WO2020170267A1

WO2020170267A1 - Method and system for compressing signals received at base station

Info

Publication number: WO2020170267A1
Application number: PCT/IN2020/050102
Authority: WO
Inventors: Aswathylakshmi P; RadhaKrishna GANTI
Original assignee: Indian Institute of Technology Madras
Current assignee: Indian Institute of Technology Madras
Priority date: 2019-02-19
Filing date: 2020-01-31
Publication date: 2020-08-27
Anticipated expiration: 2021-08-19

Abstract

Embodiments herein provide a method for compressing signals received at a base station. The method includes receiving, by a communicator of the base station, a plurality of signals using an array of antenna in the base station over a time span and compressing, by a radio unit of the BS, the plurality of signals by selecting basis signals and representing the plurality of signals in terms of the basis signals, where the basis signals are a set of signals of the plurality of signals which are received at a set of antennas of the array of antenna. Further, the method includes transmitting, by the RU of the BS, the basis signal and the plurality of signals represented in terms of the basis signals for each of the received signals of the plurality of signals to a distributed unit.

Description

METHOD AND SYSTEM FOR COMPRESSING SIGNALS RECEIVED AT BASE STATION

FIELD OF INVENTION

[0001] The embodiments herein relate to wireless communication. More particularly relates to a method and system for compressing signals received at a base station (BS). This application is based on Indian provisional application number 201941006504 filed on 19^th February 2019, the disclosure of which is incorporated herein.

BACKGROUND OF THE INVENTION

[0002] In general, Massive multiple-input and multiple-output (MEMO) base station which comprises a large number of antennas supports a plurality of users simultaneously through spatial multiplexing. This improves spectral efficiency and increases the network capacity. However, the huge processing complexity that such a system entails makes the centralized radio access network (C-RAN) a more suitable architecture for the implementation. In the C-RAN, the base station is split into two parts: a pooled baseband unit (BBU) at a centralized location common to several cells, and a number of remote radio heads (RRH) distributed geographically over these cells, connected to the central BBU (as shown in FIG. 3). The pooling of baseband resources can meet the processing requirements of the massive MEMO systems as well as offer the potential for cooperative radio to reduce interference. Furthermore, network operators can drive down the cost through the concentration of resources at the BBU and the deployment of limited-functionality RRHs in the cells. The twin advantages of reduced cost and interference in C-RAN become conducive to network densification, a key driver for 5G, by allowing a higher density of RRHs to be put in place. Thus massive MIMO combined with C-RAN can potentially support the ultra-high data rates envisioned in 5G. However, the tight latency constraints and large bandwidths of the 5G require high-speed data transfer in the links between the BBU and the RRH, called the fronthaul. Moreover, this fronthaul capacity demand scales with a number of antennas at the RRH and laying such high capacity optical fibers for each antenna stream in the BBU-RRH link would drive up the cost too much for the network operators. Therefore, there is a need to address the transmission of the signals in the fronthaul which will also curb the cost constraints at the same time does not compromise on the quality and performance of the signals being transmitted.

[0003] The above information is presented as background information only to help the reader to understand the present invention. Applicants have made no determination and make no assertion as to whether any of the above might be applicable as prior art with regard to the present application.

OBJECT OF INVENTION

[0004] The principal object of the embodiments herein is to provide a method and system for compressing signals received at a base station.

[0005] Another object of the embodiments herein is to receive a plurality of signals using an array of antenna in the base station over a time span.

[0006] Another object of the embodiments herein is to compress the plurality of signals by selecting basis signals and representing the plurality of signals in terms of the basis signals.

[0007] Another object of the embodiments herein is to transmit the basis signal and the plurality of signals represented in terms of the basis signals for each of the received signals of the plurality of signals to a distributed unit (150).

[0008] Another object of the embodiments herein is to reconstruct each of the sub-matrices by determining a function of the basis matrix and the projection matrix of each of the sub-matrices^ [0009] Another object of the embodiments herein is to reconstruct a time domain matrix by determining a function of the basis matrix and the projection matrix for the time domain matrix.

SUMMARY

[0010] Accordingly, the embodiments herein provide a method for compressing signals received at a base station (100). The method includes receiving, by a communicator (126) of the base station (100), a plurality of signals using an array of antenna in the base station (100) over a time span and compressing, by a radio unit (120) of the BS (100), the plurality of signals by selecting basis signals and representing the plurality of signals in terms of the basis signals, where the basis signals are a set of signals of the plurality of signals which are received at a set of antennas of the array of antenna. Further, the method includes transmitting, by the RU (120) of the BS (100), the basis signal and the plurality of signals represented in terms of the basis signals for each of the received signals of the plurality of signals to a distributed unit (140).

[0011] In an embodiment, the compression is obtained by performing a QR decomposing technique.

[0012] In an embodiment, the method of compressing, by the RU (120) of the BS (100), the plurality of signals by selecting the basis signals and representing the plurality of signals in terms of the basis signals includes constructing, by the RU (120) of the BS (100), a time-domain signal matrix based on the plurality of signals received at the array of antenna, wherein the time-domain signal matrix comprises a block of time-domain samples of the signals received at each antenna of the array of antenna and generating, by the RU (120) of the BS(100), a frequency domain signal matrix using the time- domain signal matrix. Further, the method also includes performing, by the RU (120) of the BS (100), resource element de-mapping by dividing the frequency domain signal matrix into sub-matrices corresponding to different users; and compressing, by the RU (120) of the BS (100), each of the sub-matrices of each of the users to obtain component matrixes for each of the sub-matrices of each of the user, wherein the component matrixes comprises the basis signals and the plurality of signals in terms of the basis signals. Further, the method also includes receiving, by the DU (140) of the BS (100), a basis matrix and a projection matrix for each of the sub-matrices of each of the users; and reconstructing, by the DU (140) of the BS (100), each of the sub-matrices by determining a function of the basis matrix and the projection matrix of each of the sub-matrices.

[0013] In an embodiment, the method of compressing, by the RU (120) of the BS (100), the plurality of signals by selecting the basis signals and representing the plurality of signals in terms of the basis signals includes constructing, by the RU (120) of the BS (100), a time-domain signal matrix based on the plurality of signals received at the array of antenna, wherein the time-domain signal matrix comprises a block of time-domain samples of the received signal at each antenna of the array of antenna; and compressing, by the RU (120) of the BS (100), the time-domain signal matrix to obtain component matrixes, wherein the component matrixes comprises the basis signals and the plurality of signals in terms of the basis signals. Further, the method also includes receiving, by the DU (140) of the BS (100), a basis matrix and a projection matrix for the time domain matrix and reconstructing, by the DU (140) of the BS (100), the time domain matrix by determining a function of the basis matrix and the projection matrix for the time domain matrix.

[0014] In an embodiment, the two component matrixes comprise a basis matrix and a projection matrix.

[0015] In an embodiment, the component matrixes represents an approximation of contents of the time-domain signal matrix with fewer number of time-domain samples as compared to the block of the time-domain samples of the time-domain signal matrix which provides the compression. [0016] In an embodiment, the entries in columns of the time-domain signal matrix represent the signals received at each of the antenna of the array of antenna over a time span of N samples.

