WO2023191796A1

WO2023191796A1 - Apparatus and method for data compression and data upsampling

Info

Publication number: WO2023191796A1
Application number: PCT/US2022/022889
Authority: WO
Inventors: Chengzhi LI; Shuang TIAN
Original assignee: Zeku, Inc.
Priority date: 2022-03-31
Filing date: 2022-03-31
Publication date: 2023-10-05

Abstract

In certain aspects, apparatuses and methods for data compression and data upsampling are provided. The apparatus includes one or more processors and memory storing instructions that, when executed by the one or more processors, cause the one or more processors to receive a data sequence including a first data set and a second data set. A project matrix is obtained based on a correlation matrix between the first data set and the second data set. An extracted data sequence is obtained from the second data set based on a size O of the overlapping samples and a dimension of one data set of the first data set and the second data set. Second compressed data, corresponding to the second data set, is obtained based on the project matrix, the extracted data sequence, and first compressed data corresponding to the first data set.

Description

APPARATUS AND METHOD FOR DATA COMPRESSION AND DATA UPSAMPLING

BACKGROUND

[0001] Embodiments of the present disclosure relate to apparatuses and methods for data compression and data upsampling.

[0002] Data compression is a process of encoding or transforming data using fewer bits for representation than its original data size. It can be done by a program that uses functions or algorithms which purport to shrink the data size. Compressed data can help decrease the required data storage space and reduce the amount of bandwidth required on a communication link, thereby achieving higher transmission rates. Faster data transmission rates are crucial in channel communication, especially when the bandwidth is constrained. Therefore, data compression can reduce incurred costs and enhance system productivity.

[0003] Based on the applied techniques, data compression can often be broken into two primary forms: one is commonly referred to as “lossy” data compression, and the other is “lossless” data compression. Data compression is commonly used in the computer and communication fields. In communication systems, the compressed strings of data may be transmitted over a channel and can be reconstructed into their original forms upon reception. In computers, data compression is frequently applied to audio, image, and video data files to offer a storage advantage.

SUMMARY

[0004] According to one aspect of the present disclosure, the present disclosure provides an apparatus for data compression and data upsampling. The apparatus may include one or more processors and memory storing instructions that, when executed by the one or more processors, may cause the one or more processors to receive a data sequence including a first data set and a second data set. The first data set and the second data set may include a size O of overlapping samples in the data sequence, and O may be a positive integer. A project matrix may be obtained based on a correlation matrix between the first data set and the second data set. The project matrix may correspond to one or more significant eigenvalues of the correlation matrix. An extracted data sequence may be obtained from the second data set based on the size O of the overlapping samples and a dimension of one data set of the first data set and the second data set. The extracted data sequence may include no overlapping with the first data set. Second compressed data, corresponding to the second data set, may be obtained based on the project matrix, the extracted data sequence, and first compressed data corresponding to the first data set.

[0005] According to another aspect of the present disclosure, a system-on-chip (SoC) for data compression and data upsampling, at a receiver, is provided. The SoC may include a channel estimation module, one or more processors, and memory storing instructions that, when executed by the one or more processors, may cause the one or more processors to obtain a project matrix based on a correlation matrix between the first data set and the second data set. The project matrix may correspond to one or more significant eigenvalues of the correlation matrix. An extracted data sequence may be obtained from the second data set based on a size O of overlapping samples between the first data set and the second data set and a dimension of one data set of the first data set and the second data set. The extracted data sequence may include no overlapping with the first data set, and O may be a positive integer. Second compressed data, corresponding to the second data set, may be obtained based on the project matrix, the extracted data sequence, and first compressed data corresponding to the first data set.

[0006] According to still another aspect of the present disclosure, a method for data compression and data upsampling is provided. The method may include receiving a data sequence including a first data set and a second data set. The first data set and the second data set may include a size O of overlapping samples in the data sequence, and O may be a positive integer. A project matrix may be obtained based on a correlation matrix between the first data set and the second data set. The project matrix may correspond to one or more significant eigenvalues of the correlation matrix. An extracted data sequence may be obtained from the second data set based on the size O of the overlapping samples and a dimension of one data set of the first data set and the second data set. The data sequence may include no overlapping with the first data set. Second compressed data, corresponding to the second data set, may be obtained based on the project matrix, the extracted data sequence, and first compressed data corresponding to the first data set.

[0007] These illustrative embodiments are mentioned not to limit or define the present disclosure, but to provide examples to aid understanding thereof. Additional embodiments are described in the Detailed Description, and further description is provided there.

BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The accompanying drawings, which are incorporated herein and form a part of the specification, illustrate embodiments of the present disclosure and, together with the description, further serve to explain the principles of the present disclosure and to enable a person skilled in the pertinent art to make and use the present disclosure.

[0009] FIG. 1 illustrates a schematic diagram showing overlapping data sets in a data sequence with respect to a time domain, according to some embodiments of the present disclosure. [0010] FIG. 2A illustrates a block diagram of an exemplary apparatus implementing data compression, according to some embodiments of the present disclosure.

[0011] FIG. 2B illustrates a block diagram of an exemplary apparatus implementing data upsampling, according to some embodiments of the present disclosure.

[0012] FIG. 2C illustrates a block diagram of an exemplary apparatus implementing data compression and data upsampling, according to some embodiments of the present disclosure.

[0013] FIG. 3 illustrates a block diagram of a system that includes a compression unit and an upsampling unit, according to some embodiments of the present disclosure.

[0014] FIG. 4 illustrates a block diagram of a compression module in the compression unit of FIG. 3, according to some embodiments of the present disclosure.

[0015] FIG. 5 illustrates a flow chart of an exemplary method for data compression, according to some embodiments of the present disclosure.

[0016] FIG. 6 illustrates a flow chart of an exemplary method for data upsampling, according to some embodiments of the present disclosure.

[0017] FIG. 7 illustrates an exemplary wireless network, according to some embodiments of the present disclosure.

[0018] FIG. 8 illustrates a block diagram of a communication system including an apparatus that has an antenna, a radio frequency (RF) chip, and a baseband chip, according to some embodiments of the present disclosure.

[0019] Embodiments of the present disclosure will be described with reference to the accompanying drawings.

DETAILED DESCRIPTION

[0020] Although some configurations and arrangements are discussed, it should be understood that this is done for illustrative purposes only. A person skilled in the pertinent art will recognize that other configurations and arrangements can be used without departing from the spirit and scope of the present disclosure. It will be apparent to a person skilled in the pertinent art that the present disclosure can also be employed in a variety of other applications. [0021] It is noted that references in the specification to “one embodiment,” “an embodiment,” “an example embodiment,” “some embodiments,” “certain embodiments,” “an instance,” “some instances,” “an example,” “some examples,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases do not necessarily refer to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it would be within the knowledge of a person skilled in the pertinent art to effect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.

