Method and system for performing channel estimation in wireless communication network

Abstract

A method includes receiving, at a base station, a signal from each User Equipment (UE) via a plurality of antennas associated with the base station, wherein each UE is located within a predefined region. The method includes estimating a channel response based on the received signal. The method includes determining a channel covariance matrix,based on one or more pilot subcarriers associated with the received signal and the estimated channel response. The method includes determining one or more dominant independent vectors based on the determined channel covariance matrix. The method includes determining one or more dominant steering vectors associated with the predefined region from the one or more determined dominant independent vectors and determining the corresponding weight of each steering vector. The method includes performing the channel estimation based on the one or more determined dominant steering vectors and the corresponding determined weight of each steering vector.

Description

METHOD AND SYSTEM FOR PERFORMING CHANNEL ESTIMATION IN WIRELESS COMMUNICATION NETWORK

[0001] The present disclosure generally relates to the field of wireless communication networks, and more specifically relates to a method and a system for performing a channel estimation in the wireless communication network.

[0002] The information disclosed in this background section is only for enhancement of understanding of the general background of the disclosure and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.

[0003] In wireless communication networks, electromagnetic wave propagation can be categorized into three main regions such as a near-field region, a far-field region, and a mixed-field region, as illustrated in FIGS. 1A, 1B, and 1C. The near-field region is an area close to a transmitting antenna. Here, the electromagnetic field is influenced significantly by the transmitting antenna, leading to strong variations in signal strength. For example, a smartphone (e.g., User Equipment or UE) near a Base Station (BS) may experience fluctuating signals due to this close proximity. In contrast, the far-field region is located farther away from the transmitting antenna, where one or more emitted waves spread out uniformly. In this region, the UE receives a more stable and consistent signal, as the antenna's effects are less noticeable. The mixed-field region is a transitional area between the near and far fields, where both reactive and radiative fields coexist. As the UE moves through this mixed-field region, the UE may encounter varying signal qualities.

[0004] In addition, Massive MIMO (mMIMO) is a technology that uses a large number of antennas at the BS to serve multiple UEs simultaneously, enhancing data capacity and signal quality. This allows several devices to communicate at once without interference. Extremely Large-Scale MIMO (XL-MIMO) technology takes this concept further by employing even more antennas, often in the hundreds or thousands, to maximize performance in densely populated areas. This is especially useful in high-demand environments like stadiums. In the near-field region, the mMIMO can provide strong signals to UEs due to the close proximity of multiple antennas, although the complex interactions can cause unpredictable signal behavior. In the far-field region, both mMIMO and XL-MIMO excel by delivering robust coverage and improved data rates, ensuring consistent service quality for UEs regardless of their distance from the BS. In the mixed-field region, performance can vary as UEs transition through this area, but the large number of antennas helps maintain a reliable connection. However, several problems are encountered in the traditional technology / systems, which are mentioned below.

[0005] In the context of 5G and 6G XL-MIMO technology, the antenna elements are significantly larger compared to those used in conventional mMIMO technology. This advancement leads to a tenfold increase in spectral efficiency, attributed to the exceptionally high number of antennas involved. However, this increase in antenna count brings about challenges, particularly in the area of channel estimation, as described in conjunction with FIG. 2, FIG. 3, and FIG. 4. Accurate channel estimation is essential but also increasingly complex due to the unique characteristics of the near-field region. Unlike the far-field region, where electromagnetic waves are planar, near-field electromagnetic waves exhibit a spherical nature. This difference complicates channel estimation because the channel becomes sparse in the polar domain rather than the angular domain, resulting in an exponential increase in computational complexity.

[0006] In addition, traditional technology / systems often rely on Orthogonal Matching Pursuit (OMP) algorithms for sparse channel estimation, which are computationally intensive, as described in conjunction with FIG. 5 . As the number of antennas grows, this complexity escalates even further, highlighting the urgent need for more efficient channel estimation algorithms.

[0007] Moreover, there is a requirement for power-efficient hybrid precoding architectures that can effectively manage the high number of antennas while ensuring low power consumption. Additionally, there is a requirement for developing computationally efficient channel estimation algorithms that are cloud-friendly for the near-field region is crucial. Such advancements may significantly improve a Block Error Rate (BLER) performance, making the XL-MIMO / mMIMO technology more robust and efficient.

[0008] Thus, it is desired to address the above-mentioned disadvantages or other shortcomings or at least provide a useful alternative for performing the channel estimation in the wireless communication network.

[0009] This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the disclosure. This summary is neither intended to identify key or essential inventive concepts of the disclosure nor is it intended for determining the scope of the disclosure.