[0017] In an embodiment, the basis matrix or a function of the basis matrix; and the projection matrix or a function of the projection matrix is transmitted through a fronthaul link (1000) of the BS (100).

[0018] In an embodiment, the frequency domain matrix is divided into the sub-matrices according to allocated sub-carriers to each of the users.

[0019] In an embodiment, the method of constructing, by the base station (100), the time-domain signal matrix based on the plurality of signals received at the plurality of antenna includes down-converting, by the RU (120) of the base station (100), the plurality of signals received at the plurality of antenna; and generating, by the RU (120) of the base station (100), the time- domain signal matrix based using the plurality of down-converted signals.

[0020] In an embodiment, the basis matrix comprises signals received at a subset of antennas or a function of the signals received at a subset of antennas from the array of antenna of the RU (120) of the BS (100).

[0021] In an embodiment, the subset of antennas comprises antennas with highest received powers or antennas with a function of the highest received powers to represent the plurality of signals received at the plurality of antennas.

[0022] In an embodiment, the projection matrix comprises description of the plurality of signals received at the plurality of antenna in terms of the basis matrix.

[0023] In an embodiment, the sub-carrier allocation of each of the users is known to the BS (100).

[0024] In an embodiment, the dimensions of the time-domain signal matrix is dependent on a total number of sub carriers allocated to all users.

[0025] In an embodiment, the method of transmitting, by the RU (120) of the BS (100), the basis signal and the plurality of signals represented in terms of the basis signal for each of the received signals of the plurality of signals to the DU (140) includes performing, by the RU (120) of the BS (100), a quantization mechanism on the basis signal and the plurality of signals represented in terms of the basis signal for each of the received signals of the plurality of signals; and transmitting, by the RU (120) of the BS (100), the quantized basis signals and the quantized signals of the plurality of signals represented in terms of the basis signal for each of the received signals of the plurality of signals to the DU (140).

[0026] Accordingly, the embodiments herein provide a base station (100) for compressing signals received at the base station (100). The base station (100) includes a Radio unit (RU) (120) connected to a distributed unit (DU) (140) through a fronthaul link (1000). The RU (120) includes a memory (122), a processor (124), and a communicator (126). The RU (120) is configured to receive a plurality of signals using an array of antenna in the base station (100) over a time span and compress the plurality of signals by selecting basis signals and representing the plurality of signals in terms of the basis signals, wherein the basis signals are a set of signals of the plurality of signals which are received at a set of antennas of the array of antenna. Further, the RU (120) is also configured to transmit the basis signal and the plurality of signals represented in terms of the basis signals for each of the received signals of the plurality of signals to the distributed unit (DU) (140) through the fronthaul link (1000).

[0027] A base station (100) for compressing signals received at the base station (100). The base station (100) includes a distributed unit (DU) (140) connected to a Radio unit (RU) (120) through a fronthaul link (1000). The DU (140) includes a memory (142), a processor (144) and a communicator (146). The DU (140) is configured to receive a basis matrix and a projection matrix for a time domain matrix from the RU (120) through the fronthaul link (1000) and reconstruct the time domain matrix by determining a function of the basis matrix and the projection matrix for the time domain matrix.

[0028] A base station (100) for compressing signals received at the base station (100). The base station (100) includes a distributed unit (DU) (140) connected to a Radio unit (RU) (120) through a fronthaul link (1000). The DU (140) includes a memory (142), a processor (144) and a communicator (146). The DU (140) is configured to receive a basis matrix and a projection matrix for each sub-matrix corresponding to each users, from the Radio unit (RU) (120) through the fronthaul link (1000); and reconstruct each of the sub- matrices corresponding to each of the users by determining a function of the basis matrix and the projection matrix for the time domain matrix.

[0029] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.

BRIEF DESCRIPTION OF FIGURES

[0030] This invention is illustrated in the accompanying drawings, throughout which like reference letters indicate corresponding parts in the various figures. The embodiments herein will be better understood from the following description with reference to the drawings, in which:

[0031] FIG. 1 is a block diagram of a system for compressing signals received at a base station (100), according to the embodiments as disclosed herein;

[0032] FIG. 2A is a flow chart 200a illustrating the method for compressing the signals received at the BS (100), according to the embodiments as disclosed herein;

[0033] FIG. 2B is another flow chart 200b illustrating the method for compressing the signals received at the BS (100), according to the embodiments as disclosed herein;

[0034] FIG. 3 is an example Massive MIMO C-RAN architecture which provides compression of signals for transmission in a fronthaul link (1000) of the BS (100), according to the embodiments as disclosed herein;

[0035] FIG. 4 illustrates proposed functional splits between a remote radio head (RRH) (120) and a baseband unit (BBU) (140) in an uplink in a massive MIMO system, according to the embodiments as disclosed herein;

[0036] FIG. 5 illustrates the compression of the signals received the BS (100) at various stages before transmission in the fronthaul link (1000) of the BS (100), according to the embodiments as disclosed herein;

[0037] FIG. 6 illustrates a method of approximating each of user sub- matrixes as a function of component matrices before transmission in the fronthaul link (1000) of the BS (100), according to the embodiments as disclosed herein;

[0038] FIG. 7 is a graph plot illustrating compression ratios (CRs) for proposed method of compression as a function of the actual number of antennas (Lu) for signal subspace of UEs u, according to the embodiments as disclosed herein;

[0039] FIG. 8A is a graph illustrating Bit Error Rate (BER) performance for QR compression for 8 users, according to the embodiments as disclosed herein;

[0040] FIG. 8B is a graph illustrating Bit Error Rate (BER) performance for QR compression for 12 users, according to the embodiments as disclosed herein; and

[0041] FIGS. 9A-9B are graph plots illustrating un-coded Bit Error Rate (BER) performance for the proposed method in comparison with the SVD compression, according to the embodiments as disclosed herein.

DETAILED DESCRIPTION OF INVENTION

[0042] Various embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. In the following description, specific details such as detailed configuration and components are merely provided to assist the overall understanding of these embodiments of the present disclosure. Therefore, it should be apparent to those skilled in the art that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness.

[0043] 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.

[0044] Herein, 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.

[0045] As is traditional in the field, embodiments may be described and illustrated in terms of blocks which carry out a described function or functions. These blocks, which may be referred to herein as managers, units, modules, hardware components or the like, are physically implemented by analog and/or digital circuits such as logic gates, integrated circuits, microprocessors, microcontrollers, memory circuits, passive electronic components, active electronic components, optical components, hardwired circuits and the like, and may optionally be driven by firmware 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.

[0046] The terms radio base station (100) and base station (100) refers to the same and may be used inter-changeably throughout the specification. The terms radio head (RRH) (120) and Radio Unit (RU) (120) refers to the same and may be used inter-changeably throughout the specification, depending on the technology referred in the specification. The terms baseband unit (BBU) (140) and Distributed Unit (DU) (140) refers to the same and may be used inter- changeably throughout the specification, depending on the technology referred in the specification.

