Cell initial access-oriented multi-dimensional parameter estimation method and system, medium and receiver

Abstract

The invention provides a cell initial access-oriented multi-dimensional parameter estimation method and system, a medium and a receiver. The method comprises the following steps of: receiving a received signal from at least one network node; generating a local reference signal corresponding to each network node signal; for each network node signal, acquiring a rough candidate parameter area based on the received signal and a corresponding local reference signal in a preset multi-dimensional parameter space; and in the rough candidate parameter region, searching the first-dimensional parameter based on a divide-and-conquer method to obtain a matched multi-dimensional parameter estimation value. According to the cell initial access-oriented multi-dimensional parameter estimation method, the cell initial access-oriented multi-dimensional parameter estimation system, the medium and the receiver, high-precision estimation of multi-dimensional parameters can be quickly and efficiently completed.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a multidimensional parameter estimation method, system, medium, and receiver for initial access to a cell. Background Technology

[0002] In modern wireless communication systems, especially 5G and future communication networks, non-terrestrial networks (NTNs) play a crucial role as a supplement to terrestrial networks. NTN systems (such as satellite communications) typically face unique challenges: First, due to the high-speed motion of satellites, the received signal will generate a huge Doppler frequency shift, or carrier frequency offset (CFO), which can range from tens to hundreds of kilohertz. Second, the receiver may simultaneously receive reference signals from multiple network nodes in the integrated space-ground network. These signals are superimposed on the same time-frequency resources, forming co-channel interference (CCI).

[0003] To achieve reliable communication, the receiver must first complete the initial synchronization of the signal, that is, accurately estimate the key parameters of the signal, such as timing delay, carrier frequency offset (CFO), and cell identifier. The cell identifier is one of the main parameters for establishing subsequent communication, and it consists of a secondary cell identifier. Level 1 Community Identification composition.

[0004] In existing technologies, a common solution is to use an exhaustive 3D search method, also known as a brute-force search. This method, under preset time delays, CFO, and... In a three-dimensional parameter space consisting of all possible combinations of values, a fine, high-resolution parameter grid is constructed, and the correlation value of each point on the grid is calculated to find the parameter combination corresponding to the correlation peak. However, its computational complexity is extremely high, and the computational load increases exponentially with the increase of search range and precision. This is unbearable for resource-constrained receiver equipment and will lead to excessively long synchronization delays.

[0005] To address the complexity issue of exhaustive search, several optimized search strategies have been explored. For example, the ternary search is a well-known and efficient algorithm often used to find the extrema of unimodal functions, with a search complexity of only logarithmic order. However, in wireless communication synchronization scenarios, the correlation volume of the parameter space is typically multimodal and severely affected by noise and interference, making it difficult to directly apply algorithms like the ternary search. If applied improperly, they can easily get trapped in local optima, leading to synchronization failure.

[0006] Therefore, there is an urgent need in this field for a parameter estimation algorithm that can significantly reduce computational complexity while ensuring estimation accuracy under conditions of large carrier frequency offset and multi-signal interference. Summary of the Invention

[0007] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a multi-dimensional parameter estimation method, system, medium and receiver for initial access to a cell, which can quickly and efficiently complete high-precision estimation of multi-dimensional parameters.

[0008] In a first aspect, the present invention provides a multidimensional parameter estimation method for initial access to a cell, the method comprising the following steps: receiving received signals from at least one network node; generating a local reference signal corresponding to each network node signal; for each network node signal, obtaining a coarse candidate parameter region based on the received signal and the corresponding local reference signal in a preset multidimensional parameter space; and searching for a first-dimensional parameter in the coarse candidate parameter region using a divide-and-conquer method to obtain a matching multidimensional parameter estimate.

[0009] In one implementation of the first aspect, the multidimensional parameters include carrier frequency offset, network node time delay, and secondary cell identifier.

[0010] In one implementation of the first aspect, the received signal is represented as Where A l s l (t), τ l and f d,l Let represent the channel gain, transmitted signal waveform, time delay, and carrier frequency offset of the l-th network node signal, respectively; n(t) represents additive white Gaussian noise; and L represents the number of network nodes.

