WO2023272653A1

WO2023272653A1 - Channel estimation compensation with constellation

Info

Publication number: WO2023272653A1
Application number: PCT/CN2021/103836
Authority: WO
Inventors: Wenliang QI; Chenhui YE; Dani Johannes KORPI
Original assignee: Nokia Shanghai Bell Co., Ltd.; Nokia Solutions And Networks Oy; Nokia Technologies Oy
Priority date: 2021-06-30
Filing date: 2021-06-30
Publication date: 2023-01-05
Also published as: CN117581498A

Abstract

Embodiments of the present disclosure relate to devices, methods, apparatuses and computer readable storage media of relaxation compensation for improved system performance. The method comprises determining a first set of estimated channel coefficients associated with a data transmission via a channel between a transmitting device and a receiving device; determining a target compensation for the channel estimation based on a reference constellation generated from a set of reference channel coefficients; and generating a second set of estimated channel coefficients by compensating the first set of estimated channel coefficients based on the target compensation. By using the proposed solution, a value-added service can be derived based on the human expert-like data analysis. The proposed solution is overhead friendly because no extra preambles or expenditure needed for the data analysis. Furthermore, this solution may be compatible with legacy frame in 5G/6G systems.

Description

CHANNEL ESTIMATION COMPENSATION WITH CONSTELLATION

FIELD

Embodiments of the present disclosure generally relate to the field of telecommunication and in particular to devices, methods, apparatuses and computer readable storage media of channel estimation compensation with constellation.

BACKGROUND

It has been considered applying artificial intelligence/machine learning (AI/ML) schemes in 6G oriented wireless air interface design. Topics may include new air interface design for performance augmentations like channel estimation, soft bit recovery, etc. AI is attractive because it is a cost-effective approach to compensate hardware imperfection.

SUMMARY

In general, example embodiments of the present disclosure provide a solution of channel estimation compensation with constellation.

In a first aspect, there is provided a method. The method comprises determining a first set of estimated channel coefficients associated with a data transmission via a channel between a transmitting device and a receiving device; determining a target compensation for the channel estimation based on a reference constellation generated from a set of reference channel coefficients; and generating a second set of estimated channel coefficients by compensating the first set of estimated channel coefficients based on the target compensation.

In a second aspect, there is provided an apparatus. The apparatus comprises at least one processor; and at least one memory including computer program codes; the at least one memory and the computer program codes are configured to, with the at least one processor, cause the apparatus at least to determine a first set of estimated channel coefficients associated with a data transmission via a channel between a transmitting device and a receiving device; determine a target compensation for the channel estimation based on a reference constellation generated from a set of reference channel coefficients; and generate a second set of estimated channel coefficients by compensating the first set of estimated channel coefficients based on the target compensation.

In a third aspect, there is provided an apparatus comprising means for determining a first set of estimated channel coefficients associated with a data transmission via a channel between a transmitting device and a receiving device; means for determining a target compensation for the channel estimation based on a reference constellation generated from a set of reference channel coefficients; and means for generating a second set of estimated channel coefficients by compensating the first set of estimated channel coefficients based on the target compensation.

In a fourth aspect, there is provided a computer readable medium having a computer program stored thereon which, when executed by at least one processor of a device, causes the device to carry out the method according to the first aspect.

Other features and advantages of the embodiments of the present disclosure will also be apparent from the following description of specific embodiments when read in conjunction with the accompanying drawings, which illustrate, by way of example, the principles of embodiments of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosure are presented in the sense of examples and their advantages are explained in greater detail below, with reference to the accompanying drawings, where

FIG. 1 illustrates an example environment in which example embodiments of the present disclosure can be implemented;

FIG. 2 shows an example of a process of channel estimation compensation with constellation according to some example embodiments of the present disclosure;

FIGs. 3A and 3B show examples of constellation image generation according to some example embodiments of the present disclosure;

FIG. 4A and 4B show examples of channel estimation compensation with constellation according to some example embodiments of the present disclosure;

FIG. 5 shows an example of a model for the pattern recognition according to some example embodiments of the present disclosure;

FIG. 6 shows a flowchart of an example process for training the model for the pattern recognition according to some example embodiments of the present disclosure;

FIG. 7 shows a simulation evaluation by using the solution according to some example embodiments of the present disclosure;

FIG. 8 shows a flowchart of an example method of channel estimation compensation with constellation according to some example embodiments of the present disclosure;

FIG. 9 shows a simplified block diagram of a device that is suitable for implementing example embodiments of the present disclosure; and

FIG. 10 shows a block diagram of an example computer readable medium in accordance with some embodiments of the present disclosure.

