Method and wireless network for managing channel state information (CSI) feedback compression in wireless network

Abstract

ABSTRACT METHOD AND WIRELESS NETWORK FOR MANAGING CHANNEL STATE INFORMATION (CSI) FEEDBACK COMPRESSION IN WIRELESS NETWORK Embodiments herein disclose methods for managing CSI feedback compression in wireless network (300) by base station (200). The method includes transmitting at least one pilot symbol over first window. Further, the method includes receiving at least one of a first type feedback and a second type feedback from a UE (100) at an end of the first window or after the first window. Further, the method includes receiving the compressed CSI feedback based on predefined precoder weights in the first window. Further, the method includes computing and predicting at least one precoder weight for the UE (100) in at least one time instant in a second window for at least one sub-band of the UE (100) based on the at least one of the first type feedback and a second type feedback. Further, the method includes managing the received CSI feedback compression based on the predicted precoder weight. FIG. 7

Description

TECHNICAL FIELD

[001] Embodiments disclosed herein relate to wireless communication networks, and more particularly to managing Channel State Information (CSI) feedback compression in a Doppler domain in the wireless communication networks.BACKGROUND

[002] Release 16 for CSI feedback reduces overhead compared to Release 15 by doing compression across sub bands. Release 18 speaks about CSI feedback for mobility. For mobility scenarios, it is possible to attain further compression in time (i.e., Doppler domain).OBJECTS

[003] The principal object of the embodiments herein is to disclose methods and an apparatus for managing CSI feedback compression in a Doppler domain in wireless communication networks.SUMMARY

[004] Accordingly, the embodiments herein provide methods for managing a Channel State Information (CSI) feedback compression in a wireless network. The method includes transmitting, by a base station, at least one pilot symbol over a first window. Further, the method includes receiving, by the base station, at least one of a first type feedback and a second type feedback from a User Equipment (UE) at an end of the first window or after the first window. Further, the method includes receiving, by the base station, a compressed CSI feedback based on predefined precoder weights in the first window or a second window. Further, themethod includes computing and predicting, by the base station, at least one precoder weight for the UE in at least one time instant in the second window for at least one sub-band of the UE based on the at least one of the first type feedback and the second type. Further, the method includes managing, by the base station, the received CSI feedback compression in the second window based on the at least one predicted precoder weight.

[005] In an embodiment, computing and predicting, by the base station, the at least one precoder weight for the UE in the at least one time instant in the second window for the at least one sub-band of the UE based on the at least one received feedback information includes extracting, by the base station, information from the second type feedback, and predicting, by the base station, the at least one precoder weight for the UE in at least one time instant in the second window for the at least one sub-band of the UE based on the extracted information.

[006] In an embodiment, the at least one of the first type feedback and the second type feedback depends on a prior configuration of the UE from the base station and a prior signalling from the UE to the base station.

[007] In an embodiment, the at least one first type feedback corresponds to all precoder weights across time instants in the first window for all sub-bands of the UE.

[008] In an embodiment, the first window is an observation window, and the second window is a prediction window.

[009] In an embodiment, the at least one pilot symbol comprises a Channel State Information Reference Signal (CSI-RS), wherein the CSI feedback compression is managed in a Doppler domain.

[0010] In an embodiment, the at least one precoder weight for the UE is predicted in the second window based on at least one of a linear prediction technique, a spectral estimation technique and a neural network (NN).

[0011] In an embodiment, the second type feedback is determined by computing the channel across the selected time instant in the first window for at least one delay or sub-band or sub-carrier or beam delay, predicting the channel using the computed channel in the first window across the selected time instant inthe second window for at least one delay or sub-band or sub-carrier or beam delay, wherein the channel across the selected time instant in the second window for the at least one delay or the sub-carrier or the sub-band or the angle delay is predicted based on at least one of a liner prediction technique, a spectral estimation technique and a neural network (NN), computing precoder for the selected time instant in the second window for all the sub-bands of the UE, and obtaining, by the base station, at least one precoder weight across the selected time instant in the second window for all the sub-bands of the UE in a compressed format.

[0012] In an embodiment, the second type feedback is determined by predicting the precoder across the selected time instant in the second window for at least one delay or sub-band, wherein the precoder across the selected time instant in the second window are predicted from precoders in first window for the at least one delay or the sub-band is predicted based on at least one of a liner prediction technique, a spectral estimation technique and a neural network (NN), obtaining, by the base station, at least one precoder weight across the selected time instant in the second window for all the sub-bands of the UE in a compressed format.

[0013] Accordingly, the embodiments herein provide methods for managing a CSI feedback compression in a wireless network. The method includes estimating, by an apparatus, at least one channel for one of a set of sub-carriers or at least one sub-band or at least one delay or at least one beam delay for a selected time instant in a first window. Further, the method includes estimating, by the apparatus, at least one precoder weight for the selected time instant across the at least one sub-band or delay for the selected time instant in the first window. Further, the method includes predicting, by the apparatus, a value of a quantity in the selected time instant across at least one sub-band or the delay in a second window based on a linear combination or a non-linear combination of quantities in one or more dimensions in the first and / or second window, wherein the linear combination or non-linear combination (learning coefficient) is learned across a plurality of candidate values in the first window. Further, the method includes managing, by the apparatus, the CSI feedback compression in the wireless network based on the predicted value of the quantity.

[0014] In an embodiment, the quantity is a channel corresponding to at least one of subcarriers, sub-bands, a frequency, delays, beam delays and antenna. In another embodiment, the quantity is the precoder weight for the beam in a subband or delay. If the quantity is channel, the dimension could be subcarrier / frequency / sub-bands / antennas / delays / beams. If the quantity is an element of w2, the dimensions could be associated beams / sub-bands. If the quantity is an element of w2~, the dimensions could be associated beams / delays.In an embodiment, the apparatus comprises at least one of a User Equipment (UE) and a base station.

[0015] Accordingly, the embodiments herein provide methods for managing a CSI feedback compression in a wireless network. The method includes estimating, by an apparatus, at least one channel for a set of sub-carriers or at least one sub-band or delays or beam delays for a selected time instant in a first window. Further, the method includes estimating, by the apparatus, at least one precoder weight for the selected time instant across the at least one sub-band or delay for the selected time instant in the first window. Further, the method includes determining, by the apparatus, an output over a period of time in a second window by performing spectral estimation associated with a quantity in the first window, where the output of the spectral estimation is an n-dimensional frequency component and an amplitude associated with the n-dimensional frequency component. Further, the method includes estimating, by the apparatus, a value of the quantity in the selected time instant in the second window based on the output. Further, the method includes managing, by the apparatus, the CSI feedback compression in the wireless network based on the estimated value of the quantity.

[0016] In an embodiment, if the quantity is an element of w2 and n=1, a frequency component is across the time, wherein if the quantity is an element of w2 and n=2, a frequency component is across the sub-bands and time, wherein if the quantity is an element of w2~ and n=1, a frequency component is across the time. If the quantity is an element of w2 and n=2, a frequency component is across the delay and time, wherein if the quantity is channel at a subcarrier / sub-band and n=1, the frequency component is across the time wherein if the quantity is channel at asubcarrier / sub-band and n=2, the frequency component is across the time and the subcarrier / sub-band, wherein if the quantity is channel at a delay and n=1, the frequency component is across the time, wherein if the quantity is channel at a delay and n=2, the frequency component is across the time and delay, and wherein if the quantity is channel at a delay, angle, and n=1, the frequency component is across the time. Accordingly, the embodiments herein provide methods for managing a CSI feedback compression in a wireless network. The method includes compressing, by a UE, at least one precoder at various time instants across subbands or delays at the UE in at least one of a first window and a second window. Further, the method includes sending, by the UE, at least one compressed precoder to a base station.

[0017] In an embodiment, further, the method includes computing, by the UE, a vector value corresponding to precoder weights of a beam, a delay across selected time instants in at least one of the first window or and the second window. Further, the method includes determining, by the UE, that computed vector value for all delays for the beam are completed. Further, the method includes determining, by the UE, that computed vector value for all beams are completed. Further, the method includes determining, by the UE, whether a time domain Doppler basis matrix for the selected time instants is different across one of a spatial domain, a frequency domain and a delay domain. In an embodiment, the method includes feedbacking a first compressed Doppler coefficient precoder vector value based on precoder weights for each beam and subband or delay across the selected time instants, a Doppler-time domain basis matrix for each beam and subband or delay associated with the first compressed vector and a spatial beam matrix value associated with all beams value in response to determining that the time domain Doppler basis matrix is different across one of the spatial domain, the delay domain and the frequency domain. In another embodiment, the method includes feedbacking a first compressed Doppler coefficient precoder matrix value based on precoder weights for all beams and subband / delay across the selected time instants, a joint Doppler-time frequency / delay domain basis matrix for all beams and subband / delay associated with the first compressed Doppler coefficient matrix anda spatial beam matrix value associated with all beams to the base station in response to determining that the time domain Doppler basis matrix is not different across one of the spatial domain, the delay domain and the frequency domain.

