Weight prediction for closed-loop mode transmit diversity

Techniques for predicting weights used for closed-loop transmit diversity. In a channel prediction scheme, channel gains for multiple transmit antennas are initially estimated (e.g., based on pilots received from these antennas) and used to derive predicted channel gains for a future time instant. The predicted channel gains are then used to derive predicted weights that are deemed to be “optimal” at the future time instant. Optimality may be determined based on one or more criteria, such as maximizing a received SNR for the received signals. In a weight prediction scheme, the channel gains for the multiple antennas are estimated and used to compute optimal weights for the current time instant. The current optimal weights are then used to predict the optimal weights at the future time instant. For both schemes, the prediction may be performed based on an adaptive filter (e.g., LMS or RLS filter) or a non-adaptive filter.

BACKGROUND

The present invention relates generally to data communication, and more specifically to techniques for predicting weights used for closed-loop transmit diversity in wireless communication systems.

In a wireless communication system, data to be transmitted is first modulated onto a radio frequency (RF) carrier signal to generate an RF modulated signal that is more suitable for transmission over a wireless channel. The transmitted RF modulated signal may reach a receiver via a number of propagation paths. The characteristics of the propagation paths may vary over time due to various factors such as, for example, fading and multipath. Consequently the transmitted RF modulated signal may experience different channel conditions and may be associated with different complex channel gains over time.

To provide diversity against deleterious path effects and improve reliability, multiple transmit antennas and/or multiple receive antennas may be used for data transmission. Transmit diversity is achieved by the use of multiple antennas to transmit data, and receive diversity is achieved by the use of multiple antennas to receive a data transmission. A transmission channel is formed between each of the transmit antenna(s) and each of the receive antenna(s). If the transmission channels for different transmit/receive antenna pairs are linearly independent (i.e., a transmission on one channel is not formed as a linear combination of the transmissions on the other channels), which is generally true to at least an extent, then diversity increases and the likelihood of correctly receiving a data transmission improves as the number of antennas increases.

For costs and other considerations, some wireless communication systems employ multiple antennas at a base station and a single antenna at a terminal for data transmission. On the downlink, transmit diversity may be achieved by transmitting data redundantly on multiple RF modulated signals from the multiple base station antennas redundantly on multiple RF modulated signals from the multiple base station antennas to the single terminal antenna. These signals typically experience different channel conditions and may be associated with different channel gains. Consequently, these signals typically arrive at the terminal antenna with different phases and amplitudes, and may add constructively or destructively.

A control loop may be maintained to determine weights to be applied to the multiple RF modulated signals, at the base station, such that these signals maximally combine at the terminal. The control loop would estimate the complex channel gain (which is also referred to as fading coefficient) between each of the multiple antennas at the base station and the single antenna at the terminal. The control loop would then determine the “optimal” weights for the RF modulated signals based on the estimated channel gains for the multiple base station antennas. The weights are then applied to the RF modulated signals prior to transmission from the base station antennas. By adjusting the phase and possibly the amplitude of the transmitted RF modulated signals, the received signals at the terminal can be assured to add constructively, and improved performance may then be achieved.

The performance of a closed-loop transmit diversity scheme, such as the one described above, is dependent on the optimality of the weights at the time that they are applied. Unfortunately, any closed-loop transmit diversity scheme will exhibit some amounts of delay between the time that the weights are computed to the time that they are applied. If the channel condition is not static or stationary during this entire delay (e.g., due to movement by the terminal), then the weights that may have been optimal at the time that they are computed may be far from optimal at the time that they are applied. This would then degrade performance.

There is therefore a need in the art for techniques for predicting weights used for closed-loop transmit diversity in wireless communication systems.

SUMMARY

Techniques are provided herein for predicting weights that are “optimal” at the time that they are applied to the RF modulated signals, instead of being optimal at the time that they are computed. These techniques may provide improved performance for non-stationary wireless channels (e.g., due to Doppler shifts caused by movement of the terminal).

The weights may be predicted using various schemes. In a channel prediction scheme, the channel gains for multiple transmit antennas are initially estimated (e.g., based on pilots received from these antennas) and used to derive predicted channel gains for a future time instant. The predicted channel gains are then used to derive predicted weights that are deemed to be optimal at the future time instant. Optimality may be determined based on one or more criteria, such as maximizing a received signal-to-noise ratio (SNR) for the received signals. In a weight prediction scheme, the channel gains for the multiple antennas are estimated and used to compute the optimal weights for the current time instant. The current optimal weights are then used to predict the optimal weights at the future time instant. For both schemes, the prediction may be performed based on an adaptive filter or a non-adaptive filter. These schemes are described in further detail below.

Various aspects and embodiments of the invention are also described in further detail below.