[0010] According to one embodiment of the present disclosure, a method for performing a channel estimation in a wireless communication network is disclosed herein. The method includes receiving, at a base station, a signal from each User Equipment (UE) via a plurality of antennas associated with the base station, wherein each UE is located within a predefined region. The method further includes estimating a channel response based on the received signal. The method further includes determining a channel covariance matrix, based on one or more pilot subcarriers associated with the received signal and the estimated channel response. The method further includes determining one or more dominant independent vectors based on the determined channel covariance matrix. The method further includes determining one or more dominant steering vectors associated with the predefined region from the one or more determined dominant independent vectors and determining the corresponding weight of each steering vector. The method further includes performing the channel estimation based on the one or more determined dominant steering vectors and the corresponding determined weight of each steering vector.

[0011] According to another embodiment of the present disclosure, a base station for performing a channel estimation in a wireless communication network is disclosed herein. The base station includes a channel estimator coupled with a memory, a processor, and a communicator. The channel estimator may receive a signal from each User Equipment (UE) via a plurality of antennas associated with the base station, wherein each UE is located within a predefined region. The channel estimator may estimate a channel response based on the received signal. The channel estimator may determine a channel covariance matrix,based on one or more pilot subcarriers associated with the received signal and the estimated channel response. The channel estimator may determine one or more dominant independent vectors based on the determined channel covariance matrix. The channel estimator may determine one or more dominant steering vectors associated with the predefined region from the one or more determined dominant independent vectors and determining the corresponding weight of each steering vector. The channel estimator may perform the channel estimation based on the one or more determined dominant steering vectors and the corresponding determined weight of each steering vector.

[0012] To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will be rendered by reference to specific embodiments thereof, which are illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the disclosure and are therefore not to be considered limiting of its scope. The disclosure will be described and explained with additional specificity and detail in the accompanying drawings.

[0013] These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:

[0014] FIGS. 1A, 1B, and 1C illustrate a plurality of operations associated with a near-field region, a far-field region, and a mixed-field region, according to the prior art;

[0015] FIG. 2 illustrates a signal model of the near-field region, according to prior art;

[0016] FIGS. 3A-3B illustrates an example scenario of wavefronts of the near-field region and the far-field region, according to prior art;

[0017] FIG. 4 illustrates an extremely Large-Scale MIMO (XL-MIMO) communication system with hybrid precoding and a problem associated with channel estimation in the near-field region, according to prior art;

[0018] FIG. 5 is a flow diagram illustrating an existing Orthogonal Matching Pursuit (OMP) method, according to prior art;

[0019] FIG. 6 is a diagram of an example of an implementation environment in which a system and / or method, described herein, may be implemented, according to an embodiment as disclosed herein;

[0020] FIG. 7 illustrates a block diagram of a base station for performing channel estimation in a wireless communication network, according to an embodiment as disclosed herein;

[0021] FIGS. 8A-8B is an example scenario illustrating the method for selecting an antenna pair mechanism, according to an embodiment as disclosed herein;

[0022] FIG. 9 is a flow diagram illustrating a method for performing the channel estimation based on a fractional fourier transform for near field channel, according to an embodiment as disclosed herein;

[0023] FIG. 10 is a flow diagram illustrating a method for estimating one or more dominant paths based on a projection of eigen vectors for the channel estimation, according to an embodiment as disclosed herein;

[0024]

[0025] FIG. 12 illustrates an uplink use case based on the disclosed channel estimation mechanism, according to an embodiment as disclosed herein;

[0026] FIG. 13 illustrates a downlink use case based on the disclosed channel estimation mechanism, according to an embodiment as disclosed herein; and

[0027] FIG. 14 is a flow diagram illustrating a method for performing the channel estimation , according to an embodiment as disclosed herein.

[0028] Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present disclosure. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

[0029] For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as illustrated therein being contemplated as would normally occur to one skilled in the art to which the disclosure relates.

[0030] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are explanatory of the disclosure and are not intended to be restrictive thereof.

[0031] Reference throughout this specification to "an aspect", "another aspect" or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrase "in an embodiment", "in one embodiment", "in another embodiment", and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0032] The terms "comprise", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by "comprises... a" does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.

