Systems and methods for channel estimation in physical layer processing

Abstract

A method for channel estimation in physical layer processing, implemented in a user equipment (UE), is disclosed. The method may include: receiving data packets from a next-generation node B (gNB) for physical layer processing; initiating physical layer processing of the received data packets; sending a message to the gNB including at least one channel quality information (CQI) report and one or more network parameters; receiving channel information from the gNB, wherein the channel information is determined using at least one of the CQI report, one or more network parameters, and uplink packets transmitted by the UE on an uplink channel; and selecting a channel estimation technique based on the received channel information.

Description

Technical Field

[0001] This disclosure relates to the field of wireless communication systems. For example, this disclosure relates to systems and methods for channel estimation in physical layer processing. Background Technology

[0002] In the 5G New Radio (NR) protocol stack, the Physical Layer (PHY layer or Layer 1 (L1)) allows the transmission and reception of radio signals over a wireless medium. In communication, the PHY layer sits at the bottom of the 5G NR protocol stack, interfacing with the Media Access Control (MAC) sublayer via transport channels. Furthermore, the PHY layer provides its services to the MAC sublayer and is configured by the Radio Resource Control (RRC). The PHY layer supports downlink (Next Generation Node B (gNB) to User Equipment (UE)), uplink (UE to gNB), and sidelink (UE to UE) communication. For example, downlink channels can be the Physical Downlink Shared Channel (PDSCH), Physical Downlink Control Channel (PDCCH), and Physical Broadcast Channel (PBCH). Uplink channels can be the Physical Random Access Channel (PRACH), Physical Uplink Shared Channel (PUSCH), and Physical Uplink Control Channel (PUCCH). Moreover, the PHY layer plays a crucial role in converting data into a format suitable for transmission over the wireless medium. In addition, the PHY layer performs a set of operations, such as encoding, modulation, and synchronization, to ensure reliable communication between multiple devices.

[0003] Figure 1a This is a block diagram illustrating an example of L1 processing 100 according to the prior art. As shown in the figure, Figure 1a An example L1 processing block diagram 100 for a channel based on PDSCH receive (Rx) multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) is shown. L1 processing 100 includes a set of computations such as cyclic prefix removal 102, fast Fourier transform (FFT) 104, user demapping 106, channel estimation 108, equalization 110, demodulation 112, descrambling 114, rate dematching 116, low-density parity-check (LDPC) decoding 118, and cyclic redundancy check (CRC) 120. Furthermore, most of these computations are intensive and require significant time and power from the processing unit.

[0004] Figure 1b This is a block diagram illustrating an example process for performing channel estimation 122 according to the prior art. Figure 1bAs shown, the process of performing channel estimation 122 includes demodulation reference signal (DMRS) extraction 124, power boosting 126, DMRS generation 128, despreading 130, phase compensation 132, interpolation 134, signal-to-noise ratio (SNR) estimation 136, and frequency correction 138. The goal of channel estimation is to calculate the channel coefficients through estimation theory. This also involves including and using different techniques, in which trade-offs are made between accuracy and computation time.

[0005] Furthermore, there are various estimation methods based on training pilots. These methods are applicable to systems where the transmitter sends a known signal. This known transmitted signal is called a pilot signal. The process of estimating channel parameters using these received symbols or pilots is called channel estimation. Channel estimation based on training pilots can be achieved by inserting pilot tones into the subcarriers of all OFDM symbols with a specific period, or by inserting pilot tones into each OFDM symbol. The insertion configuration of these pilot tones in the frame can vary according to system requirements.

[0006] Furthermore, channel estimation techniques are generally expected to provide a combination of high data rate support and low error rate. High data rate support can be achieved if the channel estimation technique is fast. Conversely, a low error rate can be achieved if the channel estimation technique is highly accurate, which requires mathematically complex techniques. Therefore, OFDM systems require estimators with low complexity and high accuracy. Thus, estimators in OFDM systems typically employ a trade-off between complexity and accuracy. For example, increased runtime and power consumption lead to increased channel estimation complexity and accuracy. Conversely, decreased channel estimation accuracy leads to decreased / increased power consumption depending on channel conditions and decreased channel estimation complexity. Two basic 1D estimators are block-based pilot estimators and comb-based estimators. Furthermore, block-based estimators can be based on least squares (LS) methods, minimum mean square error (MMSE), and modified MMSE. Additionally, comb-based pilot estimators include LS estimators with 1D interpolation, maximum likelihood estimators (MLE), and estimators based on parametric channel modeling (PCMB).

[0007] Furthermore, channel estimation techniques employ mathematical calculations such as least squares, least mean squares, correlation, complex vector multiplication, matrix multiplication, and matrix inversion. These mathematical calculations require a significant amount of computation. For example, matrix inversion requires substantial computation, and various techniques, such as matrix factorization using Cholesky, QR, and LU, are employed to reduce such computation. In addition, there are major computational challenges in estimating communication channels. For instance, channel estimation leads to significant power consumption on both the UE and gNB sides. With the increase in bandwidth and high data rates, the current frame now occupies a very small time interval. Furthermore, such frames need to be decoded with shorter transmission intervals.