[0022] In general, terminology may be understood at least in part from usage in context. For example, the term “one or more” as used herein, depending at least in part upon context, may be used to describe any feature, structure, or characteristic in a singular sense or may be used to describe combinations of features, structures, or characteristics in a plural sense. Similarly, terms, such as “a,” “an,” or “the,” again, may be understood to convey a singular usage or to convey a plural usage, depending at least in part upon context. In addition, the term “based on” or “according to” may be understood as not necessarily intended to convey an exclusive set of factors and may, instead, allow for the existence of additional factors not necessarily expressly described, again, depending at least in part on context.

[0023] Techniques for compressing data are commonly applied to computer and communication systems. In communication systems, compressed strings of data may be transmitted over channels and reconstructed into their original forms upon reception. In computer systems, data compression is frequently applied to various audio, image, and video data files so as to offer a storage advantage. Under some scenarios, e.g., in wireless channels, data in transmission is typically correlated in a time domain and/or in a frequency domain. By taking advantage of the correlation(s), the size of the data under compression can be further reduced, thereby significantly increasing the storage capacity and the data transmission rates.

[0024] FIG. 1 illustrates a schematic diagram showing overlapping data sets in a data sequence with respect to a time domain, according to some embodiments of the present disclosure. In FIG. 1, the data sequence is depicted using multiple blocks, and a label in each box represents an index associated with the data. The data sequence may include a data block (or a data set) = [h₀, h_lt ... , hfi-i ¹¹, where i denotes an index of the data block, TV is a positive integer greater than 1 and is used to represent the size or the dimension of the data set, and H (bold) denotes the Hermitian transpose. Also, as shown in FIG. 1, the data sequence also includes a next data block H_i+1 = [h_D, h_D+1, ... , h_N, ... , h_N+D-1]^H, where D is a positive integer greater than 0 and H denotes the Hermitian transpose. The data sets

and H_i+1 may include the same dimension N, and as illustrated in FIG. 1, the two data sets share O = N-D samples, where O is a positive integer, for which the data blocks

and Hj₊₁overlap with each other.

[0025] For compressing the correlated data sets, in some approaches, the technique of direct block compression may be employed. In the technique, the precedent data block =

may be first compressed. Upon completion of reception of the next data block

H_i+1 = [h_D, h_D+1, ... , h_N, ..., h_N+D-1]^H , in order to compress the current data block, the compressed form of the precedent data block

= [h₀, h_lt ... , h_N-1]^His required to be fetched and decompressed to obtain the overlapping samples that also exist in H_i+1 . Alternatively, the overlapping samples per se can be stored in advance. In either way, the requirement for overlapping samples will offset the advantages that data compression can bring. That is, in these approaches, the benefits of the data correlation are not well-considered and taken advantage of.

[0026] In view of the above and other drawbacks, some embodiments of the present disclosure provide apparatuses and methods for data compression and data upsampling, e.g., for compressing correlated data. The term “data sets” may refer to data blocks and data samples in, e.g., a vectorization form, and these terms may be used interchangeably. Further, the term “correlation” or “correlated” may be used to describe a relationship of a pair of data sets or the extent to which two data sets are related. In some sense, two data sets may change or move together if they are correlated. In some embodiments of the present disclosure, two correlated data sets may share some data or have overlapping portions of the data.

[0027] Various aspects of techniques for data compression and data upsampling will now be described with reference to various apparatuses and methods. These apparatuses and methods will be described in the following detailed description and illustrated in the accompanying drawings by various blocks, modules, units, components, circuits, steps, operations, processes, algorithms, etc. (collectively referred to as “elements”). These elements may be implemented using electronic hardware, firmware, computer software, or any combination thereof. Whether such elements are implemented as hardware, firmware, or software depends upon the particular application and design constraints imposed on the overall system.

[0028] FIG. 2A illustrates a block diagram of an exemplary apparatus implementing data compression, according to some embodiments of the present disclosure. FIG. 2B illustrates a block diagram of an exemplary apparatus implementing data upsampling, according to some embodiments of the present disclosure. According to some embodiments of the present disclosure, apparatus 100 or 200 may be applied or integrated into various systems and apparatuses capable of data processing, such as computers and wireless communication devices. For example, apparatus 100 or 200 may be the entirety or part of a mobile phone, a desktop computer, a laptop computer, a tablet, a vehicle computer, a gaming console, a printer, a positioning device, a wearable electronic device, a smart sensor, a virtual reality (VR) device, an argument reality (AR) device, or any other suitable electronic devices having data processing capability. As shown in FIGs. 2A and 2B, apparatus 100 or 200 may include a processor 102, a memory 104, and an interface 106. These components are shown as connected to one another by local wires or buses, but other connection types are also permitted. It can be understood that apparatus 100 or 200 may include any other suitable components for performing functions described here and compatible with the functions herein.

[0029] Processor 102 may include microprocessors, microcontrollers (MCUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), programmable logic devices (PLDs), state machines, gated logic, discrete hardware circuits, and other suitable hardware configured to perform the various functions described throughout the present disclosure. Although only one processor is shown in FIGs. 2A and 2B, it is understood that multiple processors can be included. Processor 102 may be a hardware device having one or more processing cores. Processor 102 may execute software. Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executables, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise. Software can include computer instructions written in an interpreted language, a compiled language, or machine code. Other techniques for instructing hardware are also permitted under the broad category of software.

[0030] Memory 104 can broadly include both memory (e.g., primary/system memory) and storage (a.k.a., secondary memory). For example, memory 104 may include random-access memory (RAM), read-only memory (ROM), static RAM (SRAM), dynamic RAM (DRAM), ferroelectric RAM (FRAM), electrically erasable programmable ROM (EEPROM), compact disc readonly memory (CD-ROM) or other optical disk storage, hard disk drive (HDD), such as magnetic disk storage or other magnetic storage devices, Flash drive, solid-state drive (SSD), or any other medium that can be used to carry or store desired program code in the form of instructions that can be accessed and executed by processor 102. Broadly, memory 104 may be embodied by any computer-readable medium, such as a non-transitory computer-readable medium. Although only one memory is shown in FIGs. 2A and 2B, it is understood that multiple memories can be included. [0031] Interface 106 can broadly include a data interface and/or a communication interface that are configured to receive and transmit a signal in a process of receiving and transmitting information with other external network elements. For example, interface 106 may include input/output (VO) devices and wired or wireless transceivers. Although only one interface is shown in FIGs. 2A and 2B, it is understood that multiple interfaces can be included.