DETAILED DESCRIPTION

The techniques described herein for predicting weights may be used for various closed-loop transmit diversity schemes and for various wireless communication systems. In general, these techniques may be used for any closed-loop transmit diversity scheme that has some inherent delay between the computation of the weights and their application. For clarity, these techniques are described specifically for the downlink in a W-CDMA communication system (i.e., a CDMA system that implements W-CDMA standard, which is known in the art).

FIG. 1shows a downlink data transmission from a base station110to a terminal120in a W-CDMA system. A base station is generally a fixed station that is used for communicating with the terminals, and may also be referred to as a Node B (in W-CDMA), an access point, or some other terminology. A terminal is a fixed or mobile station that can communicate with the base station, and may also be referred to as a user equipment (UE) (in W-CDMA), a mobile station, a remote station, an access terminal, a wireless communication device, or some other terminology.

W-CDMA supports a “closed loop mode transmit diversity” scheme that uses two antennas at the base station for data transmission on the downlink. One antenna is referred to as the reference antenna, and the other antenna is referred to as the diversity antenna.

The closed loop mode transmit diversity scheme in W-CDMA has two modes of operation—mode1and mode2. In mode1, the phase of the RF modulated signal transmitted from the diversity antenna is adjusted at the base station so that the RF modulated signals from both the reference and diversity antennas are inphase and maximally combined at the terminal. In mode2, both the phase and the amplitude of the RF modulated signal transmitted from the diversity antenna are adjusted so that the two RF modulated signals are inphase and maximally combined at the terminal.

To achieve the maximal combining at the terminal antenna, the terminal determines the optimal weights for the two RF modulated signals. Each weight is typically a complex value. For W-CDMA, the two weights are normalized so that only one normalized weight needs to be sent back to the base station. The weight sent by the terminal indicates only phase adjustment in mode1, and both phase and amplitude adjustments in mode2.

FIG. 2shows a block diagram of the processing at base station for the closed loop mode transmit diversity defined by W-CDMA. The traffic data to be transmitted by the base station is provided to a complex multiplier212, which spreads and scrambles the traffic data with a data spread/scramble code that is formed by a combination of a particular orthogonal variable spreading factor (OVSF) code and a particular scrambling code. The spreading channelizes the traffic data onto a particular physical channel that is associated with the particular OVSF code. The scrambling spectrally spreads the channelized data over the entire operating bandwidth of the system. The complex-valued data from multiplier212is then provided to two multipliers214aand214b, which respectively receive two complex-valued weight factors W1and W2for the two transmit antennas. The weight factors W1and W2are determined based on the weight feedback received from the terminal. Each multiplier214performs complex multiplication of its received data with the associated weight factor to provide complex-valued weighted data.

For W-CDMA, a pilot is sent on a common pilot channel (CPICH) for all terminals in the system. This common pilot is generated based on a first data pattern (i.e., pilot 1 data) for the reference antenna and a second data pattern (i.e., pilot 2 data) for the diversity antenna, where the two data patterns are orthogonal to one another. Thus, pilot 1 data and pilot 2 data are provided to multipliers222aand222b, respectively. Each multiplier222spreads and scrambles its received pilot data with a pilot spread/scramble code to form pilot symbols for the associated antenna. The pilot symbols for each antenna may be used by the terminals to estimate the channel gain for that antenna.

The weighted data from multiplier214ais then combined (or multiplexed) with the pilot symbols from multiplier222a, and the weighted data from multiplier214bis combined with the pilot symbols from multiplier222b. The complex-valued data streams from combiners216aand216bare then provided to, and processed by, transmitter units (TMTRs)218aand218b, respectively, to provide two RF modulated signals that are then transmitted from antennas112aand112b, respectively.

FIG. 3shows a timing diagram for the closed loop mode transmit diversity in W-CDMA. The CPICH is transmitted by the base station and includes orthogonal pilots for the two antennas. In particular, the pilot for the reference antenna is a sequence of 10 pilot symbols defined as {A, A, A, A, A, A, A, A, A, A}, and the pilot for the diversity antenna is an orthogonal sequence of 10 pilot symbols defined as {A, −A, −A, A, A, −A, −A, A, A, −A }, where A=1+j. The two pilot symbol sequences may be viewed as being generated by two data patterns. The two pilot symbol sequences are transmitted from the reference and diversity antenna in each slot, which has a duration of ⅔ msec in W-CDMA.

The terminal receives the CPICH, processes the pilot symbols, and determines the weights to use for transmit diversity. The weights are processed, compressed, quantized, and sent back to the base station in a feedback information (FBI) field of an uplink dedicated physical control channel (DPCCH). The base station receives the weight feedback, computes the weight factors W1and W2for the two antennas based on the weight feedback, and applies the weight factors at the start of a pilot field in the downlink DPCCH in either the next slot (if a 1-slot delay is specified) or the following slot (if a 2-slot delay is specified). The system determines whether 1-slot or 2-slot delay is to be used, and the terminal has knowledge of this delay.