[0011] In one implementation of the first aspect, for each network node signal, obtaining a coarse candidate parameter region based on the received signal and the corresponding local reference signal in a preset multidimensional parameter space includes the following steps:

[0012] In the multidimensional parameter space, a low-resolution parameter grid is constructed;

[0013] In the parameter grid, a coarse correlation volume is constructed based on the received signal and the corresponding local reference signal;

[0014] In the coarse correlation body, the parameter points corresponding to the peak points of the correlation values ​​are obtained as candidate parameter points;

[0015] Centered on the first dimension parameter of the candidate parameter point, with other dimension parameters as constants, a neighborhood of a preset size is defined as the coarse candidate parameter region.

[0016] In one implementation of the first aspect, constructing a coarse correlation body based on the received signal and the corresponding local reference signal in the parameter grid includes the following steps:

[0017] On the frequency dimension of the parameter grid, a candidate carrier frequency offset is selected to perform frequency compensation on the received signal;

[0018] Perform a Fast Fourier Transform on the frequency-compensated received signal to obtain the frequency domain received signal;

[0019] The frequency domain received signal is multiplied by the conjugate dot product of the frequency domain value of the local reference signal corresponding to the second dimension parameter in the multidimensional parameters, wherein the frequency domain value is the conjugate value of the local reference signal after fast Fourier transform.

[0020] The result of the conjugate dot product is subjected to an inverse fast Fourier transform and the modulus is taken to obtain a profile of the correlation between the carrier frequency offset and the second dimension parameter in the time delay dimension.

[0021] The profiles corresponding to all carrier frequency offsets and the second-dimensional parameters in the parameter grid are stacked to construct the coarse correlation body containing time delay, carrier frequency offset, and the second-dimensional parameter.

[0022] In one implementation of the first aspect, the search for the first dimension parameter based on the divide-and-conquer method in the coarse candidate parameter region to obtain matching multidimensional parameter estimates includes the following steps:

[0023] Perform a Fast Fourier Transform on the reset signal to obtain the frequency domain reset signal;

[0024] Perform a Fast Fourier Transform on the local reference signal to obtain a frequency domain reference signal;

[0025] In the search interval corresponding to the first dimension parameter in the rough candidate parameter region, multiple equally divided points are taken;

[0026] Calculate the cyclic shift number corresponding to the plurality of division points in the frequency domain;

[0027] For each division point, the frequency domain reset signal is cyclically shifted based on the corresponding cyclic shift number; the cyclically shifted frequency domain reset signal is then multiplied by its conjugate and subjected to an inverse fast Fourier transform with the frequency domain reference signal to obtain the relevant value;

[0028] Compare the relevant values ​​of the multiple equally divided points, and update the search interval according to the divide-and-conquer method until the updated search interval is less than a preset threshold interval;

[0029] The estimated value of the first dimension parameter is set to the center first dimension parameter of the last updated search interval;

[0030] The multidimensional parameter estimate is constructed based on the first-dimensional parameter of the center and the parameters other than the first-dimensional parameter of the candidate parameter points.

[0031] In one implementation of the first aspect, the method further includes detecting network node signals in the received signal based on the multidimensional parameter estimates.

[0032] Detecting network node signals in the received signal based on the multidimensional parameter estimates includes the following steps:

[0033] Based on the multidimensional parameter estimates Obtain the parameter value of the strongest signal in the received signals.

[0034] The strongest signal is reconstructed based on the parameter values ​​of the strongest signal;

[0035] Calculate the difference between the received signal and the reconstructed strongest signal to obtain the residual signal;

[0036] The multidimensional parameter estimates of the residual signal are obtained as parameter values ​​of the secondary strong signal, and the secondary strong signal is obtained until the signals of all network nodes in the received signal are detected or the obtained residual signal is less than a preset threshold.

[0037] Secondly, the present invention provides a multi-dimensional parameter estimation system for initial access to a cell, the system comprising a receiving module, a generating module, a coarse estimation module, and a fine search module;

[0038] The receiving module is used to receive signals from at least one network node;

[0039] The generation module is used to generate a local reference signal corresponding to each network node signal;

[0040] The coarse estimation module is used to obtain a coarse candidate parameter region for each network node signal in a preset multidimensional parameter space based on the received signal and the corresponding local reference signal; wherein the multidimensional parameters include at least the carrier frequency offset.