Throughout the drawings, the same or similar reference numerals represent the same or similar element.

DETAILED DESCRIPTION

Principle of the present disclosure will now be described with reference to some example embodiments. It is to be understood that these embodiments are described only for the purpose of illustration and help those skilled in the art to understand and implement the present disclosure, without suggesting any limitations as to the scope of the disclosure. The disclosure described herein can be implemented in various manners other than the ones described below.

In the following description and claims, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skills in the art to which this disclosure belongs.

References in the present disclosure to “one embodiment, ” “an embodiment, ” “an example embodiment, ” and the like indicate that the embodiment described may include a particular feature, structure, or characteristic, but it is not necessary that every embodiment includes the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an example embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.

It shall be understood that although the terms “first” and “second” etc. may be used herein to describe various elements. These elements should not be limited by these terms. These terms are only used to distinguish functionalities of various elements. As used herein, the term “and/or” includes any and all combinations of one or more of the listed terms.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a” , “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” , “comprising” , “has” , “having” , “includes” and/or “including” , when used herein, specify the presence of stated features, elements, and/or components etc., but do not preclude the presence or addition of one or more other features, elements, components and/or combinations thereof.

As used in this application, the term “circuitry” may refer to one or more or all of the following:

(a) hardware-only circuit implementations (such as implementations in only analog and/or digital circuitry) and

(b) combinations of hardware circuits and software, such as (as applicable) :

(i) a combination of analog and/or digital hardware circuit (s) with software/firmware and

(ii) any portions of hardware processor (s) with software (including digital signal processor (s) ) , software, and memory (ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions) and

(c) hardware circuit (s) and or processor (s) , such as a microprocessor (s) or a portion of a microprocessor (s) , that requires software (e.g., firmware) for operation, but the software may not be present when it is not needed for operation.

This definition of circuitry applies to all uses of this term in this application, including in any claims. As a further example, as used in this application, the term circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its (or their) accompanying software and/or firmware. The term circuitry also covers, for example and if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in server, a cellular network device, or other computing or network device.

As used herein, the term “communication network” refers to a network following any suitable communication standards, such as fifth generation (5G) systems, Long Term Evolution (LTE) , LTE-Advanced (LTE-A) , Wideband Code Division Multiple Access (WCDMA) , High-Speed Packet Access (HSPA) , Narrow Band Internet of Things (NB-IoT) and so on. Furthermore, the communications between a terminal device and a network device in the communication network may be performed according to any suitable generation communication protocols, including, but not limited to, the first generation (1G) , the second generation (2G) , 2.5G, 2.75G, the third generation (3G) , the fourth generation (4G) , 4.5G, the future fifth generation (5G) new radio (NR) communication protocols, and/or any other protocols either currently known or to be developed in the future. Embodiments of the present disclosure may be applied in various communication systems. Given the rapid development in communications, there will of course also be future type communication technologies and systems with which the present disclosure may be embodied. It should not be seen as limiting the scope of the present disclosure to only the aforementioned system.

As used herein, the term “network device” refers to a node in a communication network via which a terminal device accesses the network and receives services therefrom. The network device may refer to a base station (BS) or an access point (AP) , for example, a node B (NodeB or NB) , an evolved NodeB (eNodeB or eNB) , a NR Next Generation NodeB (gNB) , a Remote Radio Unit (RRU) , a radio header (RH) , a remote radio head (RRH) , a relay, a low power node such as a femto, a pico, and so forth, depending on the applied terminology and technology. A RAN split architecture comprises a gNB-CU (Centralized unit, hosting RRC, SDAP and PDCP) controlling a plurality of gNB-DUs (Distributed unit, hosting RLC, MAC and PHY) . A relay node may correspond to DU part of the IAB node.