[0008] Furthermore, in traditional solutions, applying high-precision channel estimation to all channels each time results in significant power consumption on both the UE and gNB sides. However, there are several use cases where applying high-precision channel estimation to all channels is unnecessary. For example, when the UE is in clear line-of-sight and there are few obstacles in the channel (environment), the channel tends to remain constant over such a short duration as the received frame. This eliminates the need to recalculate the channel parameter coefficients for each received frame using channel estimation techniques. In another example, when users employ communication technologies used in industrial and static communication environments, the control unit for each machine is located at a fixed installation. Therefore, the channel conditions between such control units (or communication units) are mostly static and do not need to change over such a short duration as the received frame. This also eliminates the need to recalculate the channel parameters for each received frame using channel estimation techniques. Additionally, when the UE is not mobile and is in an environment with minimal interference, recalculating the channel parameters is also unnecessary. Stationary objects ensure minimal changes in channel parameters in static environments such as mobile devices held at a desk, table, or in an idle state. Furthermore, when the UE is within a good beam formed by the gNB, recalculating the channel parameters is also unnecessary. In this scenario, strong beamforming ensures a good communication channel and high-power transmission. Furthermore, channel parameters are unlikely to vary between the gNB and the user. In this case, previously estimated channel coefficients and low-prediction channel estimation techniques can be applied.

[0009] Therefore, it is necessary to address the aforementioned issues associated with channel estimation in physical layer processing. Summary of the Invention

[0010] Solution to the problem

[0011] According to an example embodiment of this disclosure, a method for channel estimation in physical layer processing implemented in a user equipment (UE) is disclosed. The method includes: receiving data packets from a next-generation node B (gNB) for physical layer processing; initiating physical layer processing of the received data packets; sending a message to the gNB including at least one channel quality information (CQI) report and one or more network parameters; receiving channel information from the gNB, wherein the channel information is identified using at least one of the CQI report, one or more network parameters, and uplink packets transmitted by the UE on an uplink channel; and selecting a channel estimation technique based on the received channel information.

[0012] According to an example embodiment of this disclosure, a user equipment (UE) for performing channel estimation in physical layer processing is disclosed. The UE includes: a memory storing instructions and at least one processor including processing circuitry, wherein the instructions, when executed individually and / or jointly by the at least one processor, cause the UE to: receive data packets from a next-generation node B (gNB) for physical layer processing; initiate physical layer processing of the received data packets; control the UE to send a message to the gNB including at least one channel quality information (CQI) report and one or more network parameters; control the UE to receive channel information from the gNB, wherein the channel information is identified using at least one of the CQI report, one or more network parameters, and uplink packets transmitted by the UE on an uplink channel; and select a channel estimation technique based on the received channel information.

[0013] According to an example embodiment of this disclosure, a non-transitory computer-readable storage medium is disclosed that stores one or more programs including instructions. These instructions, when executed individually and / or jointly by at least one processor, cause the UE to: receive data packets from a next-generation node B (gNB) for physical layer processing; initiate physical layer processing of the received data packets; send a message to the gNB including at least one channel quality information (CQI) report and one or more network parameters; receive channel information from the gNB, wherein the channel information is identified using at least one of the CQI report, one or more network parameters, and uplink packets transmitted by the UE on an uplink channel; and select a channel estimation technique based on the received channel information.

[0014] To further illustrate the advantages and features of this disclosure, a more specific description of the various exemplary embodiments shown in the accompanying drawings is provided. It should be understood that these drawings depict exemplary embodiments and are therefore not intended to limit the scope of the disclosure. This disclosure will be described and explained with reference to the accompanying drawings in addition to the features and details. Attached Figure Description

[0015] The above and other features, aspects and advantages of certain embodiments of this disclosure will become more apparent from the following detailed description taken in conjunction with the accompanying drawings, wherein the same characters denote the same parts throughout the drawings, and wherein:

[0016] Figure 1a This is a block diagram illustrating an example of L1 processing according to the existing state of the prior art;

[0017] Figure 1b This is a block diagram illustrating an example process for performing channel estimation based on the existing state of the prior art;

[0018] Figure 2This is a block diagram illustrating an example configuration of a user equipment (UE) for channel estimation in physical layer processing according to various embodiments;

[0019] Figure 3 This is a flowchart illustrating example techniques for selecting channel estimation based on channel information according to various embodiments;

[0020] Figure 4 This is a diagram illustrating example operations performed by the UE for performing channel estimation according to various embodiments;

[0021] Figure 5 This is a signal flow graph illustrating example operations performed by a UE for performing channel estimation according to various embodiments; and

[0022] Figure 6 This is a flowchart illustrating example methods for performing channel estimation according to various embodiments.