[0041] The fine search module is used to search for the carrier frequency offset in the coarse candidate parameter region based on the divide-and-conquer method to obtain matching multidimensional parameter estimates.

[0042] Thirdly, the present invention provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described multidimensional parameter estimation method for initial cell access.

[0043] Fourthly, the present invention provides a receiver, comprising: a processor and a memory;

[0044] The memory is used to store computer programs;

[0045] The processor is used to execute the computer program stored in the memory, so that the receiver performs the above-described multidimensional parameter estimation method for initial cell access.

[0046] As described above, the multi-dimensional parameter estimation method, system, medium, and device for initial cell access described in this invention have the following beneficial effects:

[0047] (1) By adopting a two-stage strategy of coarse search + fine search, the search space is greatly reduced. Theoretically, the computational complexity of the fine search stage is reduced from linear to logarithmic level, which is related to the search accuracy. Thus, while ensuring the estimation accuracy, the computational speed is improved by several times. For example, in a typical 5G NTN multi-network node synchronization simulation scenario, the computational speed of this invention has been verified to be three times that of the traditional three-dimensional search method. More importantly, this speed improvement is achieved by sacrificing only a small amount of estimation accuracy. When this invention is applied to a multi-satellite timing synchronization scenario with severe signal superposition and co-channel interference, under the harsh conditions of a signal-to-noise ratio (SNR) as low as -19dB, the computational speed of the cell is improved. The detection accuracy rate can still reach 99%, which fully demonstrates the efficiency and robustness of the present invention.

[0048] (2) It solves the problem of excessive computational complexity of exhaustive search of three-dimensional parameters in the existing initial cell search process, and can perform high-efficiency and high-precision synchronization parameter estimation in multi-signal superposition interference environment.

[0049] (3) It is especially suitable for non-terrestrial network communication scenarios. Attached Figure Description

[0050] Figure 1 The flowchart shown is an embodiment of the multi-dimensional parameter estimation method for initial cell access according to the present invention;

[0051] Figure 2 The diagram shown is a structural schematic of a multi-dimensional parameter estimation system for initial cell access according to an embodiment of the present invention.

[0052] Figure 3 The diagram shown is a structural schematic of the receiver of the present invention in one embodiment. Detailed Implementation

[0053] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0054] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0055] The technical solutions of the present invention will now be described in detail with reference to the accompanying drawings.

[0056] like Figure 1 As shown, in one embodiment, the multi-dimensional parameter estimation method for initial cell access of the present invention includes steps S1-S4.

[0057] Step S1: Receive a received signal from at least one network node.

[0058] Specifically, in this invention, the received signal R received by the receiver x (t) can be modeled as the superposition of L signals from different network nodes and additive white Gaussian noise (AWGN), specifically expressed as: Where A l s l (t), τ l and f d,lLet represent the channel gain, transmitted signal waveform, time delay, and carrier frequency offset of the signal from the l-th network node, respectively; n(t) represents additive white Gaussian noise; and L represents the number of network nodes, where L ≥ 1. The objective of this invention is to obtain the received signal R... x The parameter set of each network node signal is accurately estimated in (t).

[0059] Step S2: Generate the local reference signal corresponding to the signal of each network node.

[0060] Specifically, the receiver generates a set of local reference signals (such as the primary synchronization signal PSS) according to the communication protocol (such as 5G NR).

[0061] Step S3: For each network node signal, obtain a rough candidate parameter region in a preset multidimensional parameter space based on the received signal and the corresponding local reference signal.

[0062] Specifically, this step is used to roughly obtain the approximate range where the actual parameters may exist. It should be noted that the synchronization signals (such as PSS / SSS) transmitted by the multiple network nodes are generated using different Physical Cell Identifiers (PCIs) or sequence indices, thus they have good cross-correlation orthogonality (i.e., low cross-correlation characteristics).

[0063] During parameter estimation, the receiver uses locally stored reference signals corresponding to different network node identifiers to perform correlation operations with the received signal. Due to the orthogonality of the sequences, when matching is performed using the reference template of network node A, only the signal component from network node A will produce a significant correlation peak, while the signal components from other network nodes will exhibit noise-like interference. This enables independent differentiation and estimation of signal parameters (time delay, Doppler shift, etc.) of different network nodes.