The closed loop mode transmit diversity in W-CDMA is described in detail in 3GPP TS 25.214, entitled “Physical Layer Procedures (FDD),” which is publicly available and incorporated herein by reference.

In any closed-loop transmit diversity scheme, such as the one supported by W-CDMA and shown inFIGS. 2 and 3, there will inherently be some delay from the time that the weights are computed (e.g., by the receiver) to the time that the weights are applied by the transmitter. For W-CDMA, the delay is approximately one or two slots (which is selectable by the system). This delay includes (1) processing delay at the terminal to determine the weight feedback, (2) propagation delay to send the weight feedback, and (3) processing delay at the base station to process and apply the weight feedback.

The delay in the closed-loop transmit diversity scheme may cause significant degradation in performance under certain situations. For example, moderate to higher Doppler shifts caused by movement of the terminal can result in significant changes in the propagation paths between the two transmit antennas and the receive antenna. In this case, although the weights may have been optimal when computed for a particular channel condition, they may be far from optimal at the time that they are applied because the channel may have changed considerably.

The techniques described herein can provide improved performance, especially for a non-stationary wireless channel, by predicting weights that are optimal at the time that they are applied to the RF modulated signals, instead of being optimal at the time that they are computed. As described in further detail below, the weights are computed based on estimates of the channel gains (or fading coefficients) between the two transmit antennas at the base station and the single receive antenna at the terminal.

The optimal weights may be predicted using various schemes. In a channel prediction scheme, the channel gains for the two transmit antennas are initially estimated and used to derive predicted channel gains for a future time instant. The predicted channel gains are then used to derive the predicted weights that are deemed to be optimal at the future time instant. In a weight prediction scheme, the channel gains for the multiple antennas are estimated and used to compute the optimal weights for the current time instant. The current optimal weights are then used to predict the optimal weights at the future time instant. Both of these schemes are described in further detail below.

FIG. 4Ashows a block diagram of the processing at a terminal120afor the channel prediction scheme. Terminal120ais one embodiment of terminal120inFIG. 1. The two downlink RF modulated signals transmitted by the base station are received by antenna122, and the signal from antenna122is processed by a receiver unit (RCVR)412to provide a stream of samples. The samples are then provided to a descrambler/despreader414and descrambled and despread with a pilot descramble/despread code that is complementary to the pilot spread/scramble code used at the base station.

The despread pilot symbols from unit414are then provided to multipliers416aand416b. Multiplier416areceives and multiplies the conjugate of the pilot symbols for the reference antenna (i.e., conjugated pilot 1 data) with the despread pilot symbols from unit414to provide “de-patterned” pilot symbols p0(t) for the reference antenna. Similarly, multiplier416breceives and multiplies the conjugate of the pilot symbols for the diversity antenna (i.e., conjugated pilot 2 data) with the despread pilot symbols from unit414to provide de-patterned pilot symbols p1(t) for the diversity antenna. The de-patterned pilot symbols p0(t) and p1(t) may be expressed as:
p0(t)=s0(t)·h0(t)+n0(t), and  Eq(1)
p1(t)=s1(t)·h1(t)+n1(t),
where s0(t) and s1(t) are the pilot symbols transmitted from the reference and diversity antennas, respectively;h0(t) and h1(t) are the channel gains or fading coefficients that are indicative of the complex gains for the transmission channels between the reference and diversity antennas, respectively, and the terminal antenna; andn0(t) and n1(t) are the noise associated with the transmission channels for the reference and diversity antennas, respectively.

A processing unit420athen receives and operates on the de-patterned pilot symbols p0(t) and p1(t), in accordance with the channel prediction scheme, to provide predicted weights {tilde over (w)}cp,0(t+Δ) and {tilde over (w)}cp,1(t+Δ) for the reference and diversity antennas, respectively. The weights {tilde over (w)}cp,0(t+Δ) and {tilde over (w)}cp,1(t+Δ) are predicted to be optimal at a future time instant t+Δ, which is Δ seconds from the current time instant t. The delay Δ is the known delay between the current time instant (which is the time associated with the more recent de-patterned pilot symbols p0(t) and p1(t) that are used to derive the predicted weights) and the future time instant when the predicted weights are applied at the base station. The current time instant may not be the time the weights are computed, since there may be a delay from the most recent pilot symbols to the time the weight computation is performed. However, for simplicity, the description throughout assumes that the weight computation occurs at (or coincide with) the most recent de-patterned pilot symbols (i.e., the weight computation is performed at time t). For W-CDMA, Δ is between one and two slots for the closed loop mode transmit diversity.