[0047] Accordingly, the embodiments herein provide a method for compressing signals received at a base station (100). The method includes receiving, by a communicator (120) of the base station (100), a plurality of signals using an array of antenna in the base station (100) over a time span and compressing, by a radio unit (120) of the BS (100), the plurality of signals by selecting basis signals and representing the plurality of signals in terms of the basis signals, where the basis signals are a set of signals of the plurality of signals which are received at a set of antennas of the array of antenna. Further, the method includes transmitting, by the RU (120) of the BS (100), the basis signal and the plurality of signals represented in terms of the basis signals for each of the received signals of the plurality of signals to a distributed unit (150). [0048] Referring now to the drawings, and more particularly to FIGS. 1 through 9B, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments.

[0049] FIG. 1 is a block diagram of system for compressing signals received at a base station (100), according to the embodiments as disclosed herein.

[0050] A radio base station (100) or a base station (100) has a remote radio head (RRH) (120) that performs radio frequency and low-PHY layer processing, and a baseband unit (BBU) (140) that performs high-PHY, MAC and RLC layer processing. Fronthaul (1000) is a data transport link that connects the RRH (120) and BBU (140). In 5G, the RRH (120) is called Radio Unit (RU) (120) and the BBU (140) is called Distributed Unit (DU) (140). A base station (100) that supports massive Multiple Input Multiple Output (MIMO) has the RU (120) equipped with antenna arrays that have a large number of antennas for transmission/reception so that many users can be supported simultaneously. Having large number of antennas increases the amount of data that has to be transported in the fronthaul link (1000), whose capacity is limited. Therefore, compression techniques are required to reduce the data load in the fronthaul (1000).

[0051] Referring to the FIG. 1, the base station (100) includes a radio unit (RU) (120) and a distributed unit (DU) (140) connected through a fronthaul link (1000).

[0052] The RU (120) includes a memory (122), a processor (124), a communicator (124), a down conversion management engine (128), a component matrix generation engine (130) and a resource element (RE) demapping engine (132). The RU (120) performs radio frequency and low- PHY layer processing.

[0053] In an embodiment, the memory (122) can 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 (122) 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 (122) is non-movable. In some examples, the memory (122) is 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).

[0054] In an embodiment, the processor (124) is configured to coordinate the functions of the hardware elements of the base station (100).

[0055] In an embodiment, the communicator (126) is configured to receive a plurality of signals using an array of antenna in the base station (100) over a time span.

[0056] In an embodiment, the fronthaul link (1000) is configured to transmit the basis signal and the plurality of signals represented in terms of the basis signals to the DU (140).

[0057] In an embodiment, the down conversion management engine (128) is configured to down-convert the plurality of signals received at the plurality of antennas of the base station (100).

[0058] In an embodiment, the component matrix generation engine (130) is configured to generate the time-domain signal matrix using the plurality of down-converted signals. The time-domain signal matrix includes a block of time-domain samples of the signals received at each antenna of the array of antenna. Further, the component matrix generation engine (130) is configured to generate a frequency domain signal matrix using the time-domain signal matrix and compress each of the sub-matrices of each of the users to obtain component matrixes for each of the sub-matrices of each of the user. The component matrixes include the basis signals and the plurality of signals in terms of the basis signals. Further, the component matrix generation engine (130) is configured to compress each of the sub-matrices of each of the users to obtain component matrixes for each of the sub-matrices of each of the user. The component matrixes comprise the basis signals and the plurality of signals in terms of the basis signals. The frequency domain matrix is divided into the sub- matrices according to allocated sub-carriers to each of the users.

[0059] In another embodiment, the component matrix generation engine (130) is configured to generate the time-domain signal matrix based on the plurality of signals received at the array of antenna and compress the time- domain signal matrix to obtain the component matrixes. The time-domain signal matrix includes a block of time-domain samples of the received signal at each antenna of the array of antenna and the dimensions of the time-domain signal matrix is dependent on a total number of sub carriers allocated to all users. Further, the component matrix generation engine (130) is configured to compress the time-domain signal matrix to obtain component matrixes, wherein the component matrixes comprise the basis signals and the plurality of signals in terms of the basis signals. The compression is obtained by performing a decomposition technique such as for example but not limited to: a QR decomposing technique, singular value decomposition (SVD) compression technique and the like.

[0060] The two component matrix includes a basis matrix and a projection matrix. The component matrixes represents an approximation of the contents of the time-domain signal matrix with fewer number of time-domain samples as compared to the block of the time-domain samples of the time- domain signal matrix which provides the compression. The entries in the columns of the time-domain signal matrix represent the signals received at each of the antenna of the array of antenna over a time span of N samples. The basis matrix or a function of the basis matrix and the projection matrix or a function of the projection matrix of each of the user is transmitted through the fronthaul link (1000) of the BS (100). The basis matrix includes the signals received at a subset of antennas or a function of the signals received at a subset of antennas from the array of antenna of the RU (120) of the BS (100). The subset of antennas includes the antennas with the highest received powers or antennas with a function of the highest received powers to represent the plurality of signals received at the plurality of antennas. The projection matrix encompasses the description of the plurality of signals received at the plurality of antenna in terms of the basis matrix.

[0061] In an embodiment, the RE demapping engine (132) is configured to perform the resource element de-mapping by dividing the frequency domain signal matrix into the sub-matrices corresponding to different users.

[0062] The distributed unit (DU) (150) includes a memory (142), a processor (144), a communicator (146) and a reconstruction management engine (148). The DU (140) performs high-PHY, MAC and RLC layer processing.

[0063] In an embodiment, the memory (142) can 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 (142) 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 (142) is non-movable. In some examples, the memory (142) is 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). [0064] In an embodiment, the processor (144) is configured to coordinate the functions of the hardware elements of the base station (100).

[0065] In an embodiment, the communicator (146) is configured to receive the basis matrix and the projection matrix from the DU (140) through the fronthaul link (1000).

[0066] In an embodiment, the reconstruction management engine (148) is configured to receive a basis matrix and a projection matrix for the time domain matrix and reconstruct the time domain matrix by determining a function of the basis matrix and the projection matrix for the time domain matrix.

[0067] In another embodiment, the reconstruction management engine (148) is configured to receive a basis matrix and a projection matrix for each of the sub-matrices of each of the users and reconstruct each of the sub-matrices by determining a function of the basis matrix and the projection matrix of each of the sub-matrices.

[0068] Although the FIG. 1 shows the hardware elements of the base station (100) but it is to be understood that other embodiments are not limited thereon. In other embodiments, the base station (100) may include less or more number of elements. Further, the labels or names of the elements are used only for illustrative purpose and does not limit the scope of the invention. One or more components can be combined together to perform same or substantially similar function.

[0069] FIG. 2A is a flow chart 200a illustrating the method for compressing the signals received at the BS (100), according to the embodiments as disclosed herein.

[0070] Referring to the FIG. 2A, at step 202a, the BS (100) receive the plurality of signals using the array of antenna in the base station (100) over the time span. For example, in the BS (100) as illustrated in the FIG. 1, the communicator (126) of the RU (120) is configured to receive the plurality of signals using the array of antenna in the base station (100) over the time span.

[0071] At step 204a, the BS (100) constructs the time-domain signal matrix based on the plurality of signals received at the array of antenna. For example, in the BS (100) as illustrated in the FIG. 1, the component matrix generation engine (130) is configured to constructs the time-domain signal matrix based on the plurality of signals received at the array of antenna.