[0064] In one embodiment, the multidimensional parameters include carrier frequency offset f. d,l The time delay τ of network nodes l and secondary community signage The following explanation uses carrier frequency offset as the first dimension parameter and secondary cell identifier as the second dimension parameter as an example.

[0065] For each network node signal, obtaining a coarse candidate parameter region in a preset multidimensional parameter space based on the received signal and the corresponding local reference signal includes the following steps:

[0066] 31) In the multidimensional parameter space, construct a low-resolution parameter grid.

[0067] Among them, a time delay τ and carrier frequency offset f are included. d and In a three-dimensional parameter space, a sparse, low-resolution parameter grid is defined. For example, the search step for carrier frequency offset can be set to 200 Hz or greater.

[0068] It should be noted that the terms "sparse" and "low resolution" are relative to the final fine-tuning accuracy and the system subcarrier spacing (SCS).

[0069] In this embodiment, the specific resolution of the parameter grid is set as follows:

[0070] In the carrier frequency offset (CFO) dimension, the low resolution refers to a grid step size set in the range of hundreds of hertz to thousands of hertz (e.g., 200 Hz to 3000 Hz).

[0071] The principle for selecting the upper limit is: the grid step size is set to be smaller than the subcarrier spacing (SCS) of the signal (e.g., 15kHz or 30kHz). As long as the step size is small enough to ensure that the energy of the correlation peak does not suffer devastating attenuation due to frequency offset (e.g., energy loss is controlled within 3dB or 6dB), thus ensuring that the presence of the signal can be detected through the correlation peak, it falls within the coarse search range described in this invention.

[0072] The lower limit is selected based on the principle that the grid step size is significantly larger than the accuracy achievable in subsequent fine-tuning correction steps. Subsequent fine-tuning correction typically converges the frequency error to the order of tens of hertz (e.g., 10Hz-50Hz), an accuracy sufficient to meet the requirements of subsequent signal demodulation and serial interference cancellation (SIC). Therefore, this step uses a coarse step size of several hundred hertz or more, aiming to achieve extremely high search speed with an acceptable loss of accuracy.

[0073] 32) In the parameter grid, a coarse correlation body is constructed based on the received signal and the corresponding local reference signal.

[0074] In this embodiment, the multidimensional related body V(τ,f) d The construction of α) is achieved by traversing the carrier frequency offset f on a preset parameter grid. d Second dimension parameter α (such as cell identifier) This is achieved by calculating the cross-correlation function between the cross-correlation function and the time delay τ.

[0075] In one embodiment, when the first dimension parameter is a carrier frequency offset in the parameter grid, constructing a coarse correlation body based on the received signal and the corresponding local reference signal includes the following steps:

[0076] a) Select a candidate carrier frequency offset f in the frequency dimension of the parameter grid. i Frequency compensation is performed on the received signal.

[0077] In order to eliminate the impact of the carrier frequency offset on the correlation calculation, the received signal R is... x (t) Perform reverse phase rotation (De-rotation), i.e. in It is based on the current carrier frequency offset f i The generated complex exponential twitch factor, F s The sampling rate is used. This step cancels out the assumed frequency offset in the time domain, "pulling" the signal back to baseband.

[0078] b) The received signal after frequency compensation Performing a Fast Fourier Transform (FFT) yields the received signal in the frequency domain, i.e.

[0079] c) Perform a conjugate dot product between the frequency domain received signal and the frequency domain value of the local reference signal corresponding to the second dimension parameter in the multidimensional parameters, wherein the frequency domain value is the conjugate value of the local reference signal after fast Fourier transform.

[0080] Wherein, the second dimension parameter α (e.g. The time-domain sequence of the corresponding local reference signal is: The frequency domain value k is the frequency index, and conj(·) denotes the complex conjugate operation. Utilizing the frequency-domain equivalence of time-domain correlation (i.e., frequency-domain multiplication), the frequency domain value of the compensated received signal is multiplied by the conjugate of the corresponding local reference signal's frequency domain value, i.e., This step completes the calculation of the carrier frequency offset f through a single frequency domain multiplication. i Preparation for matching calculations of all possible time delays τ under the second-dimensional parameter α.