[0018] Accordingly, the embodiments herein provide a base station including a CSI controller coupled with a processor and a memory. The CSI controller is configured to transmit at least one pilot symbol over a first window and receive at least one of a first type feedback and a second type feedback from a UE at an end of the first window or after the first window. Further, the CSI controller is configured to receive a compressed CSI feedback based on predefined precoder weights in the first window or a second window and compute and predict at least one precoder weight for the UE in at least one time instant in the second window for at least one sub-band of the UE based on the at least one of the first type feedback and the second type feedback. Further, the CSI controller is configured to manage the received CSI feedback compression based on the at least one predicted precoder weight in the second window.

[0019] Accordingly, the embodiments herein provide an apparatus including a CSI controller coupled with a processor and a memory. The CSI controller is configured to estimate at least one channel for one of a set of subcarriers or at least one sub-band or at least one delay or at least one beam delay for a selected time instant in a first window. Further, the CSI controller is configured to estimate at least one precoder weight for the selected time instant across the at least one sub-band or delay for the selected time instant in the first window. Further, the CSI controller is configured to predict a value of a quantity in the selected time instant across at least one sub-band or delay in a second window based on a linear combination or a non-linear combination (learning coefficient) of quantities in one or more dimensions in the first and / or second window, wherein the linear combination learning coefficient is learned across a plurality of candidate values in the first window, wherein the linear combination learning coefficient corresponds to quantities in one or more dimensions in the first window. Further, the CSI controller is configured to manage the CSI feedback compression in the wireless network based on the predicted value of the quantity.

[0020] Accordingly, the embodiments herein provide an apparatus including a CSI controller coupled with a processor and a memory. The CSI controller is configured to estimate at least one channel for a set of sub-carriers or at least one sub-band or delays or beam delays for a selected time instant in a first window. Further, the CSI controller is configured to estimate at least one precoder weight for the selected time instant across the at least one sub-band or delay for the selected time instant in the first window. Further, the CSI controller is configured to determine an output over a period of time in a second window by performing spectral estimation associated with a quantity in the first window, where the output of the spectral estimation is an n-dimensional frequency component and an amplitude associated with the n-dimensional frequency component. Further, the CSI controller is configured to estimate a value of the quantity in the selected time instant in the second window based on the output. Further, the CSI controller is configured to manage the CSI feedback compression in the wireless network based on the estimated value of the quantity.

[0021] Accordingly, the embodiments herein provide a UE including a CSI controller coupled with a processor and a memory. The CSI controller is configured to compress at least one precoder at various time instants across sub-bands or delays at the UE in at least one of a first window and a second window. Further, the CSI controller is configured to send at least one compressed precoder to a base station.

[0022] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating at least one embodiment and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.BRIEF DESCRIPTION OF FIGURES

[0023] The embodiments disclosed herein are illustrated in the accompanying drawings, throughout which like reference letters indicate corresponding parts in the various figures. The embodiments herein will be better understood from the following description with reference to the drawings, in which:

[0024] FIG. 1 illustrates a wireless network for managing a CSI feedback compression, according to embodiments as disclosed herein;

[0025] FIG. 2 shows various hardware components of an apparatus (i.e., UE or base station), according to the embodiments as disclosed herein;

[0026] FIG. 3 depicts the mode of operation, according to embodiments as disclosed herein;

[0027] FIG. 4A and FIG. 4B depict LCP, according to embodiments as disclosed herein;

[0028] FIG. 4C depicts 2D-LCP, according to embodiments as disclosedherein;

[0029] FIG. 5 depicts the process of computing Doppler coefficients, according to embodiments as disclosed herein;

[0030] FIG. 6 depicts the low frequency Doppler delay components in a 2D frequency grid, according to embodiments as disclosed herein;

[0031] FIG. 7 is a flow chart illustrating a method for managing the CSI feedback compression in the wireless network, according to embodiments as disclosed herein;

[0032] FIG. 8 is an example flow chart illustrating a method for managing the CSI feedback compression in the wireless network, according to embodiments as disclosed herein;

[0033] FIG. 9 is an example flow chart illustrating a method for determining a first feedback type while managing the CSI feedback compression in the wireless network, according to embodiments as disclosed herein;

[0034] FIG. 10 is an example flow chart illustrating a method for determining a second feedback type while managing the CSI feedback compression in the wireless network, according to embodiments as disclosed herein;

[0035] FIG. 11 and FIG. 12 are example flow charts illustrating a method for determining a precoder weights while managing the CSI feedback compression in the wireless network, according to embodiments as disclosed herein;

[0036] FIG. 13 is an example flow chart illustrating a method for managing the CSI feedback compression in the wireless network using a linear prediction, according to embodiments as disclosed herein;

[0037] FIG. 14 is an example flow chart illustrating a method for managing the CSI feedback compression in the wireless network using a spectral estimation, according to embodiments as disclosed herein;

[0038] FIG. 15 is an example flow chart illustrating a method for managing the CSI feedback compression in the wireless network using Doppler-Delay coefficients, according to embodiments as disclosed herein; and

[0039] FIG. 16 is an example flow chart illustrating a method for managing the CSI feedback compression in the wireless network using the non-linear prediction or a neural network, according to embodiments as disclosed herein.DETAILED DESCRIPTION

[0040] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein can be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

[0041] The embodiments herein achieve methods for managing a CSI feedback compression in a wireless network. The method includes transmitting, by a base station, at least one pilot symbol over a first window. Further, the method includes receiving, by the base station, at least one of a first type feedback and a second type feedback from a UE at an end of the first window or after the first window. Further, the method includes receiving the compressed CSI feedback based on predefined precoder weights in the first window or a second window. Further, the method includes computing and predicting, by the base station, at least one precoder weight for the UE in at least one time instant in the second window for at least one sub-band of the UE based on the at least one of the first type feedback and the second type feedback. Further, the method includes managing, by the base station, the received CSI feedback compression based on the at least one predicted precoder weight in the second window.

[0042] The methods can be used for managing the CSI feedback compression in the Doppler domain. As in Doppler scenarios feedback is increased, a compressed mechanism ensures the amount of feedback is greatly reduced and compression works as the feedback is highly correlated due to the Doppler scenario.

[0043] Referring now to the drawings, and more particularly to FIGS. 1 through 16, where similar reference characters denote corresponding features consistently throughout the figures, there are shown at least one embodiment.

[0044] The following notifications have been used in the patent disclosure:a) Matlab notation to access matrices / vectors.b) An estimate of a quantity x is denoted by x.c) A quantity x approximated due to quantization is denoted by x.d) Matrices will be uppercase BOLD, vectors lower case bold and scalars arenormal fonts.e) '*' denotes conjugate.

[0045] The W2 matrix is compressed across subbands and is feedback to the base station. Assuming a system with Nj subbands and 2L SD beams, the frequency domain correlation inside W2 can be exploited by applying DFT compression on top of W2 which is of size 2L X N3.

[0046] A frequency compression matrix of size N3 X M, Wf is selected from the columns of an oversampled DFT codebook, where it forms an orthogonal subset of the basis set found in the DFT codebook.

[0047] M < N3 is the number of frequency domain (FD) basis vectors, that are selected after compression. FD compression is applied to each layer I to obtain a matrix of linear combination coefficients W2:VK2 = W2Wf (4)

[0048] Wf can be regarded as the equivalent of the 2N1N2 X 2L matrix for frequency compression. The final precoder format can be written as:W = W1VV2WfH (5)

[0049] The elements inside W2 are referred to as FD coefficients.

[0050] Only one Rx antenna, or Rank 1 CSI-Type 2 feedback assumed for simplicity and ease of depiction though it can apply easily for higher ranks.

