value also assumed constant across the symbol, Nl is the discretized time delay for the lth tap, and Nsymbis the OFDM symbol length. The Nsymbsamples are collected into a vector y[k] according to the following equation (21):
y[k]=[y[Nsymbk], . . . , y[Nsymb(k+1)−1]]T(21)
and the received signal power for an OFDM symbol is given as the squared norm of the vector y[k], i.e., y[k]]2. The channel predictor301is modified to predict each of the L taps that characterize the channel one OFDM symbol ahead using pilot signals in both frequency and time dimensions, giving a predicted channel value per tap l ĥp[l,k+1] with the corresponding predicted error variances per tap {circumflex over (σ)}p2[l,k+1], which are collected into vectors according to the following equations (22) and (23):
ĥp[k+1]=[ĥp1,k+1], . . . , ĥp[L,k+1]]T(22)
{circumflex over (σ)}p[k+1]={circumflex over (σ)}p2[1,k+1], . . . , {circumflex over (σ)}p2[L,k+1]]T(23)
The equations (12)-(19) are modified by replacing the signal power terms y2with the squared norm terms y2, and similarly the channel power terms h2and ĥp2with the squared norm counterparts h2and ∥ĥp∥2, respectively.

A method of fast predictive automatic gain control according to one embodiment includes estimating channel gain applied to a received signal, predicting channel gain at a subsequent time by applying temporal correlation statistics to the estimated channel gain, determining a predicted receiver gain which reduces variance between the predicted channel gain and a predetermined target power level, and applying the predicted receiver gain to the received signal. In one embodiment, predicting channel gain may include applying linear minimum mean-squared error (LMMSE) prediction to the estimated channel gain. In another embodiment, the method may include predicting error variance at the subsequent time by applying the temporal correlation statistics to the estimated channel gain and combining the predicted channel gain and the predicted error variance.

The method may include estimating channel gain of pilot symbols within the received signal, estimating a temporal correlation function using the estimated channel gain of the pilot symbols, and determining predicted channel gain using the estimated channel gain of the pilot symbols and the estimated temporal correlation function. In one embodiment, the method may further include using estimated channel gain values of the pilot symbols to determine temporal autocorrelation values, interpolating between the temporal autocorrelation values to determine temporal cross-correlation values, determining predictor coefficients using the temporal autocorrelation values and the temporal cross-correlation values, and linearly combining the estimated channel gain values of the pilot symbols and the predictor coefficients to determine predicted channel gain values.

The method may further include combining the temporal autocorrelation values and the temporal cross-correlation values to determine corresponding predicted error variance values, and combining the predicted channel gain values and the predicted error variance values to determine corresponding predicted receiver gain values. The method may further include sampling the received signal to provide signal samples, down sampling the signal samples at pilot locations to provide pilot symbol samples, storing the predicted receiver gain values to provide current gain values, down sampling the current gain values at the pilot locations to provide pilot gain values, and combining the pilot symbol samples, the pilot gain values, and known pilot symbols. The method may further include converting the current gain values to a gain signal, and amplifying the received signal by the gain signal.

A channel estimation and gain prediction system for a receiver for fast predictive automatic gain control according to one embodiment includes a channel gain estimator, a channel gain predictor, a predictive gain controller, and a gain circuit. The channel gain estimator estimates channel gain applied to a received signal and determines an estimated channel gain. The channel gain predictor predicts channel gain at a subsequent time by applying temporal autocorrelation statistics to the estimated channel gain to provide a predicted channel gain. The predictive gain controller uses the predicted channel gain to determine a predicted receiver gain to reduce variance between the predicted channel gain and a target power level. The gain circuit applies the predicted receiver gain to the received signal.

In one embodiment, the channel gain predictor determines a predicted error variance, where the predictive gain controller uses the predicted channel gain and the predicted error variance to determine the predicted receiver gain. In another embodiment, the channel gain estimator determines a least-squares estimate of the channel gain of known pilot symbols within the received signal. In another embodiment, the channel gain predictor applies linear minimum mean-squared error (LMMSE) prediction to the estimated channel gain to determine the predicted channel gain.

The channel estimation and gain prediction system may further include a variable range analog to digital converter (ADC) and a dynamic range determination circuit. The variable resolution ADC has a first input receiving the received signal, a second input receiving a resolution control signal, and an output providing received signal samples. The dynamic range determination circuit has an input receiving the received signal samples and an output providing the resolution control signal. The dynamic range determination circuit controls the resolution control signal to reduce resolution of the variable resolution ADC based on dynamic range of the received signal samples.