[0032] Processor 102, memory 104, and interface 106 may be implemented in various forms in apparatus 100 or 200 for performing data compression and data upsampling in addition to various functions. The operations of the data compression or the data upsampling may be compatible with the other functions of these elements. In some embodiments, processor 102, memory 104, and interface 106 of apparatus 100 or 200 are implemented (e.g., integrated) on one or more system-on-chips (SoCs). In one example, processor 102, memory 104, and interface 106 may be integrated on an application processor (AP) SoC that handles application processing in an operating system (OS) environment, including running data compression and upsampling applications. In another example, processor 102, memory 104, and interface 106 may be integrated on a specialized processor chip for a specified purpose, such as a baseband chip dedicated for baseband signal processing.

[0033] As shown in FIG. 2 A, in apparatus 100, processor 102 may include one or more processing units, such as a compression unit 101. Although FIG. 2 A shows that compression unit 101 is within a single processor 102, it can be understood that compression unit 101 may include one or more sub-units or sub-modules that can be implemented on different processors located closely or remotely with each other. Compression unit 101 (and any corresponding sub-modules or sub-units) can be hardware units (e.g., portions of an integrated circuit) of processor 102 designed for use with other components or software units implemented by processor 102 through executing at least part of a program, i.e., instructions. The instructions of the program may be stored on a computer-readable medium, such as memory 104, and when executed by processor 102, it may perform a process having one or more functions related to data compression, such as data reception, downsampling, data multiplication, data transforming, data filtering, etc., as described below in detail.

[0034] Similarly, as shown in FIG. 2B, in apparatus 200, processor 102 may include one or more units, such as an upsampling unit 201. Although FIG. 2B shows that upsampling unit 201 is within a single processor 102, it is understood that upsampling unit 201 may include one or more sub-units or sub-modules that can be implemented on different processors located closely or remotely with each other. Upsampling unit 201 (and any corresponding sub-modules or sub-units) can be hardware units (e.g., portions of an integrated circuit) of processor 102 designed for use with other components or software units implemented by processor 102 through executing at least part of a program, i.e., instructions. The instructions of the program may be stored on a computer- readable medium, such as memory 104, and when executed by processor 102, it may perform a process having one or more functions related to data upsampling, such as data reception, upsampling, data multiplication, data transformation, data filtering, etc., as described below in detail.

[0035] Some embodiments of the present disclosure may be employed in a wireless communication system. For example, apparatus 100 may be arranged at a transmitter side, while apparatus 200 may be arranged at a receiver side. Data may be compressed in apparatus 100, at the transmitter side, prior to the transmission. At the receiver side, in apparatus 200, the compressed data may be reconstructed to its original form for subsequent operations. Through this manner, the sizes of the transmission buffers and the amount of bandwidth required on the communication link can be reduced, thereby arriving at a higher transmission rate.

[0036] FIG. 2C illustrates a block diagram of an exemplary apparatus implementing both data compression and data upsampling, according to some embodiments of the present disclosure. In some embodiments, a single apparatus 202 as illustrated in FIG. 2C may include a compression unit 101 and an upsampling unit 201. Similar to apparatuses 100 and 200, apparatus 202 may include a processor 102, a memory 104, and an interface 106. Compression unit 101 and upsampling unit 201 may be implemented on processor 102. Compression unit 101 may be configured to generate compressed data. The compressed data may be transmitted to and stored in memory 104 for later processing. Upsampling unit 201 may be configured to fetch and transform the compressed data to its original form. Although FIG. 2C shows that there is only one processor 102 and compression unit 101 is within the single processor 102, it can be understood that compression unit 101 may include one or more sub-units or sub-modules that can be implemented on different processors located closely or remotely with each other. Similarly, upsampling unit 201 may include one or more sub-units or sub-modules. It can also be understood that upsampling unit 201 may be arranged in a different processor from compression unit 101, and/or the one or more sub-units or sub-modules of upsampling unit 201 can be implemented on different processors located closely or remotely with each other. These elements in FIG. 2C may have a similar or identical connection, configuration, functions, quantities of those as described above in regard to FIGs. 2A and 2B. It is understood that the connection, configuration, functions, and quantities of these elements in FIGs. 2A-2C can be flexibly adjusted, depending on the applications and system requirements/constraints.

[0037] Now returning to FIG. 1, as described above, the data sequence may include the data sets = [h₀, h₁, ... , h_N-1]^H and H_i+1 = [h_D, h_D+1, ... , h_N, ... , h_N+[)-1]^H that are correlated. Therefore, a correlation matrix R_H can be defined to describe a correlation level between the two data sets by finding eigenvalues and eigenvectors of the correlation matrix R_H. As a result, the correlation matrix R_H for a data set H can be decomposed and expressed as:

R_H = E(HH^H) = BVB^H (1), where E denotes an expected value of HH^H, B denotes the eigenvectors of the correlation matrix R_H, V denotes the eigenvalues of the correlation matrix R_H, and H denotes the Hermitian transpose. [0038] In some instances, the data sets

= [h₀, h₁, ..., h_N-1]^H and H_i+1 = [h_D, h_D+1, ... , h_N, ..., h_N+D-1]^H can be used to represent spatially correlated antenna signals of a wireless communication system. Under this scenario, the data may be associated with the wireless channels in the frequency domain or in the time domain. Each of the indices 0 to N+D-l can be either a frequency tone index in a given time or a particular time index, and the correlation matrix R_H can represent a full-channel spatial correlation between any pair of antenna signals.

[0039] In some embodiments, the eigenvalues V of the correlation matrix R_H may be sorted in descending order for selecting one or more eigenvalues from V. The one or more eigenvalues may be labeled as being significant. For example, in response to determining that an eigenvalue from Eis greater than a threshold, the eigenvalue can be identified as a significant eigenvalue. The threshold may be preset. Consequently, K significant eigenvalue(s) can be determined, where K is a positive integer and used to represent the number (or the dimension) of the significant eigenvalue(s). In the interpretation, the K significant eigenvalue(s) can explain the majority of variance in the data sets. Due to the correlation, it can be expected that K is less than N. That is, K < N. The term “significant” may be used to describe that a significant eigenvector from the eigenvectors B of the correlation matrix, corresponding to the “significant” eigenvalue, can be used to construct a 0_K domain. The 0_K domain with the reduced dimension K is suitable for the data transformation to reduce the size of the data.