[0072] At step 206a, the BS (100) generates the frequency domain signal matrix using the time-domain signal matrix. For example, in the BS (100) as illustrated in the FIG. 1, the component matrix generation engine (130) is configured to generate the frequency domain signal matrix using the time- domain signal matrix.

[0073] At step 208a, the BS (100) performs the resource element de- mapping by dividing the frequency domain signal matrix into the sub-matrices corresponding to different users. For example, in the BS (100) as illustrated in the FIG. 1, the RE demapping engine (132) is configured to perform the resource element de-mapping by dividing the frequency domain signal matrix into the sub-matrices corresponding to different users.

[0074] At step 210a, the BS (100) compresses each of the sub-matrices of each of the users to obtain the component matrixes for each of the sub- matrices of each of the user. For example, in the BS (100) as illustrated in the FIG. 1, the component matrix generation engine (130) is configured to compress each of the sub-matrices of each of the users to obtain the component matrixes for each of the sub-matrices of each of the user.

[0075] At step 212a, the BS (100) transmits the component matrixes for each of the sub-matrices of each of the user. For example, in the BS (100) as illustrated in the FIG. 1, the fronthaul link (1000) is configured to transmit the component matrixes for each of the sub-matrices of each of the user. [0076] At step 214a, the BS (100) receives the basis matrix and the projection matrix for each of the sub-matrices of each of the users. For example, in the BS (100) as illustrated in the FIG. 1, the communicator (146) at the DU (140) is configured to receive the basis matrix and the projection matrix for each of the sub-matrices of each of the users.

[0077] At step 216a, the BS (100) reconstructs each of the sub-matrices by determining the function of the basis matrix and the projection matrix of each of the sub-matrices. For example, in the BS (100) as illustrated in the FIG. 1, the reconstruction management engine (148) is configured to reconstruct each of the sub-matrices by determining the function of the basis matrix and the projection matrix of each of the sub-matrices.

[0078] The various actions, acts, blocks, steps, or the like in the method may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some of the actions, acts, blocks, steps, or the like may be omitted, added, modified, skipped, or the like without departing from the scope of the invention.

[0079] FIG. 2B is another flow chart 200b illustrating the method for compressing the signals received at the BS (100), according to the embodiments as disclosed herein.

[0080] Referring to the FIG. 2B, at step 202b, the BS (100) receive the plurality of signals using the array of antenna in the base station (100) over the time span. For example, in the BS (100) as illustrated in the FIG. 1, the communicator (126) of the RU (120) is configured to receive the plurality of signals using the array of antenna in the base station (100) over the time span.

[0081] At step 204b, the BS (100) constructs the time-domain signal matrix based on the plurality of signals received at the array of antenna. For example, in the BS (100) as illustrated in the FIG. 1, the component matrix generation engine (130) is configured to construct the time-domain signal matrix based on the plurality of signals received at the array of antenna. [0082] At step 206b, the BS (100) compresses the time-domain signal matrix to obtain the component matrixes. For example, in the BS (100) as illustrated in the FIG. 1, the component matrix generation engine (130) is configured to compress the time-domain signal matrix to obtain the component matrixes.

[0083] At step 208b, the BS (100) transmits the component matrixes which comprise the basis matrix and the projection matrix for the time-domain signal matrix. For example, in the BS (100) as illustrated in the FIG. 1, the fronthaul link (1000) is configured to transmit the component matrixes which comprise the basis matrix and the projection matrix for the time-domain signal matrix.

[0084] At step 210b, the BS (100) receives the basis matrix and the projection matrix for the time domain matrix from the RU (140) through the fronthaul link (1000). For example, in the BS (100) as illustrated in the FIG. 1, the communicator (146) at the DU (140) is configured to receive the basis matrix and the projection matrix for the time domain matrix from the RU (140) through the fronthaul link (1000).

[0085] At step 212b, the BS (100) reconstructs the time domain matrix by determining function of the basis matrix and the projection matrix for the time domain matrix. For example, in the BS (100) as illustrated in the FIG. 1, the reconstruction management engine (148) is configured to reconstruct the time domain matrix by determining function of the basis matrix and the projection matrix for the time domain matrix.

[0086] The various actions, acts, blocks, steps, or the like in the method may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some of the actions, acts, blocks, steps, or the like may be omitted, added, modified, skipped, or the like without departing from the scope of the invention. [0087] FIG. 3 is an example Massive MIMO C-RAN architecture which provides the compression of the signals for transmission in the fronthaul link (1000) of the BS (100), according to the embodiments as disclosed herein.

[0088] Referring to the FIG. 3, a massive MIMO base station comprises a large number of antennas which has the ability to support many users simultaneously through spatial multiplexing, which improves spectral efficiency and increases the network capacity. The processing complexity that such a system requires makes centralized radio access network (C-RAN) a more suitable architecture for implementation. In C-RAN, the base station (100) is split into two parts: a pooled baseband unit (BBU) (140) at a centralized location common to several cells; and a number of remote radio heads (RRH) (120) distributed geographically over the cells and connected to the central BBU (140). The pooling of the baseband resources can meet the processing requirements of the massive MIMO systems as well as offer the potential for cooperative radio to reduce interference. Further, network operators reduce the cost of implementation due to the concentration of the resources at the BBU (140) and deployment of limited-functionality RRHs in the cells.

[0089] The massive MIMO combined with the C-RAN can potentially support the ultra-high data rates envisioned in the 5G. However, the tight latency constraints and large bandwidths of the 5G require high-speed data transfer in the links between the BBU (140) and the RRH (120) called the fronthaul link (1000). Moreover, the fronthaul capacity demand scales with the number of antennas at the RRH (120) and laying such high capacity optical fibers for each of the antenna stream in the BBU-RRH link would drive up the cost for the network operators. For example, using the latest standard of the Common Public Radio Interface (eCPRI) for the fronthaul data transport using 64 RRH antennas, the low-PHY functional split between the BBU (140) and RRH (120) requires a data rate up to 236 Gbps for a 100MHz bandwidth. With the use of millimeter-wave (mmWave) and terahertz (THz) communication that cover huge swathes of bandwidth, the strain on the fronthaul capacity increases and hence the compression techniques that reduce the fronthaul load becomes instrumental to any practical implementation of the massive MIMO systems in the C-RAN.

[0090] The conventional methods used for uplink fronthaul compression are for example: point-to-point (P2P) compression, distributed source coding, compressed sensing (CS) and spatial filtering. However, a key drawback of the approaches is a need to divide the antenna array into many groups and then apply the processing separately. In the conventional methods and systems, the set of antennas that are not selected either remain inactive or the data received at the antennas are discarded.

[0091] The Principal Component Analysis (PCA) compression algorithm uses the inherent sparsity of the MIMO channels to reduce the number of links required in the fronthaul. The PCA compression performs a low-rank approximation of the matrix consisting of the received signals by leveraging the signal correlation across space and time. But the PCA compression requires computing the singular value decomposition (SVD) of the matrix wherein the complexity is prohibitively high for large matrix dimensions as in the massive MIMO case, since the RRH (120) has limited processing resources. Therefore, the above mentioned issues are addressed by the proposed method which uses QR decomposition technique which sends the received signal matrix as the component matrix of the basis matrix and the projection matrix. The projection matrix comprises description of the plurality of signals received at the plurality of antenna in terms of the basis matrix.