The channel estimation and gain prediction system may further include a memory device and a digital to analog converter (DAC). The predictive gain controller has an output providing predicted receiver gain values. The memory device has an input receiving the predicted receiver gain values and an output providing current receiver gain values. The DAC has an input receiving the current receiver gain values and an output providing a gain signal. In this case, the gain circuit has a first input receiving the received signal, a second input receiving the gain signal, and an output providing a gain-controlled received signal.

The received signal may include known periodic symbols. In one embodiment the channel estimation and gain prediction system may further include an ADC having an input receiving the received signal and an output providing received signal samples, and a memory device having an input receiving the predicted receiver gain and an output providing current receiver gain values. In this case the channel gain estimator has a first input receiving the received signal samples, a second input receiving the current receiver gain values, and an output providing estimated channel gain values based on the known periodic symbols, the received signal samples at periodic symbol locations, and the current receiver gain values corresponding to the periodic symbol locations.

The channel estimation and gain prediction system may further include a channel statistics estimator having an input receiving the estimated channel gain and an output providing temporal correlation values. In this case the channel gain predictor has a first input receiving the estimated channel gain, a second input receiving the temporal correlation values, and a first output providing predicted channel gain values based on the estimated channel gain and the temporal correlation values. The channel gain predictor may further have a second output providing predicted error variance values. The predictive gain controller may have a first input receiving the predicted channel gain values, a second input receiving the predicted error variance values, and an output providing predicted receiver gain values.

A receiver according to another embodiment includes a radio, an ADC, a channel estimation and gain prediction circuit, and a DAC. The radio receives and converts a transmitted signal into a received signal and amplifies the received signal by a predictive gain signal to provide a gain-controlled received signal. The ADC has an input receiving the gain-controlled received signal and an output providing received signal samples. The channel estimation and gain prediction circuit includes a channel predictor, a predictive gain controller, and a memory device. The channel predictor has a first input receiving the received signal samples, a second input receiving current gain values, a first output providing predictive channel gain values and a second output providing predictive error variance values. The predictive channel gain values and the predictive error variance values are determined using a temporal correlation function. The predictive gain controller has a first input receiving the predictive channel gain values, a second input receiving the predictive error variance values, and an output providing predictive gain values. The memory device has an input receiving the predictive gain values and an output providing the current gain values. The DAC has an input receiving the current gain values and an output providing the predictive gain signal.

The ADC of the radio may be a variable resolution ADC having a resolution adjust input. In this case, the receiver may further include a dynamic range determination circuit having an input receiving the received signal samples and an output providing a resolution control signal to the adjust input of the variable resolution ADC. The dynamic range determination circuit controls the resolution control signal to reduce resolution of the variable resolution ADC based on dynamic range of the received signal samples.

The channel predictor of the receiver may include a channel gain estimator, a channel statistics estimator, and a channel gain predictor. The channel gain estimator has a first input receiving the received signal samples, a second input receiving the current gain values, a third input receiving known pilot symbols, and an output providing channel gain estimate values. The channel statistics estimator has an input receiving the channel gain estimate values and an output providing temporal correlation values. The channel gain predictor has a first input receiving the channel gain estimate values, a second input receiving the temporal correlation values, a first output providing the predictive channel gain values and a second output providing the predictive error variance values.

Although the invention is described herein with reference to specific embodiments, various modifications and changes can be made without departing from the scope of the present invention as set forth in the claims below. For example, the illustrated embodiment shows prediction one symbol ahead where it is understood that prediction may be performed for other future times, such as up to any number of future symbol times. The present invention is illustrated for a single carrier system but applies to multiple carrier systems. Although the illustrated embodiment shows a variable resolution ADC, embodiments employing a fixed resolution ADC are contemplated as well. It should be understood that all circuitry or logic or functional blocks described herein may be implemented either in silicon or another semiconductor material or alternatively by software code representation of silicon or another semiconductor material. Accordingly, the specification and figures are to be regarded in an illustrative rather than a restrictive sense, and all such modifications are intended to be included within the scope of the present invention. Any benefits, advantages, or solutions to problems that are described herein with regard to specific embodiments are not intended to be construed as a critical, required, or essential feature or element of any or all the claims.