[0033] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. Also, the various embodiments described herein are not necessarily mutually exclusive, as some embodiments can be combined with one or more other embodiments to form new embodiments. The term "or" as used herein, refers to a non-exclusive or unless otherwise indicated. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein can be practiced and to further enable those skilled in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

[0034] As is traditional in the field, embodiments may be described and illustrated in terms of blocks that carry out a described function or functions. These blocks, which may be referred to herein as units or modules or the like, are physically implemented by analog or digital circuits such as logic gates, integrated circuits, microprocessors, microcontrollers, memory circuits, passive electronic components, active electronic components, optical components, hardwired circuits, or the like, and may optionally be driven by firmware and software. The circuits may, for example, be embodied in one or more semiconductor chips, or on substrate supports such as printed circuit boards and the like. The circuits constituting a block may be implemented by dedicated hardware, or by a processor (e.g., one or more programmed microprocessors and associated circuitry), or by a combination of dedicated hardware to perform some functions of the block and a processor to perform other functions of the block. Each block of the embodiments may be physically separated into two or more interacting and discrete blocks without departing from the scope of the disclosure. Likewise, the blocks of the embodiments may be physically combined into more complex blocks without departing from the scope of the disclosure.

[0035] The accompanying drawings are used to help easily understand various technical features and it should be understood that the embodiments presented herein are not limited by the accompanying drawings. As such, the present disclosure should be construed to extend to any alterations, equivalents, and substitutes in addition to those which are particularly set out in the accompanying drawings. Although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are generally only used to distinguish one element from another.

[0036] FIGS. 1A,1Band1Cillustrate a plurality of operations associated with a near-field region, a far-field region, and a mixed-field region, according to the prior art.

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[0048] In the context of the near-field region and the far-field region, Table 1 outlining the difference between the near-field region and the far-field region for the channel estimation process is provided below.

[0049] Near-fieldFar-field

[0050] FIG. 4 illustrates an extremely Large-Scale MIMO (XL-MIMO) communication system 400 with hybrid precoding and a problem associated with channel estimation in the near-field region, according to prior art.

[0051]

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[0055] FIG. 5is a flow diagram illustrating an existing Orthogonal Matching Pursuit (OMP) method 500, according to prior art. The OMP method 500 is a systematic approach used for signal recovery and channel estimation in various applications, particularly in the context of hybrid beamforming.

[0056]

[0057] To enhance the signal quality, at operation 502, the method 500 includes implementing a noise pre-whitening process. This crucial operation mitigates the noise introduced during the hybrid beamforming process, effectively reducing the noise variance and improving the accuracy of subsequent projections. At operation 503, the method 500 includes applying a brute-force approach to project the residual matrix "R" onto each basis vector within the steering vector matrix "W". This projection entails calculating the inner product between the residual and each steering vector, thereby determining the correlation of the residual with each basis.

[0058]

[0059]

[0060]

[0061] To address the above-mentioned challenges, a disclosed system and / or method provides a unique strategy for performing the channel estimation in a wireless communication network, as described in conjunction with FIGS. 6 to 14.

[0062] Referring now to the drawings, and more particularly to FIGS. 6 to 14, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments.

[0063] FIG. 6is a diagram of an example of an implementation environment 600 in which a system and / or method, described herein, may be implemented, according to an embodiment as disclosed herein. The implementation environment 600 includes a Base Station (BS) 601, a User equipment (UE) 602 (e.g., 602a, 602b, and 602c), a RF chain 603, and a digital processor 604. The RF chain 603 may have same functionality as the RF chain 403 and the digital processor 604 may have same functionality as the baseband processing 404.

[0064] In some example embodiments, the UE 602 is a device that communicates with the BS 601. The UE 602 receives information from the BS 601 and / or sends information to the BS 601. Also, the UE 602 may generate and / or store information to be transmitted, as necessary. Also, the UE 602 may store and / or process information that is received, as necessary. The example FIG. 6 refers to the "UE". However, it should be understood by those skilled in the art that general terms such as "user device," "terminal," "terminal device," "communication device," and "communication terminal" can be used interchangeably with the term "UE." For example, the UE 602 may include a mobile phone (e.g., a smart phone, a radiotelephone, etc.), a wearable device (e.g., a pair of smart glasses or a smart watch), or a similar device.

[0065] In some example embodiments, the BS 601 may relate with one or more wired and / or wireless networks (wireless communication network). For example, the BS 601 may relate to a cellular network (e.g., a Fifth Generation (5G) network, a Long-Term Evolution (LTE) network, a Third Generation (3G) network, a Code Division Multiple Access (CDMA) network, etc.), a Public land mobile network (PLMN), a Local Area Network (LAN), a Wide Area Network (WAN), a Metropolitan Area Network (MAN), a telephone network (e.g., the Public Switched Telephone Network (PSTN)), a private network, an ad hoc network, an intranet, the Internet, a fiber optic-based network, or the like, a non-terrestrial network (NTN), and / or a combination of these or other types of networks.

[0066] In some example embodiments, the BS 601 may be a part of a network. For example, in a 5G network that includes a RAN, a transport network, and a core network, the network can be at least one of the RAN, the transport network, or the core network.

