Authorization-free random access method for large-scale MIMO (Multiple Input Multiple Output) system

Abstract

The invention provides an authorization-free random access method for a large-scale MIMO (Multiple Input Multiple Output) system. The authorization-free random access method comprises the following steps: establishing a wireless communication system model; receiving signals of the base station are obtained, the part with the large target receiving power is selected, and the minimum description length criterion is adopted to estimate the number of the user equipment; estimating a preamble sequence and time offset of a signal sent by each user equipment by using a preamble detection algorithm; the base station performs channel estimation on each user equipment by using the PI estimation value and the TO estimation value, and performs channel equalization on the received signal; the base station decodes the estimated value of the uplink service data of each user equipment to realize authorization-free random access; the preamble detection is carried out based on the maximum likelihood criterion, a detection threshold does not need to be set, and the detection performance is excellent; secondly, the complexity of an ML preamble detection algorithm is reduced based on a single-source approximation method; in addition, binary search is further utilized to assist detection, and the precision of TO estimation is improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and more specifically, to a method for random access without authorization for a large-scale multiple-input multiple-output system. Background Art

[0002] With the rapid development of wireless networks, how to provide reliable connections for massive user equipment (UE) has become one of the key issues in future communication systems. Massive Multiple-Input Multiple-Output (MIMO) technology deploys a large number of antennas in the base station (BS), which is considered to be a promising solution to meet the massive access needs. Since random access (RA) is a necessary process for UE to establish a communication link with BS, quickly processing the random access request of UE is a key technical issue in massive MIMO systems. In recent years, the research on RA has gradually received attention, and researchers have proposed many RA schemes for massive MIMO scenarios.

[0003] The Long-Term Evolution (LTE) and New Radio (NR) standards use an authorization-based RA process, which is a four-step handshake procedure. In the authorization-based RA process, the accessing UE first selects a preamble index (PI) and sends the corresponding preamble sequence to the base station. The UE can only transmit uplink data if it receives the RA response sent by the BS. Therefore, when the UE has a small amount of data, especially when a large amount of small-scale data needs to be sent repeatedly, the authorization-based RA process will generate more signaling overhead, resulting in a significant reduction in system efficiency.

[0004] One way to reduce signaling overhead is to use the unlicensed RA process. In this process, the UE sends uplink data while sending the preamble sequence, and the BS can detect the data from the UE with the help of a large antenna array. Due to the omission of the request-grant process, the signaling overhead of unlicensed RA is significantly reduced compared to the grant-based RA.

[0005] In the unlicensed RA process, preamble detection (PD) is a necessary step for the BS to identify the preamble sequence transmitted by the access UE, and is also the basis for implementing subsequent channel estimation and data detection operations. Most of the existing PD algorithms designed for unlicensed RA assume that the UE is synchronized. In an orthogonal frequency division multiplexing (OFDM) system, UE synchronization can be achieved by transmitting the preamble sequence on a single subcarrier and multiple consecutive symbols. In this case, the effect of the timing offset (TO) can be ignored, because the phase offset caused by TO is a constant for the same subcarrier and can be merged into the channel estimation module for transparent processing. However, if the preamble sequence is transmitted on multiple consecutive subcarriers, the UE's TO cannot be ignored because it will cause a linearly varying phase shift in the frequency domain.

[0006] If the preamble sequence is transmitted on multiple consecutive subcarriers, even if different UEs select the same preamble sequence, the BS still has the opportunity to distinguish these UEs by the difference in TO, which makes it possible to improve the RA performance by utilizing the TO diversity of UE. At present, researchers have considered the different TOs of UEs and proposed some PD algorithms designed for authorization-based RA. For example, the frequency domain correlation (FDC) algorithm proposed in the literature "B. Liang, Z. He, K. Niu, B. Tian, ​​and S. Sun, "The research on random access signal detection algorithm in LTE systems," in Proc. IEEE Int. Symp. MAPE Wireless Commun., Chengdu, China, Oct. 2013, pp. 115-118.", in which PI and TO are determined according to the strength and position of the correlation peak respectively; further, this method can also be extended to an enhanced version for frequency selective channels or high mobility scenarios. In addition, the FDC algorithm ignores the preamble sequence conflicts between UEs, while the User Grouping (UG) algorithm proposed in the document "S.Mukherjee, AKSinha, and SKMohammed, "Timingadvance estimation and beamforming of random access response in crowded TDDmassive MIMO systems," IEEE Trans.Commun., vol.67, no.6, pp.4004-4019, Jun.2019." takes this conflict into consideration. Through the grouping operation, if the TO difference between multiple UEs that have selected the same preamble sequence is large enough, these UEs can be detected separately. Therefore, compared with the FDC algorithm, the UG algorithm can adapt to more UE access scenarios.

[0007] However, the above algorithms that take TO into consideration (FDC algorithm and UG algorithm) all need to set a detection threshold, and the value of the threshold is related to system parameters such as noise power, leading sequence length, and target false alarm probability. Generally, when the algorithm is applied to different scenarios with multiple system parameter combinations, it is difficult to determine the optimal value of the detection threshold. In addition, the relevant operations of such algorithms are generally implemented through Fast Fourier Transform (FFT), so the estimation accuracy of TO is limited by the FFT length. This disadvantage of limited TO estimation accuracy will affect the channel estimation and data detection performance in unlicensed RA, thereby reducing the UE access success rate. Therefore, it is necessary to design a new PD scheme for unlicensed RA to improve the access success rate. Summary of the invention

[0008] In order to overcome the defects of the unlicensed random access preamble detection algorithm in the above-mentioned prior art that a detection threshold needs to be set and the estimation accuracy is limited, the present invention provides an unlicensed random access method for a large-scale multi-input multi-output system, which performs preamble detection based on the maximum likelihood (ML) criterion, does not need to set a detection threshold and has excellent detection performance; secondly, the present invention is based on a single-source approximation method, which further reduces the complexity of the ML preamble detection algorithm; in addition, the present invention further utilizes binary search to assist detection, thereby improving the accuracy of TO estimation.

[0009] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0010] A method for random access without authorization for a large-scale multiple-input multiple-output system comprises the following steps:

[0011] S1: Establishing a wireless communication system model based on orthogonal frequency division multiplexing, in which a plurality of user equipments respectively send signals to a base station through different types of PRACH resource blocks orthogonal in the frequency domain;

[0012] The transmission signal includes a preamble sequence and encoded uplink service data;

[0013] S2: obtaining a received signal of the base station, selecting a part of the received signal with a larger target received power, and estimating the number of user equipments by using a minimum description length criterion;

[0014] S3: using a preset preamble detection algorithm to estimate the preamble sequence and time offset of the signal sent by each user equipment, and obtaining a PI estimation value and a TO estimation value of each user equipment;

[0015] The preset preamble detection algorithm is specifically: any one of the ML preamble detection algorithm, the AML preamble detection algorithm and the DS-AML preamble detection algorithm;

[0016] S4: The base station performs channel estimation for each user equipment using the PI estimation value and the TO estimation value, and performs channel equalization on the received signal to obtain an estimation value of uplink service data of the corresponding user equipment;

[0017] S5: The base station decodes the estimated value of the uplink service data of each user equipment, completes the reception of the uplink service data, and implements unlicensed random access.