[0040] In some embodiments of the present disclosure, one data set H (either Hi , H_i+1, or any other data set) may be projected to the domain 0_K having the dimension K to reduce its data size. This projection can be expressed as:

OK = Uk, 0N-K]B^HH = B»H (2), where B_K is a project matrix corresponding to the 0_K domain and may include one or more significant eigenvectors of the correlation matrix R_H corresponding to the K significant eigenvalue(s). H denotes the Hermitian transpose, h denotes an identity matrix of dimension K, and 0_N-K denotes a zero matrix of dimension N-K.

[0041] By applying Equation (2) with the proj ect matrix B_K, a data set H having dimension N in the data sequence can be compressed to a smaller dimension K.

[0042] Similarly, for any data set Hi with index z in the data sequence, it can be compressed as:

where B_K is the project matrix corresponding to the 0_K domain, H denotes the Hermitian transpose, and K denotes the number of the significant eigenvalues.

[0043] Due to the O overlapping samples with H_h as illustrated in FIG. 1, the next data set Hi+i can be produced by removing the first D non-overlapping samples from H, and adding new D samples AHj₊₁ at the end of the data sequence. Consequently, the next data set H_l+i can be expressed as:

where the extracted data sequence AHj₊₁ can be expressed as AHj₊₁ = [h_w, ... h_N+[)-1 ^H, IN-D denotes an identity matrix of dimension N-D, and 0_D denotes a zero matrix with dimension D. D represents the size of the non-overlapping samples.

[0044] Therefore, the compressed data 0i_+1.K corresponding to the data set H_i+1 can be obtained as:

where B_KF0 — [I_o, OQXD BK , B_KLD — [0_OxD, ID]B_K, and B_KL0 - [0_OxD, IQ]B_K.

[0045] By analyzing the matrix components of the three element matrices B_KFo, B_KLD, ^and B_KLO , it ^can be found that B_KFo represents the first O rows of the project matrix B_K , B_KLD represents the last D rows of the project matrix B_K , and B_KLo represents the last O rows of the project matrix B_K. That is, once the project matrix B_K is given, the three element matrices B_KFo, B_KLD, ^ar|d B_KLO ^can be obtained, and an operator matrix Bk ’=[BK_F0 B_KLO > BKLD ^can be calculated prior to the data compression.

[0046] As shown in FIG. 1, the relationship between the size O of the overlapping samples and the size D of the non-overlapping samples can be expressed as:

0 = N - D (6), where N denotes the dimension of one data set H (either

, H_i+1, or any other data set).

[0047] Based on the element matrices, the project matrix B_k can be expressed as:

[0048] By analyzing Equation (5), it can be observed that the compressed data 0i_+1:K corresponding to H_i+1 is a multiplication of the operator matrix Bk ’ (that includes the three element matrices B_KFo, BKLD, ^and B_KLo) ^and ^a data matrix (combining the previously compressed data 0_i:K corresponding to the data set

and the extracted new data sequence

It can be also seen that the O overlapping samples between the data sets

and H_i+1 do not appear in Equation (5), and thus the overlapping samples are not required in the provided data compression. As a result, the compressed data may be obtained in a form, according to Equation (5), independent of the overlapping samples between the data sets. This feature facilitates the data compression and increases the compression performance.

[0049] FIG. 3 illustrates a block diagram of a system 300 that includes a compression unit 101 and an upsampling unit 201, according to some embodiments of the present disclosure. System 300 may be a wired or wireless communication system. System 300 may implement software and/or hardware components to realize Equation (5). In some embodiments, system 300 may in part implement the elements as shown in FIGs. 2A-2C. For example, compression unit 101 may include a compression module 302. In response to the previously compressed data 0_{i K} and the current data set H_i+1, compression module 302 may process the inputs to generate the compressed data 0i_+liK, corresponding to the data set H_i+1, according to Equation (5). Further, upsampling unit 201 may include an upsampling module 304, details of which will be described below.

[0050] For simplicity of illustration, FIG. 3 merely depicts particular elements for an exemplary purpose, not with an attempt to bring limitations to the present disclosure. For example, compression unit 101 and upsampling unit 201 may further include other functional blocks and/or modules configured to perform other aspects of the data compression and data decompression, respectively. For example, in some examples, compression unit 101 may further include a downsampling module, and upsampling unit 201 may further include a first-in-first-out (FIFO) buffer. One or more of other modules and/or functional blocks are also possible.

[0051] FIG. 4 illustrates a block diagram of a compression module 302 in the compression unit 101 of FIG. 3, according to some embodiments of the present disclosure. FIG. 5 illustrates a flow chart of an exemplary method 500 for data compression, according to some embodiments of the present disclosure. In the following, the structure of compression module 302 and its operations will be described with reference to FIGs. 4 and 5.

[0052] In some embodiments, compression module 302 may be configured to compress one data set H (either

or any other data set) according to Equation (5). For reference convenience, Equation (5) is reproduced as follows:

where B_KF0 represents the first O rows of the project matrix B_K , B_KLD represents the last D rows of the project matrix B_K , and B_KL0 represents the last O rows of the project matrix B_K.

[0053] In order to implement Equation (5), the project matrix B_K and its corresponding three element matrices B_KF0, B_KLD, and B_KL0 are required to be obtained in advance. For that purpose, compression module 302 may include a project matrix calculator 402, a project matrix extractor 404, and an operator matrix generator 406. A method according to some embodiments of the present disclosure may proceed to 502 in FIG. 5. Project matrix calculator 402 may receive a data set H (either

, H_i+1, or any other data set) having a dimension of N and may also receive the number K of the significant eigenvalue(s) to calculate the project matrix BK corresponding to the 0_K domain. [0054] The selection of the K significant eigenvalues may be determined by a correlation level between the data sets. In one instance, the higher the correlation is, the smaller K may be selected. That is, a compression ratio can be increased. In some embodiments, K may be fixed, while in other embodiments, during the data compression, K may be flexibly adjusted so as to arrive at desired compression ratio and compression performance.

[0055] Further, the method may proceed to 504 in FIG. 5. Project matrix extractor 404 may extract the three element matrices from the project matrix BK as obtained in 502 based on the size O of the overlapping samples between the two data sets and the dimension N of the data set.

