FIELD

Aspects of the present disclosure relate to communication channel estimation.

BACKGROUND

The physical downlink shared channel (PDSCH) is a physical channel that is generally used to carry user data, dedicated control and user-specific higher layer information and downlink system information. The resource blocks (RBs) for PDSCH may be allocated in bundles of two or more RBs. Resource blocks within a bundle may be precoded in the same manner and other resource blocks mapped to different bundles may be independently precoded based on the decision by the radio node (e.g., a 5G logical radio node, i.e., gNB).

Under a recent specification released by the 3rd Generation Partnership Project (3GPP), there are two scenarios for resource allocation for PDSCH, namely, precoding all bundles (with the same or different precoding) or not precoding at all. The latter is equivalent to the identical precoding across all bundles. A user equipment (UE) can take advantage of this configuration to improve the channel estimation. When the same precoding is applied to all bundles, a frequency domain signal is homogeneous in terms of the precoding so that it can be converted to a time domain by taking inverse fast fourier transform (IFFT). This means that the denoising in a time domain can be utilized based on the user equipment's estimated power delay profile (PDP), which recursively provides an advantage to estimate PDP for the next time/frequency slot as measuring channel powers with infinite impulse response (IIR) filters.

However, this approach is not applicable when each bundle is independently precoded with its own selection, as the user equipment is not aware of the radio node's precoding selection at each bundle. The time domain conversion through IFFT is not feasible since the effective channels combined with the precoding are no longer homogeneous across bundles. The user equipment then has to estimate channels based on the information in the frequency domain.

The above information disclosed in this Background section is only for enhancement of understanding of the background of the disclosure and therefore it may contain information that does not constitute prior art.

SUMMARY

Aspects of example embodiments of the present disclosure are directed to a system and a method for performing channel estimation by utilizing a neural network (e.g., a deep-learning neural network). According to some embodiments, the neural network is trained with samples having precoded channel correlation with labels of unprecoded channel correlation, and can thus convert channel correlation to be homogeneous. In some embodiments, the channel estimator performs post-processing on the output of the neural network to further refine the estimation and allow it to be denoised in the time domain via inverse fast fourier transform (IFFT). This allows the channel estimator to estimate channel correlation in the frequency domain even when random precoding is configured across bundles.

According to some embodiments of the present disclosure, there is provided a method of channel estimation for a precoded channel, the method including: generating an initial frequency autocorrelation of the precoded channel for a current bundle of a received data transmission; generating an expanded frequency autocorrelation based on the initial frequency autocorrelation of the precoded channel; providing the expanded frequency autocorrelation to a neural network; generating, by the neural network, an estimated frequency autocorrelation of an unprecoded channel based on the expanded frequency autocorrelation; and generating an estimated power distribution profile of the unprecoded channel based on the estimated frequency autocorrelation.

In some embodiments, the current bundle includes a plurality of resource blocks, each one of the resource blocks including a plurality of subcarriers.

In some embodiments, the unprecoded channel is an estimate of the precoded channel absent precoding.

In some embodiments, the generating the expanded frequency autocorrelation includes: performing edge expansion on the initial frequency autocorrelation to expand a size of the estimate frequency autocorrelation to a fast fourier transform (FFT) size, wherein the FFT size is an input size of the neural network.

In some embodiments, the edge expansion includes a linear interpolation of values of the initial frequency autocorrelation via an expansion matrix.

In some embodiments, the providing the expanded frequency autocorrelation to the neural network includes: providing a first half of values of the expanded frequency autocorrelation to the neural network, wherein a second half of values of the expanded frequency autocorrelation are complex conjugates of the first half of values of the expanded frequency autocorrelation.

In some embodiments, the generating the estimated frequency autocorrelation by the neural network includes: generating, by the neural network, a first half of values of the estimated frequency autocorrelation of the unprecoded channel based on the expanded frequency autocorrelation; and calculating a second half of values of values of the estimated frequency autocorrelation of the unprecoded channel as a complex conjugate of the first half of values.

In some embodiments, the generating the estimated power distribution profile includes: filtering the estimated frequency autocorrelation output by the neural network via a low pass filter to generate a refined autocorrelation of the unprecoded channel; and performing an inverse FFT (IFFT) operation on the refined autocorrelation to generate the estimated power distribution profile.

In some embodiments, the low pass filter is a moving average filter.

In some embodiments, the generating the initial frequency autocorrelation of the precoded channel for the current bundle includes: generating a time autocorrelation for a previous bundle of the received data transmission; generating a previous frequency autocorrelation for the previous bundle based on a previous estimated power distribution profile; generating an estimated channel input response based on the time autocorrelation and the previous frequency autocorrelation; and generating the initial frequency autocorrelation of the precoded channel for the current bundle based on the estimated channel input response.

In some embodiments, the method further includes: generating a truncated estimated power distribution profile by truncating a size of the estimated power distribution profile to match a size of the initial frequency autocorrelation of the precoded channel.

In some embodiments, the method further includes: normalizing the truncated estimated power distribution profile to a unit power to generate a normalized estimated power distribution profile.

In some embodiments, the truncated estimated power distribution profile has a length of a maximum delay spread of the precoded channel.

According to some embodiments of the present disclosure, there is provided a system for channel estimation of a precoded channel, the system including: a processor;

and memory storing instructions that, when executed on the processor, cause the processor to perform: generating an initial frequency autocorrelation of the precoded channel for a current bundle of a received data transmission; generating an expanded frequency autocorrelation based on the initial frequency autocorrelation of the precoded channel; providing the expanded frequency autocorrelation to a neural network; generating, by the neural network, an estimated frequency autocorrelation of an unprecoded channel based on the expanded frequency autocorrelation; and generating an estimated power distribution profile of the unprecoded channel based on the estimated frequency autocorrelation.

According to some embodiments of the present disclosure, there is provided a method of channel estimation of a precoded channel, the method including: generating an initial frequency autocorrelation of the precoded channel for a current bundle of a received data transmission; providing the initial frequency autocorrelation to a policy network; generating, by the policy network, an estimated frequency autocorrelation of an unprecoded channel based on the initial frequency autocorrelation; determining, by a value network, an instantaneous reward based on the estimated frequency autocorrelation; determining an advantage based on the instantaneous reward and a predicted total reward of forward propagation at the value network; and updating a policy of the policy network based on the advantage via back propagation to reduce a block error rate.

In some embodiments, the updating the policy of the policy network includes: determining a policy gradient based on the advantage; and updating coefficients of the policy network based on the policy gradient.

In some embodiments, the policy network and the value network are multi-layer perceptrons.

In some embodiments, the method further includes: adding gaussian noise to the estimated frequency autocorrelation to convert a discrete action space of the policy network to a continuous action space.

In some embodiments, the method further includes: generating an expanded frequency autocorrelation based on the initial frequency autocorrelation of the precoded channel, wherein the providing the initial frequency autocorrelation to the policy network includes: providing the expanded frequency autocorrelation to the policy network, and wherein the generating the estimated frequency autocorrelation of the unprecoded channel is based on the expanded frequency autocorrelation.

In some embodiments, the method further includes: filtering the estimated frequency autocorrelation via a low pass filter to generate a refined autocorrelation of the unprecoded channel; and performing an inverse FFT (IFFT) operation on the refined autocorrelation to generate an estimated power distribution profile.