The term “terminal device” refers to any end device that may be capable of wireless communication. By way of example rather than limitation, a terminal device may also be referred to as a communication device, user equipment (UE) , a subscriber station (SS) , a portable subscriber station, a mobile station (MS) , or an access terminal (AT) . The terminal device may include, but not limited to, a mobile phone, a cellular phone, a smart phone, voice over IP (VoIP) phones, wireless local loop phones, a tablet, a wearable terminal device, a personal digital assistant (PDA) , portable computers, desktop computer, image capture terminal devices such as digital cameras, gaming terminal devices, music storage and playback appliances, vehicle-mounted wireless terminal devices, wireless endpoints, mobile stations, laptop-embedded equipment (LEE) , laptop-mounted equipment (LME) , USB dongles, smart devices, wireless customer-premises equipment (CPE) , an Internet of Things (IoT) device, a watch or other wearable, a head-mounted display (HMD) , a vehicle, a drone, a medical device and applications (e.g., remote surgery) , an industrial device and applications (e.g., a robot and/or other wireless devices operating in an industrial and/or an automated processing chain contexts) , a consumer electronics device, a device operating on commercial and/or industrial wireless networks, and the like. The terminal device may also correspond to Mobile Termination (MT) part of the integrated access and backhaul (IAB) node (a. k. a. a relay node) . In the following description, the terms “terminal device” , “communication device” , “terminal” , “user equipment” and “UE” may be used interchangeably.

Although functionalities described herein can be performed, in various example embodiments, in a fixed and/or a wireless network node, in other example embodiments, functionalities may be implemented in a user equipment apparatus (such as a cell phone or tablet computer or laptop computer or desktop computer or mobile IoT device or fixed IoT device) . This user equipment apparatus can, for example, be furnished with corresponding capabilities as described in connection with the fixed and/or the wireless network node (s) , as appropriate. The user equipment apparatus may be the user equipment and/or or a control device, such as a chipset or processor, configured to control the user equipment when installed therein. Examples of such functionalities include the bootstrapping server function and/or the home subscriber server, which may be implemented in the user equipment apparatus by providing the user equipment apparatus with software configured to cause the user equipment apparatus to perform from the point of view of these functions/nodes.

FIG. 1 shows an example communication network 100 in which embodiments of the present disclosure can be implemented. As shown in FIG. 1, the communication network 100 may comprise a transmitting device 110 and a receiving device 120. The transmitting device 110 and the receiving device 120 via a channel 102. For example, a data transmission from the transmitting device 110 to the receiving device 120 can be carried by the channel 102. It is to be understood that the transmitting device 110 may also act as a receiving device in some scenarios and the receiving device 120 may also be act as a transmitting device in some scenarios.

It is to be understood that the number of transmitting device and receiving device shown in FIG. 1 is given for the purpose of illustration without suggesting any limitations. The communication network 100 may include any suitable number of transmitting devices and receiving devices.

The hardware imperfections and compounded wireless channel usually cause the wireless communication performance drop. Current 5G system has not yet been equipped with such elaborate intelligent analysis capability or any corresponding actions to follow. Therefore, potential value-added services, e.g., root cause of certain performance issues has not been considered, leaving most of data discarded before being fully exploited.

For this reason, it has been considered apply the AI/ML scheme for new air interface design, because it is a cost-effective approach to compensate hardware imperfection.

In general, variant carrier frequency offsets (CFOs) between the transmitting device and the receiving device may cause different degrees of constellation drifts. AI based computer vision (CV) with pattern recognition can enable 6G wireless system with human expert-like cognition capability based on non-intuitive pattern recognition from data analysis. Based on that, the machine can then automatically compensate the impairments accordingly.

Therefore, the present disclosure proposed a solution of channel estimation compensation with constellation. In this solution, the receiving device may determine a first set of estimated channel coefficients associated with a data transmission via a channel between a transmitting device and a receiving device and a target compensation for the channel estimation based on a reference constellation generated from a set of reference channel coefficients. The receiving device may generate a second set of estimated channel coefficients for recovering original data transmitted from the transmitting device from the received data by compensating the first set of estimated channel coefficients based on the target compensation.

Principle and implementations of the present disclosure will be described in detail below with reference to FIG. 2, which shows an example of a process of channel estimation compensation with constellation according to some example embodiments of the present disclosure. For the sake of brevity, FIG. 2 only illustrates a basic structure of channel estimation compensation with constellation. It is to be understood that some blocks for the process of channel estimation compensation may be omitted in FIG. 2.