[0056] According to Equation (5), the three element matrices include the first O rows of the project matrix B_K (B_KF0), the last D rows of the project matrix B_K (B_KLD), and the last O rows of the project matrix B_K (B_KL0). The size D of the non-overlapping samples can be obtained by D =N-0 according to Equation (6). Based on the three element matrices, at 506, operator matrix generator 406 may generate the operator matrix Bk’ as:

B' ⁼ [BKFO BKLO > BKLD] (&)•

[0057] In some embodiments, the size O and the dimension N may be identical for all the data sets. Once the project matrix B_K and its corresponding operator matrix B_k' are calculated, they can be used for the later data compression and data upsampling. In some embodiments, however, the project matrix B_K and its corresponding operator matrix B_k' may be provided by a source or a device external to compression module 302, to which the present disclosure does not place limitation thereto.

[0058] As illustrated in FIG. 4, compression module 302 may further include a data extractor 408, a data matrix generator 410, and a matrix multiplier 412. The method may proceed to 508, data extractor 408 may extract new D samples AHj₊₁ from the current data set H_l+i. The new D samples (AHj₊₁) represent the D non-overlapping samples at the end of the current data set H_l+i. Subsequently, the method may proceed to 512, based on the new D samples (AHj₊₁) and the previously compressed data 0_{i K} as obtained at 510, data matrix generator 410 may generate a data matrix Equation (5).

[0059] In some embodiments, the process of data matrix generator 410 may be performed in parallel with the process of operator matrix generator 406 so as to reduce the system latency. The method may proceed to 514. Matrix multiplier 412 may receive the operator matrix Bk’ and the data matrix to perform matrix multiplication to obtain the compressed data 0i_+1:K at 516, corresponding to the data set Hi+i, according to Equation (5).

[0060] Based on the above description, the compressed data 0t₊₁,_K corresponding to the data block H_i+1 can be directly obtained from the data set H, /, the project matrix Bk, and the previously compressed data, without using the overlapping samples. Therefore, an extra storage space, as required in the other approaches, for saving the overlapping samples is not necessary. Consequently, the storage capacity and the transmission rates can be enhanced, and thus the power consumption can be reduced. The features are particularly beneficial for a system with constrained resources, such as mobile devices.

[0061] On the other hand, to decompress and reconstruct the data block H_h matrix multiplication may be applied in Equation (3) with the project matrix B_K to obtain:

[0062] In some embodiments, interpolation or upsampling may be further applied to the data Hi with an interpolation matrix AT to obtain upsampled data corresponding to H, as follows:

7T_l = MH_i = MB_K0_{i K} = M0_{i K} (10), where M is an interpolation matrix corresponding to the compressed domain 0_K and can be obtained as:

M = MB_K (11), where B_K is the project matrix, and AT is the interpolation matrix corresponding to the data set H having size N.

[0063] In view of Equation (10), by combing the decompression operation and the upsampling operation, without reconstructing the data set H_h the upsampled data H_L may be directly obtained from the compressed data 0 _K with the interpolation matrix M corresponding to the compressed domain 0_K. In the following, therefore, the term “upsampling” may include and also refer to as a decompression process.

[0064] FIG. 6 illustrates a flow chart of an exemplary method 600 for data upsampling, according to some embodiments of the present disclosure. In the following, the structure of upsampling module 304 in FIG. 3 and its operations are described with reference to FIG. 6.

[0065] As illustrated in FIG. 3, upsampling unit 201 may include upsampling module 304. The method according to some embodiments may process to 602. Upsampling module 304 may calculate the interpolation matrix M corresponding to the compressed domain 0_K based on the project matrix B_K and the interpolation matrix AT corresponding to the original data set. At 604, in response to reception of the compressed data 0i₊₁,K, upsampling module 304 may process the compressed data 0i_+1.K with the interpolation matrix M obtained at 602 to obtain the upsampled data H_t, according to Equation (10).

[0066] While the above embodiments describe that the matrix multiplication may be applied to the project matrix B_K and the interpolation matrix M in advance to obtain the new interpolation matrix M before the data upsampling. In other embodiments, however, these operations may be integrated into one step of the matrix multiplication.

[0067] Through the proposed scheme according to some embodiments, without a decompression operation to transform the compressed data 0_{i K} back to H_h the upsampled data H_l can be directly obtained from the compressed data 0_{i K}.

[0068] Consistent with the scope of the present disclosure, the proposed methods for data compression and data upsampling may be employed in various communication and computer systems. For example, FIG. 7 illustrates an exemplary wireless network 700, in which certain aspects of the present disclosure may be implemented, according to some embodiments of the present disclosure. As shown in FIG. 7, wireless network 700 may include a network of nodes, such as a user equipment (UE) 702, an access node 704, and a core network element 706. UE 702 may be any terminal device, such as a mobile phone, a desktop computer, a laptop computer, a tablet, a vehicle computer, a gaming console, a printer, a positioning device, a wearable electronic device, a smart sensor, or any other device capable of receiving, processing, and transmitting information, such as any member of a vehicle to everything (V2X) network, a cluster network, a smart grid node, or an Intemet-of-Things (loT) node. It is understood that UE 702 is illustrated as a mobile phone simply by way of illustration and not by way of limitation.

[0069] Access node 704 may be a device that communicates with UE 702, such as a wireless access point, a base station (BS), a Node B, an enhanced Node B (eNodeB or eNB), a next-generation NodeB (gNodeB or gNB), a cluster master node, or the like. Access node 704 may have a wired connection to UE 702, a wireless connection to UE 702, or any combination thereof. Access node 704 may be connected to UE 702 by multiple connections, and UE 702 may be connected to other access nodes in addition to access node 704. Access node 704 may also be connected to other user equipments. It is understood that access node 704 is illustrated by a radio tower by way of illustration and not by way of limitation.

[0070] Core network element 706 may serve access node 704 and UE 702 to provide core network services. Examples of core network element 706 may include a home subscriber server (HSS), a mobility management entity (MME), a serving gateway (SGW), or a packet data network gateway (PGW). These are examples of core network elements of an evolved packet core (EPC) system, which is a core network for the Long Term Evolution (LTE) 4G system. Other core network elements may be used in LTE and in other communication systems. In some embodiments, core network element 706 includes an access and mobility management function (AMF) device, a session management function (SMF) device, or a user plane function (UPF) device, of a core network for the New Radio (NR) 5G system. It is understood that core network element 706 is shown as a set of rack-mounted servers by way of illustration and not by way of limitation.

[0071] Core network element 706 may connect with a large network, such as the Internet 708, or another Internet Protocol (IP) network, to communicate packet data over any distance. In this way, data from UE 702 may be communicated to other user equipments connected to other access points, including, for example, a computer 710 connected to Internet 708, for example, using a wired connection or a wireless connection, or to a tablet 712 wirelessly connected to Internet 708 via a router 714. Thus, computer 710 and tablet 712 provide additional examples of possible user equipments, and router 714 provides an example of another possible access node.