[0018] Preferably, in step S1, the wireless communication system adopts OFDM to transmit signals, wherein part of the time-frequency resources are allocated as a physical random access channel; the transmission of the transmission signal of the user equipment in the physical random access channel is divided into a preamble transmission phase and a data transmission phase, and the transmission signal of the user equipment constitutes two continuous OFDM symbols in the time domain;

[0019] Several PRACH resource blocks are set within the bandwidth of the wireless communication system; in each PRACH resource block, the bandwidths allocated in the preamble transmission phase and the data transmission phase are LΔf and LΔf respectively. Where L is the number of symbols contained in the leading sequence, is the number of data symbols, Δf is the subcarrier spacing; the type of the PRACH resource block includes any one of the following: Type 1: Type 2: and type 3:

[0020] Preferably, in the wireless communication system model of step S1, K user equipments access one base station, K is unknown; each user equipment sends a signal through a single transmitting antenna, and the base station uses M receiving antennas to receive the signal; the sequence number of the user equipment is denoted as k, satisfying k=1,…,K; the distance between the user equipment k and the base station is d k , and the maximum distance d between the user equipment and the base station is known max ; The TO of user equipment k is c = 3 × 10 8 m / s represents the speed of light, and the value range of TO is [0,τ max ],

[0021] In the preamble transmission phase, each user equipment receives a predefined preamble sequence set {x q |q=1,…,Q}, select a preamble sequence of length L and send it, where Q is the number of preamble sequences, x q ∈C L×1 represents the leading sequence with sequence number q, C represents a complex set; the leading sequence selected by user equipment k is recorded as q k , the leading sequence sent is xqk ;

[0022] In the preamble transmission phase, the received signal of the mth antenna of the base station is denoted as y m , m=1,…,M, specifically:

[0023]

[0024] in, represents the Hadamard product, γ k >0 is the transmit power of each subcarrier of user equipment k during the preamble transmission phase, h k,m is the channel coefficient between user equipment k and base station antenna m in the PRACH resource block, β k and v k,m Represent the large-scale and small-scale fading coefficients respectively; a(τ k )∈C L×1 Indicates that in the preamble transmission phase, τ k The phase shift caused by m ∈C L×1 is the Gaussian white noise vector in the preamble transmission stage;

[0025] In the data transmission phase, user equipment k generates a data symbol vector by constellation mapping the encoded uplink service data. And send, the base station receiving signal in the data transmission stage is recorded as Specifically:

[0026]

[0027] in, is the transmit power per subcarrier of user equipment k during the data transmission phase; Indicates that during the data transmission phase, τ k The phase shift caused by is the Gaussian white noise vector in the data transmission stage;

[0028] y m and Convert them into matrix forms respectively to obtain the wireless communication system model:

[0029]

[0030] in,

[0031]

[0032] in,

[0033] Preferably, in step S2, estimating the number of user equipments by using the minimum description length criterion includes the following steps:

[0034] Select the part of the base station's received signal with a larger target received power and calculate the sample covariance matrix R:

[0035]

[0036] Among them, ρ and The target receiving power of the signal sent by the user equipment during the preamble transmission phase and the data transmission phase reaching the base station;

[0037] Calculate the eigenvalues ​​of R {λ m |m=1,…,M}, according to λ 1 ≥…≥λ M The estimated number of user devices is Calculated by the following formula:

[0038]

[0039] Among them, μ is the intermediate variable, μ=0,…,M-1; D(μ) is the intermediate function.

[0040] Preferably, in step S3, the objective function of the ML leader detection algorithm is expressed as:

[0041]

[0042] in, For the matrix The projection matrix; It means to find the square of the F norm of the matrix; and are the test values ​​of PI and TO respectively; and They represent the PI estimation value and TO estimation value output by the ML leader detection algorithm respectively;

[0043] In the continuous interval [0,τ max ] densely sampled to obtain a discrete set Discrete the set As the new value range of TO; and Exhaustively search all possible combinations of values, solve the objective function of the ML leader detection algorithm, and obtain the PI estimate and TO estimates

[0044] Preferably, in step S3, the ML leader detection algorithm is improved based on the single-source approximation method to obtain the AML leader detection algorithm;

[0045] The objective function of the AML leader detection algorithm is expressed as:

[0046]

[0047] in, is the intermediate matrix, for y m The conjugate vector of ; is the experimental value of normalized TO, which is recorded as ε=τNΔf, where N≥L is the fast Fourier transform length, and ε max is the maximum value of ε; For is the phase shift of the variable, Indicates rounding up;

[0048] According to the discrete set Determine the experimental value of normalized TO The value range of is solved by fast Fourier transform All values ​​of , output local maximum values, and the PI corresponding to each local maximum value is used as the PI estimate Using the normalized TO corresponding to each local maximum Further calculation of TO estimate

[0049] Preferably, in step S3, the accuracy of TO estimation in the AML leader detection algorithm is further improved based on a binary search method to obtain a DS-AML leader detection algorithm;

[0050] The PI estimate output by the DS-AML leader detection algorithm

[0051] In the DS-AML preamble detection algorithm, based on the estimated number of user devices and user equipment κ selected The base station performs a binary search operation on each user equipment for the normalized TO one by one, and the number of iterations of the binary search is I; wherein, for The κth element in ;

[0052] The estimated value of the normalized TO of the user equipment κ at the i-th iteration is recorded as Its initial value is given by the AML leader detection algorithm, that is, for The κth element in ; in the binary search process, according to The objective function of the AML leader detection algorithm is searched in the order of exist The peak value near the edge of the y-axis is continuously updated during the iteration process. 0 ,ψ - ,ψ +}, used to determine The update direction of {ψ 0 ,ψ - ,ψ + The initial value of} is set to:

[0053]

[0054] During the binary search process, the search step size of each iteration becomes the size of the previous iteration. According to the i-th iteration ψ - and ψ + The size relationship, The update method is:

[0055]

[0056] Completed in one iteration After the update of {ψ - ,ψ +} to determine the update direction of the normalized TO at the next iteration: If ψ + >ψ - , then ψ - ←ψ 0 , ψ + remains unchanged; otherwise ψ + ←ψ 0 , ψ - remains unchanged; at the same time 0 Updated to:

[0057]

[0058] After I iterations, the estimated value of the normalized TO of the user equipment κ is The estimated value of TO is

[0059] The normalized TO estimate output by the DS-AML leader detection algorithm is Further calculation of TO estimate

[0060] Preferably, in step S4, an equivalent channel matrix G of each user equipment in the preamble transmission phase and an equivalent channel matrix G of each user equipment in the data transmission phase are constructed.