[0067] In some example embodiments, the BS 601 may receive one or more signals from the at least one UE 602 (e.g., 602a, 602b, and 602c) from the near-field region. The BS 601 may further extract channel information by computing a covariance matrix which is computed from one or more pilot subcarriers. The BS 601 may further determine one or more dominant independent vectors of the channel from the covariance matrix. The BS 601 may further determine one or more unknown parameters of steering vectors of the near-field region from the independent vectors. The BS 601 may further determine a weight of each steering vector and then find the estimated channel from these weights and steering vectors, as described in conjunction with FIG. 7 to FIG. 14. The weight of each steering vector is a scalar value indicating a relative contribution of that steering vector in overall signal processing, used to adjust one or more amplitudes and phases of the received signal processed by the plurality of antennas.

[0068] The number and arrangement of devices and networks shown in FIG. 6 are provided as an example. It should be understood that any changes that may be implemented by those skilled in the art, such as the addition or rearrangement of well-known devices or networks at the time of implementation, are included in this disclosure.

[0069] FIG. 7illustrates a block diagram of the BS 601 for performing channel estimation in the wireless communication network, according to an embodiment as disclosed herein.

[0070] In an embodiment, the BS 601 comprises a system 601a. The system 601a may include a memory 610, a processor 620, a communicator 630, and a channel estimator 640. In one or more embodiments, the system 601a may be implemented on one or more electronic devices, (not shown in FIG. 6).

[0071] In some example embodiments, the memory 610 stores instructions to be executed by the processor 620 for performing the channel estimation in the wireless communication network, as discussed throughout the disclosure. The memory 610 may include non-volatile storage elements. Examples of such non-volatile storage elements may include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. In addition, the memory 610 may, in some examples, be considered a non-transitory storage medium. The term "non-transitory" may indicate that the storage medium is not embodied in a carrier wave or a propagated signal. However, the term "non-transitory" should not be interpreted that the memory 610 is non-movable. In some examples, the memory 610 can be configured to store larger amounts of information than the memory. In certain examples, a non-transitory storage medium may store data that can, over time, change (e.g., in Random Access Memory (RAM) or cache). The memory 610 can be an internal storage unit, or it can be an external storage unit of the BS 601, a cloud storage, or any other type of external storage.

[0072] In some example embodiments, the processor 620 communicates with the memory 610, the communicator 630, and the channel estimator 640. The processor 620 is configured to execute instructions stored in the memory 610 and to perform various processes for performing the channel estimation in the wireless communication network, as discussed throughout the disclosure. The processor 620 may include one or a plurality of processors, maybe a general-purpose processor, such as a Central Processing Unit (CPU), an Application Processor (AP), or the like, a graphics-only 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).

[0073] In some example embodiments, the communicator 630 is configured for communicating internally between internal hardware components and with external devices (e.g., server) via one or more networks (e.g., radio technology). The communicator 630 includes an electronic circuit specific to a standard that enables wired or wireless communication.

[0074] In some example embodiments, the channel estimator 640 is implemented by processing circuitry such as logic gates, integrated circuits, microprocessors, microcontrollers, memory circuits, passive electronic components, active electronic components, optical components, hardwired circuits, or the like, and may optionally be driven by firmware. The circuits may, for example, be embodied in one or more semiconductor chips, or on substrate supports such as printed circuit boards and the like.

[0075] In some example embodiments, the channel estimator 640 may include a channel response estimator module 641, a channel covariance matrix determination module 642, a dominant independent vector determination module 643, and a dominant steering vector determination module 644.

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[0092] In some example embodiments, a function associated with the various components of the BS 601 may be performed through the non-volatile memory, the volatile memory, and the processor 620. One or a plurality of processors controls the processing of the input data in accordance with a predefined operating rule or AI model stored in the non-volatile memory and the volatile memory. The predefined operating rule or AI model is provided through training or learning. Here, being provided through learning means that, by applying a learning algorithm to a plurality of learning data, a predefined operating rule or AI model of the desired characteristic is made. The learning may be performed in a device itself in which AI according to an embodiment is performed, and / or may be implemented through a separate server / system. The learning algorithm is a method for training a predetermined target device (for example, a robot) using a plurality of learning data to cause, allow, or control the target device to decide or predict. Examples of learning algorithms include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

[0093] In some example embodiments, the AI model may consist of a plurality of neural network layers. Each layer has a plurality of weight values and performs a layer operation through a calculation of a previous layer and an operation of a plurality of weights. Examples of neural networks include, but are not limited to, convolutional neural network (CNN), deep neural network (DNN), recurrent neural network (RNN), restricted Boltzmann Machine (RBM), deep belief network (DBN), bidirectional recurrent deep neural network (BRDNN), generative adversarial networks (GAN), and deep Q-networks.