[0072] A generic example of a rack-mounted server is provided as an illustration of core network element 706. However, there may be multiple elements in the core network including database servers, such as a database 716, and security and authentication servers, such as an authentication server 718. Database 716 may, for example, manage data related to user subscription to network services. A home location register (HLR) is an example of a standardized database of subscriber information for a cellular network. Likewise, authentication server 718 may manage authentication of users, sessions, and so on. In the NR system, an authentication server function (AUSF) device may be the specific entity to perform user equipment authentication. In some embodiments, a single server rack may manage multiple such functions, such that the connections between core network element 706, authentication server 718, and database 716, may be local connections within a single rack.

[0073] FIG. 8 illustrates a block diagram of a communication system 80 including an apparatus 800 that has an antenna 802, a radio frequency (RF) chip 804, and a baseband chip 806, according to some embodiments of the present disclosure. Apparatus 800 may further include other functional units, such as a host chip, to perform various functions. Apparatus 800 may be an example of any suitable node of wireless network 700 in FIG. 7, such as UE 702 or access node 704. In some embodiments, baseband chip 806 may be implemented by a processor and a local memory 8066, and RF chip 804 may be implemented by a processor, a memory, and a transceiver (not shown). Besides the on-chip memory (also known as “internal memory” or “local memory,” e.g., registers, buffers, or caches) on each chip 804 or 806, apparatus 800 may further include an external memory (e.g., the system memory or main memory) that can be shared by each chip through the system/main bus. Although baseband chip 806 is illustrated as a standalone SoC in FIG. 8, it is understood that in one example, baseband chip 806 and RF chip 804 may be integrated as one SoC; in another example, baseband chip 806 and the host chip may be integrated as one SoC; in still another example, baseband chip 806, RF chip 804, and the host chip may be integrated as one SoC.

[0074] FIG. 8 merely shows explementary downlink of wireless communication. In downlink, antenna 802 may receive RF signals and pass the RF signals to a receiver of RF chip 804. RF chip 804 may perform any suitable front-end RF functions, such as filtering, direct current (DC) offset compensation, IQ imbalance compensation, down-conversion, or sample-rate conversion, and convert the RF signals into low-frequency digital signals (baseband signals) that can be processed by baseband chip 806. In downlink, baseband chip 806 may demodulate and decode the baseband signals to extract raw data that can be processed by the host chip. Baseband chip 806 may perform additional functions, such as error checking, de-mapping, channel estimation, descrambling, etc. The raw data provided by baseband chip 806 may be sent to the host chip directly or stored in the external memory.

[0075] In certain implementations of the downlink, baseband chip 806 in FIG. 8 may implement the compression and upsampling techniques according to some embodiments of the present disclosure. Baseband chip 806 may include a plurality of functional modules, e.g., a channel estimation module 8062, a compression module 8064, an upsampling module 8068, and a demodulation module 8070, as shown in FIG. 8. Before the data is compressed, it may be transmitted to channel estimation module 8062 for channel estimation and channel performance measurements, such as reference signal received power (RSRP), reference signal received quality (RSRQ), noise variance estimation, frequency offset estimation, etc. Compression module 8064 may be configured to perform the data compression according to some embodiments of the present disclosure. The compressed data may be stored or saved in a local memory 8066. Subsequently, in response to a decompression/upsampling request, upsampling module 8068 may upsample the compressed data to obtain upsampled data for later processing, such as performing demodulation in demodulation module 8070.

[0076] It can be understood that FIG. 8 merely depicts in part the functional units of baseband chip 806 to describe some application examples of the present disclosure. Baseband chip 806 may include some functional units other than those described above. For example, in the uplink, the host chip may generate raw data and send it to baseband chip 806 for encoding, modulation, and mapping. Baseband chip 806 may include one or more modules configured to perform those functions. Baseband chip 806 may send the modulated signal to RF chip 804. RF chip 804 may convert the modulated signal in the digital form into analog signals, i.e., RF signals, and perform any suitable front-end RF functions, such as filtering, digital pre-distortion, up-conversion, or sample-rate conversion. Antenna 802 (e.g., an antenna array) may transmit the RF signals provided by a transmitter of RF chip 804.

[0077] Consistent with the scope of the present disclosure, under the scenarios of the correlated data sets, the overlapping samples are not required for the data compression. Therefore, there is no requirement for extract storage space to save the overlapping samples. Compression performance can be enhanced. Meanwhile, in the data upsampling, the compressed data can be directly transformed into the upsampled data. As a result, a decompression operation is not required. Accordingly, the system performance can be increased.

[0078] According to one aspect of the present disclosure, an apparatus for data compression and data upsampling is provided. The apparatus may include one or more processors and memory storing instructions that, when executed by the one or more processors, may cause the one or more processors to receive a data sequence including a first data set and a second data set. The first data set and the second data set may include a size O of overlapping samples in the data sequence, and O may be a positive integer. A project matrix may be obtained based on a correlation matrix between the first data set and the second data set. The project matrix may correspond to one or more significant eigenvalues of the correlation matrix. An extracted data sequence may be obtained from the second data set based on the size O of the overlapping samples and a dimension of one data set of the first data set and the second data set. The extracted data sequence may include no overlapping with the first data set. Second compressed data, corresponding to the second data set, may be obtained based on the project matrix, the extracted data sequence, and first compressed data corresponding to the first data set.

[0079] In some embodiments, the instructions may further cause the one or more processors to extract three element matrices from the project matrix based on the size O and the dimension of the data set. The three element matrices may include different components of the project matrix.

[0080] In some embodiments, the instructions may further cause the one or more processors to generate a data matrix based on the first compressed data and the extracted data sequence. An operator matrix may be obtained based on the three element matrices. The operator matrix may be multiplied with the data matrix to obtain the second compressed data.

[0081] In some embodiments, the instructions may further cause the one or more processors to extract first O rows of the project matrix to obtain a first element matrix, extract last D rows of the project matrix to obtain a second element matrix, and extract last O rows of the project matrix to obtain a third element matrix. The three element matrices may include the first element matrix, the second element matrix, and the third element matrix. D may indicate a size of non-overlapping samples between the first data set and the second data set, and D may be a positive integer.

[0082] In some embodiments, the correlation matrix may include one or more eigenvectors and one or more eigenvalues. The one or more eigenvalues and the one or more eigenvectors of the correlation matrix may be indicative of a level of correlation between the first data set and the second data set. The project matrix may include one or more significant eigenvectors, corresponding to the one or more significant eigenvalues, from the one or more eigenvectors of the correlation matrix. The one or more significant eigenvalues may be selected from the one or more eigenvalues of the correlation matrix.