[0023] Furthermore, those skilled in the art will understand that the elements in the accompanying drawings are shown for simplicity and may not necessarily be drawn to scale. For example, flowcharts illustrate the method in terms of the steps / operations involved to aid in understanding various aspects of this disclosure. Additionally, regarding the construction of the device, one or more components of the device may have been represented by conventional symbols in the drawings, and the drawings may show those specific details relevant to understanding the various embodiments of this disclosure so as not to obscure the drawings with details readily understood by those skilled in the art from the description herein. Detailed Implementation

[0024] First, it should be understood that although illustrative embodiments of various example examples of this disclosure are shown below, this disclosure can be implemented using any number of techniques, whether currently known or existing. This disclosure should in no way be limited to the illustrative embodiments, figures, and techniques described below, but includes the exemplary designs and implementations illustrated and described herein, and modifications can be made within the full scope of the appended claims and their equivalents.

[0025] The term "some" as used herein can mean, for example, "none," "one," "more than one," or "all." Therefore, the terms "none," "one," "more than one," "more than one but not all," or "all" all fall under the definition of "some." The term "some embodiments" can refer to one embodiment, several embodiments, or all embodiments. Therefore, the term "some embodiments" can refer to "no embodiment," "one embodiment," "more than one embodiment," or "all embodiments."

[0026] The terminology and structure used herein are intended to describe, teach, and illustrate various embodiments and their specific features and elements, and do not limit, constrain, or diminish the spirit and scope of the claims or their equivalents.

[0027] For example, unless otherwise stated, any term used herein, such as but not limited to “including,” “contains,” “has,” “has,” and its grammatical variations, does not specify exact limitations or constraints, and certainly does not preclude the possible addition of one or more features or elements, and furthermore, unless otherwise stated in restrictive language such as “must include” or “requires inclusion,” it does not preclude the possible removal of one or more listed features and elements.

[0028] Whether a feature or element is limited to being used only once, it may still be referred to as "one or more features," "one or more elements," "at least one feature," or "at least one element." Furthermore, the use of the terms "one or more" or "at least one" features or elements does not preclude the absence of that feature or element, unless otherwise specified by restrictive language such as "one or more are required" or "one or more elements are required."

[0029] Unless otherwise defined, all terms used herein, in particular any technical and / or scientific terms, may be considered to have the same meaning as commonly understood by one of ordinary skill in the art.

[0030] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings.

[0031] Figure 2 This is a block diagram illustrating an example configuration of a user equipment (UE) 200 for channel estimation in physical layer processing according to various embodiments. Figure 2 The example block diagram of UE 200 shown can be understood as part of the configuration of UE 200. In the following text, it should be understood that terms including "unit" or "module" as shown in the figures can refer to a unit used to process at least one function or operation, and can be implemented in hardware, software, or a combination of hardware and software.

[0032] refer to Figure 2UE 200 may include one or more processors (e.g., including processing circuitry) 202, communication units (e.g., including communication circuitry) 204 (e.g., a communicator or communication interface), and memory 206. As an example, UE 200 may be an electronic device, such as, but not limited to, a cellular phone, mobile phone, smartphone, tablet computer, computing device, personal digital assistant (PDA), or other device that can communicate via a cellular network (such as 3G, 4G, 5G, 6G, and super 6G networks or any future wireless communication network). Communication unit 204 may perform functions for transmitting and receiving signals via a wireless channel.

[0033] As an example, one or more processors 202 may be a single processing unit or multiple units, all of which may include multiple computing units. One or more processors 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuits, and / or any device that manipulates signals based on operating instructions. Among other capabilities, one or more processors 202 are configured to acquire and execute computer-readable instructions and data stored in memory 206. One or more processors 202 may include one or more processors. In this case, one or more processors 202 may be a general-purpose processor (such as a central processing unit (CPU), application processor (AP), etc.), a pure graphics processing unit (such as a graphics processing unit (GPU), a visual processing unit (VPU)), and / or an artificial intelligence (AI) dedicated processor (such as a neural processing unit (NPU)). One or more processors 202 may control the processing of input data according to specified operating rules or AI models stored in non-volatile memory and volatile memory. Specified operating rules or AI models are provided through training or learning. Furthermore, processor 202 may include various processing circuits and / or multiple processors. For example, as used herein, including the claims, the term "processor" can include various processing circuitry, including at least one processor, wherein one or more of the at least one processor can be configured individually and / or collectively in a distributed manner to perform the various functions described herein. As used herein, when "processor," "at least one processor," and "one or more processors" are described as being configured to perform a number of functions, these terms cover, for example, but not limited to, cases where one processor performs some of the functions and another processor performs other functions, and cases where a single processor can perform all of the functions. Additionally, at least one processor can include, for example, a combination of processors performing the various described / disclosed functions in a distributed manner. At least one processor can execute program instructions to implement or perform the various functions.