[0094] AlthoughFIG. 7shows various hardware components of the BS 601, but it is to be understood that other embodiments are not limited thereon. In other embodiments, the BS 601 may include less or more number of components. Further, the labels or names of the components are used only for illustrative purposes and do not limit the scope of the disclosure. One or more components can be combined to perform the same or substantially similar functions for performing channel estimation in the wireless communication network.

[0095] FIGS. 8A-8Bis an example scenario illustrating the method for selecting an antenna pair mechanism, according to an embodiment as disclosed herein.

[0096]

[0097]

[0098] In some example embodiments, the channel estimator 640 may detect few antenna elements are muted / switched OFF (e.g., N-2, N-1, etc.), as shown in second scenario 802. Hence only a subset antenna array can be active. In this application, the channel estimator 640 may perform channel estimation only from the active antennas (e.g., 0, 1, 2, 4, etc.). The channel estimator 640 may select the reference antenna (e.g., 2), which is optimally such that the maximum number of antenna pairs are formed. In this second scenario 802, by using the reference antenna index as 2, antenna pairs (1,3), and (0,4) are formed and we can estimate the channel by solving the independent equations at these antenna pairs, as discussed in FIG. 7.

[0099] In some example embodiments, the channel estimator 640 may determine the antenna pair and its symmetric equations by changing the reference antenna as each antenna element. As a result, the number of antenna pairs is increased to exploit the spatial diversity. The accuracy of the channel estimator 640 may increase exponentially by deriving all possible antenna pairs.

[0100] a. For instance, in Case-A 803, by using the reference antenna index as 2, the symmetric antenna pairs are (0,4) and (1,1).

[0101] b. For instance, in Case-B 804, by using the reference antenna index as 5, the symmetric antenna pair is (2,4).

[0102] FIG. 9 is a flow diagram illustrating a method 900 for performing the channel estimation based on a fractional fourier transform for near field channel, according to an embodiment as disclosed herein. The method 900 may execute multiple operations to perform the channel estimation, which are given below.

[0103]

[0104] Instead of relying on the brute-force method, which is computationally inefficient and yields poorer performance compared to the disclosed method 900, the channel estimator 640 may intelligently identify the "L" dominant paths that provide high signal power and low interference in a single step. Utilizing AI and machine learning, RIC or the channel estimator 640 may determine the minimum number of "L" eigenvectors required based on various factors, including hard-packet deadlines, packet loss tolerance, doppler frequency, and UE throughput requirements.

[0105]

[0106]

[0107]

[0108] FIG. 10 is a flow diagram illustrating a method 1000 for estimating one or more dominant paths based on a projection of eigen vectors for the channel estimation, according to an embodiment as disclosed herein. The method 1000 may execute multiple operations to perform the channel estimation, which are given below.

[0109]

[0110] At operation 1002, the method 1000 includes determining / computing the limit inferior that minimizes the nth norm of received and transmitted complex elemental signal that belongs to complex space. Using Least mean square method, find the channel on pilot subcarriers (From step a), the channel estimator 640 may compute the instantaneous covariance matrix. At operation 1003, the method 1000 includes selecting only independent vectors, after performing the EVD. The method 1000 includes ordering eigenvectors in a decreasing fashion based on the eigenvalues. At operation 1004, the method 1000 includes By projection based method, determining the dominant steering vector corresponding to each eigen vector.

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[0115] At operation 1102, the method 1100 includes determining / computing the limit inferior that minimizes the nth norm of received and transmitted complex elemental signal that belongs to complex space. Using Least mean square method, find the channel on pilot subcarriers (From step a), the channel estimator 640 may compute the instantaneous covariance matrix. At operation 1103, the method 1100 includes selecting only independent vectors, after performing EVD, select only independent vectors. Based on the eigen values, eigen vectors are ordered in the decreasing fashion. The method 1100 includes ordering eigenvectors in the decreasing fashion based on the eigenvalues. At operation 1104, the method 1100 includes determining the dominant eigenvectors (L) based on the threshold value of lambda which is evaluated by the at least one AI-ML model.

[0116]

[0117]

[0118] FIG. 12illustrates an uplink use case 1200 based on the disclosed channel estimation mechanism, according to an embodiment as disclosed herein. The uplink use case involves a series of message exchanges between the BS 601 and the UE 602.

[0119] At operation 1201, initially, a Location Management Function (LMF) module within the UE 602 receives a location service request (request for UE location) from the BS 601. At operation 1202, utilizing current location data, the LMF module determines a precise geographical position of the UE 602 (reports UE location), which is then transmitted to the BS 601 (e.g., gNodeB (gNodeB)) to enhance the network's awareness of the UE's location.