[0051] The feedback is W2, W2. The problem for the simple case of feedback of channel coefficients across time and frequency is first formulated. Based on the motivation of these approaches, these approaches can be extended to the case where elements of W2, W2 are fed back.Linear prediction:

[0052] It is well known that channels in time and frequency are governed by prediction coefficients. In the simplest case, a future channel value in timedomain is a linear combination (using linear prediction coefficients) of past channel values. The error in prediction is much lesser than the error between a current and previous value. So to feedback values of lesser magnitude will result in lesseroverhead. The linear predictor coefficients are provided as feedback. Doppler Components:

[0053] Just as the frequency-selective channel varies slowly and as such can be quite accurately represented by a few FFT bins (if FFT is taken of the channel across frequency), the same holds for channel variation across time. If there are N values across time of the channel and if N-point FFT is taken, the channel can be quite accurately reconstructed using a few (a) FFT bins (lesser than N). These a FFT bins can be learnt using a subset N1 < N and N1 > a samples. Once the a FFT bins are learnt, the remaining N-N1 samples can be reconstructed based on these bins. If done on multipaths in time domain, it is called as delay-Doppler model. Kalman Filter:

[0054] Kalman filter does a MMSE estimate of a noisy signal, if the variation of the signal is captured in a state equation. Kalman filter can have the linear channel predictor coefficients (LCP) as part of the state equation. LCP can predict a future channel value and the Kalman filter can correct the predicted value thereby minimising the error. The Kalman filter works with Noisy observations. The feedback is quantized, so the channel reconstructed at the BS is a noisy version of the true channel. The Kalman filter can give better estimate of the downlink channel. For this the error variance of linear prediction, LCP and error variance due to the quantization need to be provided as feedback. The base station need not work with quantized channel values, instead it will work with more accurate values, mainly to reduce quantization noise (if that has an impact).

[0055] FIG. 1 illustrates a wireless network (300) for managing a CSI feedback compression, according to embodiments as disclosed herein. The wireless network (300) can be, for example, but not limited to a Long-Term Evolution (LTE) network, a fifth generation (5G) network, an Open Radio Access Network (ORAN) network or the like. The wireless network (300) includes a UE (100) and a basestation (200). The UE (100) can be, for example, but not limited to a smart laptop, a smart computer, a smart Device-to-Device (D2D) device, a smart vehicle to everything (V2X) device, a smartphone, a smart foldable phone, a smart TV, an immersive device, an internet of things (IoT) device or the like. The base station (200) is also referred as a gNB, a eNB, a new radio (NR) base station or the like.

[0056] In an embodiment, the base station (200) transmits a pilot symbol (e.g., CSI-RS or the like) over a first window (e.g., observation window or the like). Further, the base station (200) receives at least one of a first type feedback and a second type feedback from the UE (100) at an end of the first window or after the first window.

[0057] In an embodiment, the second type feedback is determined by computing the channel across the selected time instant in the first window for at least one delay or sub-band or sub-carrier or beam delay, predicting a channel using the computed channel in the first window across a selected time instant in a second window (e.g., prediction window or the like) for at least one delay or sub-band or sub-carrier or beam delay, computing a precoder for the selected time instant in the second window for all the sub-bands of the UE (100), and obtaining at least one precoder weight across the selected time instant in the second window for all the sub-bands of the UE (100). The channel across the selected time instant in the second window for the at least one delay or the sub-carrier or the sub-band or the angle delay is predicted based on at least one of a liner prediction technique, a spectral estimation technique and a neural network (NN).

[0058] In another embodiment, the second type feedback is determined by predicting the precoder across the selected time instant in the second window for at least one delay or sub-band, and obtaining at least one precoder weight across the selected time instant in the second window for all the sub-bands of the UE (100). The precoder across the selected time instant in the second window for the at least one delay or the sub-band is predicted from precoders in the first window based on at least one of the liner prediction technique, the spectral estimation technique and the NN.

[0059] The at least one of the first type feedback and the second type feedback depends on the prior configuration of the UE (100) from the base station (200) and a prior signalling from the UE (100) to the base station (200). The at least one first type feedback corresponds to all precoder weights across time instants in the first window for all sub-bands of the UE (100). The at least one precoder weight for the UE (100) is predicted based on at least one of the linear prediction technique, the spectral estimation technique and the NN.

[0060] Based on the at least one received feedback information, the base station (200) computes and predicts at least one precoder weight for the UE (100) in at least one time instant in the second window (e.g., prediction window or the like) for at least one sub-band of the UE (100). In an embodiment, the base station (200) extracts information from the second type feedback and predicts the at least one precoder weight for the UE (100) in at least one time instant in the second window for the at least one sub-band of the UE (100) based on the extracted information.

[0061] Based on the at least one predicted precoder weight in the second window, the base station (200) manages the received CSI feedback compression. The CSI feedback compression is managed in a Doppler domain.

[0062] In another embodiment, the apparatus (e.g., UE (100), base station (200) or the like) estimates the at least one channel for one of a set of sub-carriers or at least one sub-band or at least one delay or at least one beam delay for a selected time instant in the first window. Further, the apparatus estimates at least one precoder weight for the selected time instant across the at least one sub-band or delay for the selected time instant in the first window. Further, the apparatus predicts a value of a quantity in the selected time instant in a second window across at least one sub-band or delay based on a linear combination (learning coefficient) or a non-linear combination (learning coefficient) of quantities in one or more dimensions in the first and / or second window. The linear combination or non-linear combination is learned across a plurality of candidate values in the first window. Based on the predicted value of the quantity, the apparatus manages the CSI feedback compression in the wireless network (300).

[0063] In another embodiment, the apparatus estimates the at least one channel for a set of sub-carriers or at least one sub-band or delays or beam delays for the selected time instant in the first window. Further, the apparatus estimates the at least one precoder weight for the selected time instant across the at least one subband or delay for the selected time instant in the first window. Further, the apparatus determines an output over a period of time in a second window by performing spectral estimation associated with a quantity in the first window. The output of the spectral estimation is an n-dimensional frequency component and an amplitude associated with the n-dimensional frequency component. Based on the output, the apparatus estimates a value of the quantity in the selected time instant in the second window. Based on the estimated value of the quantity, the apparatus manages the CSI feedback compression in the wireless network (300).

[0064] In another embodiment, the UE (100) compresses the at least one precoder at various time instants across sub-bands or delays at the UE (100) in at least one of the first window and the second window. Further, the UE (100) sends the at least one compressed precoder to the base station (200).

[0065] Further, the UE (100) computes a vector value corresponding to precoder weights of the beam, the delay and the doppler coefficient across selected time instants in at least one of the first window or and the second window. Further, the UE (100) determines that the computed vector value for all delays for the beam are completed and all beams are completed. Further, the UE (100) determines whether a time domain Doppler matrix is different across one of a spatial domain, a delay domain and a frequency domain. In response to determining that the time domain Doppler matrix is different across one of the spatial domain, the delay domain and the frequency domain, the UE (100) feedbacks a compressed Doppler coefficient precoder vector value based on precoder weights for each beam and subband or delay across the selected time instants, a Doppler-time domain basis matrix for each beam and subband or delay associated with the first compressed vector and a spatial beam matrix value associated with all beams. . In response to determining that the time domain Doppler matrix is not different across one of the spatial domain, the delay domain and the frequency domain, the UE (100)feedbacks the first compressed Doppler coefficient precoder matrix value based on precoder weights for all beams and subband / delay across the selected time instants, a joint Doppler-time frequency / delay domain basis matrix for all beams and subband / delay associated with the first compressed Doppler coefficient matrix and a spatial beam matrix value associated with all beams. Mode of operation:

[0066] FIG. 3 depicts the mode of operation. Here N1 samples belong to first or observation window and N2 samples belong to second or prediction window. Consider that N=N1+ N2. In N1, feedback is send as per Rel. 16. During this time, the UE (100) calculates LCP / error variance of LCP, quantization error variance / Doppler coefficients etc. At point A, the UE (100) feeds back LCP / error variance of LCP, quantization error variance / Doppler coefficients to the BS (200). This time instant is called as feedback point or FP. During N2, the feedback is provided using embodiments as disclosed herein. CSI-RS is sent over N1 and N2 regions (samples).Linear Channel Prediction:

[0067] Let H(f,n;a) be the channel at frequency location f and time instant n. The sampling instants can be anything in time and frequency. For example f could be once every RB / subband and n could be once every slot / 10 slots. It corresponds to oth Tx antenna. For simplicity throughout we assume one Rx antenna. Let hi(n; a) be the Zth multipath time domain component at time instant n and oth Tx antenna. a) be the vector of stacked up H(f, n; a) for various f and h(n; a) be the vector of stacked up hi(n; a) for various I. Let F be the sampled FFT matrix with rows corresponding to indices of f and columns corresponding to indices of I. a) = F h(n; a). Assume length of a) > length of h(n; a). ht(n; a) = [hi(n - P + 1; a) ... hi(n;a)] is defined, where P is order of prediction and a is a P X 1 vector of LPC coefficients (assumed, for simplicity, to be same across Z multipaths).Linear channel prediction (1D): Training:

[0068] The coefficient vector a is learnt. Happens in the first Ni time instants. The training equation to learn a is(Equation)

[0069] Training can happen across Tx antennas. The predictor coefficients are roughly the same across Tx antennas and subbands. The LCP vector a is assumed, for simplicity, to be constant across all Z multipaths (though not necessary).Prediction:

[0070] hi(n + 1;a) = hi(n;a)a typically happens after the N1 timedomain sample instants.Feedback to BS:

[0071] Quantized LPC a. (optional). Quantized channel error value e(n; a) for n > N1. Feedback of e(n; a) is assumed to take lesser bits than feedback of hi(n; a) - hi(n - 1; a). (Need to be verified by simulations, but generally prediction error is less than error w.r.t previous sample).Computing ei(n; a):

[0072] For the first N1 samples (n < N1), hi(n; a) is fed back as usual. For n > N1, hi(n; a) = ahi(n - 1; a) and ei(n; a) = hi(n; a) - ahi(n - 1; a), feed back e(n; a). The BS (200) computes (Equation)LCP (other forms of prediction):

[0073] FIG. 4A and FIG. 4B depict LCP. At (A), the BS (200) determines channel using H(n) = h(n). It can also use 2D channel prediction using neighbouring pilot symbols across frequency / time at this instant and before this instant to determine the channel here. At (B), the BS (200) determines channel here using time domain interpolation, or 2D channel prediction. At (C), prediction can also happen in frequency domain along a given subcarrier across time. Pilotlocations can be diamond shape as well, not necessarily rectangular (as depicted in FIG. 4B).LCP (2D prediction):

[0074] FIG. 4C depicts 2D-LCP. Pt is a prediction order in time domain, and depends on coherence time. Pf is prediction order in frequency domain, which can be done using neighboring subbands, and depends on coherence bandwidth. The frequency domain correlation can be harnessed, in addition to time domain, reduces prediction error and hence the feedback. The LCP coefficients are roughly constant across subbands, antennas and small durations of time as it is mainly dependent on Doppler frequency. The LCP coefficient vector a1 is a PtPfx1 vector. Linear Channel Prediction:(Equation)where N3 > PtPf. Training can happen at more than one time instant n and also across antennas a. Coefficients can be learnt using Neural networks like RNN also. Doppler Coefficients:

[0076] For the first N1 points, the 'a' low-pass FFT bins from the N1 samples are learned and fedback to the BS (200) at feedback point. The N1 samples come from channel estimation of CSI-RS. For the next N2 samples (where N2 = N-N1), the signal is predicted using the quantized 'a' low pass FFT bins, subtracted from the actual value (assuming CSI-RS is present) and the error is feedback to the BS (200). Alternatively, if no CSI-RS here and just BS (200) use the reconstructed signal over this region. The a=a1+a2 low pass FFT bins, a<<N, characterizes all N samples. This process is depicted in FIG. 5. Here N1 samples belong to first or observation window and N2 samples belong to second or prediction window.

[0077] For n<N1 and assuming N1 > a Doppler coefficients are computedas(Equation)

[0078] where di is ax1 vector of Doppler coefficients fed back to the BS (200) and fj is the ith column of N x N FFT matrix.

[0079] For n > Ni, hl(n) as(Equation)

[0080] The UE (100) feeds back to BS at FP along with e(n) where et(n) = ht(n) - fii(n). The BS (200) computes ht(n) as ht(ri) + et(n). Alternatively, if there is no CSI-RS during the N2 samples, the BS (200) just uses hl(ri).2D-FFT:

[0081] The channel varies slowly in both time and frequency. The Doppler component method can also be extended to two dimensions (time and frequency) and based on 2D-FFT method.Kalman filters:

[0082] For fading, Kalman estimators give MMSE estimates if LCP coefficients, channel predictor error variance and error variance associated with observation are known. Here, the observation is quantized and the observation noise is quantization noise. If the feedback channel predictor error variance and quantization noise variance along with LPC coefficients are fedback, the BS (200) can estimate the channel more accurately (overcomes quantization loss). Since Kalman does prediction and correction as against the prediction method, which does only prediction, Kalman can have low prediction orders than prediction method. N2 can be bigger for Kalman method than channel prediction method, thereby having lesser feedback.Feedback of CSI:

[0083] All channel feedback H(f,n) or hi(ri) are per link (Tx-Rx) or per antenna. For simplicity, one layer is assumed. This will consume large feedback even with LCP. So W- W2 domain is considered. The relationships between H(f, ri) or hi(ri) and elements of W- W2 are determined. Let G be an estimate of channel matrix of dimensions 2NT X N3 X N (2 no. of Tx antennas x no. of subbands x no. of time instants). We have G(:,:,ri) = W1W2(:,: ,ri) = W1W2(:,: ,ri)Wf . Here G is an approximation of channel matrix such that elements of W2 correspond to conjugate of channel gains associated with the best set of orthonormal beams, a subset of which are the columns of Wi. W-l, W2, W2, Wf be extended across multiple time instants (third dimension). Wt be 2NT X 2L (2 no. of Tx antennas x 2 no. of beams). W2 be 2L X N3 X N (2 no. of beams x no. of subbands x no. of time instants). W2 be 2L X M X N (2 no. of beams x no. of FD compressed elements x no. of time instants). Wf is N3xM matrix.Option 1 (Rel. 15 W2 type of feedback):

[0084] The LPC coefficients of all elements of W2(:,:,ri) are related. W(a,: )G(: ,b,ri) = W2(a, b, ri). From literature, it is known that LCP of H(f, ri) (a1) for a given f across time instant n is roughly the same across f and antennas. So LPC of W2(a,b,ri) is also a. Only one set of LCP coefficients are feedback and LCP coefficients of all elements of W2 can be calculated. This is an important aspect for compression.Option 2 (Rel. 16 W2 type of feedback):

[0085] The LCP coefficients of all elements of W2(:,:,ri) are related. Wi(a,: )G(:,:, ri)Wf(:, b) = W2(a, b, ri). From literature, it is known that LCP of H(f,ri) (a) for a given f across time instant n is roughly the same across f and antennas. So LPC of W2(a,b,ri).is a. Only one set of LPC coefficients have to be feedback and LPC coefficients of all elements of W2 can be calculated.Option 3:

[0086] Both the BS (200) and UE (100) have access to G(:,:,n) for n< N±. So, the BS (200) and the UE (100) can both calculate the LCP coefficients. Ifthere is more than one way of calculating LCP coefficients, the UE(100) / BS (200) just has to signal the method used to the BS (200) / UE (100) and BS (200) / UE (100) can use the same method to calculate LCP coefficients. Correlation among elements of W2:

[0087] Wi(a, :)G(:, b, n) = W2(a, b, n). This corresponds to the ath beam, bth subband at time instant n. W2(ai,b,n) and W2(a2,b,n) are uncorrelated. This is because WI(ai,:)Wi(:, a2) = 0 and E[lG(a,b,n)l2} the channel power is constant as a (antenna index) varies. Intuitively they correspond to different beams. So for time and frequency domain prediction orders Pt and Pf, the apparatus canpredict W2(ai,b,n) from W2(ai,b- 1), ...,(Equation)- Pt + l). The values used for prediction are captured in a row-vectorW(vec\avb,n) (in any order). For the element of W2 corresponding to the ath beam, bth subband and nth time instant, a linear combination of elements of W2Pf-i Pf+icorresponding to the ath , subband beams b -to b + and time instantsn - Pt + 1 to n - 1 are used. The LCP coefficients associated with all elements of W2 is one and the same and is a1 (the same as that which is associated with channel values H(f, n; a)). Correlation among elements of W? :