In the embodiment shown inFIG. 4A, for the channel prediction scheme, processing unit420aincludes prediction filters422aand422band a weight computation unit424. Prediction filter422areceives and processes the de-patterned pilot symbols p0(t) to provide a predicted channel gain {tilde over (h)}0(t+Δ) for the reference antenna. Similarly, prediction filter422breceives and processes the de-patterned pilot symbols p1(t) to provide a predicted channel gain {tilde over (h)}1(t+Δ) for the diversity antenna. The predicted channel gains {tilde over (h)}0(t+Δ) and {tilde over (h)}1(t+Δ) are the gains predicted for transmission channels associated with the reference and diversity antennas at the future time instant t+Δ (instead of the current time instant t). Weight computation unit424then computes the predicted weights {tilde over (w)}cp,0(t+Δ) and {tilde over (w)}cp,1(t+Δ) based on the predicted channel gains {tilde over (h)}0(t+Δ) and {tilde over (h)}1(t+Δ).

Prediction filters422aand422bmay be implemented with any filter that can predict future channel gains based on current noisy pilot symbols. Each prediction filter422may be implemented with an adaptive filter or a non-adaptive filter. Examples of adaptive filters include least mean square (LMS) filter, recursive least square (RLS) filter, Kalman filter, and so on. Adaptive and non-adaptive filters may be implemented using an infinite impulse response (IIR) filter, a finite impulse response (FIR) filter, or some other filter structure. Adaptive filters can track changes in the wireless channel based on statistics derived from the filter inputs. Non-adaptive filters normally need to be provided with additional information that characterizes, models, and/or predicts the channel.

FIG. 4Bshows a block diagram of an embodiment of a prediction filter422x, which may be used for each of prediction filters422aand422binFIG. 4A. Prediction filter422xincludes an estimation filter432coupled to a prediction filter434.

Estimation filter432receives and processes de-patterned pilot symbols pi(t) for a particular antenna i, where iε {0, 1}, to provide an estimate of the channel gain ĥi(t) for the transmission channel associated with that antenna. Estimation filter432may be implemented as an IIR filter, a FIR filter, or some other filter. The characteristics of the estimation filter may be selected to pass the desired signal with as little distortion as possible and to suppress as much noise as possible. Estimation filter432may also be implemented with any other type of filter that can estimate the channel gain based on the noisy de-patterned pilot symbols.

Prediction filter434receives and processes the estimated channel gain ĥi(t) for the current time instant t to provide the predicted channel gain {tilde over (h)}i(t+Δ) for the future time instant t+Δ. Prediction filter434may be implemented as an adaptive filter or a non-adaptive filter and with an IIR, FIR, or some other filter structure. For an adaptive filter, the LMS, RLS, or some other adaptive algorithm may be used to adapt the filter. In a specific embodiment, prediction filter434is implemented as an RLS filter. The predicted channel gain {tilde over (h)}i(t+Δ) may then be computed as follows:
ki(t)=λ−1Pi(t−1)ĥi(t),  Eq(2b)
αi(t)=1−kiH(t)ĥi(t),  Eq(2b)

g_i⁡(t)=k_i⁡(t)αi⁡(t),Eq⁢⁢(2⁢c)
Pi(t)=λ−1Pi(t−1)−gi(t)kiH(t),  Eq(2d)
ei(t)=ĥi(t)−ciH(t−1)ĥi(t),  Eq(2e)
ci(t)=ci(t−1)+gi(t)ei*(t),  Eq(2f)
{tilde over (h)}i(t+Δ)=ciH(t)ĥi(t+Δ),  Eq(2g)
whereĥi(t) is an N×1 vector of prior estimated channel gains (i.e.,ĥi(t)=[ĥi(t−Δ) ĥi(t−Δ−1) . . . ĥi(t−Δ−N+1)]T);Pi(t) is an N×N inverse correlation matrix that is initialized asPi(Δ+N−1)=δ−1I, where δ is a small positive value andIis the identity matrix;ki(t) is an N×1 vector for the adaptation gain for a priori RLS filter;gi(t) is an N×1 vector for the adaptation gain for a posteriori RLS filter;ei(t) is a priori error;αi(t) is a conversion factor;ci(t) is an N×1 vector of coefficients used to compute the predicted channel gain and is initialized to all zeros, orci(Δ+N−1)=0;λ is a memory factor for the channel, which may be set to value between zero and one (i.e., 0<λ≦1), where a small value may be used for a fast changing channel;N is the number of estimated channel gains used to derive the predicted channel gain; and“T” denotes a transpose, “*” denotes a conjugate, and “H” denotes a Hermitian or conjugate transpose.

In equation set (2), the first six equations (2a) through (2f) are for the RLS filter that is used to derive the coefficient vectorci(t), and the last equation (2g) is the computation to derive the predicted channel gains {tilde over (h)}i(t+Δ), for iε {0, 1}. The RLS filter may be updated whenever new estimated channel gains ĥi(t) are available, which may be for each pair of pilot symbols received for the two transmit antennas. The channel gain computation in equation (2g) may be performed whenever the predicted channel gains are needed, which may be whenever the predicted weights are needed. In general, the updating of the RLS filter and the predicted channel gain computation may be performed at the same or different rates.