[0034] Memory 206 may include any non-transitory computer-readable medium known in the art, including, for example, volatile memory (such as static random access memory (SRAM) and dynamic random access memory (DRAM)) and / or non-volatile memory (such as read-only memory (ROM), erasable programmable ROM, flash memory, hard disk, optical disk and magnetic tape).

[0035] The various example embodiments disclosed herein can be implemented using processing circuitry. For example, some example embodiments disclosed herein can be implemented using at least one software program that runs on at least one hardware device and performs network management functions to control elements.

[0036] In embodiments of this disclosure, one or more processors 202 of the UE 200 are configured to receive data packets from a next-generation node B (gNB) for physical layer processing. In embodiments of this disclosure, physical layer processing may refer to, for example, a set of operations and manipulations performed at the physical layer (L1) of a communication system.

[0037] In addition, one or more processors 202 can be configured to initiate physical layer processing of received data packets.

[0038] Furthermore, one or more processors 202 may be configured to send a message to the gNB or base station including a channel quality information (CQI) report and one or more network parameters. In embodiments of this disclosure, the network evaluates the quality of the radio channel between the UE 200 and the base station. The CQI report provides information about channel conditions, allowing the network to optimize various aspects of the communication link. In example embodiments of this disclosure, one or more network parameters may include estimated frequency offset, phase offset, observed Doppler shift, or any combination thereof. In embodiments of this disclosure, based on the sent message, the decision engine of the gNB or base station is notified of the channel state between the gNB and the UE 200. Furthermore, this can be supplemented by similar parameters calculated at the gNB end for uplink frames received from the UE 200. This information can significantly improve the decision quality of the decision engine in classifying the channel.

[0039] For example, according to the specification TR 138 901-V14.3.0-5G, one or more network parameters are used to model different channel categories. However, these one or more network parameters are not transmitted to the UE 200, but instead serve as a standardized static guideline for L1 processing. If the Tap Delay Channel (TDL) channel model is considered in Section 7.7.2 of the TR 138 901-V14.3.0-5G specification, then Doppler shift is considered. Three TDL models, namely TDL-A, TDL-B, and TDL-C, are constructed to represent three different channel distributions for non-line-of-sight (NLOS), while TDL-D and TDL-E are constructed for line-of-sight (LOS). The Doppler spectrum of each tap is characterized by a classical (Jakes) spectral shape and a maximum Doppler shift f_d, where These TDL models are normalized and can be scaled over the delay, allowing the desired root mean square (RMS) delay spread to be achieved using equation (1):

[0040] (1)

[0041] In equation (1), τ n. scaled It is the normalized delay value of the nth cluster in the CDL or TDL model. Furthermore, τ n. model This is the new delay value for the nth cluster (in [ns]). Furthermore, DS desired This is the required latency spread (in [ns]).

[0042] In the embodiments of this disclosure, Table 1 shows various models of fading channels and their corresponding DS. desired Furthermore, the example parameters given in Table 1 do not preclude the use of other scaling values ​​(if appropriate is found). For example, if additional scenes are introduced, or if it is necessary to capture the effects of beamforming in the TDL. Both of these examples could potentially lead to an increase in the range of experienced RMS delay spread.

[0043] Table 1

[0044]

[0045] Furthermore, one or more processors 202 may be configured to receive channel information from the gNB. In embodiments of this disclosure, the channel information is calculated using CQI reports, one or more network parameters, and uplink packets received by the gNB from the UE 200 on the uplink channel, or any combination thereof.

[0046] In addition, one or more processors 202 can be configured to select a channel estimation technique based on the received channel information.

[0047] In addition, one or more processors 202 can be configured to send data packets to the upper layer of the UE based on a selected channel estimation technique when physical layer processing is completed.

[0048] When selecting a channel estimation technique based on the received channel information, one or more processors 202 can be configured to receive one or more updated transmission parameters from the gNB. In embodiments of this disclosure, the gNB obtains its input from multiple sources, such as CQI reports, synchronization signal block (SSB) reports (beamforming information), noise and interference power information, signal-to-interference-plus-noise ratio (SINR), etc.

[0049] In embodiments of this disclosure, the gNB updates one or more transmission parameters based on channel information. In example embodiments of this disclosure, the one or more updated transmission parameters include demodulation reference signal (DMRS) configuration, signal-to-noise ratio of one or more transmission parameters, transmission bandwidth priority, one or more beamforming parameters, etc. In embodiments of this disclosure, the one or more updated transmission parameters are shared with the UE 200 as part of an information communication frame, such as Channel State Information (CSI) indication, Downlink Control Information (DCI) information blocks, etc. Furthermore, one or more processors 202 can be configured to select a channel estimation technique based on the received channel information and the received one or more updated transmission parameters.

[0050] In embodiments of this disclosure, the gNB at the decision engine can classify the channel based on the necessity / severity of one or more transmission parameters to be estimated. The decision engine can employ one or more techniques for classification, such as, but not limited to, experiment-based thresholding techniques, machine learning-based decision techniques, convolutional neural networks (CNNs), evolutionary decision models based on recurrent neural networks (RNNs), etc.