[0092] FIG. 4 illustrates the proposed functional splits between the BBU (140) and the RRH (120) in the uplink in the massive MIMO system, according to the embodiments as disclosed herein.

[0093] Referring to the FIG. 4, the data rate in the fronthaul link (1000) is impacted by the functional split between the BBU (140) and the RRH (120). The data rate is almost halved when moving from a split A to a split B, as the cyclic prefix (CP) and the guard bands are removed. At the split C the resource elements (RE) are demapped and the users are separated which results in the reduction in the data rate which is also dependent on the resource block utilization. The split D depends on the modulation order where for large modulation orders, the split D can increase the data rate, as more bits are required to represent each of the samples. Finally, greater than 90 percent reduction in the data rate is achieved with a split E compared to the split A, but the high reduction in the data rate is achieved at the cost of requiring all PHY layer processing to be at the RRHs which increases the complexity and decreases flexibility. Though the data rate decreases from the split A to the split E, the required control information increases.

[0094] In the proposed method, the uplink fronthaul data rate is reduced in two stages with a split at C as shown in the FIG. 4 where the CP and the guard band are removed and the RE demapping is completed at the RRH (120). Also, at the split C, the users are separated and a low complexity algorithm based on the QR decomposition is used to approximate the matrix composed of the complex baseband received signals. The advantages of the proposed compression method includes a user-specific compression technique; provides a good approximation of the received signal matrix; information from the antennas that are not selected are not lost while achieving high compression ratios; and provides denoising gain leading to better error performance compared to conventional uncompressed system.

[0095] FIG. 5 illustrates the compression of the signals received the BS (100) at various stages before transmission in the fronthaul link (1000) of the BS (100), according to the embodiments as disclosed herein.

[0096] FIG. 6 illustrates the method of approximating each of the user sub-matrixes as the function of the component matrices before transmission in the fronthaul link (1000) of the BS (100), according to the embodiments as disclosed herein.

[0097] Referring to the FIG. 6, Consider a massive MIMO 5G base station with Nr antennas at the RRH (120) which receives signals from single antenna users in the uplink. Let the number of users in the system be Nu. In 5G, the uplink multiple access scheme is Orthogonal frequency-division multiple access (OFDMA). Therefore, the bit-stream from each user undergoes the M- QAM symbol mapping followed by the OFDM modulation. The OFDM modulation consists of the sub-carrier mapping according to the resources allocated to the user, IFFT, and addition of a cyclic prefix (CP). The OFDM symbols reach the RRH (120) of the base station (100) through multi-path channels. The received signal at antenna r at sampling instant n is

where xu is the OFDM symbol from the user u, hr,u is the multipath channel response from the user u to antenna represents the

convolution output between the OFDM symbols of the user u and the multi- path channel response from the user u to the antenna r, and wr is the additive white Gaussian noise (AWGN) with variance s²at antenna r.

[0098] For the proposed QR compression algorithm, consider a block of N time-domain samples received at the RRH antennas. The channel is assumed to remain constant for the duration of the N samples. The received signal matrix Y at the RRH (120) is:

Each column of the received signal matrix Y represents the signal received at each antenna over a time span of N samples. Assuming a maximum of L multi- paths for each user, equation (1) is expanded to get:

In the equation (2),

is the matrix of complex channel gains for the i^th multi-path,

is the matrix composed of transmitted symbols from the users, with symbols from user u experiencing delay i_u passing through 1^th multi-path, and W is the N_r X N matrix of the complex AWGN at the RRH (120).

[0099] The received signal matrix Y needs to be sent from the RRH (120) to the BBU (140) via the fronthaul link (1000). Since the dimension of the Y, N XN_r, is large in the massive MIMO system the dimension of the Y needs to be reduced to achieve compression.

[00100] In general, low rank approximation is a tool used in signal processing to represent a matrix of large dimension using a lower dimensional subspace for analysis while retaining the essential information contained in the original matrix. From (2), we observe that the received signal matrix in the absence of noise would simply be Therefore, the true rank of the

matrix Y is the rank of the noiseless matrix and is atmost N_UL.

For Nu < N_r and N_u < N,

(3)

Since the dimension of each Hi is N_r XN_U and Nu < Nr, rank (Hi) £ N_u- Similarly, for each Xi, rank (Xi) £ N_u.

For each product term ¾Xi,

Now, for the sum over L such matrices,

[00101] In the massive MIMO system, the number of active users is typically far lesser than the number of antennas at the base station (100). Therefore, Nu N_r is a reasonable assumption. Further, if L Nr, then the product N_uL < N_r, and Y is approximated using a lower dimensional signal subspace of the N_UL components.

[00102] The simplest way to reduce the dimension of Y is to select the N_uL columns of Y that have the largest vector norms, which correspond to the antennas with the highest received powers. However, if this selection criterion is applied in the time-domain, where the users are not separated, the set of antennas chosen will be common to all users. Since we choose the antennas based on their total received powers, the users nearer to the RRH (120) that contribute more power, will be favored over the users farther from the RRH (120).

[00103] The proposed method avoids the users nearer to the RRH (120) that contribute more power from being favored over the users farther from the RRH (120) by separating the users as shown in FIG. 4 before reducing the dimension of the matrix Y. In order to perform the same, the matrix Y is converted to the frequency domain by applying FFT so that the users can be separated according to the sub-carriers allocated to the users. The sub-carrier allocation of each user is known to the base station (100). Further, the matrix Y can be decomposed in the frequency domain into the sub-matrices corresponding to each of the users and then the proposed method selects the best antennas in each of the sub-matrices. [00104] If Y_f is the frequency domain received signal matrix obtained from Y, then the true rank (Y_f ) £ N_UL.

[00105] Conversion of Y to the frequency domain is equivalent to multiplication of Y with the DFT matrix, F, of appropriate dimension, i.e.,

is the actual signal component in the Y_f and therefore, the true

rank of Y_f is the rank of Now, the DFT matrix, F is unitary

(full rank). Since multiplication with a full rank matrix is rank-preserving and using equation (4):

[00106] If the users are independent, then the true rank of each of the user sub-matrices obtained from the Y_f can atmost be L. Therefore, the proposed method can choose up-to L largest norm columns from each of these sub-matrices to represent each user’s signal subspace. Since there is correlation between the antennas, the signal subspace chosen must have dimension L which is got by applying Gram-Schmidt orthogonalisation to each successive antenna chosen until up-to L orthogonal components are obtained for each user.

[00107] Further, the data from the ( N_r - L) antennas are projected onto the previously obtained signal subspace. The advantage of projecting the signals include: since the signal subspace is of dimension atmost L, the rest of the (N_r - L) components represent noise directions and by projecting the signals onto the signal subspace, only the desired signal components are extracted from the antennas while eliminating the noise components. Due to this the post- processing SNR increased by contributing to the overall signal power and simultaneously decreasing the overall noise power. Therefore, a denoising gain is provided that leads to better error performance with the compressed data compared to using the uncompressed data which is similar to the denoising gain of the low rank approximation observed in image processing applications. This can be accomplished using column-pivoting QR factorization of each of the user sub-matrices where each user sub-matrix is approximated as the function of two matrices: an orthogonal matrix Q (called the basis matrix) with upto L columns representing the basis for the signal subspace, and a projection matrix R that is upper triangular upto the column L. In the proposed method, the Q and the R matrices of all the users are sent via the fronthaul link (1000) instead of the matrix Y (as shown in FIG. 6).