[0083] In some embodiments, the instructions may further cause the one or more processors to obtain the project matrix defined in a second domain corresponding to the one or more significant eigenvalues of the correlation matrix. The second compressed data may be obtained by projecting the second data set in a first domain having the dimension of the data set to the second domain corresponding to the one or more significant eigenvalues. A dimension of the second domain may be less than a dimension of the first domain.

[0084] In some embodiments, the instructions may further cause the one or more processors to obtain an interpolation matrix corresponding to the second domain based on an interpolation matrix corresponding to the first domain. Upsampled data, corresponding to the second data set, may be obtained based on the second compressed data and the interpolation matrix corresponding to the second domain.

[0085] In some embodiments, the instructions may further cause the one or more processors to obtain the second compressed data in a format independent of the overlapping samples between the first data set and the second data set.

[0086] In some embodiments, a selection of the one or more significant eigenvalues may depend on the dimension of the data set and the size O of the overlapping samples.

[0087] In some embodiments, the instructions may further cause the one or more processors to obtain upsampled data, corresponding to the second data set, based on the second compressed data and an interpolation matrix defined in a domain corresponding to the one or more significant eigenvalues.

[0088] According to another aspect of the present disclosure, a system-on-chip (SoC) for data compression and data upsampling, at a receiver, is provided. The SoC may include a channel estimation module, one or more processors, and memory storing instructions that, when executed by the one or more processors, may cause the one or more processors to obtain a project matrix based on a correlation matrix between the first data set and the second data set. The project matrix may correspond to one or more significant eigenvalues of the correlation matrix. An extracted data sequence may be obtained from the second data set based on a size O of overlapping samples between the first data set and the second data set and a dimension of one data set of the first data set and the second data set. The extracted data sequence may include no overlapping with the first data set, and O may be a positive integer. Second compressed data, corresponding to the second data set, may be obtained based on the project matrix, the extracted data sequence, and first compressed data corresponding to the first data set.

[0089] In some embodiments, the instructions may further cause the one or more processors to obtain the project matrix defined in a second domain corresponding to the one or more significant eigenvalues of the correlation matrix. The second compressed data may be obtained by projecting the second data set in a first domain of the dimension of the data set to the second domain corresponding to the one or more significant eigenvalues. A dimension of the second domain is less than a dimension of the first domain.

[0090] In some embodiments, the instructions may further cause the one or more processors to obtain the second compressed data in a format independent of the overlapping samples between the first data set and the second data set.

[0091] In some embodiments, the instructions may further cause the one or more processors to obtain upsampled data, corresponding to the second data set, based on the second compressed data and an interpolation matrix defined in a domain corresponding to the one or more significant eigenvalues.

[0092] According to still another aspect of the present disclosure, a method for data compression and data upsampling is provided. The method may include receiving a data sequence including a first data set and a second data set. The first data set and the second data set may include a size O of overlapping samples in the data sequence, and O may be a positive integer. A project matrix may be obtained based on a correlation matrix between the first data set and the second data set. The project matrix may correspond to one or more significant eigenvalues of the correlation matrix. An extracted data sequence may be obtained from the second data set based on the size O of the overlapping samples and a dimension of one data set of the first data set and the second data set. The data sequence may include no overlapping with the first data set. Second compressed data, corresponding to the second data set, may be obtained based on the project matrix, the extracted data sequence, and first compressed data corresponding to the first data set.

[0093] In some embodiments, three element matrices may be extracted from the project matrix based on the size O and the dimension of the data set. The three element matrices may include different components of the project matrix.

[0094] In some embodiments, the correlation matrix may include one or more eigenvectors and one or more eigenvalues. The one or more eigenvalues and the one or more eigenvectors of the correlation matrix may be indicative of a level of correlation between the first data set and the second data set. The project matrix may include one or more significant eigenvectors, corresponding to the one or more significant eigenvalues, from the one or more eigenvectors of the correlation matrix. The one or more significant eigenvalues may be selected from the one or more eigenvalues of the correlation matrix.

[0095] In some embodiments, the project matrix may be defined in a second domain corresponding to the one or more significant eigenvalues of the correlation matrix. The second data set in a first domain of the dimension of the data set may be projected to the second domain corresponding to the one or more significant eigenvalues to obtain the second compressed data. A dimension of the second domain may less than a dimension of the first domain.

[0096] In some embodiments, an interpolation matrix corresponding to the second domain may be obtained based on an interpolation matrix corresponding to the first domain. Upsampled data, corresponding to the second data set, may be obtained based on the second compressed data and the interpolation matrix corresponding to the second domain.

[0097] In some embodiments, the second compressed data may be obtained in a format independent of the overlapping samples between the first data set and the second data set.

[0098] The foregoing description of the embodiments will so reveal the general nature of the present disclosure that others can, by applying knowledge within the skill of the art, readily modify and/or adapt for various applications such embodiments, without undue experimentation, without departing from the general concept of the present disclosure. Therefore, such adaptations and modifications are intended to be within the meaning and range of equivalents of the disclosed embodiments, based on the teaching and guidance presented herein. It is to be understood that the phraseology or terminology herein is for the purpose of description and not of limitation, such that the terminology or phraseology of the present specification is to be interpreted by the skilled artisan in light of the teachings and guidance.

[0099] Embodiments of the present disclosure have been described above with the aid of functional building blocks illustrating the implementation of specified functions and relationships thereof. The boundaries of these functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternate boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed.

[0100] The Summary and Abstract sections may set forth one or more but not all exemplary embodiments of the present disclosure as contemplated by the inventor(s), and thus, are not intended to limit the present disclosure and the appended claims in any way.

[0101] Various functional blocks, modules, and steps are disclosed above. The arrangements provided are illustrative and without limitation. Accordingly, the functional blocks, modules, and steps may be reordered or combined in different ways than in the examples provided above. Likewise, some embodiments include only a subset of the functional blocks, modules, and steps, and any such subset is permitted.

[0102] The breadth and scope of the present disclosure should not be limited by any of the above-described exemplary embodiments but should be defined only in accordance with the following claims and their equivalents.

Claims

WHAT IS CLAIMED IS:

1. An apparatus for data compression and data upsampling, comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the one or more processors to: receive a data sequence comprising a first data set and a second data set, the first data set and the second data set comprising a size O of overlapping samples in the data sequence, and O being a positive integer; obtain a project matrix based on a correlation matrix between the first data set and the second data set, the project matrix corresponding to one or more significant eigenvalues of the correlation matrix; obtain an extracted data sequence from the second data set based on the size O of the overlapping samples and a dimension of one data set of the first data set and the second data set, the extracted data sequence comprising no overlapping with the first data set; and obtain second compressed data, corresponding to the second data set, based on the project matrix, the extracted data sequence, and first compressed data corresponding to the first data set.