[0081] d) Perform an inverse fast Fourier transform on the result of the conjugate dot product and take the modulus to obtain a profile of the correlation between the carrier frequency offset and the second dimension parameter in the time delay dimension.

[0082] Among them, the above product results Perform an inverse fast Fourier transform (IFFT) and take the modulus to obtain the value at the current carrier frequency offset f. i A profile showing the change of the correlation value with time delay τ under the second-dimensional parameter α.

[0083] e) Stack the profiles corresponding to all carrier frequency offsets and the second dimension parameters in the parameter grid to construct the coarse correlation body containing time delay, carrier frequency offset and the second dimension parameter.

[0084] This involves iterating through all carrier frequency offsets f in the parameter grid. i Second dimension parameter α (e.g.) Taking values ​​of 0, 1, and 2, calculate all relevant profiles C(τ,f). i α) Stacked according to the corresponding dimensions, they eventually form a coarsely related volume in three dimensions (or higher).

[0085] 33) In the coarse correlation body, the parameter points corresponding to the peak points of the correlation values ​​are obtained as candidate parameter points.

[0086] Within the coarse correlation matrix, the peak point with the maximum correlation value is identified. The parameter combination (τ) corresponding to this peak point is determined. coarse ,f d,coarse ,NID2coarse) was identified as a candidate parameter point.

[0087] 34) Taking the first dimension parameter of the candidate parameter point as the center, and the other dimension parameters as constant values, a neighborhood of a preset size is the coarse candidate parameter region.

[0088] Wherein, parameter τ coarse and NID2 coarse Keep it unchanged, with f d,coarse The range of ±100Hz represents the approximate candidate parameter region.

[0089] Step S4: In the coarse candidate parameter region, search the first dimension parameter based on the divide-and-conquer method to obtain the matching multidimensional parameter estimate.

[0090] Specifically, this step is used to perform a fine search for the carrier frequency offset within the coarse candidate parameter region. In each step of the divide-and-conquer method, such as the three-part iteration, the process of calculating the correlation value of a specific parameter point is to simulate the effect of the frequency offset by performing a cyclic shift operation of the Fast Fourier Transform (FFT), thereby avoiding repetitive and computationally complex FFT operations. The divide-and-conquer method based on FFT cyclic shift reduces the computational complexity of the fine search stage from linear to logarithmic, thus achieving a several-fold increase in computational speed while maintaining estimation accuracy.

[0091] Taking the carrier frequency offset as the first-dimensional parameter and the three-part divide-and-conquer method as an example, in one embodiment, the search for the first-dimensional parameter based on the three-part divide method in the coarse candidate parameter region to obtain the matching multi-dimensional parameter estimate includes the following steps:

[0092] 41) Regarding the reset signal R x Perform a fast Fourier transform to obtain the frequency domain reset signal X(f).

[0093] During the entire search process, only one Fast Fourier Transform is performed on the reset signal.

[0094] 42) Perform a fast Fourier transform on the local reference signal to obtain a frequency domain reference signal.

[0095] 43) In the search interval corresponding to the carrier frequency offset in the coarse candidate parameter region, take two trisection points.

[0096] Wherein, the current search interval for the carrier frequency offset is set to [f left ,f right Take two trisection points f. m1 and f m2 The calculation formula is as follows:

[0097]

[0098] 44) Calculate the cyclic shift number corresponding to the two trisection points in the frequency domain.

[0099] Among them, the trisection point f d The corresponding cyclic shift number in the frequency domain Where Δf is the frequency resolution (or subcarrier spacing) of the frequency domain reset signal, and round(·) represents the rounding operation.

[0100] 45) For each trisection point, the frequency domain reset signal is cyclically shifted based on the corresponding cyclic shift number; the cyclically shifted frequency domain reset signal is then multiplied by conjugate and subjected to inverse fast Fourier transform with the frequency domain reference signal to obtain the correlation value.