[00108] The process of compression of the signals at the RRH (120) and the decompression at the BBU (140) in the MIMO C-RAN architecture is provided. At step 1, the RF down-conversion is performed on the signals received at the RRH (120). Further, at step 2, the RF down-converted signals are used to construct the baseband signal matrix Y using the signals received at the N_r antennas over a time span of N symbols. Therefore, the baseband signal matrix has a dimension of N X N_r. Without the loss of generality, N is chosen to be the duration of one OFDM symbol.

[00109] At step 3, the RRH (120) removes the CP and applies the fast Fourier transform (FFT) to the Y to convert the baseband signal matrix to the frequency domain signal matrix Y_f. If the total number of sub-carriers allocated to all the users is N_f, then the Y_f is of dimension N_f X N_r. The removal of the CP and the guard-bands (which do not need to be sent to the BBU (140)) almost halves the amount of data to be sent. Further, at step 4, the RRH (120) performs the resource element (RE) demapping for separating the signals from the different users. The RE demapping is performed by dividing the Y_f into the plurality of sub-matrices corresponding to the different users according to the sub-carriers allocated to the individual users. The sub-matrix of the user u is represented as Y_u. If N_fu is the number of sub-carriers allotted to the user u, then the Y_u has a dimension of N_fu X N_r. Further, the RRH (120) assumes that the users are independent and L < Nr < N_fu. Therefore, the true rank of the Y_u will be less than the N_r and equal to the number of independent multi-paths in the channel for user u.

[00110] At step 5, the RRH (120) applies the QR compression algorithm to each of the Y_u, for example as described below:

[00111] Thus for each of the user u, the low-rank approximated matrix Y_uO = Q_uR_u. The RRH (120) chooses the L_u antennas having the highest received powers from the Y_u to form the columns of the Q_u. The value of the L_u is chosen based on the channel state information and the required error performance for the user u. The L is the maximum number of multi-paths that are assumed for each of the users but the L_u is the actual number of antennas chosen for the signal subspace of the user u depending on the channel and requirements. Further, y_un denotes column vector n of the Y_u, qi and n denote column vector i of Q_u and R_u, respectively. e^ki denotes column vector of length k with first component 1 and rest 0s.

[00112] For the proposed method and compression scheme the fronthaul compression ratio (CR) is given by:

[00113] In the absence of any compression, the samples of the received signal matrix Y (of dimension N X N_r) quantized to b_Q bits are sent to the BBU via the fronthaul link (1000). Therefore, the number of bits before compression, B_org is given by

[00114] During compression, the Y is converted to the frequency domain and divided into the plurality of the sub-matrices Y_u corresponding to each user u. The Y_u is of dimension N_fu X N_r, where the N_fu is the number of sub-carriers allotted to the user u. Each Y_u is then approximated to the product of the matrices Q_u and R_u by the QR approximation. The Q_u is of the dimension N_fu X L_u and the R_u of dimension L_u X N_r, where L_u < N_r. Uniform scalar quantization of b_Q bits is applied to the samples of each Q_u and R_u. Therefore, the number of bits after compression, B_cmp is given by:

[00115] The order in which the columns of the Y_u are chosen to construct the Q_u and the R_u also needs to be sent to the BBU (140) for proper reconstruction of the Y_u at the BBU (140). Since the proposed method needs log2 N_r bits to represent the index of each of the N_r antennas in the Y_u, the log2 N_r bits amounts to an overhead of B_ovh bits given by:

Thus, combining (8), (9) and (10), the fronthaul CR is

which gives the fronthaul compression ratio (CR) i.e., equation (7).

[00116] At step 6, the BBU (140) receives the Q_u and the R_u for the plurality of sub-matrices. Further, the BBU (140) reconstructs all the Y_uo by taking the product of the corresponding Q_u and R_u. Further, at step 7, the decoding is initiated with the assumption that the channel is known at the BBU (140) and a zero-forcing equalization is performed on each of the reconstructed Y_uo. At step 8, the BBU (140) performs the joint decoding where each user’s symbols from all the N_r antennas are combined and the M-QAM symbols are demodulated.

[00117] FIG. 7 is a graph plot illustrating the compression ratios (CRs) for the proposed method of compression as a function of the actual number of antennas (Lu) for the signal subspace of UEs u, according to the embodiments as disclosed herein.

[00118] Compression Ratios (CRs) for the proposed method as a function of L_u, for 256 RRH antennas, 8 users and 12 users. The CR is inversely proportional to Lu, as observed from the FIG. 7. [00119] The proposed method of compressing the signals before transmission through the fronthaul link (1000) using the QR compression scheme is compared with the singular value decomposition (S VD) compression scheme. In, the low-rank approximation using the SVD compression scheme, the SVD compression algorithm is applied directly to the matrix Y in the time- domain, after removing the CP. However, the guard bands are also sent to the BBU which may be inefficient as removing the guard bands provides considerable compression gain. The users are also not separated in the SVD compression scheme which does not have the flexibility of choosing user- specific trade-off between the compression ratio and the error performance present in the proposed method. Further, in another example even if the SVD compression is applied in the frequency-domain after the removal of the guard band and the user separation, calculating the SVD compression of each of the user matrix is more expensive in terms of complexity than the proposed method which uses the QR decomposition.

[00120] FIG. 8A is a graph illustrating Bit Error Rate (BER) performance for QR compression for 8 users, according to the embodiments as disclosed herein.

[00121] FIG. 8B is a graph illustrating Bit Error Rate (BER) performance for QR compression for 12 users, according to the embodiments as disclosed herein.

[00122] The Bit Error Rate (BER) performance of the proposed method is determined through Monte Carlo simulations using a massive MIMO uplink link level simulator in the baseband.

[00123] A 3 GPP tapped delay line (TDL) Rayleigh fading channel model for 5G is used for the BER performance evaluation. Further, a 100MHz bandwidth and 30kHz sub-carrier spacing is considered for which the FFT length is estimated to be 4096 and the CP length is 288. Therefore, the length of one OFDM symbol is 4384, which is the number of time samples N that is considered for one compression block. Further, 64-QAM is used with 256 receive antennas at the RRH (120). Thus, the dimension of the received signal matrix Y that is to be compressed is 4384 X 256. The simulation parameters are provided in Table.1.

Table. 1

[00124] The BER performance is compared for an un-coded system using the proposed compression method against the SVD compression. Further, the BER of the baseline uncompressed system is also plotted for reference. Assuming uniform linear array, an antenna correlation is generated at the RRH (120) according to an exponential correlation model, with correlation coefficient of 0.7.

[00125] Consider two example cases based on the number of users i.e., FIG. 8A with Nu=8 and FIG. 8B with Nu=12. The UEs are allocated resource blocks (RBs) according to the received SNRs at the RRH. The UEs with higher SNRs are allocated more RBs than those with lower SNRs. For 8 UEs, after arranging the UEs in the increasing order of their received SNRs at the RRH, the number of RBs allocated is 26, 28, 30, 32, 34, 36, 38, 40, respectively. Similarly, for 12 UEs, the RB allocation is 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, respectively. In both the cases, the total number of RBs allocated should not exceed 273.