Now the reference is made to FIG. 2, the transmitting device 110 may initiate a data transmission from the transmitting device 110 to the receiving device 120. The original data transmitted from the transmitting device 110 may be distorted during the data transmission. That is, the received data received by the receiving device 120 may be considered as the distorted original data. The distortion of the received data can be associated with the channel characteristics of a channel for carry the data transmission and/or the intrinsic feature of the hardware, i.e., the transmitting device 110 and the receiving device 120.

Based on the received data, the receiving device 120 may perform the channel estimation in channel estimation block 210 to correct the distortion of the received data. The channel estimation block 210 may be considered as a ML based channel estimation module.

In some example embodiments, before the channel estimation to be performed in the block 210, a raw channel estimation may be applied for the received data. A set of raw channel coefficients can be fed to the channel estimation block 210 as input and the channel estimation block 210 may perform the channel estimation by interpolation and output a set of estimated channel coefficients.

For example, 1 Demodulation Reference Signal (DMRS) pilot based PHY channel estimation scheme can be applied for the channel estimation at the receiving device. In the

channel estimation block

210, 1 DMRS with known pilots allocated in frequency axis compensates only the frequency selective channel fading but leaving inter-symbol constellation rotation uncompensated in time-domain, which may be caused either by hardware frequency offset or Dopplers. Thus, the output of the channel estimation block 210 are imperfect channel coefficients.

As described above, the imperfection may leave its trace in the constellation pattern. Thus, the imperfection of the set of estimated channel coefficients output by the channel estimation block 210 may be further recognized in the Data Pattern Recognition (DPR) block 220 to compensate the hardware/environment caused impairments from a constellation image. Hereinafter the DPR block 220 may be referred to as a human expert-like pattern recognition (EDPR) , which may analyze the pattern and adaptively restore the constellation rotation.

In some example embodiments, the DPR block 220 may consist of a data statistics block 221 and a pattern recognition block 222. A set of OFDM symbols associated with the received data can be equalized by using the the set of estimated channel coefficients output by the channel estimation block 210 and the set of equalized OFDM symbols can be converted by the data statistics block 221 to a statistic constellation image.

In some example embodiments, a conventions or rules for the subsequent data statistic may be determined in advance at the data statistics block 221. Then, the set of equalized OFDM symbols are fed to the data statistics block 221, to gather the statistical raw image.

In some example embodiments, the statistical raw image generated by the data statistics block 221 may also be post-processed, to generate a statistic constellation image to be fed for pattern recognition block 222.

For example, the generation of the statistic constellation image can be performed at the data statistics block 221 by grid design. FIG. 3A shows an example of constellation image generation according to some example embodiments of the present disclosure.

As shown in FIG. 3A, the real and imaginary parts of the equalized symbols may be treated as x and y axis and [x1, x2] and [y1, y2] may determine the range of the grid in which all equalized data should fall in. The resolution may refer the square size, for example, (x2-x1) /6 in real axis and (y2-y1) /5 in imaginary axis. In FIG. 3A, n may refer to number of equalized symbols falling in the grey square and the raw image may be generated accordingly.

In the post-process, the counted raw image can be further process by

Then the final statistic image can be formed by raw image accordingly. FIG. 3B show an examples of generated constellation image according to some example embodiments of the present disclosure. The constellation rotation, which may be considered as the trace of imperfection, can be seen from the FIG. 3B.

Then the statistic constellation image can be fed for pattern recognition block 222. The statistic constellation image may visualize the impairments brought by hardware and/or environment. The pattern recognition block 222 may extract the features from the statistic constellation images and generates the expertise incorporated remedy, for example vector as the compensation, which may be further fed back to channel estimation to augment the performance. The neural network (NN) for pattern recognition can be constructed as the entity of pattern recognition block 222.

The NN for pattern recognition may characterize a correlation between a set of history-determined compensations and a plurality of history constellations images. The NN for pattern recognition may be trained by plurality of history constellations images. The structure of the NN used for the pattern recognition block 222 and the training process may be description later.

Based on the pattern recognition block 222, the impairments brought by hardware and/or environment can be recognized and the compensation for the set of estimated channel coefficients output by the channel estimation block 210 can be generated.