[0101] For example, for carrier frequency offset f m1 Calculate the corresponding cyclic shift Δ in the frequency domain. shift,1 Perform a cyclic shift operation on the frequency domain reset signal X(f) to obtain X′(f) = circshift(X(f), Δ shift,1 This operation is equivalent to multiplying the reset signal by a complex exponent in the time domain (i.e., frequency shifting) and then performing a Fast Fourier Transform. The correlation value C(f) is obtained by conjugate multiplication of X′(f) with the corresponding frequency domain reference signal and then performing an inverse Fast Fourier Transform. m1 Similarly, the carrier frequency offset f can be calculated. m2 The correlation value C(f) m2 ).

[0102] 46) Compare the correlation values ​​of the two trisection points and update the search interval according to the three-part rule until the updated search interval is less than the preset threshold interval.

[0103] This involves comparing the magnitudes of two relevant values ​​and updating the search interval according to the ternary trigonometric method. That is, if C(f m1 )>C(f m2 If ), then the new search range is updated to [f]. left ,f m2 Otherwise, the new search interval is updated to [f]. m1 ,f right Repeat steps 43)-46) above until the updated search interval (f) is reached. right -f left () is less than the preset threshold range.

[0104] 47) Set the estimated value of the carrier frequency offset to the center carrier frequency offset of the last updated search interval.

[0105] Specifically, the center carrier frequency offset of the updated search interval is calculated.

[0106] 48) Construct the multidimensional parameter estimate based on parameters other than the center carrier frequency offset and the carrier frequency offset of the candidate parameter points.

[0107] In the multidimensional parameter estimates, the carrier frequency offset is the center frequency offset, and the time delay and the delay τ of the secondary cell identifier are the coarse parameter points. coarse and secondary cell identifier NID2 coarse That is, the estimated value of the multidimensional parameters is...

[0108] In one embodiment, the multidimensional parameter estimation method for initial cell access of the present invention further includes detecting network node signals in the received signal based on the multidimensional parameter estimation values.

[0109] Detecting network node signals in the received signal based on the multidimensional parameter estimates includes the following steps:

[0110] a) Based on the multidimensional parameter estimates Obtain the parameter value of the strongest signal in the received signals.

[0111] b) Generate a reconstructed strongest signal based on the parameter values ​​of the strongest signal.

[0112] c) Calculate the difference between the received signal and the reconstructed strongest signal to obtain the residual signal.

[0113] d) Obtain the multidimensional parameter estimates of the residual signal as parameter values ​​of the secondary strong signal, and obtain the secondary strong signal until the signals of all network nodes in the received signal are detected or the obtained residual signal is less than a preset threshold.

[0114] Specifically, the residual signal is processed based on steps S1-S4 to obtain the corresponding multidimensional parameter estimates and generate a secondary strong signal. The residual signal is updated based on the difference between the residual signal and the secondary strong signal, and steps c)-d) are iteratively executed to obtain the next strength signal until the signals of all network nodes in the received signal are detected or the obtained residual signal is less than a preset threshold.

[0115] The scope of protection of the multi-dimensional parameter estimation method for initial cell access described in this embodiment is not limited to the execution order of the steps listed in this embodiment. Any scheme implemented by adding, subtracting, or replacing steps in the prior art based on the principles of this invention is included within the scope of protection of this invention.

[0116] This invention also provides a multi-dimensional parameter estimation system for initial cell access. The multi-dimensional parameter estimation system for initial cell access can implement the multi-dimensional parameter estimation method for initial cell access described in this invention. However, the implementation device of the multi-dimensional parameter estimation system for initial cell access described in this invention includes, but is not limited to, the structure of the multi-dimensional parameter estimation system for initial cell access listed in this embodiment. All structural modifications and substitutions of the prior art made according to the principles of this invention are included within the protection scope of this invention.

[0117] like Figure 2 As shown, in one embodiment, the multi-dimensional parameter estimation system for initial cell access of the present invention includes a receiving module 21, a generating module 22, a coarse estimation module 23, and a fine search module 24.

[0118] The receiving module 21 is used to receive received signals from at least one network node.

[0119] The generation module 22 is used to generate a local reference signal corresponding to the signal of each network node.

[0120] The coarse estimation module 23 is connected to the receiving module 21 and the generating module 22, and is used to obtain a coarse candidate parameter region for each network node signal based on the received signal and the corresponding local reference signal in a preset multidimensional parameter space.