[0120] Subsequently, a RAN Intelligent Controller (RIC) module at the BS 601 leverages this location information, along with a coverage area classified radio map generated through a reinforcement learning algorithm, to classify the user. This classification allows the RIC module to activate an appropriate Channel Estimation (CE) algorithm, which improves the BLER and minimizes packet delays, by performing the below-mentioned operations.

[0121] a. The channel estimator 640 may employ artificial intelligence and machine learning techniques, the RIC module calculates the minimum required number of eigenvectors, referred to as "L", based on critical parameters such as hard-packet deadlines, packet loss tolerance, Doppler frequency, and throughput requirements.

[0122] b. The channel estimator 640 may then compute the limit inferior that minimizes the nth norm of the received and transmitted complex elemental signals within a complex signal space.

[0123] c. The channel estimator 640 may then determine the instantaneous covariance matrix, which is essential for understanding signal characteristics.

[0124] d. The channel estimator 640 may then select the Independent vectors after decomposing the covariance matrix.

[0125] e. The channel estimator 640 may then ordering the eigenvalues and corresponding eigenvectors in decreasing significance. The RIC module computes the base pivot steering vector for each eigenvector using established methodologies, followed by an assessment of each steering vector's contribution to the channel through a projection-based algorithm. With the estimated steering vectors and their associated gains, the channel is computed, enabling the UE 602 to decode the data effectively.

[0126] At operations 1203-1204, as the BLER performance improves, the BS 601 schedules a higher Modulation and Coding Scheme (MCS) for the UE 602, thereby optimizing data transmission. This shift to higher modulation schemes results in increased uplink throughput, enhancing overall communication system performance. At operation 1205, the BS 601 continues to utilize the selected algorithm until a significant change occurs in the UE's position relative to the classified radio map, ensuring that the system 201a adapts dynamically to user mobility and environmental changes.

[0127] FIG. 13illustrates a downlink use case 1300 based on the disclosed channel estimation mechanism, according to an embodiment as disclosed herein. The downlink use case involves a series of message exchanges between the BS 601 and the UE 602.

[0128] At operation 1301, initially, the LMF module in the UE 602 receives a location service request from the Base Station (BS). Utilizing the current location information, the LMF module identifies the UE's precise location. At operation 1302, Subsequently, the BS 601 (e.g., gNodeB (gNB)) collects information about the UE 602.

[0129] At operations 1303-1304, by leveraging this location data, the BS 601 generates a radio map using a reinforcement learning algorithm to assess the channel scenario. This process enables the BS 601 to select the most suitable channel estimation algorithm for optimal performance, by performing the below-mentioned operations.

[0130] a. To enhance signal quality, the BS 601 employs AI and ML to identify the number of dominant signal paths between the BS 601 and the UE 602, ensuring high signal strength and minimal interference.

[0131] b. Additionally, the BS 601 analyzes the characteristics of the UE 602 and its applications such as video, audio, or text to determine the number of signal taps (L) relevant to that specific UE.

[0132] c. The BS 601 also automatically detects channel characteristics, such as fading types, and configures the necessary parameters for the CE algorithm accordingly.

[0133] d. Once configured, the BS 601 schedules these parameters of CE algorithm to the UE 602.

[0134] At operation 1305, upon receiving the CE algorithm parameters, the UE 602 undertakes several operations:

[0135] a. First, it computes the limit inferior to minimize the nth norm of the received and transmitted complex elemental signal within a complex space.

[0136] b. From this calculation, the UE 602 derives the instantaneous covariance matrix and selects independent vectors through decomposition.

[0137] c. The eigenvalues are then ordered in decreasing fashion, and for each eigenvector, the UE 602 computes the corresponding base pivot steering vector for the near field using machine learning techniques.

[0138] d. Finally, with the updated channel estimate, the UE 602 decodes the transmitted data.

[0139] At operations 1306-1307, as a result of improved BLER performance, the UE 602 provides favorable Channel Quality Indicator (CQI) reports back to the BS 601. This enhanced decoding capability allows the BS 601 to schedule higher precoding matrices for subsequent downlink transmissions, thereby optimizing overall network performance.

[0140] FIG. 14is a flow diagram illustrating a method 1400 for performing the channel estimation, according to an embodiment as disclosed herein. The method 1400 may execute multiple operations to perform the channel estimation, which are given below.

[0141]

[0142] In some example embodiments, the disclosed method has several advantages over the existing method, which are stated below.