[00126] The true rank of each user sub-matrix Yu is found to be 12, corresponding to the 12 non-zero taps in the multipath channel model used. FIG. 7 shows the CRs achieved for different values of the Lu. Further observations reveal that lower the value of Lu, higher is the CR achieved. The BER plots for two values of Lu i.e., 12 and 24 are provided in the FIGS. 8A- 8B to evaluate the impact of Lu on the performance of the proposed method. The observations reveal that the proposed method performs better for the higher value of the Lu for both the 8 UEs and the 12 UEs. Thus, the choice of the Lu in the proposed method is a trade-off between a desired CR and a required error performance. Table. 2 shows the CRs achieved for the different values of the Lu and the Nu.

Table. 2

[00127] FIGS. 9A-9B are graph plots illustrating un-coded Bit Error

Rate (BER) performance for the proposed method in comparison with the S VD compression, according to the embodiments as disclosed herein.

[00128] Referring to the FIG. 9A, is a graph plot for the un-coded BERs of the proposed method with the Lu=24 compared with the SVD compression for the same CR for the number of users is 8 and the FIG. 9B for the number of users is 12, with 256 RRH antennas. The BER for the uncompressed system for both user cases are also plotted as reference. Hence, the BERs are plotted for both the proposed method and the SVD compression with the constant value of the CR.

[00129] In case of the SVD compression the samples are not converted into the frequency domain from the time domain. Also, in case of the SVD compression the guard bands are not removed and the UEs are not separated resulting in a greater number of samples to be sent to the BBU than in the proposed method. Further, the number of bits allocated for the samples is the only quantity that can be reduced in the SVD compression to match the CRs achieved in the proposed method. Therefore, the proposed method performs better in both the cases when the number of UEs is N_u=8 (as shown in FIG. 9A) and when the number of UEs is N_u=12 (as shown in FIG. 9B).

[00130] The plots in FIGS. 9A-9B also include the BER for the uncompressed system for both the number of UEs is 8 and 12 respectively. The BER improvement for the proposed method compared to no compression is obtained due to the denoising gain of the low rank approximation applied in the proposed method. Therefore, the link level simulations show that the proposed method achieves up-to 17.4 X compression for 8 users and 14.5 X compression for 12 users in the wireless communication system.

[00131] 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 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 spirit and scope of the embodiments as described herein.

Claims

STATEMENT OF CLAIMS We claim:

1. A method of compressing signals received at a base station (100), comprising:

receiving, by a communicator (126) of the base station (100), a plurality of signals using an array of antenna in the base station (100) over a time span;

compressing, by a radio unit (RU) (120) of the BS (100), the plurality of signals by selecting basis signals and representing the plurality of signals in terms of the basis signals, wherein the basis signals are a set of signals of the plurality of signals which are received at a set of antennas of the array of antenna; and

transmitting, by the RU (120) of the BS (100), the basis signal and the plurality of signals represented in terms of the basis signals for each of the received signals of the plurality of signals to a distributed unit (DU) (140).

2. The method as claimed in claim 1, wherein the compression is obtained by performing a QR decomposing technique.

3. The method as claimed in claim 1, wherein compressing, by the RU (120) of the BS (100), the plurality of signals by selecting the basis signals and representing the plurality of signals in terms of the basis signals comprising:

constructing, by the RU (120) of the BS (100), a time-domain signal matrix based on the plurality of signals received at the array of antenna, wherein the time-domain signal matrix comprises a block of time-domain samples of the signals received at each antenna of the array of antenna;

generating, by the RU (120) of the BS(100), a frequency domain signal matrix using the time-domain signal matrix; performing, by the RU (120) of the BS (100), resource element de-mapping by dividing the frequency domain signal matrix into sub- matrices corresponding to different users; and

compressing, by the RU (120) of the BS (100), each of the sub- matrices of each of the users to obtain component matrixes for each of the sub-matrices of each of the user, wherein the component matrixes comprises the basis signals and the plurality of signals in terms of the basis signals.

4. The method as claimed in claim 1, wherein compressing, by the RU (120) of the BS (100), the plurality of signals by selecting the basis signals and representing the plurality of signals in terms of the basis signals comprising:

constructing, by the RU (120) of the BS (100), a time-domain signal matrix based on the plurality of signals received at the array of antenna, wherein the time-domain signal matrix comprises a block of time-domain samples of the received signal at each antenna of the array of antenna; and

compressing, by the RU (120) of the BS (100), the time-domain signal matrix to obtain component matrixes, wherein the component matrixes comprises the basis signals and the plurality of signals in terms of the basis signals.

5. The method as claimed in claim 4, further comprising:

receiving, by the DU (140) of the BS (100), a basis matrix and a projection matrix for the time domain matrix; and

reconstructing, by the DU (140) of the BS (100), the time domain matrix by determining a function of the basis matrix and the projection matrix for the time domain matrix.

6. The method as claimed in claim 3 or claim 4, wherein the two component matrixes comprises a basis matrix and a projection matrix.

7. The method as claimed in claim 6, wherein the component matrixes represents an approximation of contents of the time-domain signal matrix with fewer number of time-domain samples as compared to the block of the time-domain samples of the time-domain signal matrix which provides the compression.

8. The method as claimed in claim 3 or claim 4, wherein entries in columns of the time-domain signal matrix represents the signals received at each of the antenna of the array of antenna over a time span of N samples.

9. The method as claimed in claim 6, wherein the basis matrix or a function of the basis matrix; and the projection matrix or a function of the projection matrix is transmitted through a fronthaul link (1000) of the BS (100).

10. The method as claimed in claim 3, further comprising:

receiving, by the DU (140) of the BS (100), a basis matrix and a projection matrix for each of the sub-matrices of each of the users; and reconstructing, by the DU (140) of the BS (100), each of the sub- matrices by determining a function of the basis matrix and the projection matrix of each of the sub-matrices.

11. The method as claimed in claim 3, wherein the frequency domain matrix is divided into the sub-matrices according to allocated sub-carriers to each of the users.

12. The method as claimed in claim 3 or claim 4, wherein constructing, by the base station (100), the time-domain signal matrix based on the plurality of signals received at the plurality of antenna comprises:

down-converting, by the RU (120) of the base station (100), the plurality of signals received at the plurality of antenna; and

generating, by the RU (120) of the base station (100), the time- domain signal matrix based using the plurality of down-converted signals.

13. The method as claimed in claim 6, wherein the basis matrix comprises signals received at a subset of antennas or a function of the signals received at a subset of antennas from the array of antenna of the RU (120) of the BS (100).

14. The method as claimed in claim 13, wherein the subset of antennas comprises antennas with highest received powers or antennas with a function of the highest received powers to represent the plurality of signals received at the plurality of antennas.

15. The method as claimed in claim 6, wherein the projection matrix comprises description of the plurality of signals received at the plurality of antenna in terms of the basis matrix.

16. The method as claimed in claim 3, wherein the sub-carrier allocation of each of the users is known to the BS (100).

17. The method as claimed in claim 3 or claim 4, wherein dimensions of the time-domain signal matrix is dependent on a total number of sub carriers allocated to all users.