[0121] The fine search module 24 is connected to the coarse estimation module 23 and is used to search for the first dimension parameter in the coarse candidate parameter region based on the divide-and-conquer method to obtain the matching multidimensional parameter estimate.

[0122] The structure and principle of the receiving module 21, generating module 22, coarse estimation module 23 and fine search module 24 correspond one-to-one with the steps in the multi-dimensional parameter estimation method for initial access to the cell described above, so they will not be repeated here.

[0123] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, or methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of apparatuses or modules or units may be electrical, mechanical, or other forms.

[0124] The modules / units described as separate components may or may not be physically separate. The components shown as modules / units may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules / units can be selected to achieve the objectives of the embodiments of the present invention, depending on actual needs. For example, the functional modules / units in the various embodiments of the present invention may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0125] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0126] This invention also provides a computer-readable storage medium. Those skilled in the art will understand that all or part of the steps in the multi-dimensional parameter estimation method for initial cell access described above can be implemented by a program instructing a processor. This program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof. The storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. This available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state drive (SSD)).

[0127] This invention also provides a receiver. The receiver includes a processor and a memory.

[0128] The memory is used to store computer programs.

[0129] The memory includes various media capable of storing program code, such as ROM, RAM, magnetic disk, USB flash drive, memory card, or optical disk.

[0130] The processor is connected to the memory and is used to execute the computer program stored in the memory so that the receiver performs the above-described multi-dimensional parameter estimation method for initial cell access.

[0131] Preferably, the processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0132] like Figure 3As shown, the receiver of the present invention is presented in the form of a general-purpose computing device. The components of the receiver may include, but are not limited to: one or more processors or processing units 31, a memory 32, and a bus 33 connecting different system components (including the memory 32 and the processing unit 31).

[0133] Bus 33 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0134] Receivers typically include a variety of computer system-readable media. These media can be any available media that can be accessed by the receiver, including volatile and non-volatile media, and removable and non-removable media.

[0135] Memory 32 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 321 and / or cache memory 322. The receiver may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 323 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 3 Not shown; usually referred to as a "hard drive"). Although Figure 3 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 33 via one or more data media interfaces. Memory 32 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0136] A program / utility 324 having a set (at least one) of program modules 3241 may be stored, for example, in memory 32. Such program modules 3241 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 3241 typically perform the functions and / or methods described in the embodiments of the present invention.

[0137] The receiver can also communicate with one or more external devices (e.g., keyboard, pointing device, display, etc.), one or more devices that enable user interaction with the receiver, and / or any device that enables the receiver to communicate with one or more other computing devices (e.g., network interface card, modem, etc.). This communication can be performed through input / output (I / O) interface 34. Furthermore, the receiver can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 35. Figure 3 As shown, network adapter 35 communicates with other modules of the receiver via bus 33. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the receiver, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0138] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A multi-dimensional parameter estimation method for initial cell access, characterized in that, The method includes the following steps: Receive signals from at least one network node; Generate a local reference signal corresponding to the signal of each network node; For each network node signal, a rough candidate parameter region is obtained in a preset multidimensional parameter space based on the received signal and the corresponding local reference signal. Within the coarse candidate parameter region, the first-dimensional parameter is searched using a divide-and-conquer method to obtain matching multidimensional parameter estimates.

2. The multi-dimensional parameter estimation method for initial cell access according to claim 1, characterized in that, The multidimensional parameters include carrier frequency offset, network node time delay, and secondary cell identifier.

3. The multi-dimensional parameter estimation method for initial cell access according to claim 1, characterized in that, The received signal is represented as Where A l s l (t), τ l and f d,l Let represent the channel gain, transmitted signal waveform, time delay, and carrier frequency offset of the l-th network node signal, respectively; n(t) represents additive white Gaussian noise; and L represents the number of network nodes.

4. The multi-dimensional parameter estimation method for initial cell access according to claim 1, characterized in that, For each network node signal, obtaining a coarse candidate parameter region in a preset multidimensional parameter space based on the received signal and the corresponding local reference signal includes the following steps: In the multidimensional parameter space, a low-resolution parameter grid is constructed; In the parameter grid, a coarse correlation volume is constructed based on the received signal and the corresponding local reference signal; In the coarse correlation body, the parameter points corresponding to the peak points of the correlation values ​​are obtained as candidate parameter points; Centered on the first dimension parameter of the candidate parameter point, with other dimension parameters as constants, a neighborhood of a preset size is defined as the coarse candidate parameter region.