[0143] a. Reduced computational complexity: The disclosed method significantly lowers computational demands compared to existing OMP-based methods, particularly as the number of antennas at the BS601increases. Complexity is reduced by approximately 16,000 times, making it feasible for practical applications:

[0144]

[0145] b. Suitability for low latency applications: The disclosed method is specifically designed for low latency requirements of 5G and 6G networks, addressing a critical need that existing algorithms (e.g., OMP-based methods) fail to meet.

[0146] c. Enhanced Performance in the near-field scenarios: The proposed algorithm demonstrates superior performance in near-field channel estimation, aligning closely with theoretical expectations, unlike existing projection-based methods.

[0147] d. Improved user experience: By utilizing this efficient estimation method, BS 601 (e.g., gNodeBs) can enhance cellular access mechanisms, leading to a better overall user experience.

[0148] e. Adaptability to fast-fading channels: The disclosed method is effective even in fast-fading channel conditions, ensuring reliable performance in dynamic environments.

[0149] f. Cloud-friendly design: The computational efficiency of the disclosed method makes it suitable for cloud-based implementations, facilitating scalability and flexibility in network management.

[0150] g. Lower BLER: The disclosed method improves BLER performance, contributing to more reliable data transmission.

[0151] The various actions, acts, blocks, steps, or the like in the flow diagrams may be performed in the order presented, in a different order, or simultaneously. Further, in some embodiments, some of the actions, acts, blocks, steps, or the like may be omitted, added, modified, skipped, or the like without departing from the scope of the disclosure.

[0152] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one ordinary skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are illustrative only and not intended to be limiting.

[0153] While specific language has been used to describe the present subject matter, any limitations arising on account thereto, are not intended. As would be apparent to a person in the art, various working modifications may be made to the method to implement the inventive concept as taught herein. The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment.

[0154] The embodiments disclosed herein can be implemented using at least one hardware device and performing network management functions to control the elements.

[0155] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and / or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the scope of the embodiments as described herein.