[0088] W?(a,:)G(:,:,ri)Wf(:,b) = W2(a,b,n). This corresponds to the ath beam, bth multipath (or FD component) at time instant n. W2(ai,b,n) and W2(a2, b, n) are uncorrelated. This is because WI(ai, : )Wi(:, a2) = 0 and fading is uncorrelated across antennas. Intuitively they correspond to different beams. W2(a,bi,n) and W2(a,b2,n) are uncorrelated. This is becausei,:)Wf(:, b2) = 0 and fading is uncorrelated across antennas, correlation between neighboring subbands is constant across subbands. Intuitively, they correspond to different multipaths in the same beam. So for time domain prediction order Pt, it can be predicted that W2(a,b,n) from W2(a,b,n-1), ..., W2(a,b,n - Pt + 1). The values used for prediction are captured in a row-vectorW(vec) (a, b, n). For the element of W2 corresponding to the oath beam, bth multipath and nth time instant, we use linear combination of elements of W2 corresponding to the ath beam, multipath b only (as uncorrelated across multipaths)and time instants n - Pt + 1 to n- 1. The LCP coefficients associated with all elements of W2 is one and the same and is a (the same as that which is associated with channel values hi(n; a)).LCP for elements of W2:

[0089] At each time instant n, both the BS (200) and UE (100) store anestimate of W2 as W2. The vector W(vec\a, b, ri) is formed by using elements of W2. For time instants n < N1, we have W2(a,b,ri) = W2(a,b,n), the quantized versions feedback by UE->BS. For ri > N1. As an example for ri = N1 + 1. The UE (100) predicts W(vec)(a,b,ri)a1 = W2(a,b,ri). The UE (100) computes error as e(a, b, ri) = W2(a, b, ri)-W2(a, b, ri). The UE (100) sends the quantized version e(a,b,ri) to the BS (200). The BS (200) and the UE (100) store W2(a,b,ri) = W2(a,b,ri)+ e(a,b,ri). At feedback point (FP) after the first N1 time instants Pt, Pf, a1 are exchanged between BS (200) and UE (100) (Either from BS (200) to UE (100) or from UE (100) to BS (200)). Alternately, at FP, an index of a method to calculate a1from W2 for time instants ri < N1 can be signalled between the BS (200) and the UE (100) (both directions). LCP of elements of W2:

[0090] At each time instant n, both the BS (200) and the UE (100) store anestimate of W2 as W2. The vector w(vec)(a, b, ri) is formed by using elements ofW2. For time instants ri < Nx, W2(a,b,ri) = W2(a,b,ri), the quantized versions feedback provided by the UE (100) to the BS (200). For ri > Nv As an example for ri = N1 + 1- The UE (100) predicts W(vec\a, b, ri)a = W2(a, b, n).- The UE(100) computes error as e(a, b, ri) = W2(a, b, ri)-W2(a, b, ri).- The UE (100) sends to BS (200) the quantized version e(a, b, ri).- The BS (200) and the UE (100) store W2(a, b, ri) = W2(a, b, ri)+ e(a, b, ri).

[0091] At feedback point (FP) after the first N1 time instants Pt, a are exchanged between the BS (200) and the UE (100) (either from the BS (200) to the UE (100) or from the UE (100) to the BS). Alternately, at FP, an index of a method to calculate a from W2 for time instants n <Ni can be signalled between the BS (200) and the UE (100) (both directions).Doppler coefficients (1D):

[0092] For n<Ni and assuming Ni > a compute Doppler coefficients as(Equation) - 1)]db,a W2(a,b,0) W2(a,b, Ni - 1)where dba is ax1 vector of Doppler coefficients and f is the ith column of N x N FFT matrix.

[0093] For n > Ni, reconstruct W2(a,b,n)as1(Equation)

[0094] The UE (100) feeds back dbia to BS (200) for all b and a along with<?b,a(n) where eba(n) = W2(a,b,n) - W2(a,b,n). The BS (200) computesW2(a, b, n)as W2(a, b, n) + eb,a(n). Alternatively, if there is no CSI-RS during theN2 samples, the BS just uses. W2(a, b, n). Doppler-Delay coefficients (2D):

[0095] There are N3 subbands and N samples. The 2D-FFT of elements of W2 for the bth subband and ath beam and nth time instant (as b and n vary) is defined as(Equation)

[0096] Let the vector equivalent of matrix F be denoted by vec(F). Lowfrequency components of W2 J(p,q; a) will be used for learning / prediction. FIG. 6 depicts the low frequency Doppler delay components in a 2D frequency grid. Doppler coefficients (2D):Vq

[0097] The matrix Fp,q is defined, whose (b,n)th element is given by(Equation)(Equation)Doppler coefficients. For n <N1 and assuming N1> a compute Doppler coefficients as[vec(F0i0(:, 0: N - 1)) vec(Fai-i,ai-i(:, 0:Ni - 1)) ... vec., 0: N - 1)) vec(W2(a, :,0:N1-1)where d a is axl vector of Delay-Doppler coefficients.

[0098] For n > N1, W2(a, b, n) is reconstructed as[vec(F00(:,Ni:end)) vec(Fa1-i,a1-i(:,Ni:end)) vec(FN -a3,N3-a3(:, N: end)) vec(W2(a, :, Nend))

[0099] The UE (100) feeds back da to BS along with ëba(n) where eb,a(n) = W2(a,b,n) - W2(a,b,n). The BS (200) computes W2(a,b,n)as W2( a,b,n) + eba(n). Alternatively, if there is no CSI-RS during the N2 samples, BS just uses. W2(a, b, n).

[00100] The above techniques applies compression to both subbands and time and is a 2D compression via FFT. The apparatus can alternatively first compress via subbands and then the compressed subband coefficients can be further compressed along time. This we call as proposed compression algorithm or PCA. We shall first give the PCA and then later mention it's variants.

[00101] We denote the W2 matrix of earlier releases (up to Release 17) at the ith time instant (For Release 18 there are N4 time instants- These time instantscan lie in observation or prediction window but we assume each time instant has an associated channel and associated and W2) as W2i (of dimension 2L x M). There are M delays ( for the elements of W2) that are the result of compressing N3 subbands (for the elements of W2 ).

[00102] For each beam b do the following (note there are 2L beams, L per polarization, each associated with a column of called as spatial beam matrix).

[00103] For each delay d corresponding to the beam do the following

[00104] Compute the N4 x 1 vector x(b,d) corresponding to the bth beamand dth delay as x(b,d) = [W2:1(b,d) ... W2,(b,d)] , T is the transpose operation. This is compressed to a D x 1 vector, called as compressed Doppler coefficient precoder vector, x(b,d) = W(b,d)+x(b,d) where x+is the pseudo-inverse of x. W(b,d is the Doppler time domain basis matrix for the bth beam and dth delay.

[00105] End of delay loop

[00106] End of beam loop

[00107] First variant for compression technique (PCA): If the W(b,d is constant across beams and delays (in spatial and frequency domains) then the apparatus can have one compression that is feedback as follows.

[00108] of dimension NT x 2L, Wf of dimension N3 x M, Wt of dimension N4 x D, W2 of dimension 2L x MD, Wf t of dimension N3N4 x MD. Nt = 2N1N2 is number of transmit antennas and D N4 and M N3 implying compression in frequency and time domains. can be identity matrix, implying no compression in time-domain for that beam and delay.

[00109] Wf,t is kron(Wf, Wt) called as joint Doppler-time frequency / delay domain basis matrix. Kron is Kronecker operation of matrices(Equation)N2 is formed as N2 =(Equation)Doppler coefficient precoder matrix .called as compressed

[00110] So if the final precoders are stacked column-wise, such that precoders are stacked first by time and then by subband (i.e., for a given subband stack up for all time instants and do the same for other subbands) and if this precoder matrix is called as , we have W = W1W2Wft.

[00111] Second variant for compression technique (PCA):

[00112] If the W(b,d) is not constant across beams and delays (in spatial and frequency domains) then we report all W(b'ddl and x(b,d for all beams b and delays d. As regards W(b,d\ enough information is only reported so that gNB (BS) can reconstruct W(b,d We also report W.

[00113] In some cases, for a given beam b and delay d, W(b'ddl would mean a selected submatrix ( consisting of selected columns only) of a Doppler basis matrix Wt in which case for each beam b and d, only the selected column information is reported for W(b,d)

[00114] Third variant for compression technique (PCA):

[00115] PCA variant 1-2 computed delays W2 i for each time instant i. For a given beam b and delay d, it accumulated N4 values and compressed it in time.

[00116] In this variant we can work with W2ii which is the Release-15 / 16 / 17 matrix W2 associated with subbands at time instant i. We take the N4 values corresponding to a beam b and subband 5, compress it in time, and then the compressed values are further compressed across subbands using an appropriate basis (example FFT). The final compressed values are fedback.