For W-CDMA, the weight computation (and thus the predicted channel gain computation) is typically performed for each slot, in which case t may be an index for slots. In one embodiment, one pair of estimated channel gains ĥ0(t) and ĥ1(t) is derived for each slot by the estimation filters based on all de-patterned pilot symbols received for that slot. In another embodiment, the RLS filter may be updated for each pair of de-patterned pilot symbols. For this embodiment, the indices for the equations in equation set (2) may be revised accordingly. For simplicity, the updating of the RLS filter and the predicted channel gain computation are assumed to be performed at the same rate.

As shown in equation set (2), for the RLS filter, the cross-correlation between N prior estimated channel gains, from ĥi(t−Δ) to ĥi(t−Δ−N+1), is determined and accumulated in the correlation matrixPi(t). The coefficient vectorci(t) is then updated based on the vectorĥi(t) of prior estimated channel gains, the correlation matrixPi(t), the current estimated channel gain ĥi(t), and the prior coefficient vectorci(t−1). For the channel gain computation, the predicted channel gain {tilde over (h)}i(r+Δ) is computed based on the current coefficient vectorci(t) and the vectorĥi(t+Δ) of N most recent estimated channel gains, from ĥ1(t) to ĥi(t−N+1).

The RLS algorithm is described in further detail by D. G. Manolakis et al. in “Statistical and Adaptive Signal Processing,” 1st edition, 2000, McGraw-Hill.

Referring back toFIG. 4A, weight computation unit424receives the predicted channel gains {tilde over (h)}0(t+Δ) and {tilde over (h)}1(t+Δ) from prediction filters422aand422b, respectively. Unit424then computes the weights {tilde over (w)}cp,0(t+Δ) and {tilde over (w)}cp,1(t+Δ), which are predicted to be optimal at the future time instant t+Δ when the weights are applied at the base station. Optimality may be determined based on one or more criteria. In an embodiment, the optimal weights are the weights that would result in the highest received signal-to-noise ratio (SNR) for the received signals at the terminal. The computation for the predicted optimal weights may then be expressed as:

w~cp,0⁡(t+Δ)=h~0⁡(t+Δ)2h~0⁡(t+Δ)2+h~1⁡(t+Δ)2,andEq⁢⁢(3⁢a)w~cp,1⁡(t+Δ)=h~1⁡(t+Δ)2h~0⁡(t+Δ)2+h~1⁡(t+Δ)2·ⅇj⁢⁢θ⁡(t+Δ),Eq⁢⁢(3⁢b)
where |{tilde over (h)}0(t+Δ)|2is the squared magnitude of the predicted channel gain {tilde over (h)}0(t+Δ) for the reference antenna (i.e., |{tilde over (h)}0(t+Δ)|2={tilde over (h)}0(t+Δ){tilde over (h)}0*(t+Δ));|{tilde over (h)}1(t+Δ)|2is the squared magnitude of the predicted channel gain {tilde over (h)}1(t+Δ) for the diversity antenna (i.e., |{tilde over (h)}1(t+Δ)|2={tilde over (h)}1(t+Δ){tilde over (h)}1*(t+Δ)); andθ(t+Δ) is the angle between the two predicted weights, which can be expressed as:

Referring back toFIG. 1, for the downlink, two RF modulated signals are transmitted from two antennas at the base station. Due to artifacts (e.g., buildings, trees, and so on) in the wireless channel, each RF modulated signal may reach the antenna at the terminal via multiple propagation paths. The signal at the terminal antenna may thus include multiple instances (or multipath components) of each transmitted RF modulated signal. Each multipath component corresponds to a specific RF modulated signal that is received via a specific propagation path.

For a CDMA system, a rake receiver is often used to process a number of multipath components of each RF modulated signal of interest. The rake receiver typically includes a number of demodulation elements (often referred to as “fingers”). Each finger may be assigned to process a specific multipath component of a specific RF modulated signal, which may be selected based on received signal strength. The demodulated data from all assigned fingers are then combined to obtain an improved estimate of the transmitted data.

Each assigned finger may be operated as described above to provide a pair of predicted channel gains {tilde over (h)}0,j(t+Δ) and {tilde over (h)}1,j(t+Δ) for the two antennas for the j-th multipath component assigned to that finger. The predicted weight {tilde over (w)}cp,0(t+Δ) and {tilde over (w)}cp,1(t+Δ) may then be computed based on the predicted channel gains for all assigned fingers such that, for example, the highest received SNR is achieved at the terminal for all assigned multipath components. The computation for the predicted weights based on the predicted channel gains for M assigned multipath components may then be expressed as:

w~cp,0⁡(t+Δ)=h_~0⁡(t+Δ)h_~0⁡(t+Δ)+h_~1⁡(t+Δ),andEq⁢⁢(5⁢a)w~cp,1⁡(t+Δ)=h_~1⁡(t+Δ)h_~0⁡(t+Δ)+h_~1⁡(t+Δ)·ⅇj⁢⁢θ⁡(t+Δ),Eq⁢⁢(5⁢b)
where{tilde over (h)}0(t+Δ) is an M×1 vector of the predicted channel gains for the M multipath components of the RF modulated signal transmitted from the reference antenna (i.e.,{tilde over (h)}0(t+Δ)=[{tilde over (h)}0,1(t+Δ) {tilde over (h)}0,2(t+Δ) . . . {tilde over (h)}0,M(t+Δ)]T);{tilde over (h)}1(t+Δ) is an M×1 vector of the predicted channel gains for the M multipath components of the RF modulated signal transmitted from the diversity antenna (i.e.,{tilde over (h)}1(t+Δ)=[{tilde over (h)}1,1(t+Δ) {tilde over (h)}1,2(t+Δ) . . . {tilde over (h)}1,M(t+Δ)]T);∥{tilde over (h)}0(t+Δ)∥ is the norm of{tilde over (h)}0(t+Δ), or ∥{tilde over (h)}0(t+Δ)∥={tilde over (h)}0H(t+Δ){tilde over (h)}0(t+Δ);∥{tilde over (h)}1(t+Δ)∥ is the norm of{tilde over (h)}1(t+Δ), or ∥{tilde over (h)}1(t+Δ)∥={tilde over (h)}1H(t+Δ){tilde over (h)}1(t+Δ); andθ(t+Δ) is the angle between the two predicted weights, which can be expressed as:

In any case, regardless of the number of assigned multipath components, the two predicted weight {tilde over (w)}cp,0(t+Δ) and {tilde over (w)}cp,1(t+Δ) may be normalized such that the weight for the reference antenna is set to 1.0 and the weight for the diversity antenna is given as {tilde over (w)}cp(t+Δ)={tilde over (w)}cp,1(t+Δ)/{tilde over (w)}cp,0(t+Δ). The normalization results in only one predicted weight (instead of two) needing to be sent back to the base station, which then reduces the amount of overhead signaling. The normalized weight {tilde over (w)}cp(t+Δ) for the diversity antenna is then compressed and quantized (e.g., as specified by W-CDMA) and sent back to the base station.

For the weight prediction scheme, the optimal weights for the current time instant are first computed based on the estimated channel gains. The current optimal weights are then used to predict the optimal weights at the future time instant.

FIG. 5shows a block diagram of the processing at a terminal120bfor the weight prediction scheme. Terminal120bis another embodiment of terminal120inFIG. 1. The two downlink RF modulated signals transmitted by the base station are received by antenna122and processed as described above to provide de-patterned pilot symbols p0(t) and p1(t). A processing unit420bthen receives and operates on the de-patterned pilot symbols p0(t) and p1(t), in accordance with the weight prediction scheme, to provide weights {tilde over (w)}wp,0(t+Δ) and {tilde over (w)}wp,1(t+Δ), which are predicted to be optimal at the future time instant t+Δ.

In the embodiment shown inFIG. 5, for the weight prediction scheme, processing unit420bincludes estimation filters442aand442b, a weight computation unit444, and a prediction filter446. Each estimation filter442receives and processes the de-patterned pilot symbols pi(t) for a particular antenna i, where iε {0, 1}, to provide an estimate of the channel gain ĥi(t) for that antenna. Each estimation filter442may be implemented as a lowpass filter, such as an IIR filter or a FIR filter, or some other type of filter that can estimate the channel gain based on noisy de-patterned pilot symbols.

Weight computation unit444then receives the estimated channel gains ĥ0(t) and ĥ1(t) from estimation filters442aand442b, respectively. Unit444then computes the current weights w0(t) and w1(t), which are deemed to be optimal for the current time instant t. The computation for the current optimal weights may be expressed as:

w0⁡(t)=h^0⁡(t)2h^0⁡(t)2+h^1⁡(t)2,andEq⁢⁢(7⁢a)w1⁡(t)=h^1⁡(t)2h^0⁡(t)2+h^1⁡(t)2·ⅇj⁢⁢∠⁢⁢h^0⁡(t)⁢⁢h^1*⁡(t),Eq⁢⁢(7⁢b)
where |ĥ0(t)|2is the squared magnitude of the estimated channel gain ĥ0(t) for the reference antenna;|ĥ1(t)|2is the squared magnitude of the estimated channel gain ĥ1(t) for the diversity antenna; and∠ĥ0(t)ĥ1*(t) is the angle between the two current weights.