[0061]

[0062] And meet

[0063] The base station uses the PI estimation value and TO estimates Estimate the equivalent channel matrix G of each user equipment in the preamble transmission phase, specifically:

[0064]

[0065] in, is the estimated value of G; (·) + represents a pseudo-inverse operation;

[0066] Further calculate the equivalent channel matrix in the data transmission stage Estimated value of Channel estimation is completed.

[0067] Preferably, the transmit power of user equipment k is calculated as follows:

[0068]

[0069] Among them, P max is the maximum total transmit power.

[0070] Preferably, in step S4, channel equalization is performed on the received signal to obtain an estimated value of the uplink service data of the corresponding user equipment. It is expressed as:

[0071]

[0072] in, represents the zero-forcing equalizer, * ” denotes matrix conjugation.

[0073] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0074] The present invention provides a method for unlicensed random access for a large-scale multiple-input multiple-output system. First, a wireless communication system model based on orthogonal frequency division multiplexing is established. Then, a received signal of a base station is obtained, a part of the received signal with a larger target received power is selected, and the number of user devices is estimated by using a minimum description length criterion. A preamble detection algorithm is used to estimate the preamble sequence and time offset of a signal sent by each user device, and a PI estimation value and a TO estimation value of each user device are obtained. Then, the base station uses the PI estimation value and the TO estimation value to perform channel estimation on each user device, and performs channel equalization on the received signal to obtain an estimation value of uplink service data of the corresponding user device. Finally, the base station decodes the estimation value of the uplink service data of each user device, completes the reception of the uplink service data, and realizes unlicensed random access.

[0075] Different from the existing threshold-based algorithms, the ML preamble detection algorithm based on the ML criterion designed by the present invention does not need to set the detection threshold, and can jointly detect the PI and TO of multiple UEs, wherein the number of UEs is calculated by the minimum description length (MDL) criterion; the algorithm has excellent detection performance, and its complexity increases rapidly with the increase of the number of UEs. It is suitable for application scenarios that need to obtain the absolute optimal performance under the condition that the computing power allows, and can also be used as a theoretical upper limit reference for the PD solution in the unlicensed RA problem;

[0076] Secondly, based on the similarity between the Direction-of-Arrival (DOA) estimation problem and the PD problem, the present invention draws on the approximate ML estimation method of DOA to reduce the complexity of the ML leader detection algorithm; specifically, the present invention extends the single-source approximation method for estimating continuous DOA and applies it to the PD problem with both discrete and continuous variables, thereby obtaining a detection algorithm based on the Approximated ML (AML) criterion, which can significantly reduce the computational complexity of detection while maintaining better performance;

[0077] In addition, in order to solve the problem that the TO estimation accuracy is limited by the FFT length, the present invention further improves the aforementioned AML preamble detection algorithm by using binary search, which is called the Dichotomous Search aided AML (DS-AML) detection algorithm based on binary search; when the FFT length is small, the DS-AML algorithm can provide a larger performance gain than the AML preamble detection algorithm while still maintaining a similar computational complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 This is a flow chart of a method for unlicensed random access for a large-scale multiple-input multiple-output system provided by the present invention.

[0079] Figure 2 This is a schematic diagram of the PRACH resource block structure provided by the present invention.

[0080] Figure 3 This is a schematic diagram of the authorization-free RA process provided by the present invention.

[0081] Figure 4 This is a schematic diagram of the convergence performance of the DS-AML leader detection algorithm provided in Example 4.

[0082] Figure 5 This is a performance diagram of RA success rate of different detection algorithms provided in Example 4.

[0083] Figure 6This is a performance diagram of RA success rate of different detection algorithms provided in Example 4 when Q=K=2. DETAILED DESCRIPTION

[0084] The drawings are for illustrative purposes only and should not be construed as limiting the present patent;

[0085] In order to better illustrate the present embodiment, some parts in the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product;

[0086] It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0087] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0088] like Figure 1 As shown, the present invention provides a method for random access without authorization for a large-scale multiple-input multiple-output system, comprising the following steps:

[0089] S1: Establishing a wireless communication system model based on orthogonal frequency division multiplexing, in which a plurality of user equipments respectively send signals to a base station through different types of PRACH resource blocks orthogonal in the frequency domain;

[0090] The transmission signal includes a preamble sequence and encoded uplink service data;

[0091] S2: obtaining a received signal of the base station, selecting a part of the received signal with a larger target received power, and estimating the number of user equipments by using a minimum description length criterion;

[0092] S3: using a preset preamble detection algorithm to estimate the preamble sequence and time offset of the signal sent by each user equipment, and obtaining a PI estimation value and a TO estimation value of each user equipment;

[0093] The preset preamble detection algorithm is specifically: any one of the ML preamble detection algorithm, the AML preamble detection algorithm and the DS-AML preamble detection algorithm;

[0094] S4: The base station performs channel estimation for each user equipment using the PI estimation value and the TO estimation value, and performs channel equalization on the received signal to obtain an estimation value of uplink service data of the corresponding user equipment;

[0095] S5: The base station decodes the estimated value of the uplink service data of each user equipment, completes the reception of the uplink service data, and implements unlicensed random access.

[0096] like Figure 2As shown, the wireless communication system considered in the present invention adopts OFDM transmission, in which part of the time-frequency resources are allocated as a physical random access channel (PRACH); Figure 2 As shown, the signal transmission of UE in PRACH is divided into two phases: Preamble Transmission Phase (PTP) and Data Transmission Phase (DTP), and its transmission signal constitutes two continuous OFDM symbols in the time domain;

[0097] In addition to the conventional service data blocks, multiple PRACH resource blocks can be set within the system bandwidth. In each PRACH resource block, the bandwidths allocated to PTP and DTP are LΔf and LΔf, respectively. Where L is the number of symbols contained in the leading sequence, is the number of data symbols, Δf is the subcarrier spacing; Figure 2 In the PRACH structure scheme shown, the PRACH resource blocks can be of different types. For each PRACH resource block, L and There can be different value combinations, namely type 1: Type 2: and type 3: And they are all applicable to the authorization-free RA method proposed in the present invention;

[0098] According to different application scenarios, different types of PRACH resource blocks that are orthogonal in the frequency domain can be used simultaneously in the same frame, which can have higher flexibility; for example, it can support UEs with different uplink data sizes to select PRACH resource blocks of appropriate sizes for access, thereby avoiding the waste of spectrum resources caused by uniformly using larger PRACH resource blocks to be compatible with UEs with different uplink data sizes; it is pointed out here that the unlicensed RA method proposed in the present invention is applicable to a single PRACH resource block of any type, and for Figure 2 In the case where there are multiple PRACH resource blocks within the system bandwidth shown, the method can be performed independently on these PRACH resource blocks respectively;