[00117] FIG. 2 shows various hardware components of an apparatus (i.e., UE (100) or base station (200)), according to the embodiments as disclosed herein. In an embodiment, the apparatus includes a processor (210), a communicator (220), a memory (230) and a CSI controller (240). The processor (210) is coupled with the communicator (220), the memory (230) and the CSI controller (240).

[00118] In an embodiment, the CSI controller (240) transmits the pilot symbol over the first window and receives at least one of the first type feedback and a second type feedback from the UE (100) at an end of the first window or afterthe first window. The CSI controller (240) receives the compressed CSI feedback based on the predefined precoder weights in the first window or the second window. Based on the at least one received feedback information (e.g., first type feedback and second type feedback), the CSI controller (240) computes and predicts the at least one precoder weight for the UE (100) in at least one time instant in the second window for at least one sub-band of the UE (100). Further, the CSI controller (240) manages the received CSI feedback compression based on the at least one predicted precoder weight in the second window.

[00119] In another embodiment, the CSI controller (240) estimates at least one channel for one of the set of sub-carriers or at least one sub-band or at least one delay or at least one beam delay for a selected time instant in the first window. Further, the CSI controller (240) estimates at least one precoder weight for the selected time instant across the at least one sub-band or delay for the selected time instant in the first window. Further, the CSI controller (240) predicts a value of a quantity in the selected time instant across at least one sub-band or delay in a second window based on a linear combination or a non-linear combination of quantities in one or more dimensions in the first and / or second window , wherein the linear combination or non-linear combination (learning coefficient) is learned across a plurality of candidate values in the first window. Further, the CSI controller (240) manages the CSI feedback compression in the wireless network (300) based on the predicted value of the quantity.

[00120] In another embodiment, the CSI controller (240) estimates at least one channel for a set of sub-carriers or at least one sub-band or delays or beam delays for a selected time instant in the first window. Further, the CSI controller (240) estimates at least one precoder weight for the selected time instant across the at least one sub-band or delay for the selected time instant in the first window. Further, the CSI controller (240) determines an output over a period of time in a second window by performing spectral estimation associated with the quantity in the first window, where the output of the spectral estimation is an n-dimensional frequency component and an amplitude associated with the n-dimensional frequency component. Based on the output, the CSI controller (240) estimates thevalue of the quantity in the selected time instant in the second window. Based on the estimated value of the quantity, the CSI controller (240) manages the CSI feedback compression in the wireless network (300).

[00121] In another embodiment, the CSI controller (240) compresses at least one precoder at various time instants across sub-bands or delays at the UE (100) in at least one of the first window and the second window. Further, the CSI controller (240) sends feedback containing information at least one compressed precoder to the base station (200).

[00122] The CSI controller (240) is physically implemented by analog and / or digital circuits such as logic gates, integrated circuits, microprocessors, microcontrollers, memory circuits, passive electronic components, active electronic components, optical components, hardwired circuits and the like, and may optionally be driven by firmware.

[00123] Further, the processor (210) is configured to execute instructions stored in the memory (230) and to perform various processes. The communicator (220) is configured for communicating internally between internal hardware components and with external devices via one or more networks. The memory (230) also stores instructions to be executed by the processor (210). The memory (230) may include non-volatile storage elements. Examples of such non-volatile storage elements may include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. In addition, the memory (230) may, in some examples, be considered a non-transitory storage medium. The term "non-transitory" may indicate that the storage medium is not embodied in a carrier wave or a propagated signal. However, the term "non-transitory" should not be interpreted that the memory (230) is non-movable. In certain examples, a non-transitory storage medium may store data that can, over time, change (e.g., in Random Access Memory (RAM) or cache).

[00124] Although the FIG. 2 shows various hardware components of the apparatus (200) but it is to be understood that other embodiments are not limited thereon. In other embodiments, the apparatus (200) may include less or morenumber of components. Further, the labels or names of the components are used only for illustrative purpose and does not limit the scope of the invention. One or more components can be combined together to perform same or substantially similar function in the apparatus (200).

[00125] FIG. 7 is a flow chart (700) illustrating a method for managing the CSI feedback compression in the wireless network (300), according to embodiments as disclosed herein. The operations (702-708) are handled by the CSI controller (240).

[00126] At 702, the method includes transmitting the at least one pilot symbol over the first window. At 704, the method includes receiving at least one of the first type feedback and the second type feedback from the UE (100) at an end of the first window or after the first window. At 706, the method includes computing and predicting at least one precoder weight for the UE (100) in at least one time instant in the second window for at least one sub-band of the UE (100) based on the at least one received feedback information. At 708, the method includes managing the received CSI feedback compression that could be based on the at least one precoder weight or used to compute at least one predicted precoder weight in the second window.

[00127] FIG. 8 is an example flow chart (800) illustrating a method for managing the CSI feedback compression in the wireless network (300), according to embodiments as disclosed herein.

[00128] At 802, the gNB transmits the pilot symbols over the observation window, where the pilot symbol is an CSI-RS. At 804, at the end of the observation window or the after observation window, the UE (100) feedbacks the feedback information to the gNB. The feedback type may depend on prior configuration of the UE (100) from the gNB or prior signalling from the UE (100) to the gNB. At 806, based on the feedback type, the gNB decides a type 2 precoder weights for the UE (100) for various time instants and subbands in the prediction window.

[00129] FIG. 9 is an example flow chart (900) illustrating a method for determining the first feedback type while managing the CSI feedback compression in the wireless network (300), according to embodiments as disclosed herein.

[00130] At 902, the gNB receives the feedback type 1 from the UE (100) at the end of the observation window. The feedback type 1 corresponds to all precoder weights across time instants in the observation window for all sub-bands of the UE (100). At 904, based on the feedback type 1, the gNB can compute / predict the precoder type 2 weights for the selected time instants in the prediction window for all sub-bands of the UE (100). The prediction / reconstruction can be based on a linear prediction (also called as AR, MMSE) or based on spectral estimation or the neural networks.

[00131] FIG. 10 is an example flow chart (1000) illustrating a method for determining the second feedback type while managing the CSI feedback compression in the wireless network (300), according to embodiments as disclosed herein.

[00132] At 1002, the gNB receives the feedback type 2 from the UE (100) at the end of or after the observation window. The feedback type 2 corresponds to precoder information weights across selected time instants in the prediction window for all sub-bands of the UE (100). At 1004, based on the feedback type 2, the gNB can extract relevant information and compute the feedback type 2 precoder weights for the selected time instants in the prediction window for all subbands of the UE (100).

[00133] FIG. 11 and FIG. 12 are example flow charts (1100 and 1200) illustrating a method for determining a precoder weights while managing the CSI feedback compression in the wireless network (300), according to embodiments as disclosed herein.

[00134] As shown in FIG. 11, at 1102, the gNB transmits the pilot symbols over the observation window. At 1104, at the end of observation windows or after the observation windows, the UE (100) predicts or reconstructs the channel across the selected time instants in the prediction window for all sub-bands. The UE (100) can also predict / reconstruct the channel across the selected time instants in the prediction window for the delay or all angle (beam) delay. The prediction or reconstruction can use either linear prediction, or spectral estimation or the neural networks. At 1106, from the predicted channels, the UE (100) computes theprecoders for the selected time instants in the prediction window for all subbands of the UE (100). At 1108, all of the precoder type 2 weights in the prediction window are captured across selected time instants for all sub-bands as W = or per delay of W2i associated with the beam and compressed across the time by Doppler basis or per sub-band of W2i associated with a beam and compressed across time by the Doppler basis. At 1110, at the end of observation window or after the observation window, the UE (100) feedbacks the feedback information to the gNB. The feedback type may depend on prior configuration of the UE (100) from the gNB or prior signalling from the UE (100) to the gNB. At 1112, based on the feedback type, the gNB decides the type 2 precoder weights W-{W2 for the UE (100) for the various time instants and subbands in the prediction window.