Now referring back to FIG. 2, the generated compensation may be merged with the set of estimated channel coefficients output by the channel estimation block 210, to generate a further set of estimated channel coefficients. The distorted received data can be recovered to be the original data at augmented performance block 230 by using the further set estimated channel coefficients.

The basic concept for the channel estimation compensation with constellation has been described with FIG. 2 and FIGs. 3A and 3B. Now the reference is made to FIG. 4A and FIG. 4B, which show examples of channel estimation compensation with constellation according to some example embodiments of the present disclosure.

In FIG. 4A, a set of OFDM symbols for channel estimation at the channel estimation block 210 and a set of OFDM symbols to be equalized for data statistic at the data statistics block 221 are associated with the received data received in a same transmission time interval (TTI) . As shown, the set of estimated channel coefficients may be generated at the channel estimation block 210 based on the received data received in the TTI n and may be fed to the equalization block 410. A set of OFDM symbols associated with the received data received in the TTI n can be equalized at the equalization block 410 by using the set of estimated channel coefficients generated based on the received data received in the TTI n.

As another option, in FIG. 4B, a set of OFDM symbols for channel estimation at the channel estimation block 210 and a set of OFDM symbols to be equalized for data statistic at the data statistics block 221 are associated with the received data received in different TTIs. For example, the set of estimated channel coefficients may be generated at the channel estimation block 210 based on the received data received in the TTI n. A set of OFDM symbols associated with received data received in the TTI n-x can be equalized at the equalization block 410 by using a set of estimated channel coefficients generated based on the received data received in the TTI n-x, which can be fed from a storage device 420 for storing the history results of the channel estimations performed at the channel estimation block 210.

Other description of the blocks and process flow shown in FIG. 4A and 4B has been described with reference to FIG. 2 and will not be repeated here.

FIG. 5 shows an example of a model for the pattern recognition according to some example embodiments of the present disclosure. As described above, the NN for pattern recognition can be constructed as the entity of pattern recognition block 222. The NN used for the pattern recognition block 222 may be referred to as a spatial attention network (SAN) . The SAN can help to enhance and attenuate in certain image areas (width x height) for more effective feature extraction. For example, the SAN may comprise multiple attention blocks, such as the attention blocks 511 and 512 shown in FIG. 5. The SAN used for pattern recognition block 222 may be well-trained before generating the compensation for the channel estimation. As another option, the SAN may also be trained during the application of compensation generation.

FIG. 6 shows a flowchart of an example process for training the model for the pattern recognition according to some example embodiments of the present disclosure.

At block 602, The statistic constellation images converted from equalized symbols by the data statistic process described above may be used as input data in training NN. At block 604, the sequences of the images are disturbed and splitted for training data set and testing data set respectively.

At block 606, the training starts. At block 608, the images are fed to Pattern recognition NN (PRNN) for analysis. After obtaining the output from the PRNN, at block 610, the output may be merged back to steer channel estimation, such as by simple multiplication. It is to be understood that the merge can be performed in other ways..

At block 612, the training loss with steered result generated in the block 610 can be calculated based on target perfect channel estimation for backpropagation. At block 614, the testing loss can be calculated for monitoring the process.

At block 616, determining whether the iteration is finished. If the iteration is finished, at block 618, the training is stopped. If the iteration is not finished, the training flow may return to the block 606, to start training next epoch.

By using the proposed solution, a value-added service can be derived based on the human expert-like data analysis. The proposed solution is overhead friendly because no extra preambles or expenditure needed for the data analysis. Furthermore, this solution may be compatible with legacy frame in 5G/6G systems.

FIG. 7 shows a simulation evaluation by using the solution according to some example embodiments of the present disclosure. The performance is evaluated by simulation where hardware/environment impairment is modelled by CFO. The data fed to DPR is the equalized symbols, with which binary constellation images are formed after data statistic block as the statistic constellation images for training the NN. The configuration of the simulation is listed as below.

Table 1: List of configurations in simulation

In this simulation, the fed equalized symbols are obtained by using the channel estimation of the 1 ^st OFDM symbol so that CFO is not compensated, like the rotated constellation in FIG. 3B. The grid range of the equalized symbols is from -2 to 2 for both real and imaginary parts. The grid resolution is 0.02, thus forming a constellation image of 201x201 by pixel.