[0099] Assume that there are K UEs evenly distributed in the cell accessing the BS, whose serial numbers are recorded as k=1,…,K; the distance between UE k and the BS is d k , whose upper bound is d max ; Since each UE is located at a different position in the cell, the signals sent by each UE at the same time will reach the BS with different TOs; Under the premise of completing downlink synchronization, the TO of the UE is equal to the round trip time (RTD) between the UE and the BS; Let the TO of UE k be Where c = 3 × 10 8 m / s represents the speed of light; the maximum TO of UE is where d max is the maximum distance between UE and BS; for the sake of simplicity, the TO value range of UE is recorded as T = [0, τ max ]; In addition, assume that the BS knows the maximum distance d of the UE max ;

[0100] A. Authorization-free RA process:

[0101] like Figure 3 As shown, Figure 3 Demonstrates the typical authorization-free RA operation process;

[0102] In the PTP process, each UE starts from a predefined preamble sequence set {x q |q=1,…,Q} selects a sequence of length L and sends it, where Q is the number of leading sequences, x q ∈C L×1 represents the leading sequence with sequence number q, C represents a complex number set; the set of leading sequence numbers is recorded as Q = {q = 1, ..., Q}; each leading sequence satisfies the following constant modulus constraint:

[0103] |[x q ] l | 2 =1,l=1,…,L (1)

[0104] The PI selected by UE k is denoted as q k , then the preamble sequence sent by UE k is Assuming that each UE sends a preamble sequence through a single transmit antenna and the BS uses M receive antennas to receive the preamble sequence, the received signal of the mth (m=1,…,M)th antenna of the BS in PTP can be expressed as:

[0105]

[0106] in: represents the Hadamard product, γ k >0 is the transmit power of UE k on each subcarrier, h k,m ∈C L×1 is the vector of channel coefficients of L subcarriers between UE k and the mth antenna of BS, a(τ)∈C L×1 represents the phase shift caused by τ∈T, and its expression is:

[0107] a(τ)=[1,e -j2πΔfτ ,…,e -j2π(L-1)Δfτ ] T (3)

[0108] w m ∈C L×1 is a Gaussian white noise vector; the noise power of each subcarrier is ν = N 0 Δf, where N 0 represents the noise power spectral density;

[0109] In the DTP process, UE k generates a data symbol vector by constellation mapping for the uplink service data after channel coding. And send, the data symbol meets the power normalization condition: The received signal of BS at DTP is:

[0110]

[0111] in is the per-subcarrier transmit power of UE k in DTP, are the channel vector, phase offset vector, and Gaussian white noise vector of DTP respectively. Similar to formula (3), the expression of the phase offset vector of DTP is:

[0112]

[0113] The vectors composed of PI and TO of all UEs are denoted as q = [q 1 ,…,q K ] T ∈Q K×1 and τ = [τ 1 ,…,τ K ] T ∈T K×1 ; Based on PTP receiving signal {y m |m=1,…,M}, the PI and TO estimation values ​​of the UE obtained after the BS performs the PD operation are respectively denoted as and in is an estimate of the number of UEs, and They are and The κth element of ; Note that since the BS does not know the actual numbering order of the UEs, Usually not (q k ,τ k ), so different subscripts κ are used to avoid misleading signs;

[0114] Get an estimate of PI and the estimated value of TO After that, the channel estimation module of BS uses The channel coefficients are estimated and obtained through the channel equalization module. Obtain an estimated value of the uplink service data symbol; finally, the BS obtains the uplink service data information of the UE through channel decoding;

[0115] The details of channel estimation and channel equalization will be introduced below;

[0116] B. Channel Model:

[0117] The present invention assumes that the channel coefficients remain unchanged within a PRACH resource block, which is reasonable in some typical communication scenarios. For example, the coherence bandwidth of the urban macrocell TDL-B channel defined by 3GPP is about 2.25MHz; the sea surface channel can be regarded as a non-frequency selective fading channel within a bandwidth of 20MHz; in the LTE and NR standards, the typical bandwidth of PRACH is 1.08MHz and the subcarrier spacing is 1.25kHz. Under this configuration, it can be considered that the channel of the PRACH resource block is unchanged in the frequency domain in the urban macrocell and sea surface scenarios; at the same time, under the typical NR frequency band of 3.4GHz, in the urban scenario, the typical moving speed of pedestrians is 1.5m / s, and the coherence time of the channel is In the sea scene, the typical speed of a ship is 20 knots (about 10m / s), so the coherence time of the channel is Since the cyclic prefix (CP) duration of a typical PRACH configuration set in the LTE and NR standards is 103 μs, the duration of two consecutive OFDM symbols containing the CP is Much shorter than the channel coherence time in the above two typical application scenarios; therefore, the present invention assumes that the channel coefficient remains approximately unchanged during the duration of the PRACH resource block;

[0118] Under the assumption that the channel remains unchanged, the signal models shown in equations (2) and (4) can be rewritten as:

[0119]

[0120] where h k,m is a scalar, representing the channel coefficient between UE k and BS antenna m in the PRACH resource block, and its value is:

[0121]

[0122] where β k and v k,m denote the large-scale and small-scale fading coefficients respectively; without loss of generality, assume that β k It is known at the UE k end and unknown at the BS end. In the actual system, UE k can estimate β by measuring the downlink reference signal power. k ;

[0123] The PTP signal model shown in formula (6) can be further expressed in matrix form as follows:

[0124]

[0125] in:

[0126]

[0127] Similarly, the DTP signal model shown in equation (7) is expressed as:

[0128]

[0129] in:

[0130]

[0131] Based on the channel coefficient model of formula (8) and the received signal models of formula (9) and formula (11), the design of power control, channel estimation, channel equalization and other modules of the unlicensed RA scheme is further introduced below;

[0132] 1) Power control;

[0133] A power control method is used to enable each UE to have similar RA performance. Specifically, the target received power of the signal sent by each UE at PTP and DTP to reach the BS is set to ρ and Among them, ρ and The size of is determined by the signal-to-noise ratio (SNR) required by the BS and is broadcast by the BS to each UE in the cell; the transmission power per subcarrier of UE k is The following conditions must be met:

[0134] i) Total transmit power constraints: where Lγ k and Represents the total transmission power of all subcarriers of PTP and DTP, P max is the maximum total transmit power;

[0135] ii) Transmit power ratio constraints: The equivalent channel matrix of PTP and DTP is:

[0136]

[0137] satisfy Based on this condition, after estimating the equivalent channel matrix G of PTP, BS can directly obtain the equivalent channel matrix of DTP The estimate of

[0138] Based on the above two constraints, the transmit power of UE k is calculated as follows:

[0139]

[0140] 2) Channel estimation and channel equalization:

[0141] according to Figure 3 , BS uses the estimated value of PI and the estimated value of TO Detect the uplink service data of the UE; According to equations (9) and (13), the least squares estimate of the equivalent channel matrix of PTP is:

[0142]

[0143] in(·) + Represents a pseudo-inverse operation; according to the above conditions And equation (11), the estimated value of the uplink data symbol can be calculated by the following equation:

[0144]

[0145] in represents the zero-forcing equalizer, * ” indicates conjugation;

[0146] at last, It is sent to the channel decoder to complete the decoding of uplink service data;

[0147] C. Preamble detection algorithm:

[0148] Since the performance of channel estimation and channel equalization operations directly depends on the estimated value of PI and the estimated value of TO The quality of the preamble detection plays a very important role in the unlicensed RA process; the main goal of the present invention is to design a new type of preamble algorithm to obtain accurate estimation values ​​of PI and TO;

[0149] According to the different algorithm design purposes and performance, the PD algorithm proposed in this invention can be specifically divided into several sub-algorithms, including:

[0150] 1) ML leader detection algorithm: This algorithm is designed to achieve theoretically optimal performance. It can be used as the upper bound of other similar algorithms, or can be used in scenarios with sufficient computing power support and where absolute performance advantages are required.

[0151] 2) AML leader detection algorithm: This algorithm is designed to reduce the computational complexity of the ML leader detection algorithm; it improves computational efficiency by adopting a single-source approximation method while still maintaining good detection performance;

[0152] 3) DS-AML preamble detection algorithm: This algorithm is designed to further improve the TO estimation accuracy of the AML preamble detection algorithm. It is based on a binary search algorithm and increases the TO search range without increasing the FFT length, thereby obtaining a more accurate TO estimation.

[0153] To distinguish the estimated values ​​of PI and TO calculated by different algorithms The outputs of these three algorithms are recorded as follows: and will The κth elements of

[0154] Since all three algorithms require UE quantity information, the following first introduces the process of UE quantity estimation, and then introduces the design of the three algorithms in combination with embodiments;

[0155] The traditional threshold-based PD algorithm does not need to pre-estimate the number of UEs, while the PD algorithm based on the ML criterion needs to use the prior information of the number of UEs; specifically, the present invention adopts the MDL criterion to estimate the number of UEs; the MDL criterion is an effective method for estimating the number of target sources from a noisy signal, and has the advantages of no need to set a threshold and small estimation error; in the present invention, firstly, the part with a larger target received power in the received signal of the base station is selected, and the sample covariance matrix is ​​calculated:

[0156]

[0157] Then, calculate the eigenvalues ​​of R {λ m |m=1,…,M}, according to λ 1 ≥…≥λ M The estimated value of the number of UEs can be calculated by the following formula:

[0158]

[0159] Where D(μ) (μ=0,…,M-1) is defined as follows:

[0160]

[0161] In summary, based on formula (18), the estimated value of the number of UEs can be calculated as Used for subsequent preamble detection algorithm.

[0162] Example 1

[0163] In this embodiment, the ML preamble detection algorithm is used to estimate the preamble sequence and time offset of the signal sent by each user equipment, and obtain the PI estimation value and TO estimation value of each user equipment.

[0164] In the specific implementation process, the principle of ML detection is to select the parameter value that maximizes the probability density function as the estimate of the target parameter; substituting formula (13) into formula (9) can be obtained:

[0165]

[0166] Since W is a matrix of Gaussian white noise with power ν, the probability density function of Y is:

[0167]

[0168] In actual systems, the noise power ν may be known or unknown at the BS end, so the following discussion is divided into two cases;

[0169] 1) Case 1: The noise power ν is known at the BS:

[0170] In this case, according to equation (21), the ML estimate of the unknown parameters {q,τ,G} is:

[0171]

[0172] in Respectively represent the test values ​​of the unknown parameters {q,τ,G}; given Under the condition of The solution is:

[0173]

[0174] Will Substituting into formula (22) we get:

[0175]

[0176] in:

[0177] P M =M(M H M) -1 M H (25)

[0178] Represents the projection matrix of matrix M, Substituting into formula (25) we can get In addition, the fifth equation of formula (24) utilizes the following two properties:

[0179]

[0180] 2) Case 2: The noise power ν is unknown at the BS:

[0181] In this case, the ML estimate of the unknown parameters {q,τ,G,ν} is:

[0182]

[0183] in represents the experimental value of ν; at a given Under the condition of The solution is:

[0184]

[0185] Will Substituting equation (21) into equation (28), and using equation (29), we get:

[0186]

[0187] By comparing equations (22) and (30), it can be found that under the conditions of known and unknown noise power ν at the BS end, the ML estimation of {q, τ} has the same representation form; therefore, according to equation (24), the ML preamble detection algorithm under known and unknown noise power can be uniformly expressed as:

[0188]

[0189] The ML leader detection algorithm shown in Equation (31) (31) has no closed-form solution, so it is necessary to and All possible combinations of values ​​are exhausted to find the solution; note that since T = [0, τ max ] is a continuous interval and cannot be Therefore, the algorithm needs to use a set with a finite number of elements. Instead of T, the search space of τ becomes In order to accurately estimate τ, the set It should be obtained by dense sampling on the interval T;

[0190] Finally, through and Exhaustively search all possible combinations of values ​​and solve equation (31) to obtain the PI estimate and TO estimates

[0191] Example 2

[0192] In this embodiment, the AML preamble detection algorithm is used to estimate the preamble sequence and time offset of the signal sent by each user equipment, and obtain the PI estimation value and TO estimation value of each user equipment.

[0193] In the specific implementation process, it can be seen from Example 1 that in order to solve equation (31), it is necessary to Group Possible Substitute the value into the objective function Calculate, where |·| represents the number of elements in the set; because the computational complexity increases with the estimated number of UEs It grows exponentially, so when When the value of is large, it is difficult to directly solve formula (31) in the actual system; therefore, this embodiment further proposes a suboptimal algorithm version that is easier to implement in actual scenarios based on the ML leading detection algorithm, namely, the AML leading detection algorithm;

[0194] The approximate principle of the AML leader detection algorithm is as follows:

[0195] The signal model shown in equation (20) is highly similar to the signal model of the DOA estimation problem; more specifically, the unknown parameter combination (q k ,τ k ) can be compared to the DOA of the k-th signal source, and the M elements of the k-th row of the equivalent channel matrix G can be compared to the M snapshots of the signal transmitted by the k-th signal source; similar to the PD problem based on the ML criterion, the DOA estimation based on the ML criterion also has the problem of high computational complexity, so different suboptimal methods for this problem have been proposed; inspired by the similarity between the PD and DOA estimation problems, some suboptimal ML DOA estimation methods can be used to solve the PD problem considered in the present invention;