5. The multi-dimensional parameter estimation method for initial cell access according to claim 4, characterized in that, In the parameter grid, constructing a coarse correlation volume based on the received signal and the corresponding local reference signal includes the following steps: On the frequency dimension of the parameter grid, a candidate carrier frequency offset is selected to perform frequency compensation on the received signal; Perform a fast Fourier transform on the frequency-compensated received signal to obtain the frequency domain received signal; The frequency domain received signal is multiplied by the conjugate dot product of the frequency domain value of the local reference signal corresponding to the second dimension parameter in the multidimensional parameters, wherein the frequency domain value is the conjugate value of the local reference signal after fast Fourier transform. The result of the conjugate dot product is subjected to an inverse fast Fourier transform and the modulus is taken to obtain a profile of the correlation between the carrier frequency offset and the second dimension parameter in the time delay dimension. The profiles corresponding to all carrier frequency offsets and the second-dimensional parameters in the parameter grid are stacked to construct the coarse correlation body containing time delay, carrier frequency offset, and the second-dimensional parameter.

6. The multi-dimensional parameter estimation method for initial cell access according to claim 1, characterized in that, In the coarse candidate parameter region, the divide-and-conquer method uses a ternary search to search the first dimension parameter and obtain matching multidimensional parameter estimates, including the following steps: Perform a Fast Fourier Transform on the reset signal to obtain the frequency domain reset signal; Perform a Fast Fourier Transform on the local reference signal to obtain a frequency domain reference signal; In the search interval corresponding to the first dimension parameter in the rough candidate parameter region, three equally divided points are taken; Calculate the cyclic shift number corresponding to the two division points in the frequency domain; For each trisection point, the frequency domain reset signal is cyclically shifted based on the corresponding cyclic shift number; the cyclically shifted frequency domain reset signal is then multiplied by its conjugate and subjected to inverse fast Fourier transform with the frequency domain reference signal to obtain the relevant value; Compare the correlation values ​​of the two equally divided points, and update the search interval according to the three-part rule until the updated search interval is less than the preset threshold interval; The estimated value of the first dimension parameter is set to the center first dimension parameter of the last updated search interval; The multidimensional parameter estimate is constructed based on the first-dimensional parameter of the center and the parameters other than the first-dimensional parameter of the candidate parameter points.

7. The multi-dimensional parameter estimation method for initial cell access according to claim 1, characterized in that, It also includes detecting network node signals in the received signal based on the multidimensional parameter estimates; Detecting network node signals in the received signal based on the multidimensional parameter estimates includes the following steps: The multidimensional parameter estimate is defined as the parameter value of the strongest signal in the received signal; The strongest signal is reconstructed based on the parameter values ​​of the strongest signal; Calculate the difference between the received signal and the reconstructed strongest signal to obtain the residual signal; The multidimensional parameter estimates of the residual signal are obtained as parameter values ​​of the secondary strong signal, and the secondary strong signal is obtained until the signals of all network nodes in the received signal are detected or the obtained residual signal is less than a preset threshold.

8. A multi-dimensional parameter estimation system for initial cell access, characterized in that, The system includes a receiving module, a generating module, a coarse estimation module, and a fine search module; The receiving module is used to receive signals from at least one network node; The generation module is used to generate a local reference signal corresponding to each network node signal; The coarse estimation module is used to obtain a coarse candidate parameter region for each network node signal in a preset multidimensional parameter space based on the received signal and the corresponding local reference signal. The fine search module is used to search for the first dimension parameter in the coarse candidate parameter region based on the divide-and-conquer method to obtain the matching multidimensional parameter estimate.

9. A storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the multidimensional parameter estimation method for initial cell access as described in any one of claims 1 to 7.

10. A receiver, characterized in that, include: Processor and memory; The memory is used to store computer programs; The processor is used to execute the computer program stored in the memory to cause the receiver to perform the multidimensional parameter estimation method for initial cell access as described in any one of claims 1 to 7.

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