[00135] As shown in FIG. 12, at 1202, the gNB transmits the pilot symbols over the observation window. At 1204, at the end of observation windows or after the observation window, the UE (100) computes the precoder weights or W2 or W2 for all time instants in the observation window. Based on the precoder weights so computed, the UE (100) predicts or reconstructs the precoder weights for the selected time instants in the prediction window across all subbands using linear prediction, or AR prediction or neural networks or reconstruction by estimating Doppler / frequency components via spectral estimation. At 1206, all of this precoder type 2 weights in the prediction window are captured across selected time instants for all sub-bands as W = or per delay of W2i associated with the beamand compressed across the time by Doppler basis or per sub-band of W2i£ associated with a beam and compressed across time by Doppler basis. At 1208, at the end of observation window or after the observation window, the UE (100) feedbacks to the feedback information to the gNB. The feedback type may depend on prior configuration of the UE (100) from the gNB or prior signalling from the UE (100) to the gNB. At 1210, based on the feedback type, the gNB decides the type 2 precoder weights W-1W2 for the UE (100) for the various time instants and subbands in the prediction window.

[00136] FIG. 13 is an example flow chart (1300) illustrating a method for managing the CSI feedback compression in the wireless network (300) using the linear prediction, according to embodiments as disclosed herein.

[00137] At 1302, the gNB transmits the pilot symbols over the observation window. At 1304, the apparatus (e.g., gNb or the UE (100)) uses the quantity at time instants t, t-1 and t-a across one or more dimensions associated with time instants to predict the quantity at time instant t+b. The prediction at time instants t+b is a linear combination of the quantities in one or more dimensions at time instants t, t-1, and t-a. Here, t, t-1, t-a and t-b are all time instants in the observation windows.

[00138] At 1306, in order to learn the linear combination coefficients learning happens across many candidates across many candidate values of the time in the observation windows. For each value of b, there is one set of learning coefficients. At 1308, after the learning is done, the apparatus (e.g., gNb or the UE (100)) predicts the value of the quantity at t+b, where t+b is a time instant in the prediction windows using the quantities in one or more dimensions at t, t-1 and t-a. One or more or all time instants t, t-1, .. .t-a can be in the observation window. For the time instants in the prediction windows (If any) the quantities used as inputs for the prediction are the ones which were predicated in an earlier iteration for the associated time instants t1<t.

[00139] If the quantity is channel, the dimension could be subcarrier / frequency / sub-bands / antennas / delays / beams. If the quantity is an element of w2, the dimensions could be associated beams / sub-bands. If the quantity is an element of w2~, the dimensions could be associated beams / delays.

[00140] FIG. 14 is an example flow chart (1400) illustrating a method for managing the CSI feedback compression in the wireless network (300) using the spectral estimation, according to embodiments as disclosed herein. At 1402, the gNB transmits the pilot symbols over the observation window. At 1404, the apparatus (e.g., gNb or the UE) uses the quantity and does the spectral estimation of the quantity over the time instants in the observation windows. The results of the spectral estimation is n-dimensional frequency components and associatedamplitude. At 1406, the apparatus uses the n-dimensional frequency components and associated amplitude and evaluates the values of the quantity in the selected time instants of the prediction windows.

[00141] If the quantity is an element of w2 and n=1, a frequency component is across the time (1 dimension). If the quantity is an element of w2 and n=2, a frequency component is across the sub-bands and time (two dimensions). If the quantity is an element of w2~ and n=1, a frequency component is across the time (one dimension). If the quantity is an element of w2 and n=2, a frequency component is across the delay and time (two dimensions). If the quantity is channel at a subcarrier / sub-band and n=1, the frequency component is across the time (one dimensions). If the quantity is channel at a subcarrier / sub-band and n=2, the frequency component is across the time and the subcarrier / sub-band (two dimensions). If the quantity is channel at a delay and n=1, the frequency component is across the time (one dimension). If the quantity is channel at a delay and n=2, the frequency component is across the time and delay (two dimensions). If the quantity is channel at a delay, angle, and n=1, the frequency component is across the time (one dimension).

[00142] FIG. 15 is an example flow chart (1500) illustrating a method for managing the CSI feedback compression in the wireless network (300) using the Doppler-Delay coefficients, according to embodiments as disclosed herein.

[00143] At 1502, the precoders at the various time instants across subbands / delays are present at the UE (100) in the observation window and / or the prediction window that need to compressed and sent to the gNB. These are denoted by and W2 i, where i is the time instant. The precoders are as per release 17 or before. Initialize b=1 and d=1. At 1504, the UE (100) computes the #4x1 vector x(b,di corresponding to bth beam and dth delay along with Doppler basis W(b,d\ At 1506, the method includes determining whether the processing done for all delays for the beam. If the processing is not done for all delays for the beam, at 1508, the method goes to the next delay d=d+1 for the beam. If the processing is done for all delays for the beam, at 1510, the method includes determining whether the processing is done for all the beams. If the processing is not done for all the beamsthen, at 1512, the method goes to next beam (i.e., b=b+1). If the processing is done for all the beams then, at 1514, method includes determining whether the time domain Doppler basis matrix is different across spatial / frequency domain. If the time domain Doppler basis matrix is not different across spatial / frequency domain then, at 1516, the method includes feedbacking W1,W2,Wft (Here ,W2 is as per

[00109] ) to the gNB. If the time domain doppler basis matrix is different across the spatial / frequency domain then, at 1518, the method includes feedbacking tox(b-d)and W(b,ddl for all beams b and delay d.

[00144] FIG. 16 is an example flow chart (1600) illustrating a method for managing the CSI feedback compression in the wireless network (300) using the non-linear prediction or a neural network, according to embodiments as disclosed herein.

[00145] As shown in FIG. 16, at 1602, the gNB transmits the pilot symbols (e.g., CSI-RS) over the observation window. At 1604, the apparatus (e.g., gNb or the UE) uses the quantity at time instants t, t-1 and t-a across one or more dimensions associated with time instants to predict the quantity at time instant t+b. The prediction at time instants t+b is a non-linear combination of the quantities in one or more dimensions at time instants t, t-1, and t-a. Here, t, t-1, t-a and t-b are all time instants in the observation windows. At 1606, in order to learn the non-linear combination coefficients / aspects learning happens across many candidate values of the time in one or more observation windows (or a separate training dataset). For each value of b, there is one set of learning coefficients. At 1608, after the learning is done, the apparatus (e.g., gNb or the UE) predicts the value of the quantity at t+b, where t+b is a time instant in the prediction windows using the quantities in one or more dimensions at t, t-1 and t-a. One or more or all time instants t, t-1, .. .t-a can be in the observation window. For the time instants in the prediction windows (If any) the quantities used as inputs for the predication are the ones which were predicated in an earlier iteration for the associated time instants t1<t.

[00146] The various actions, acts, blocks, steps, or the like in the flow charts (700-1600) may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some of the actions, acts, blocks,steps, or the like may be omitted, added, modified, skipped, or the like without departing from the scope of the invention.

[00147] The embodiments disclosed herein can be implemented through at least one software program running on at least one hardware device and performing network management functions to control the elements. The elements can be at least one of a hardware device, or a combination of hardware device and software module.

[00148] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and / or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of at least one embodiment, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.

Claims

1. A method for managing a Channel State Information (CSI) feedback compression in a wireless network (300), comprising: transmitting, by a base station (200), at least one pilot symbol over a first window; receiving, by the base station (200), at least one of a first type feedback and a second type feedback from a User Equipment (UE) (100) at an end of the first window or after the first window; receiving, by the base station (200), a compressed CSI feedback based on predefined precoder weights in the first window or a second window; computing and predicting, by the base station (200), at least one precoder weight for the UE (100) in at least one time instant in the second window for at least one sub-band of the UE (100) based on the at least one of the first type feedback and the second type feedback; and managing, by the base station (200), the received CSI feedback compression based on the at least one predicted precoder weight in the second window.

2. The method as claimed in claim 1, wherein computing and predicting, by the base station (200), the at least one precoder weight for the UE (100) in the at least one time instant in the second window for the at least one sub-band of the UE (100) based on the at least one received feedback information comprises: extracting, by the base station (200), information from the second type feedback; and predicting, by the base station (200), the at least one precoder weight for the UE (100) in at least one time instant in the second window for the at least one sub-band of the UE (100) based on the extracted information.

3. The method as claimed in claim 1, wherein the at least one of the first type feedback and the second type feedback depends on at least one of a prior configuration of the UE (100) from the base station (200) and a prior signalling from the UE (100) to the base station (200).

4. The method as claimed in claim 1, wherein the at least one first type feedback corresponds to all precoder weights across the at least one time instants in the first window for all sub-bands of the UE (100).

5. The method as claimed in claim 1, wherein the first window is an observation window, and the second window is a prediction window.