Claims

1.A method performed by a base station for performing a channel estimation in a wireless communication network, the method comprising:receiving a signal from each user equipment (UE) via a plurality of antennas associated with the base station, wherein each UE is located within a predefined region;estimating a channel response based on the received signal;determining a channel covariance matrix, based on one or more pilot subcarriers associated with the received signal and the estimated channel response;determining one or more dominant independent vectors based on the determined channel covariance matrix;determining one or more dominant steering vectors associated with the predefined region from the one or more determined dominant independent vectors and determining the corresponding weight of each steering vector; andperforming the channel estimation based on the one or more determined dominant steering vectors and the corresponding determined weight of each steering vector.2.The method as claimed in claim 1, wherein the predefined region comprises a near-field region and a mixed-field region.3.The method as claimed in claim 1, wherein estimating the channel response based on the received signal comprises:converting, based on a hybrid precoding mechanism, the received signal to one or morechains using an analog combining matrix; andestimating the channel response of the one or morechains based on a least squares (LS) mechanism, wherein estimation is performed on one or more predefined subcarriers and timeslots that correspond to the received signal.4.The method as claimed in claim 1, wherein determining the one or more dominant independent vectors based on the determined channel covariance matrix comprises:performing an autocorrelation operation on the determined channel covariance matrix;decomposing the autocorrelated channel covariance matrix through an eigen value decomposition (EVD) comprising one or more eigenvalues and one or more corresponding eigenvectors; anddetermining the one or more dominant independent vectors from the decomposed autocorrelated channel covariance matrix.5.The method as claimed in claim 1, wherein determining the one or more dominant steering vectors associated with the predefined region from the one or more determined dominant independent vectors and the corresponding weight of each steering vector comprises:determining a first parameter and a second parameter for each eigenvector by using an antenna pair mechanism associated with the plurality of antennas; anddetermining the one or more steering vectors based on the determined first parameter and the determined second parameter.6.The method as claimed in claim 5, wherein the antenna pair mechanism comprises one or more operations, the one or more operations comprise:determining an operational status of each antenna within the plurality of antennas; andin response to determining that each antenna within the plurality of antennas is operational, determining a reference antenna from the plurality of antennas that utilize a symmetric antenna structure to determine the first parameter and the second parameter for each eigenvector.7.The method as claimed in claim 5, wherein the antenna pair mechanism comprises one or more operations, the one or more operations comprise:determining an operational status of each antenna within the plurality of antennas; andin response to determining that one or more antennas within the plurality of antennas are not operational, determining a reference antenna from a plurality of operational antennas to optimize a formation of a maximum number of antenna pairs, to determine the first parameter and the second parameter for each eigenvector.8.The method as claimed in claim 1, wherein performing the channel estimation comprises:determining a magnitude of each eigenvector associated with the channel from the corresponding eigenvalues;rearranging the eigenvectors in accordance with the magnitudes of the corresponding eigenvalues;identifying, by utilizing at least one Artificial intelligence (AI) - Machine learning (ML) model, one or more dominant eigenvectors based on at least one of one or more UE characteristics or one or more channel characteristics,wherein the predefined-optimal eigenvectors　represent a number of dominant delay taps, as automatically detected by the base station;in response to identifying one or more dominant eigenvectors contributing to the channel, determining a gain associated with each dominant eigenvector; andperforming the channel estimation based on the determined gain and one or more identified dominant eigenvectors.9.The method as claimed in claim 1, wherein performing the channel estimation comprises:identifying, by utilizing at least one artificial intelligence (AI) - machine learning (ML) model, a minimum of eigenvalue (), based on at least one of a number of the plurality of antennas, an operating frequency, and a type of UE traffic;determining one or more dominant eigenvectors that correspond to eigenvalues exhibiting magnitudes exceeding the identified minimum of eigenvalue;in response to identifying one or more dominant eigenvectors contributing to the channel, determining a gain associated with each eigenvector; andperforming the channel estimation based on the determined gain and one or more identified dominant eigenvectors.10.The method as claimed in claim 1, comprising:identifying each eigenvector of the covariance matrix allows for a calculation of an alpha-weighted kernel transform for each dominant eigenvector;extracting one or more unknown parameters for each eigenvector is achieved by analyzing one or more peak locations of a resulting 2D curves;determining a base steering vector corresponding to each dominant eigenvector within a mixed field framework;determining a gain of each base steering vector relative to the channel is performed through a projection-based algorithm;integrating all steering vectors from both near-field region and far-field region into a matrix to effectively support a system where the base station receives signals from both near field and far field regions;utilizing the alpha-weighted kernel transform of each dominant eigenvector accommodates both near-field and far-field regions ;estimating the one or more unknown parameters is accomplished by analyzing the one or more peak locations identified within the kernel transforms of each eigenvector; andreflecting the dominant basis of the mixed field occurs through the kernel transform of the eigenvector in the mixed field region.11.The method as claimed in claim 1,wherein the channel covariance matrix is defined as a mathematical representation that characterizes one or more statistical properties of a wireless channel, to capture one or more correlations between multiple transmission paths of the received signal;wherein the one or more dominant independent vectors are defined as principal components or significant eigenvectors derived from the channel covariance matrix, represents one or more influential directions in a signal space that contribute to the channel response;wherein the one or more steering vectors are defined as a mathematical representation that defines directionality of signal transmission or reception at the plurality of antennas, represents as complex exponentials corresponding to specific angles of arrival or departure of the received signal; andwherein the weight of each steering vector is a scalar value indicating a relative contribution of that steering vector in overall signal processing, used to adjust one or more amplitudes and phases of the received signal processed by the plurality of antennas.12.A base station for performing a channel estimation in a wireless communication network, wherein the system comprising:memory, including one or more storage media, storing instructions;a processor including processing circuitry; andcommunicator circuitry;wherein the instructions, when executed by the processor, cause the base station to:receive a signal from each user equipment (UE) via a plurality of antennas associated with the base station, wherein each UE is located within a predefined region;estimate a channel response based on the received signal;determine a channel covariance matrix, based on one or more pilot subcarriers associated with the received signal and the estimated channel response;determine one or more dominant independent vectors based on the determined channel covariance matrix;determine one or more dominant steering vectors associated with the predefined region from the one or more determined dominant independent vectors and determining the corresponding weight of each steering vector; andperform the channel estimation based on the one or more determined dominant steering vectors and the corresponding determined weight of each steering vector.13.The base station as claimed in claim 12, wherein the predefined region comprises a near-field region and a mixed-field region.14.The base station as claimed in claim 12, wherein to estimate the channel response based on the received signal, the instructions cause the base station to:convert, based on a hybrid precoding mechanism, the received signal to one or morechains using an analog combining matrix; andestimate the channel response of the one or morechains based on a least squares (LS) mechanism, wherein estimation is performed on one or more predefined subcarriers and timeslots that correspond to the received signal.15.The base station as claimed in claim 12, wherein to determine the one or more dominant independent vectors based on the determined channel covariance matrix, the instructions cause the base station to:perform an autocorrelation operation on the determined channel covariance matrix;decompose the autocorrelated channel covariance matrix through an eigen value decomposition (EVD) comprising one or more eigenvalues and one or more corresponding eigenvectors; anddetermine the one or more dominant independent vectors from the decomposed autocorrelated channel covariance matrix.

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