[0051] Furthermore, when selecting a channel estimation technique based on the received channel information, one or more processors 202 can be configured to determine that the received channel information indicates a channel with relatively small changes. Additionally, one or more processors 202 can be configured to select the least computationally intensive and low-complexity channel estimation technique when determining that the received channel information indicates a channel with relatively small changes. In embodiments of this disclosure, the least computationally intensive and low-complexity channel estimation technique corresponds to the least squares technique.

[0052] Furthermore, when selecting a channel estimation technique based on the received channel information, one or more processors 202 can be configured to determine that the received channel information indicates a moderately variable channel. Additionally, one or more processors 202 can be configured to select a channel estimation technique of moderate computational and moderate complexity when determining that the received channel information indicates a moderately variable channel. In embodiments of this disclosure, the channel estimation technique of moderate computational and moderate complexity corresponds to the minimum mean square error (MMSE) technique.

[0053] Furthermore, when selecting a channel estimation technique based on the received channel information, one or more processors 202 can be configured to determine that the received channel information indicates a highly variable channel. Additionally, one or more processors 202 can be configured to select a computationally intensive and complex channel estimation technique when it is determined that the received channel information indicates a highly variable channel. In embodiments of this disclosure, the computationally intensive and complex channel estimation technique corresponds to the Discrete Cosine Transform (DCT) technique or the Discrete Fourier Transform (DFT).

[0054] In embodiments of this disclosure, one or more processors 202 may be configured to select a DS based on channel type and delay spread conditions. desired The associated parameter values ​​are shown in Table 2. If the model is AI-based, such as CNN, RNN, or Deep Neural Network (DNN), these parameters can be considered as input to the decision engine. One or more network parameters enable the AI-based model to make decisions for channel classification. Furthermore, one or more parameters are supplemented by similar parameters computed by the gNB using uplink frames. Examples of similar parameters may include, but are not limited to, channel state information, signal-to-noise ratio, and timing offset.

[0055] Table 2

[0056]

[0057] Furthermore, one or more processors 202 can be configured to determine, based on received channel information, that a previously used channel is optimal for physical layer processing. One or more processors 202 can be configured to use the previously used channel to perform physical layer processing when it is determined that the previously used channel is optimal for physical layer processing. References have been made to at least... Figure 4 and Figure 5 The details of UE 200 operation are explained.

[0058] Figure 3 This is a flowchart illustrating example techniques for selecting channel estimates based on channel information, according to various embodiments. (Reference) Figure 2 Explain the choice of channel estimation techniques.

[0059] In operation 302, UE 200 receives channel information from the gNB. In embodiments of this disclosure, the channel information is calculated using CQI reports, one or more network parameters, and uplink packets. In operation 304, the system determines that the received channel information indicates a frequent / highly variable channel. Furthermore, in operation 306, when the system determines that the received channel information indicates a highly variable channel, it selects a computationally intensive and complex channel estimation technique (e.g., MSME). In embodiments of this disclosure, the computationally intensive and complex channel estimation technique corresponds to DCT or DFT techniques 308.

[0060] In operation 310, the system determines that the received channel information indicates a moderately / slightly varied channel. In operation 312, when the system determines that the received channel information indicates a moderately varied channel, it selects a channel estimation technique of moderate computational and moderate complexity (e.g., high or low precision channel estimation) (e.g., MSME or least squares method). In embodiments of this disclosure, the selection module of UE 200 uses MMSE / DFT technique 314 or MMSE / least squares (LS) technique 316.

[0061] In operation 318, the system determines that the received channel information indicates a channel with minor changes. In operation 320, the system selects a channel estimation technique with minimal computational intensity and low complexity, using previously estimated coefficients, and estimates the channel after a certain number of frames or based on channel parameters transmitted by the gNB when determining that the received channel information indicates a channel with minor changes. In embodiments of this disclosure, this decision can be modified based on circumstances. For example, when UE 200 experiences consecutive CRC check failures. In embodiments of this disclosure, the selection module of UE 200 uses LS technique 322 or uses previously estimated coefficients 324.

[0062] Figure 4 This is a diagram illustrating example operations performed by UE 200 for performing channel estimation according to various embodiments. Reference Figure 2 Explain the details regarding the performance of channel estimation.

[0063] As shown in the figure, in operation 402, the gNB receives one or more network parameters (e.g., locally calculated channel parameters) from the uplink frame. Furthermore, the decision engine 404 at the gNB performs channel classification in operation 406, such as frequently changing channels, slightly changing channels, minimally changing channels, etc.

[0064] In operation 408, the gNB adjusts one or more transmission parameters on an optional basis. In an example embodiment of this disclosure, one or more transmission parameters include DMRS configuration 410, signal-to-noise ratio (SNR) 412 of one or more transmission parameters, transmission (Tx) bandwidth priority ordering 414, and one or more beamforming parameters 416.