In this evaluation, the channel estimation is constructed by using machine learning approach in frequency domain. Mean square error (MSE) is used as loss function to compare with perfect channel estimation. In training phase, only SNRs of 20, 24, 28 and 32 dB are trained, and the performance is validated in a wider SNR range.

The simulation result is shown in FIG. 7. The decoded BER curves are averaged BER for un-pretrained CFOs only which is more interesting and significant. The curve ‘Perfect ChanEst’ 730 refers to results by using the perfect known channel estimation. The curve ‘Uncompensated’ 710 refers to results by directly applying the channel estimation from the 1st OFDM symbol to all the frequency-time grid without CFO compensation. The curve ‘DPR’ 720 refers to results by using DPR to compensate CFO from the images.

The simulation results demonstrate that the impairment is well extracted from the ‘constellation images’ with the implemented DPR. It is also noteworthy that the implemented DPR performs extremely well with the CFOs that have never been trained, which proves that the proposed invention owns strong transferability in untrained conditions (e.g., un-pretrained CFO) .

FIG. 8 shows a flowchart of an example method 800 of relaxation compensation according to some example embodiments of the present disclosure. The method 800 can be implemented at the receiving device 120 as shown in FIG. 1. For the purpose of discussion, the method 800 will be described with reference to FIG. 1.

At 810, the receiving device determines a first set of estimated channel coefficients associated with a data transmission via a channel between a transmitting device and a receiving device.

In some example embodiments, the receiving device may perform a channel estimation based on received data from the data transmission and determine the first set of estimated channel coefficients based on a result of the channel estimation.

At 820, the receiving device determines a target compensation for the channel estimation based on a reference constellation generated from a set of reference channel coefficients.

In some example embodiments, the receiving device may determine at least one portion of the first set of estimated channel coefficients as the set of reference channel coefficients.

In some example embodiments, the receiving device may determine a plurality of symbols associated with received data from the data transmission and equalize the plurality of symbols based on the at least one portion of the first set of estimated channel coefficients. The receiving device may further generate the reference constellation based on the plurality of equalized symbols and obtain a correlation between a set of reference compensations and a plurality of history constellations. The receiving device may further determine the target compensation based on the reference constellation and the correlation.

In some example embodiments, the receiving device may obtain a third set of estimated channel coefficients associated with a further data transmission via the channel, the further data transmission being earlier than the data transmission; and determine the third set of estimated channel coefficients as the set of reference channel coefficients.

In some example embodiments, the receiving device may determine a plurality of symbols associated with received data from the further data transmission and equalize the plurality of symbols based on the third set of estimated channel coefficients. The receiving device may further generate the reference constellation based on the plurality of equalized symbols and obtain a correlation between a set of reference compensations and a plurality of history constellations. The receiving device may further determine the target compensation based on the reference constellation and the correlation.

In some example embodiments, the receiving device may obtain the correlation by according to a neural network model with the plurality of history constellations as an input.

At 830, the receiving device generates a second set of estimated channel coefficients by compensating the first set of estimated channel coefficients based on the target compensation.

In some example embodiments, the receiving device may recovery original data from received data based on the second set of estimated channel coefficients, the original data being transmitted from the transmitting device being distorted due to at least of one of an intrinsic feature of the transmitting device, an intrinsic feature of the receiving device, or characteristics of the channel.

In some example embodiments, an apparatus capable of performing the method 800 (for example, implemented at the receiving device 120) may comprise means for performing the respective steps of the method 800. The means may be implemented in any suitable form. For example, the means may be implemented in a circuitry or software module.

In some example embodiments, the apparatus comprises means for determining a first set of estimated channel coefficients associated with a data transmission via a channel between a transmitting device and a receiving device; means for determining a target compensation for the channel estimation based on a reference constellation generated from a set of reference channel coefficients; and means for generating a second set of estimated channel coefficients by compensating the first set of estimated channel coefficients based on the target compensation.

FIG. 9 is a simplified block diagram of a device 900 that is suitable for implementing embodiments of the present disclosure. The device 900 may be provided to implement the communication device, for example the receiving device 120 as shown in FIG. 1. As shown, the device 900 includes one or more processors 910, one or more memories 940 coupled to the processor 910, and communication modules 940 coupled to the processor 910.