2. The apparatus of claim 1, wherein the instructions further cause the one or more processors to: extract three element matrices from the project matrix based on the size O and the dimension of the data set, the three element matrices comprising different components of the project matrix.

3. The apparatus of claim 2, wherein the instructions further cause the one or more processors to: generate a data matrix based on the first compressed data and the extracted data sequence; generate an operator matrix based on the three element matrices; and multiply the operator matrix with the data matrix to obtain the second compressed data.

4. The apparatus of claim 2, wherein the instructions further cause the one or more processors to: extract first O rows of the project matrix to obtain a first element matrix; extract last D rows of the project matrix to obtain a second element matrix, wherein D indicates a size of non-overlapping samples between the first data set and the second data set, D being a positive integer; and extract last O rows of the project matrix to obtain a third element matrix, the three element matrices comprising the first element matrix, the second element matrix, and the third element matrix.

5. The apparatus of claim 1, wherein: the correlation matrix comprises one or more eigenvectors and one or more eigenvalues, the one or more eigenvalues and the one or more eigenvectors of the correlation matrix being indicative of a level of correlation between the first data set and the second data set; and the project matrix comprises one or more significant eigenvectors, corresponding to the one or more significant eigenvalues, from the one or more eigenvectors of the correlation matrix, the one or more significant eigenvalues being selected from the one or more eigenvalues of the correlation matrix.

6. The apparatus of claim 1, wherein the instructions further cause the one or more processors to: obtain the proj ect matrix defined in a second domain corresponding to the one or more significant eigenvalues of the correlation matrix; and obtain the second compressed data by projecting the second data set in a first domain having the dimension of the data set to the second domain corresponding to the one or more significant eigenvalues, wherein a dimension of the second domain is less than a dimension of the first domain.

7. The apparatus of claim 6, wherein the instructions further cause the one or more processors to: obtain an interpolation matrix corresponding to the second domain based on an interpolation matrix corresponding to the first domain; and obtain upsampled data, corresponding to the second data set, based on the second compressed data and the interpolation matrix corresponding to the second domain.

8. The apparatus of claim 1, wherein the instructions further cause the one or more processors to obtain the second compressed data in a format independent of the overlapping samples between the first data set and the second data set.

9. The apparatus of claim 1, wherein a selection of the one or more significant eigenvalues depends on the dimension of the data set and the size O of the overlapping samples.

10. The apparatus of claim 1, wherein the instructions further cause the one or more processors to obtain upsampled data, corresponding to the second data set, based on the second compressed data and an interpolation matrix defined in a domain corresponding to the one or more significant eigenvalues.

11. A system-on-chip (SoC) for data compression and data upsampling, at a receiver, comprising: a channel estimation module configured to estimate performance of a channel, between a transmitter and the receiver, based on a first data set and a second data set; one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the one or more processors to: obtain a project matrix based on a correlation matrix between the first data set and the second data set, the project matrix corresponding to one or more significant eigenvalues of the correlation matrix; obtain an extracted data sequence from the second data set based on a size O of overlapping samples between the first data set and the second data set and a dimension of one data set of the first data set and the second data set, the extracted data sequence comprising no overlapping with the first data set, and O being a positive integer; and obtain second compressed data, corresponding to the second data set, based on the project matrix, the extracted data sequence, and first compressed data corresponding to the first data set.

12. The SoC of claim 11, wherein the instructions further cause the one or more processors to: obtain the proj ect matrix defined in a second domain corresponding to the one or more significant eigenvalues of the correlation matrix; and obtain the second compressed data by projecting the second data set in a first domain of the dimension of the data set to the second domain corresponding to the one or more significant eigenvalues, wherein a dimension of the second domain is less than a dimension of the first domain.

13. The SoC of claim 11, wherein the instructions further cause the one or more processors to obtain the second compressed data in a format independent of the overlapping samples between the first data set and the second data set.

14. The SoC of claim 11, wherein the instructions further cause the one or more processors to obtain upsampled data, corresponding to the second data set, based on the second compressed data and an interpolation matrix defined in a domain corresponding to the one or more significant eigenvalues.

15. A method for data compression and data upsampling, comprising: receiving, through one or more processors, a data sequence comprising a first data set and a second data set, the first data set and the second data set comprising a size O of overlapping samples in the data sequence, and O being a positive integer; obtaining, through the one or more processors, a project matrix based on a correlation matrix between the first data set and the second data set, the project matrix corresponding to one or more significant eigenvalues of the correlation matrix; obtaining, through the one or more processors, an extracted data sequence from the second data set based on the size O of the overlapping samples and a dimension of one data set of the first data set and the second data set, the data sequence comprising no overlapping with the first data set; and obtaining, through the one or more processors, second compressed data, corresponding to the second data set, based on the project matrix, the extracted data sequence, and first compressed data corresponding to the first data set.

16. The method of claim 15, further comprising: extracting three element matrices from the project matrix based on the size O and the dimension of the data set, the three element matrices comprising different components of the project matrix.

17. The method of claim 15, wherein: the correlation matrix comprises one or more eigenvectors and one or more eigenvalues, the one or more eigenvalues and the one or more eigenvectors of the correlation matrix being indicative of a level of correlation between the first data set and the second data set; and the project matrix comprises one or more significant eigenvectors, corresponding to the one or more significant eigenvalues, from the one or more eigenvectors of the correlation matrix, the one or more significant eigenvalues being selected from the one or more eigenvalues of the correlation matrix.

18. The method of claim 15, wherein: obtaining the project matrix comprises obtaining the project matrix defined in a second domain corresponding to the one or more significant eigenvalues of the correlation matrix; and obtaining the second compressed data comprises projecting the second data set in a first domain of the dimension of the data set to the second domain corresponding to the one or more significant eigenvalues to obtain the second compressed data, wherein a dimension of the second domain is less than a dimension of the first domain.

19. The method of claim 18, further comprising: obtaining an interpolation matrix corresponding to the second domain based on an interpolation matrix corresponding to the first domain; and obtaining upsampled data, corresponding to the second data set, based on the second compressed data and the interpolation matrix corresponding to the second domain.

20. The method of claim 15, wherein: obtaining the second compressed data comprises obtaining the second compressed data in a format independent of the overlapping samples between the first data set and the second data set.