[0065] In operation 418, the gNB adds the channel category to the transmission frame for UE 200 and sends the transmission frame to UE 200. In operation 420, UE 200 selects a channel estimation technique based on the received channel category information. Furthermore, in operation 422, UE 200 performs channel equalization to recover the actually transmitted data. In operation 424, UE 200 calculates one or more network parameters, such as estimated frequency offset, phase offset, observed Doppler shift, SINR, etc. In operation 426, UE 200 sends one or more network parameters to the gNB via a CQI report or DCI information frame. Furthermore, the gNB performs channel classification based on the received CQI report or DCI information frame (e.g., repeating operations 402-426 consecutively).

[0066] Figure 5 This is a signal flow diagram illustrating example operations performed by UE 200 for performing channel estimation according to various embodiments. (Reference) Figure 2 Explain the details regarding the performance of channel estimation.

[0067] In operation 502, the gNB sends a downlink packet (which can be any downlink packet) to the UE 200. Upon reception, in operation 504, the UE 200 performs L1 processing on the received downlink packet and calculates different parameters, such as SNR, frequency offset, and phase offset, based on the received downlink packet for enhanced CQI reporting. In embodiments of this disclosure, the UE 200 employs various channel estimation techniques, such as high computationally intensive techniques, low computationally intensive techniques, and low computationally intensive techniques. In parallel, in operation 506, the UE 200 can send any uplink packet to the gNB, as it is a duplex system. This packet can also be analyzed by the gNB during the L1 processing phase to derive corresponding channel information. In embodiments of this disclosure, the UE 200 derives enhanced CQI parameters, such as frequency offset and phase offset. After deriving the enhanced CQI parameters from the downlink packet, in operation 508, the UE 200 provides these enhanced parameters as feedback to the gNB using CQI indications. In operation 510, the information received at the gNB from operations 506 and 508 is fed back to the decision engine at the gNB as input parameters. Furthermore, the gNB's decision engine intelligently performs channel classification.

[0068] In operation 512, the gNB can also use the channel category information to adjust different parameters for the next downlink frame, such as DMRS configuration, SINR, beamforming parameters, etc. In operation 514, the channel category information is transmitted to UE 200 using a DCI / CSI report (or any other configured report). Upon receiving the CCI, in operation 516, UE 200 decides which channel estimation technique to use, or whether the previously estimated channel can be reused for L1 processing. In parallel, UE 200 recalculates the enhanced CQI parameters in operation 518 and reports them as feedback to the gNB, as done in operation 508. Furthermore, this system is a feedback-assisted robust approach to simultaneously reduce L1 processing time and power consumption.

[0069] Figure 6 This is a flowchart illustrating example methods for performing channel estimation according to various embodiments. Figure 6 The method 600 shown is implemented in UE 200 for performing channel estimation.

[0070] In operation 602, method 600 includes receiving data packets from a next-generation node B (gNB) for physical layer processing.

[0071] In operation 604, method 600 includes physical layer processing to initiate the reception of data packets.

[0072] In operation 606, method 600 includes sending a message to the gNB including a channel quality information (CQI) report and one or more network parameters. In an example embodiment of this disclosure, the one or more network parameters are an estimated frequency offset, a phase offset, an observed Doppler shift, or any combination thereof.

[0073] In operation 608, method 600 includes receiving channel information from the gNB. In embodiments of this disclosure, the channel information is calculated using at least one of a CQI report, one or more network parameters, and uplink packets received by the gNB from the UE 200 on the uplink channel.

[0074] In operation 610, method 600 includes selecting a channel estimation technique based on received channel information. When selecting a channel estimation technique based on the received channel information, method 600 includes receiving one or more updated transmission parameters from the gNB. In embodiments of this disclosure, the one or more transmission parameters are updated by the gNB based on the channel information. In exemplary embodiments of this disclosure, the one or more updated transmission parameters include demodulation reference signal (DMRS) configuration, signal-to-noise ratio of one or more transmission parameters, transmission bandwidth priority, one or more beamforming parameters, etc. Furthermore, method 600 includes selecting a channel estimation technique based on the received channel information and the received one or more updated transmission parameters.

[0075] When selecting a channel estimation technique based on received channel information, method 600 may include: determining that the received channel information indicates a channel with relatively small changes. Method 600 may also include: selecting a channel estimation technique with minimal computation and low complexity when determining that the received channel information indicates a channel with relatively small changes. In embodiments of this disclosure, the channel estimation technique with minimal computation and low complexity corresponds to the least squares technique.

[0076] When selecting a channel estimation technique based on received channel information, method 600 may include: determining that the received channel information indicates a moderately varying channel. Method 600 may include selecting a channel estimation technique of moderate computational and moderate complexity when determining that the received channel information indicates a moderately varying channel. In embodiments of this disclosure, a channel estimation technique of moderate computational and moderate complexity corresponds to the minimum mean square error (MMSE) technique.