The communication module 940 is for bidirectional communications. The communication module 940 has one or more communication interfaces to facilitate communication with one or more other modules or devices. The communication interfaces may represent any interface that is necessary for communication with other network elements. In some example embodiments, the communication module 940 may include at least one antenna.

The processor 910 may be of any type suitable to the local technical network and may include one or more of the following: general purpose computers, special purpose computers, microprocessors, digital reference signal processors (DSPs) and processors based on multicore processor architecture, as non-limiting examples. The device 900 may have multiple processors, such as an application specific integrated circuit chip that is slaved in time to a clock which synchronizes the main processor.

The memory 920 may include one or more non-volatile memories and one or more volatile memories. Examples of the non-volatile memories include, but are not limited to, a Read Only Memory (ROM) 924, an electrically programmable read only memory (EPROM) , a flash memory, a hard disk, a compact disc (CD) , a digital video disk (DVD) , and other magnetic storage and/or optical storage. Examples of the volatile memories include, but are not limited to, a random access memory (RAM) 922 and other volatile memories that will not last in the power-down duration.

A computer program 930 includes computer executable instructions that are executed by the associated processor 910. The program 930 may be stored in the ROM 920. The processor 910 may perform any suitable actions and processing by loading the program 930 into the RAM 920.

The embodiments of the present disclosure may be implemented by means of the program 930 so that the device 900 may perform any process of the disclosure as discussed with reference to FIGs. 2 to 8. The embodiments of the present disclosure may also be implemented by hardware or by a combination of software and hardware.

In some embodiments, the program 930 may be tangibly contained in a computer readable medium which may be included in the device 900 (such as in the memory 920) or other storage devices that are accessible by the device 900. The device 900 may load the program 930 from the computer readable medium to the RAM 922 for execution. The computer readable medium may include any types of tangible non-volatile storage, such as ROM, EPROM, a flash memory, a hard disk, CD, DVD, and the like. FIG. 10 shows an example of the computer readable medium 1000 in form of CD or DVD. The computer readable medium has the program 930 stored thereon.

Generally, various embodiments of the present disclosure may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device. While various aspects of embodiments of the present disclosure are illustrated and described as block diagrams, flowcharts, or using some other pictorial representations, it is to be understood that the block, device, system, technique or method described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.

The present disclosure also provides at least one computer program product tangibly stored on a non-transitory computer readable storage medium. The computer program product includes computer-executable instructions, such as those included in program modules, being executed in a device on a target real or virtual processor, to carry out the method 800 as described above with reference to FIG. 8. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, or the like that perform particular tasks or implement particular abstract data types. The functionality of the program modules may be combined or split between program modules as desired in various embodiments. Machine-executable instructions for program modules may be executed within a local or distributed device. In a distributed device, program modules may be located in both local and remote storage media.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing device, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowcharts and/or block diagrams to be implemented. The program code may execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present disclosure, the computer program codes or related data may be carried by any suitable carrier to enable the device, device or processor to perform various processes and operations as described above. Examples of the carrier include a reference signal, computer readable medium, and the like.

The computer readable medium may be a computer readable reference signal medium or a computer readable storage medium. A computer readable medium may include but not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. More specific examples of the computer readable storage medium would include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM) , a read-only memory (ROM) , an erasable programmable read-only memory (EPROM or Flash memory) , an optical fiber, a portable compact disc read-only memory (CD-ROM) , an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the present disclosure, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment may also be implemented in multiple embodiments separately or in any suitable sub-combination.

Although the present disclosure has been described in languages specific to structural features and/or methodological acts, it is to be understood that the present disclosure defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

A method comprising:

determining a first set of estimated channel coefficients associated with a data transmission via a channel between a transmitting device and a receiving device;

determining a target compensation for the channel estimation based on a reference constellation generated from a set of reference channel coefficients; and

generating a second set of estimated channel coefficients by compensating the first set of estimated channel coefficients based on the target compensation.
The method of Claim 1, further comprising:

determining at least one portion of the first set of estimated channel coefficients as the set of reference channel coefficients.
The method of Claim 2, wherein determining the target compensation comprises:

determining a plurality of symbols associated with received data from the data transmission;

equalizing the plurality of symbols based on the at least one portion of the first set of estimated channel coefficients;