Again, for a CDMA system, a number of fingers may be assigned to process a number of multipath components of each of the two RF modulated signals. Each assigned finger may be operated as described above to provide the estimated channel gains ĥ0,j(t) and ĥ1,j(t) for the two antennas for the j-th multipath component assigned to the finger. The current weights w0(t) and w1(t) may then be computed based on the estimated channel gains associated with all assigned multipath components. The computation for the current weights based on the estimated channel gains for M assigned multipath components may be expressed as:

w0⁡(t)=h_^0⁡(t)h^_0⁡(t)+h_^1⁡(t),andEq⁢⁢(8⁢a)w1⁡(t)=h_^1⁡(t)h_^0⁡(t)+h_^1⁡(t)·ⅇj⁢⁢∠⁢⁢h_^1H⁡(t)⁢⁢h_^0⁡(t),Eq⁢⁢(8⁢b)
whereĥ0(t) is an M×1 vector of the estimated channel gains for the M multipath components of the RF modulated signal transmitted from the reference antenna (i.e.,ĥ0(t)=[ĥ0,1(t) ĥ0,2(t) . . . ĥ0,M(t)]T);ĥ1(t) is an M×1 vector of the estimated channel gains for the M multipath components of the RF modulated signal transmitted from the diversity antenna (i.e.,ĥ1(t)=[ĥ1,1(t) ĥ1,2(t) . . . ĥ1,M(t)]T);∥ĥ0(t)∥is the norm ofĥ0(t), or∥ĥ0(t)∥=ĥ0H(t)ĥ0(t);∥ĥ1(t)∥is the norm ofĥ1(t), or ∥ĥ1(t)∥=ĥ1H(t)ĥ1(t); and∠ĥ1H(t)ĥ0(t) is the angle between the two predicted weights.

In any case, prediction filter446receives and processes the current optimal weights w0(t) and w1(t) to provide the predicted optimal weights {tilde over (w)}wp,0(t+Δ) and {tilde over (w)}wp,1(t+Δ) for the future time instant t+Δ. Prediction filter446may be implemented as an adaptive filter or a non-adaptive filter and with an IIR, FIR, or some other filter structure. For an adaptive filter, the LMS, RLS, or some other adaptive algorithm may be used to adapt the filter. The adaptation criterion for the adaptive filter may be expressed as:
Min∥w0(t)−{tilde over (w)}0(t)∥ and Min∥w1(t)−{tilde over (w)}1(t)∥,  Eq(9)
wherew0(t) andw1(t) are N×1 vectors of current computed weights for the reference and diversity antennas, respectively;{tilde over (w)}0(t) and{tilde over (w)}1(t) are N×1 vectors of prior predicted weights for the reference and diversity antennas, respectively; and∥x∥ represents the norm of vectorx(i.e., the sum of the squared magnitude of the individual elements ofx).
The weight vectors may be given as:
w0(t)=[w0(t)w0(t−1) . . .w0(t−N+1)]T,{tilde over (w)}0(t)=[{tilde over (w)}0(t){tilde over (w)}0(t−1) . . .{tilde over (w)}0(t−N+1)]T,
w1(t)=[w1(t)w1(t−1) . . .w1(t−N+1)]T,{tilde over (w)}1(t)=[{tilde over (w)}1(t−1) . . .{tilde over (w)}1(t−N+1)]T.

In a specific embodiment, prediction filter446is implemented as an adaptive LMS filter. The predicted optimal weights {tilde over (w)}wp,0(t+Δ) and {tilde over (w)}wp,1(t+Δ) may then be computed as follows:
w′0(t)=b0H(t−1)w0(t) andw′1(t)=b1H(T−1)w1(t),  Eq(10a)
e0(t)=w0(t)−w′0(t) ande1(t)=w1(t)−w′1(t), Eq(10b)
b0(t)=b0(t−1)+2μw0(t)e0*(t) andb1(t)=b1(t−1)+2μw1(t)e1*(t), Eq(10c)
{tilde over (w)}0(t+Δ)=b0H(t)w0(t+Δ) and{tilde over (w)}1(t+Δ)=b1H(t)w1(t+Δ), Eq (10d)
wherewi(t), for iε{0, 1}, is an N×1 vector of prior computed weights (i.e.,
wi(t)=[wi(t−Δ)wi(t −Δ−1) . . .wi(t−Δ−N+1)]T);bi(t) is an n×1 vector of coefficients used to compute the predicted weights and is initialized to all zeros, orbi(Δ+N−1)=0;ei(t) is an error in the predicted weight w′i(t); andμ is a step size, which is selected to be small enough to ensure convergence.

In equation set (10), the first three equations (10a) through (10c) are for the LMS filter that is used to derive the coefficient vectorbi(t), and the last equation (10d) is the computation to derive the predicted weight {tilde over (w)}wp,i(t+Δ) for the future time instant t+Δ. The LMS filter may be updated whenever new computed weights w0(t) and w1(t) are available, and the weight computation may be performed whenever the predicted weights are needed. In general, the updating of the LMS filter and the weight computation may be performed at the same or different rates.