[0077] When selecting a channel estimation technique based on received channel information, method 600 may include: determining that the received channel information indicates a highly variable channel. Method 600 may include selecting a computationally intensive and complex channel estimation technique when determining that the received channel information indicates a highly variable channel. In embodiments of this disclosure, the computationally intensive and complex channel estimation technique corresponds to one of the Discrete Cosine Transform (DCT) and Discrete Fourier Transform (DFT).

[0078] Method 600 may include: upon completion of physical layer processing, sending data packets to the upper layer of UE 200 based on a selected channel estimation technique.

[0079] Method 600 may include determining, based on received channel information, that a previously used channel is optimal for physical layer processing. Method 600 may include, when determining that a previously used channel is optimal for physical layer processing, using the previously used channel to perform physical layer processing.

[0080] Although described in a specific order Figure 6 The steps / operations shown above are not identical to those described herein; however, according to various embodiments of this disclosure, the steps / operations may occur in different orders. Furthermore, for the sake of brevity, they will not be repeated in detail here. Figure 6 Details related to various steps / operations, these details are already in... Figures 2-5 This is covered in the relevant description.

[0081] This disclosure provides various technological advancements based on the key features discussed above. Furthermore, the objective of this disclosure is to reduce the computational and power requirements of communication systems. For each received frame, it is not necessary to continuously, frequently, and accurately estimate channel coefficients in real time. This disclosure intelligently adjusts aspects that reduce power consumption and time requirements without compromising its robustness. Considering that the reference symbols to be processed account for approximately 15% of the target frame, approximately 15% of the power consumption of L1 processing operations can be saved when the UE decides to skip the power of several frames. For example, consider two reference symbols per transmission time interval (TTI). Since the mathematical calculations are very complex, saving these calculations can show a significant power gain. Furthermore, since input parameters such as reference symbol configuration again depend on channel classification, the power savings can be increased when using higher-order or denser reference symbols. Because the channel estimation technique is intelligently variable, this disclosure can be more power-robust and fault-robust compared to channel estimation techniques operating on a single computationally intensive technique (consuming more power) or a less computationally intensive technique (more prone to failure). Because the decision engine at the gNB uses enhanced CQI parameters and locally computed parameters, it has better intelligence to predict the correct channel conditions for a given UE. In the case of channel prioritization, this disclosure promotes channels that change less frequently, thus making it more adaptive and power efficient.

[0082] Furthermore, this disclosure discloses a technique for intelligently reducing the overhead of estimating the channel for each received frame processed by the physical layer. This disclosure can reduce computation because a time slot is shorter at high frequencies. Additionally, this disclosure is useful for power consumption. This disclosure can also be used to increase the amount of data per frame by reducing the number of reference symbols.

[0083] Although this disclosure has been described in specific language, it is not intended to impose any limitations on it. It will be apparent to those skilled in the art that various working modifications can be made to the method to implement the disclosed concepts.

[0084] The accompanying drawings and the foregoing description provide exemplary embodiments. Those skilled in the art will understand that one or more of the described elements can be well combined into a single functional element. Some elements may be divided into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, the order of the processes described herein may be changed and is not limited to the manner described herein.

[0085] Furthermore, the actions in any flowchart need not be performed in the order shown; nor is it necessary to execute all actions. Moreover, those actions that do not depend on other actions can be performed in parallel with other actions. The scope of the embodiments is by no means limited to these specific examples. Many variations are possible, such as differences in structure, size, and material use, whether expressly given in this disclosure or not. The scope of the embodiments is at least as broad as that given by the appended claims.

[0086] While this disclosure has been shown and described with reference to various exemplary embodiments, it should be understood that these exemplary embodiments are intended to be illustrative and not restrictive. Those skilled in the art will further understand that various changes in form and detail may be made without departing from the true spirit and full scope of this disclosure (including the appended claims and their equivalents). It should also be understood that any embodiment described herein may be used in conjunction with any other embodiment described herein.

Claims

1. A method implemented in a user equipment (UE) for channel estimation in physical layer processing, the method comprising: Receive (602) data packets from the next-generation node B (gNB) for physical layer processing; Initiate (604) physical layer processing of received data packets; Send a message (606) to the gNB including at least one Channel Quality Information (CQI) report and one or more network parameters; Receive (608) channel information from gNB, wherein the channel information is identified using at least one of the following: CQI report, one or more network parameters, and uplink packets transmitted by the UE on the uplink channel; and Channel estimation techniques based on received channel information (610) are selected.

2. The method according to claim 1, further comprising: Based on the completed physical layer processing and the selected channel estimation technique, data packets are sent to the upper layer of the UE.

3. The method according to claim 1, wherein, The one or more network parameters include at least one of the estimated frequency shift, phase shift, and observed Doppler shift.

4. The method according to claim 1, wherein, Channel estimation techniques based on channel information selection include: Receive one or more updated transmission parameters from the gNB, wherein the one or more transmission parameters are updated by the gNB based on channel information, and wherein the one or more updated transmission parameters include demodulation reference signal (DMRS) configuration, signal-to-noise ratio of one or more transmission parameters, transmission bandwidth priority, and / or one or more beamforming parameters; and Channel estimation techniques are selected based on channel information and updated transmission parameters.