generating the reference constellation based on the plurality of equalized symbols;

obtaining a correlation between a set of reference compensations and a plurality of history constellations; and

determining the target compensation based on the reference constellation and the correlation.
The method of Claim 1, further comprising:

obtaining a third set of estimated channel coefficients associated with a further data transmission via the channel, the further data transmission being earlier than the data transmission; and

determining the third set of estimated channel coefficients as the set of reference channel coefficients.
The method of Claim 4, wherein determining the target compensation comprises:

determining a plurality of symbols associated with received data from the further data transmission;

equalizing the plurality of symbols based on the third set of estimated channel coefficients;

generating the reference constellation based on the plurality of equalized symbols;

obtaining a correlation between a set of reference compensations and a plurality of history constellations; and

determining the target compensation based on the reference constellation and the correlation.
The method of Claim 3 or 5, wherein obtaining the correlation comprises:

obtaining the correlation by according to a neural network model with the plurality of history constellations as an input.
The method of Claim 1, wherein determining the first set of estimated channel coefficients comprises:

performing a channel estimation based on received data from the data transmission; and

determining the first set of estimated channel coefficients based on a result of the channel estimation.
The method of Claim 1, further comprising:

recovering original data from received data based on the second set of estimated channel coefficients, the original data being transmitted from the transmitting device being distorted due to at least of one of following:

an intrinsic feature of the transmitting device,

an intrinsic feature of the receiving device, or

characteristics of the channel.
An apparatus comprising:

at least one processor; and

at least one memory including computer program codes;

the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to:

determine a first set of estimated channel coefficients associated with a data transmission via a channel between a transmitting device and a receiving device;

determine a target compensation for the channel estimation based on a reference constellation generated from a set of reference channel coefficients; and

generate a second set of estimated channel coefficients by compensating the first set of estimated channel coefficients based on the target compensation.
The apparatus of Claim 9, wherein the apparatus is further caused to:

determine at least one portion of the first set of estimated channel coefficients as the set of reference channel coefficients.
The apparatus of Claim 10, wherein the apparatus is caused to determine the target compensation by:

determining a plurality of symbols associated with received data from the data transmission;

equalizing the plurality of symbols based on the at least one portion of the first set of estimated channel coefficients;

generating the reference constellation based on the plurality of equalized symbols;

obtaining a correlation between a set of reference compensations and a plurality of history constellations; and

determining the target compensation based on the reference constellation and the correlation.
The apparatus of Claim 9, wherein the apparatus is further caused to:

obtain a third set of estimated channel coefficients associated with a further data transmission via the channel, the further data transmission being earlier than the data transmission; and

determine the third set of estimated channel coefficients as the set of reference channel coefficients.
The apparatus of Claim 12, wherein the apparatus is caused to determine the target compensation by:

determining a plurality of symbols associated with received data from the further data transmission;

equalizing the plurality of symbols based on the third set of estimated channel coefficients;

generating the reference constellation based on the plurality of equalized symbols;

obtaining a correlation between a set of reference compensations and a plurality of history constellations; and

determining the target compensation based on the reference constellation and the correlation.
The apparatus of Claim 11 or 13, wherein the apparatus is caused to obtaining the correlation by:

obtaining the correlation by according to a neural network model with the plurality of history constellations as an input.
The apparatus of Claim 9, wherein the apparatus is caused to determine the first set of estimated channel coefficients by:

performing a channel estimation based on received data from the data transmission; and

determining the first set of estimated channel coefficients based on a result of the channel estimation.
The device of Claim 9, wherein the device is further caused to:

recovering original data from received data based on the second set of estimated channel coefficients, the original data being transmitted from the transmitting device being distorted due to at least of one of following:

an intrinsic feature of the transmitting device,

an intrinsic feature of the receiving device, or

characteristics of the channel.
An apparatus comprising:

means for determining a first set of estimated channel coefficients associated with a data transmission via a channel between a transmitting device and a receiving device;

means for determining a target compensation for the channel estimation based on a reference constellation generated from a set of reference channel coefficients; and

means for generating a second set of estimated channel coefficients by compensating the first set of estimated channel coefficients based on the target compensation.
A non-transitory computer readable medium comprising program instructions for causing an apparatus to perform at least the method of any of claims 1-9.