18. The method as claimed in claim 1, wherein transmitting, by the RU (120) of the BS (100), the basis signal and the plurality of signals represented in terms of the basis signal for each of the received signals of the plurality of signals to the DU (140) comprises:

performing, by the RU (120) of the BS (100), a quantization mechanism on the basis signal and the plurality of signals represented in terms of the basis signal for each of the received signals of the plurality of signals; and

transmitting, by the RU (120) of the BS (100), the quantized basis signals and the quantized signals of the plurality of signals represented in terms of the basis signal for each of the received signals of the plurality of signals to the DU (140).

19. A base station (100) for compressing signals received at the base station (100), the base station (100) comprising:

a Radio unit (RU) (120) connected to a distributed unit (DU) (140) through a fronthaul link (1000) comprising:

a memory (122);

a processor (124) coupled to the memory (122);

a communicator (126) coupled to the memory (122) and the processor (124), and wherein the RU (120) is configured to:

receive a plurality of signals using an array of antenna in the base station (100) over a time span;

compress the plurality of signals by selecting basis signals and representing the plurality of signals in terms of the basis signals, wherein the basis signals are a set of signals of the plurality of signals which are received at a set of antennas of the array of antenna; and transmit the basis signal and the plurality of signals represented in terms of the basis signals for each of the received signals of the plurality of signals to the distributed unit (DU) (150) through the fronthaul link (1000).

20. The BS (100) as claimed in claim 19, wherein the compression is obtained by performing a QR decomposing technique.

21. The BS (100) as claimed in claim 19, wherein the RU (120) is configured to compress the plurality of signals by selecting the basis signals and representing the plurality of signals in terms of the basis signals comprises:

construct a time-domain signal matrix based on the plurality of signals received at the array of antenna, wherein the time-domain signal matrix comprises a block of time-domain samples of the signals received at each antenna of the array of antenna;

generate a frequency domain signal matrix using the time-domain signal matrix;

perform resource element de-mapping by dividing the frequency domain signal matrix into sub-matrices corresponding to different users; and

compress each of the sub-matrices of each of the users to obtain component matrixes for each of the sub-matrices of each of the user, wherein the component matrixes comprise the basis signals and the plurality of signals in terms of the basis signals.

22. The BS (100) as claimed in claim 19, wherein the RU (120) is configured to compress the plurality of signals by selecting the basis signals and representing the plurality of signals in terms of the basis signals comprising:

construct a time-domain signal matrix based on the plurality of signals received at the array of antenna, wherein the time-domain signal matrix comprises a block of time-domain samples of the received signal at each antenna of the array of antenna; and

compress the time-domain signal matrix to obtain component matrixes, wherein the component matrixes comprise the basis signals and the plurality of signals in terms of the basis signals.

23. The BS (100) as claimed in claim 21 or claim 22, wherein the two component matrixes comprises a basis matrix and a projection matrix.

24. The BS (100) as claimed in claim 23, wherein the component matrixes represents an approximation of contents of the time-domain signal matrix with fewer number of time-domain samples as compared to the block of the time-domain samples of the time-domain signal matrix which provides the compression.

25. The BS (100) as claimed in claim 21 or 22, wherein entries in columns of the time-domain signal matrix represents the signals received at each of the antenna of the array of antenna over a time span of N samples.

26. The BS (100) as claimed in claim 23, wherein the basis matrix or a function of the basis matrix; and the projection matrix or a function of the projection matrix is transmitted through the fronthaul link (1000) of the BS (100).

27. The BS (100) as claimed in claim 21, wherein the frequency domain matrix is divided into the sub-matrices according to allocated sub-carriers to each of the users.

28. The BS (100) as claimed in claim 21 or claim 22, wherein the RU (120) is configured to construct the time-domain signal matrix based on the plurality of signals received at the plurality of antenna comprises:

down-convert the plurality of signals received at the plurality of antenna; and

generate the time-domain signal matrix based using the plurality of down-converted signals.

29. The BS (100) as claimed in claim 23, wherein the basis matrix comprises signals received at a subset of antennas or a function of the signals received at a subset of antennas from the array of antenna of the RU (120) of the BS (100).

30. The BS (100) as claimed in claim 29, wherein the subset of antennas comprises antennas with highest received powers or antennas with a function of the highest received powers to represent the plurality of signals received at the plurality of antennas.

31. The BS (100) as claimed in claim 23, wherein the projection matrix comprises description of the plurality of signals received at the plurality of antenna in terms of the basis matrix.

32. The BS (100) as claimed in claim 21, wherein the sub -carrier allocation of each of the users is known to the base station (100).

33. The BS (100) as claimed in claim 21 or claim 22, wherein dimensions of the time-domain signal matrix is dependent on a total number of sub carriers allocated to all users.

34. The BS (100) as claimed in claim 19, wherein the RU (120) is configured to transmit the basis signal and the plurality of signals represented in terms of the basis signal for each of the received signals of the plurality of signals to the DU (140) comprises:

perform a quantization mechanism on the basis signal and the plurality of signals represented in terms of the basis signal for each of the received signals of the plurality of signals; and

transmit the quantized basis signals and the quantized signals of the plurality of signals represented in terms of the basis signal for each of the received signals of the plurality of signals.

35. A base station (100) for compressing signals received at the base station (100), the base station (100) comprising:

a distributed unit (DU) (140) connected to a Radio unit (RU) (120) through a fronthaul link (1000) comprising:

a memory (142);

a processor (144) coupled to the memory (142);

a communicator (146) coupled to the memory (142) and the processor (144), and wherein the DU (140) is configured to:

receive a basis matrix and a projection matrix for a time domain matrix from the RU (120) through the fronthaul link (1000); and

reconstruct the time domain matrix by determining a function of the basis matrix and the projection matrix for the time domain matrix.

36. The BS (100) as claimed in claim 35, wherein the basis matrix and the projection matrix is formed at the RU (120) by compressing a time- domain signal matrix to obtain component matrixes, wherein the component matrixes comprises basis signals and a plurality of signals in terms of the basis signals.

37. A base station (100) for compressing signals received at the base station (100), the base station (100) comprising:

a memory (142);

a processor (144) coupled to the memory (142);

receive a basis matrix and a projection matrix for each sub-matrix corresponding to each users, from the Radio unit (RU) (120) through the fronthaul link (1000); and

reconstruct each of the sub-matrices corresponding to each of the users by determining a function of the basis matrix and the projection matrix for the time domain matrix.

38. The BS (100) as claimed in claim 37, wherein the basis matrix and the projection matrix is formed at the RU (120) by compressing each of the sub-matrices of each of the users to obtain component matrixes for each of the sub-matrices of each of the user, wherein the component matrixes comprises the basis signals and the plurality of signals in terms of the basis signals.

39. The BS (100) as claimed in claim 38, wherein the each of the sub-matrices of each of the users is obtained at the RU (120) by: construct a time- domain signal matrix based on plurality of signals received at array of antenna of the RU (120), wherein the time-domain signal matrix comprises a block of time-domain samples of the signals received at each antenna of the array of antenna;

generate a frequency domain signal matrix using the time-domain signal matrix; and

obtaining sub-matrices corresponding to different users based on a resource element de-mapping by dividing the frequency domain signal matrix into sub-matrices corresponding to different users.