5. The method according to claim 1, wherein, Channel estimation techniques based on channel information selection include: Determine if the channel information indicates a channel with relatively small changes; and Based on the determined channel information indicating a channel with relatively small changes, the channel estimation technique with the least computation and low complexity is selected, where the channel estimation technique with the least computation and low complexity corresponds to the least squares technique.

6. The method according to claim 1, wherein, Channel estimation techniques based on channel information selection include: Determine if the channel information indicates a moderately changing channel; and Based on the determined received channel information indicating a moderately varying channel, a channel estimation technique with moderate computational and moderate complexity is selected, where the channel estimation technique with moderate computational and moderate complexity corresponds to the minimum mean square error (MMSE) technique.

7. The method according to claim 1, wherein, Channel estimation techniques based on channel information selection include: The received channel information indicates a highly variable channel; and Based on the determined received channel information indicating a highly variable channel, a computationally intensive and complex channel estimation technique is selected, where the computationally intensive and complex channel estimation technique corresponds to the Discrete Cosine Transform (DCT) technique and / or the Discrete Fourier Transform (DFT).

8. The method according to claim 1, further comprising: Determining whether the previously used channel is optimal for physical layer processing based on channel information; as well as Based on the determination that the previously used channel is optimal for physical layer processing, physical layer processing is performed using the previously used channel.

9. A user equipment (UE) for channel estimation in physical layer processing, the UE comprising: Memory (206), stores instructions; and At least one processor (202), including processing circuitry, Wherein, the instructions, when executed individually and / or jointly by at least one processor, cause the UE to: Receive data packets from the next-generation node B (gNB) for physical layer processing; Initiate physical layer processing of received data packets; Send a message to the gNB that includes at least one Channel Quality Information (CQI) report and one or more network parameters; Channel information is received from the gNB, wherein the channel information is identified using at least one of the following: a CQI report, one or more network parameters, and uplink packets transmitted by the UE on the uplink channel; and Channel estimation techniques based on received channel information are selected.

10. The UE according to claim 9, wherein, When executed individually and / or jointly by at least one processor, the instruction causes the UE to: transmit data packets to the upper layer of the UE based on the completion of physical layer processing and based on a selected channel estimation technique. The one or more network parameters include at least one of the estimated frequency shift, phase shift, and observed Doppler shift.

11. The UE according to claim 9, wherein, When selecting a channel estimation technique based on channel information, the instructions, when executed individually and / or jointly by at least one processor, cause the UE to: Receive one or more updated transmission parameters from the gNB, wherein the one or more transmission parameters are updated by the gNB based on channel information, and wherein the one or more updated transmission parameters include demodulation reference signal (DMRS) configuration, signal-to-noise ratio of one or more transmission parameters, transmission bandwidth priority, and one or more beamforming parameters; and Channel estimation techniques are selected based on channel information and updated transmission parameters.

12. The UE according to claim 9, wherein, In order to select a channel estimation technique based on channel information, the instructions, when executed individually and / or jointly by at least one processor, enable the UE to: Determine if the channel information indicates a channel with relatively small changes; as well as Based on the determined channel information indicating a channel with relatively small changes, the channel estimation technique with the least computation and low complexity is selected, where the channel estimation technique with the least computation and low complexity corresponds to the least squares technique.

13. The UE according to claim 9, wherein, In order to select a channel estimation technique based on channel information, the instructions, when executed individually and / or jointly by at least one processor, enable the UE to: Determine the channel information indicating moderately changing channels; as well as Based on the determined received channel information indicating a moderately varying channel, a channel estimation technique with moderate computational and moderate complexity is selected, where the channel estimation technique with moderate computational and moderate complexity corresponds to the minimum mean square error (MMSE) technique.

14. The UE according to claim 9, wherein, In order to select a channel estimation technique based on channel information, the instructions, when executed individually and / or jointly by at least one processor, enable the UE to: The received channel information indicates a highly variable channel; as well as Based on the determined received channel information indicating a highly variable channel, a computationally intensive and complex channel estimation technique is selected, where the computationally intensive and complex channel estimation technique corresponds to the Discrete Cosine Transform (DCT) technique and / or the Discrete Fourier Transform (DFT).

15. A non-transitory computer-readable storage medium storing one or more programs, said one or more programs comprising instructions that, when executed individually or jointly by at least one processor of a user equipment (UE), cause the UE to: Receive data packets from the next-generation node B (gNB) for physical layer processing; Initiate physical layer processing of received data packets; Send a message to the gNB that includes at least one Channel Quality Information (CQI) report and one or more network parameters; Channel information is received from the gNB, wherein the channel information is identified using at least one of the following: a CQI report, one or more network parameters, and uplink packets transmitted by the UE on the uplink channel; and Channel estimation techniques based on received channel information are selected.

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