[0196] Since the single-source approximation method of ML DOA has high computational efficiency, this method is adopted in this embodiment; the basic principle of the single-source approximation method is to search for multiple local maxima of the ML objective function under the assumption of a single signal source, and regard the locations of these local maxima as the DOAs of multiple signal sources; note that in the DOA problem, the DOA of each signal source is a continuous variable; while in the PD problem, the PI q of UE k k ∈Q is a discrete variable, because Q = {1, ..., Q} is a discrete set; therefore, it is necessary to improve the single-source approximation method for DOA estimation and adapt it to the PD problem faced by this embodiment;

[0197] Based on the principle of the single-source approximation method, this embodiment first calculates the ML objective function when a single UE, namely UE 1, accesses. In this case, the target vector combination becomes a scalar combination Then the matrix combination It also becomes a vector combination accordingly Therefore, referring to the definition of formula (25), formula (31) can be transformed into:

[0198]

[0199] Where: Tr(·) represents the trace of the matrix, and the sixth equation uses and The modulus of the elements of is 1. According to formula (32), based on the principle of single-source approximation, the objective function is defined as:

[0200]

[0201] Formula (33) can be regarded as the received signal Y and the leading sequence affected by the phase offset The correlation value between Will be in and A larger value appears at

[0202] Note that a significant difference between the DOA estimation problem and the PD problem is that in the former, DOA is a continuous variable, but the latter contains a discrete variable PI; from Eq. (33), we know that PI Only used to indicate the leading sequence The serial number does not participate in any mathematical operation, so A better approach is to treat it as a sequence number The independent variable is As shown in formula (33), using Rather than As a function notation, to emphasize is Q univariate functions; Based on this processing method, an approximate solution to the ML leader detection algorithm shown in formula (31) is to find the function middle The positions of the largest local maxima, and the PIs corresponding to these local maxima are recorded as The corresponding TO is recorded as This algorithm is named as AML leader detection algorithm, and equation (33) is called AML objective function;

[0203] Similar to the ML leader detection algorithm in equation (31), when using the AML leader detection algorithm to solve, it is necessary to use a discrete set Instead of the continuous interval T; in general, the AML leader detection algorithm needs to calculate the objective function The number of times is However, the special form of the phase offset vector a(·) can be used to reduce the amount of calculation with the help of FFT;

[0204] Specifically, for a general TO variable τ∈T, the normalized TO is defined as:

[0205] ε=τNΔf (34)

[0206] Where N≥L is the FFT length; similarly, for the variables related to τ used in Example 1, their normalized versions can be defined as follows:

[0207] Maximum normalized TO of UE in the cell: ε max =τ max NΔf;

[0208] The value range of normalized TO is: E = [0, ε max ];

[0209] Normalized TO of UE k: ε k =τ k NΔf, k=1,…,K;

[0210] The vector formed by the normalized TO of K UEs: ε = [ε 1 ,…,ε K ] T ∈E K×1 ;

[0211] Experimental value of normalized TO in AML algorithm:

[0212] The estimated value of ε obtained using the AML algorithm:

[0213] According to equations (3) and (34), the phase offset vector with normalized TOε as the variable is defined as:

[0214]

[0215] Using formula (35), the AML objective function of formula (33) can be rewritten as:

[0216]

[0217] in Defined as:

[0218]

[0219] By observing equations (35) and (36), we can see that exist The value when it is an integer can be calculated by The FFT of

[0220] According to formula (36), we can find The local maximum of The local maximum value of The conditions that should be met when it is a local maximum are:

[0221]

[0222] in, The value range is Compared with formula (36), formula (38) The upper bound of the range of values ​​is from ε max Expanded to The reason is that FFT can only calculate When it is an integer The value of ε max is not necessarily an integer; in addition, to make all Whether it is a local maximum can be determined by formula (38), and the function Add the following definition:

[0223] 1) When ε max is not an integer, then at this time There is no definition, so according to formula (36), we can add the following definition:

[0224] 2) Due to and Undefined, cannot be judged using formula (38) and Is it a local maximum? Therefore, we define

[0225] In summary, the AML objective function of formula (36) is expanded to:

[0226]

[0227] Based on the above discussion, the AML leader detection algorithm proposed in this embodiment is summarized in Algorithm 1;

[0228]

[0229] In the AML leader detection algorithm, according to the discrete set Determine the experimental value of normalized TO The value range of is solved by fast Fourier transform All values ​​of , output local maximum values, and the PI corresponding to each local maximum value is used as the PI estimate Using the normalization corresponding to each local maximum Further calculation of TO estimate

[0230] Example 3

[0231] In this embodiment, the DS-AML preamble detection algorithm is used to estimate the preamble sequence and time offset of the signal sent by each user equipment, and obtain the PI estimation value and TO estimation value of each user equipment.

[0232] In the specific implementation process, in the AML leading algorithm of Example 2, the test value of normalized TO is an integer whose value range is The estimated resolution is 1; according to the definition of normalized TO in equation (34), the original TO estimate of the AML algorithm is The resolution is It is limited by the FFT length N. Using a larger FFT length N helps to obtain a more accurate TO estimate, but it also increases the computational complexity. Looking back at equations (15) and (16), both channel estimation and channel equalization require the use of the estimated value of TO. Therefore, accurate TO estimation is very important for data detection in unauthorized RA; therefore, in order to further improve the TO estimation accuracy of the AML preamble detection algorithm, this embodiment designs a DS-AML preamble detection algorithm based on the idea of ​​binary search method; it is pointed out here that the DS-AML algorithm does not affect the estimation of PI, so the PI estimation value it provides is the same as that of the AML algorithm, that is,

[0233] The following is a detailed introduction to using the DS-AML algorithm to obtain a more accurate TO estimate;

[0234] The binary search method is a low-complexity estimation method that can be used to solve the frequency estimation problem in the PD process. Substituting equation (6) into equation (37) yields:

[0235]

[0236] in, Indicates that the leading sequence is selected The set of UEs is composed of

[0237]

[0238] Indicates detection selection leading sequence The additive interference and noise corresponding to the time; Looking back at the definition of the phase deflection vector in equation (35), it can be seen that the phase deflection vector obtained by equation (40) It is similar to the signal model of superposition of multiple complex exponential sequences with different frequencies in the time domain, where It can be compared to the frequencies of different complex exponential sequences; therefore, the binary search method designed for frequency estimation can be used to estimate the normalized TO of the UE in the scenario considered in the present invention;

[0239] In this embodiment, based on the estimated number of UEs and UE selected The BS performs a binary search operation on TO for each UE one by one. The binary search is an iterative operation process under the condition of a specified number of iterations I. The estimated value of the normalized TO of UEκ at the i-th (i=1,…,I) iteration is recorded as Its initial value is given by the AML algorithm, that is, In the binary search process, according to The function given by formula (39) is searched in the order of exist In the iteration process, three variables {ψ 0 ,ψ - ,ψ +}, used to determine The update direction of {ψ 0 ,ψ - ,ψ + The initial value of} is set to:

[0240]

[0241] During the binary search process, the search step size of each iteration becomes the size of the previous iteration. Because the normalized TO estimation initial value resolution provided by the AML leader detection algorithm is 1, the search step size of the i-th iteration of the binary search is 2 -i ; According to the i-th iteration ψ - and ψ + The size relationship, The update method is:

[0242]

[0243] Finish After the update of {ψ - ,ψ +} to determine the update direction of the normalized TO in the next iteration; the update rule is: if ψ + >ψ - , then ψ - ←ψ 0 , ψ + remains unchanged; otherwise ψ + ←ψ 0 , ψ- remains unchanged; finally, ψ 0 Updated to:

[0244]

[0245] Based on the above discussion, the DS-AML leader detection algorithm proposed in this embodiment is summarized in Algorithm 2;

[0246]

[0247]

[0248] Example 4

[0249] This embodiment provides a simulation experiment of a specific scenario, which is used to test the performance of the three preamble detection algorithms provided in Embodiments 1 to 3.

[0250] In the specific implementation process, this embodiment considers a marine communication system with a cell radius of R and a center frequency of f c ;The BS is located on an island in the center of the cell, and the UEs are randomly distributed on the sea level; Unless otherwise stated, the simulation parameters are given in Table 1;

[0251] Table 1 Simulation parameters of this embodiment

[0252]

[0253] In this embodiment, the large-scale fading coefficient of the sea surface channel is modeled as:

[0254]

[0255] in Indicates the carrier wavelength, A BS and A UE Represent the altitude of BS and UE respectively; small-scale fading coefficient v k,m Modeled as a complex Gaussian random variable with zero mean and unit variance; according to the NR standard, the discrete Fourier transform (DFT) of the ZC sequence is selected as the leading sequence of the PTP:

[0256]

[0257] where c q ∈C L×1 represents a ZC sequence, whose lth element is F∈C L×L Denotes the DFT matrix, whose (l 1 ,l 2 ) elements are In the simulation, the RA process of a UE is considered successful only if the uplink data packet of the UE is correctly decoded;

[0258] Since the DS-AML leader detection algorithm proposed in Example 3 is an iterative algorithm, its convergence performance is first tested below; Figure 4 The curve of RA success rate changing with the number of iterations I when using the DS-AML algorithm is drawn; it can be seen that the RA success rate increases with the increase of I when I≤4, and almost does not change with the increase of I when I>4; therefore, in the subsequent simulation, the number of iterations of the DS-AML algorithm is set to I=4;

[0259] like Figure 5 As shown, Figure 5 The RA success rate performance curves of several preamble detection algorithms under different conditions of UE number K and cell radius R are given, including the existing FDC algorithm, UG algorithm, AML and DS-AML algorithms; it can be seen that as the FFT length N increases, all algorithms can provide more accurate TO estimation, so the RA success rates of different algorithms are improved; in addition, under the same FFT length N, the RA success rates of the AML algorithm and the DS-AML algorithm proposed in Examples 2 and 3 are higher than those of the FDC algorithm and the UG algorithm; in particular, the performance of the DS-AML algorithm when N=1024 is better than that of the FDC, UG, and AML algorithms when N=2048, indicating that the binary search operation can significantly improve the TO estimation accuracy, and thus the FFT complexity of the system can be reduced under the same performance target requirements;

[0260] In addition, since the ML leader detection algorithm has exponential complexity and is difficult to implement in actual scenarios, a set of specific parameters will be selected for testing below to verify the superiority of the ML algorithm and to prove that the AML and DS-AML algorithms proposed in Examples 2 and 3 are two better approximation methods of the ML algorithm; specifically, Q=K=2 is selected so that the ML algorithm can be implemented on a computer; for fairness in comparison, the search space of TO in the ML algorithm is consistent with the search space of TO in the FDC, UG, and AML algorithms and the initial search space of TO in the DS-AML algorithm, that is, This setting makes the search space of the ML preamble detection algorithm change with the FFT length N, even if the ML algorithm does not include FFT operations;

[0261] like Figure 6 As shown, Figure 6The RA success rate performance of different detection algorithms under Q=K=2 is plotted; when N=1024, the performance of the AML algorithm is close to that of the ML algorithm, while the DS-AML algorithm is supported by binary search, which supports a more refined search space, and thus can achieve performance better than the ML algorithm; this is because the FFT length is relatively small at this time, which limits the TO estimation accuracy of the ML algorithm, while the DS-AML algorithm can overcome the influence of the small size FFT through binary search, thereby achieving better performance than the ML algorithm; when N=2048, the FFT length is large enough, so the ML algorithm has the best performance, while AML and DS-AML have performance close to that of the ML algorithm; Figure 6 It is verified that the ML algorithm has excellent performance when the TO search scale is fine enough, that is, when N is increased, and the AML and DS-AML algorithms have performance close to that of the ML algorithm.

[0262] The same or similar reference numerals correspond to the same or similar components;

[0263] The terms used in the drawings to describe positional relationships are only used for illustrative purposes and should not be construed as limiting this patent;

[0264] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A method for random access without authorization for a large-scale multiple-input multiple-output system, characterized in that: The following steps are involved: S1: Establishing a wireless communication system model based on orthogonal frequency division multiplexing, in which a plurality of user equipments respectively send signals to a base station through different types of PRACH resource blocks orthogonal in the frequency domain; The transmission signal includes a preamble sequence and encoded uplink service data; S2: obtaining a received signal of the base station, selecting a part of the received signal with a larger target received power, and estimating the number of user equipments by using a minimum description length criterion; S3: using a preset preamble detection algorithm to estimate the preamble sequence and time offset of the signal sent by each user equipment, and obtaining a PI estimation value and a TO estimation value of each user equipment; The preset preamble detection algorithm is specifically: any one of the ML preamble detection algorithm, the AML preamble detection algorithm and the DS-AML preamble detection algorithm; S4: The base station performs channel estimation for each user equipment using the PI estimation value and the TO estimation value, and performs channel equalization on the received signal to obtain an estimation value of uplink service data of the corresponding user equipment; S5: The base station decodes the estimated value of the uplink service data of each user equipment, completes the reception of the uplink service data, and implements unlicensed random access.