6. The method as claimed in claim 1, wherein the at least one pilot symbol comprises a Channel State Information Reference Signal (CSI-RS), wherein the CSI feedback compression is managed in a Doppler domain.

7. The method as claimed in claim 1, wherein the at least one precoder weight for the UE (100) is predicted in the second window based on at least one of a linear prediction technique, a spectral estimation technique and a neural network (NN).

8. The method as claimed in claim 1, wherein the second type feedback is determined by: computing a channel across the selected time instant in the first window for at least one delay or sub-band or sub-carrier or beam delay, predicting the channel using the computed channel in the first window across the selected time instant in the second window for the at least one delay or the sub-band or the sub-carrier or the beam delay, wherein the channel across the selected time instant in the second window for the at least one delay or the sub-carrier or the sub-band or the angle delay is predicted based on at least one of a liner prediction technique, a spectral estimation technique and a neural network (NN); computing a precoder for the selected time instant in the second window for all the sub-bands of the UE (100); and obtaining, by the base station (200), at least one precoder weight across the selected time instant in the second window for all the sub-bands of the UE (100) in a compressed format.

9. The method as claimed in claim 1, wherein the second type feedback is determined by: predicting the precoder across the selected time instant in the second window for at least one delay or sub-band, wherein the precoder across the selected time instant in the second window for the at least one delay or the sub-band is predicted from precoders in first window based on at least one of a liner prediction technique, a spectral estimation technique and a neural network (NN); obtaining, by the base station (200), the at least one precoder weight across the selected time instant in the second window for all the sub-bands of the UE (100) in a compressed format.

10. A method for managing a Channel State Information (CSI) feedback compression in a wireless network (300), comprising: estimating, by an apparatus, at least one channel for one of a set of subcarriers or at least one sub-band or at least one delay or at least one beam delay for a selected time instant in a first window; estimating, by the apparatus, at least one precoder weight for the selected time instant across the at least one sub-band or the at least one delay for the selected time instant in the first window; predicting, by the apparatus, a value of a quantity in the selected time instant across at least one subband or delay in a second window based on a linear combination or a non-linear combination of quantities in one or more dimensions in the first or second window, wherein the linear combination or non-linear combination is learned across a plurality of candidate values in the first window; and managing, by the apparatus, the CSI feedback compression in the wireless network (300) based on the predicted value of the quantity.

11. The method as claimed in claim 10, wherein the at least one pilot symbol comprises a Channel State Information Reference Signal (CSI-RS), wherein the CSI feedback compression is managed in a Doppler domain.

12. The method as claimed in claim 10, wherein the quantity is channel corresponding to at least one of subcarriers, sub-bands, frequency, delays, beam delays and antenna, wherein the quantity is the precoder weight for the beam in the sub-bands or the delays.

13. The method as claimed in claim 10, wherein the apparatus comprises at least one of a User Equipment (UE) (100) and a base station (200).

14. A method for managing a Channel State Information (CSI) feedback compression in a wireless network (300), comprising: estimating, by an apparatus, at least one channel for a set of sub-carriers or at least one sub-band or delays or beam delays for a selected time instant in a first window; estimating, by the apparatus, at least one precoder weight for the selected time instant across the at least one sub-band or delay for the selected time instant in the first window; determining, by the apparatus, an output over a period of time in a second window by performing spectral estimation associated with a quantity in the first window, where the output of the spectral estimation is an n30 dimensional frequency component and an amplitude associated with the n-dimensional frequency component; estimating, by the apparatus, a value of the quantity in the selected time instant in the second window based on the output; and managing, by the apparatus, the CSI feedback compression in the wireless network (300) based on the estimated value of the quantity.

15. The method as claimed in claim 14, wherein the at least one pilot symbol comprises a Channel State Information Reference Signal (CSI-RS), wherein the CSI feedback compression is managed in a doppler domain.

16. The method as claimed in claim 14, wherein the apparatus comprises at least one of a User Equipment (UE) (100) and a base station (200), wherein if the quantity is an element of w2 and n=1, a frequency component is across the time, wherein if the quantity is an element of w2 and n=2, a frequency component is across the sub-bands and time, wherein if the quantity is an element of w2~ and n=1, a frequency component is across the time. If the quantity is an element of w2 and n=2, a frequency component is across the delay and time, wherein if the quantity is channel at a subcarrier / sub-band and n=2, the frequency component is across the time and the subcarrier / subband, wherein if the quantity is channel at a subcarrier / sub-band and n=1, the frequency component is across the time, wherein if the quantity is channel at a delay and n=1, the frequency component is across the time, wherein if the quantity is channel at a delay and n=2, the frequency component is across the time and delay, and wherein if the quantity is channel at a delay, angle, and n=1, the frequency component is across the time.

17. A method for managing a Channel State Information (CSI) feedback compression in a wireless network (300), comprising: compressing, by a User Equipment (UE) (100), at least one precoder at various time instants across sub-bands or delays at the UE (100) in at least one of a first window and a second window; and sending, by the UE (100), at least one compressed precoder to a base station (200).

18. The method as claimed in claim 17, wherein the method comprises: computing, by the UE (100), a vector value corresponding to precoder weights of a beam, and a delay across selected time instants in at least one of the first window and the second window; determining, by the UE (100), that the computed vector value for all delays for the beam are completed; determining, by the UE (100), that the computed vector value for all beams are completed; determining, by the UE (100), whether a time domain Doppler basis matrix for the selected time instants is different across one of a spatial domain, a delay domain and a frequency domain; and performing, by the UE , one of feedbacking a first compressed Doppler coefficient precoder vector value based on precoder weights for each beam and subband or delay across the selected time instants, a Doppler-time domain basis matrix for each beam and subband or delay associated with the first compressed vector and a spatial beam matrix value associated with all beams in response to determining that the time domain Doppler matrix is different across one of the spatial domain, the delay domain and the frequency domain, and feedbacking a first compressed Doppler coefficient precoder matrix value based on precoder weights for all beams and subband or delay across the selected time instants, a joint Doppler-time frequency or delay domain basis matrix for all beams and subband or delay associated with the compressed Doppler coefficient matrix and the spatial beam matrix value associated with all beams in response to determining that the time domain Doppler matrix is not different across one of the spatial domain, the delay domain and the frequency domain.

19. A base station (200), comprising: a processor (210); a memory (230); and a CSI controller (240), coupled with the processor (210) and the memory (230), configured to: transmit at least one pilot symbol over a first window; receive at least one of a first type feedback and a second type feedback from a User Equipment (UE) (100) at an end of the first window or after the first window; receive a compressed CSI feedback based on predefined precoder weights in the first window or a second window; compute and predict at least one precoder weight for the UE (100) in at least one time instant in the second window for at least one subband of the UE (100) based on the at least one received feedback information; and manage the received CSI feedback compression based on the at least one predicted precoder weight in the second window.

20. An apparatus, comprising: a processor (210); a memory (230); and a CSI controller (240), coupled with the processor (210) and the memory (230), configured to: estimate at least one channel for one of a set of sub-carriers or at least one sub-band or at least one delay or at least one beam delay for a selected time instant in a first window; estimate at least one precoder weight for the selected time instant across the at least one sub-band or delay for the selected time instant in the first window; predict a value of a quantity in the selected time instant across at least one sub-band or delay in a second window based on a linear combination or a non-linear combination of quantities in one or more dimensions of the first and / or second window, wherein the linear combination or non-linear combination is learned across a plurality of candidate values in the first window; and manage the CSI feedback compression in the wireless network (300) based on the predicted value of the quantity.

21. An apparatus, comprising: a processor (210); a memory (230); and a CSI controller (240), coupled with the processor (210) and the memory (230), configured to: estimate at least one channel for a set of sub-carriers or at least one sub-band or delays or beam delays for a selected time instant in a first window; estimate at least one precoder weight for the selected time instant across the at least one sub-band or delay for the selected time instant in the first window; determine an output over a period of time in a second window by performing spectral estimation associated with a quantity in the first window, where the output of the spectral estimation is an n-dimensional frequency component and an amplitude associated with the n-dimensional frequency component; estimate a value of the quantity in the selected time instant in the second window based on the output; and manage the CSI feedback compression in the wireless network (300) based on the estimated value of the quantity.

22. A User Equipment (UE) (100), comprising: a processor (210); a memory (230); and a CSI controller (240), coupled with the processor (210) and the memory (230), configured to: compress at least one precoder at various time instants across sub10 bands or delays at the UE (100) in at least one of a first window and a second window; and send at least one compressed precoder to a base station (200).