For the LMS filter, the N prior computed weights, from wi(t−Δ) to wi(t−Δ−N+1), and the prior coefficient vectorbi(t−1) are used to derive w′i(t), which represents the predicted weight to be applied at the current time instant and derived based on prior information. The error ei(t) between the computed weight wi(t) and the predicted weight w′i(t) is determined. The error ei(t) and the prior computed weightswi(t) are then used to update the coefficient vectorbi(t). For the weight computation, the predicted weight {tilde over (w)}i(t+Δ) for the future time instant t+Δ is computed based on the current coefficient vectorbi(t) and the vectorwi(t+Δ) of the N most recent computed weights, from wi(t) to wi(t−N+1) .

The LMS algorithm is also described in detail in the aforementioned “Statistical and Adaptive Signal Processing” reference.

FIG. 6is a block diagram of an embodiment of base station110and terminal120. On the downlink, a transmit (TX) data processor610receives data of various types and processes (e.g., formats, encodes, and interleaves) the received data. The processed data is further processed (e.g., channelized with one or more OVCF codes, spectrally spread with a scrambling code, and so on) by a modulator (MOD)612. The modulated data is then multiplied with a set of weight factors W1and W2and combined with pilot symbols, as described above forFIG. 2. Modulator612provides two complex-valued data streams to transmitter units (TMTRs)614aand614b. Each data stream is conditioned (e.g., converted to one or more analog signals, amplified, filtered, frequency upconverted, and so on) by a respective transmitter unit614to generate a downlink RF modulated signal. Two downlink RF modulated signals from transmitter units614aand614bare then transmitted from antennas112aand112b, respectively.

At terminal120, the downlink RF modulated signals are received by antenna122and provided to a receiver unit (RCVR)652. Receiver unit652conditions (e.g., filters, amplifies, and frequency downconverts) the signal from antenna122and further digitizes the conditioned signal to provide samples. A demodulator (DEMOD)654further processes (e.g., descrambles and despreads) the samples to provide de-patterned pilot symbols and data symbols. The data symbols are further processed (e.g., deinterleaved and decoded) by an RX data processor656to provided decoded data, and the de-patterned pilot symbols p0(t) and p1(t) are provided to a processor660.

Processor660uses the de-patterned pilot symbols to derive predicted weights, {tilde over (w)}0(t+Δ) and {tilde over (w)}1(t+Δ), for the future time instant t+Δ. Processor660may implement the channel prediction scheme and/or the weight prediction scheme described above. The predicted weights are further processed (e.g., normalized), compressed, and quantized to provide weight feedback, which is then sent to a TX data processor672.

On the uplink, TX data processor672receives and processes various types of data, including the weight feedback. The data from TX data processor672is further processed (e.g., spread and scrambled) by a modulator674and then conditioned by a transmitter unit652to generate an uplink RF modulated signal, which is then transmitted from antenna652.

At base station110, the uplink RF modulated signal is received by antennas112aand112b, and conditioned and digitized by receiver units614aand614bto provide samples. A demodulator632further processes the samples to recover the weight feedback, which is provided to a controller620. Controller620then derives the weight factors W1and W2based on the weight feedback. These weight factors are provided to modulator612and used to adjust the phase and possibly the amplitude of the two downlink RF modulated signals.

Controller620and processor660direct the operation of various processing units within the base station and terminal, respectively. Processor660may be designed to derive the predicted weights for the closed loop transmit diversity and may implement processing unit420ainFIG. 4Aand/or processing unit420binFIG. 5. Alternatively, the computation to derive the predicted weights may be performed by controller620based on pertinent feedback information from the terminal. In this case, controller620may implement processing unit420ainFIG. 4Aand/or processing unit420binFIG. 5. Memory units622and662may store data and program codes used by various processing units within the base station and terminal, respectively.

For clarity, various aspects and embodiments of the techniques for predicting weights used for closed-loop transmit diversity have been specifically described for a W-CDMA system. In general, these techniques may be used for various wireless communication systems that employ transmit diversity. For example, these techniques may also be used for a cdma2000 system, multiple-input single-output (MISO) systems, multiple-input multiple-output (MIMO) systems, and so on.

Moreover, the weight prediction techniques described herein may be used for various closed-loop transmit diversity schemes that employ at least two transmit antennas for data transmission. The closed loop mode transmit diversity scheme defined by W-CDMA is an example scheme where these techniques may be used. In general, these techniques may be used for any closed-loop transmit diversity scheme that exhibits some delay between the time the weights are computed to the time they are applied. Moreover, these techniques may be used with any number of transmit antennas.

The techniques described herein for predicting weights used for closed-loop transmit diversity may be implemented by various means. For example, these techniques may be implemented in hardware, software, or a combination thereof. For a hardware implementation, the elements used to perform the weight prediction may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described herein, or a combination thereof.

For a software implementation, the weight prediction may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in a memory unit (e.g., memory unit622or662inFIG. 6) and executed by a processor (e.g., controller620or processor660). The memory unit may be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is known in the art.