2. The method for granting random access to a large-scale multiple-input multiple-output system according to claim 1, characterized in that: In step S1, the wireless communication system adopts OFDM to transmit signals, wherein part of the time-frequency resources are allocated as a physical random access channel; the transmission of the transmission signal of the user equipment in the physical random access channel is divided into a preamble transmission phase and a data transmission phase, and the transmission signal of the user equipment constitutes two continuous OFDM symbols in the time domain; Several PRACH resource blocks are set within the bandwidth of the wireless communication system; in each PRACH resource block, the bandwidths allocated in the preamble transmission phase and the data transmission phase are LΔf and LΔf respectively. Where L is the number of symbols contained in the leading sequence, is the number of data symbols, Δf is the subcarrier spacing; the type of the PRACH resource block includes any one of the following: Type 1: Type 2: and type 3:

3. The method for granting random access to a large-scale multiple-input multiple-output system according to claim 2, characterized in that: In the wireless communication system model of step S1, K user equipments access one base station, and K is unknown; each user equipment sends a signal through a single transmitting antenna, and the base station uses M receiving antennas to receive the signal; the sequence number of the user equipment is denoted as k, satisfying k=1,…,K; the distance between the user equipment k and the base station is d k , and the maximum distance d between the user equipment and the base station is known max ; The TO of user equipment k is c = 3 × 10 8 m / s represents the speed of light, and the value range of TO is [0,τ max ], In the preamble transmission phase, each user equipment receives a predefined preamble sequence set {x q |q=1,…,Q}, select a preamble sequence of length L and send it, where Q is the number of preamble sequences, x q ∈C L×1 represents the leading sequence with sequence number q, C represents a complex set; the leading sequence selected by user equipment k is recorded as q k , the leading sequence sent is In the preamble transmission phase, the received signal of the mth antenna of the base station is denoted as y m , m=1,…,M, specifically: in, represents the Hadamard product, γ k >0 is the transmit power of each subcarrier of user equipment k during the preamble transmission phase, h k,m is the channel coefficient between user equipment k and base station antenna m in the PRACH resource block, β k and v k,m Represent the large-scale and small-scale fading coefficients respectively; a(τ k )∈C L×1 Indicates that in the preamble transmission phase, τ k The phase shift caused by m ∈C L×1 is the Gaussian white noise vector in the preamble transmission stage; In the data transmission phase, user equipment k generates a data symbol vector by constellation mapping the encoded uplink service data. And send, the base station receiving signal in the data transmission stage is recorded as Specifically: in, is the transmit power per subcarrier of user equipment k during the data transmission phase; Indicates that during the data transmission phase, τ k The phase shift caused by is the Gaussian white noise vector in the data transmission stage; y m and Convert them into matrix forms respectively to obtain the wireless communication system model: in, in, 4. The method for granting random access to a large-scale multiple-input multiple-output system according to claim 3, characterized in that: In step S2, estimating the number of user equipments by using the minimum description length criterion includes the following steps: Select the part of the base station's received signal with a larger target received power and calculate the sample covariance matrix R: Among them, ρ and The target receiving power of the signal sent by the user equipment during the preamble transmission phase and the data transmission phase reaching the base station; Calculate the eigenvalues ​​of R {λ m |m=1,…,M}, according to λ1≥…≥λ M The estimated number of user devices is Calculated by the following formula: Among them, μ is the intermediate variable, μ=0,…,M-1; D(μ) is the intermediate function.

5. The method for granting random access to a large-scale multiple-input multiple-output system according to claim 4, characterized in that: In step S3, the objective function of the ML leader detection algorithm is expressed as: in, For the matrix The projection matrix; It means to find the square of the F norm of the matrix; and are the test values ​​of PI and TO respectively; and They represent the PI estimation value and TO estimation value output by the ML leader detection algorithm respectively; In the continuous interval [0,τ max ] densely sampled to obtain a discrete set Discrete the set As the new value range of TO; and Exhaustively search all possible combinations of values, solve the objective function of the ML leader detection algorithm, and obtain the PI estimate and TO estimates 6. The method for unlicensed random access for a large-scale multiple-input multiple-output system according to claim 5, characterized in that: In the step S3, the ML leader detection algorithm is improved based on the single source approximation method to obtain the AML leader detection algorithm; The objective function of the AML leader detection algorithm is expressed as: in, is the intermediate matrix, for y m The conjugate vector of ; is the experimental value of normalized TO, which is recorded as ε=τNΔf, where N≥L is the fast Fourier transform length, and ε max is the maximum value of ε; For is the phase shift of the variable, Indicates rounding up; According to the discrete set Determine the experimental value of normalized TO The value range of is solved by fast Fourier transform All values ​​of , output local maximum values, and the PI corresponding to each local maximum value is used as the PI estimate Using the normalized TO corresponding to each local maximum Further calculation of TO estimates 7. The method for unlicensed random access for a large-scale multiple-input multiple-output system according to claim 6, characterized in that: In the step S3, the accuracy of TO estimation in the AML leader detection algorithm is further improved based on the binary search method to obtain the DS-AML leader detection algorithm; The PI estimate output by the DS-AML leader detection algorithm In the DS-AML preamble detection algorithm, based on the estimated number of user devices and user equipment κ selected The base station performs a binary search operation on each user equipment for the normalized TO one by one, and the number of iterations of the binary search is I; wherein, for The κth element in ; The estimated value of the normalized TO of the user equipment κ at the i-th iteration is recorded as Its initial value is given by the AML leader detection algorithm, that is, for The κth element in ; in the binary search process, according to The objective function of the AML leader detection algorithm is searched in the order of exist The peak value near the edge of the y-axis is continuously updated during the iteration process. - ,ψ + }, used to determine The update direction of {ψ0,ψ - ,ψ + The initial value of} is set to: During the binary search process, the search step size of each iteration becomes the size of the previous iteration. According to the i-th iteration ψ - and ψ + The size relationship, The update method is: Completed in one iteration After the update of {ψ - ,ψ + } to determine the update direction of the normalized TO at the next iteration: If ψ + >ψ - , then ψ - ←ψ0,ψ + remains unchanged; otherwise ψ + ←ψ0,ψ - remains unchanged; at the same time, ψ0 is updated to: After I iterations, the estimated value of the normalized TO of the user equipment κ is The estimated value of TO is The normalized TO estimate output by the DS-AML leader detection algorithm is Further calculation of TO estimates 8. A method for unlicensed random access in a large-scale multiple-input multiple-output system according to any one of claims 5 to 7, characterized in that: In step S4, an equivalent channel matrix G of each user equipment in the preamble transmission phase and an equivalent channel matrix G in the data transmission phase are constructed. And meet The base station uses the PI estimation value and TO estimates Estimate the equivalent channel matrix G of each user equipment in the preamble transmission phase, specifically: in, is the estimated value of G; (·) + represents a pseudo-inverse operation; Further calculate the equivalent channel matrix in the data transmission stage Estimated value of Complete channel estimation.

9. The method for granting random access to a large-scale multiple-input multiple-output system according to claim 8, characterized in that: The transmit power of user equipment k is calculated as follows: Among them, P max is the maximum total transmit power.

10. The method for granting random access to a large-scale multiple-input multiple-output system according to claim 9, characterized in that: In step S4, channel equalization is performed on the received signal to obtain an estimated value of the uplink service data of the corresponding user equipment. It is expressed as: in, represents zero-forcing equalizer, and "*" represents matrix conjugation.

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