A joint pilot data transmission grant-free random access method

Abstract

The present application relates to the technical field of wireless communication, and specifically relates to a kind of joint pilot data transmission grant-free random access method, constructs joint pilot data transmission system model in no-cell large-scale MIMO, deduces user spectral efficiency closed expression, establishes maximum minimum user power allocation optimization problem;The objective function and constraint condition in optimization problem are reconstructed, the inter-AP power allocation optimization problem model is constructed, it is converted into equivalent convex feasibility problem using dichotomy, and optimal power allocation is carried out;With the spectral efficiency after optimal allocation, constraint condition is constructed, and the maximum number of non-conflict users is used as objective function, and the model is solved using successive approximation method and iterative algorithm, to obtain the configuration parameter of the maximum number of users in joint pilot data transmission system model access. The method meets the multiple scenarios of large-scale user simultaneous access, signal processing overhead is small, delay is low, signal processing complexity is small, and the optimal configuration scheme can be implemented, and the feasibility is good.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication, in particular to a joint pilot data transmission grant-free random access method. BACKGROUND

[0002] At present, since the cell-free massive MIMO can realize communication with the central processor through the backhaul network, improve the spatial utilization rate, increase the spectrum utilization rate and other advantages, it is considered as a key candidate technology in 6G and future wireless communication. The patents related to cell-free massive MIMO are also emerging in an endless stream. However, most of the related patents consider a relatively idealized environment. In the future communication system, the number of communication users per square kilometer may be as high as 10^7, and the large-scale access problem will become increasingly important. The traditional random access protocol is mainly divided into two categories: grant-based access protocol and grant-free access protocol. The grant-based random access depends on the handshaking mechanism, allocates a dedicated channel to each accessed user equipment and performs authentication, which is limited by limited channel resources and is not capable of coping with large-scale random access scenarios. At the same time, it brings extremely high signal processing burden, which is difficult to meet the needs of future communication systems. In view of this, based on the advantages of low signal processing overhead and low delay, the grant-free random access has received extensive attention.

[0003] However, due to the excessively large user scale, the orthogonality between the sequences is no longer possessed, thereby introducing additional multiple access interference, and further increasing the processing complexity of the receiver. Usually, the receiver processing process often relies on the compression sensing technology to recover the sparse signal. However, although the compression sensing performs well in signal recovery, the processing complexity and resource overhead brought by the compression sensing cannot be ignored.

[0004] Therefore, how to provide a new data transmission scheme to solve the deficiencies of the traditional grant-free random access method in the cell-free massive MIMO, solve some problems in the use of the grant-free random access method with orthogonal joint pilot data transmission, and remove the problem of increasing the signal processing complexity of the receiver by the compression sensing method and the like are problems that persons skilled in the art need to solve urgently. SUMMARY

[0005] Therefore, the present application provides a joint pilot data transmission grant-free random access method to solve the technical problems in the above technical background. The method meets the demand of large-scale random access in various application scenarios in the Internet of Things era, and has the characteristics of easy landing, good universality, small signal processing overhead, low delay and low complexity.

[0006] In order to achieve the above purpose, the present application adopts the following technical scheme:

[0007] S1, based on a cell-free massive MIMO system, a joint pilot-data transmission system model is constructed in combination with a backoff mechanism and frequency division multiplexing;

[0008] S2, according to the constructed joint pilot-data transmission system model, a closed-form expression of user spectral efficiency is derived, and a maximum-minimization user power allocation optimization problem is established;

[0009] S3, based on the objective function and constraint conditions in the user power allocation optimization problem, a power allocation optimization problem model between APs is constructed, and the power allocation optimization problem model is converted into multiple equivalent convex feasibility problems by using a bisection method, and optimal power allocation is performed;

[0010] S4, based on the user spectral efficiency after optimal power allocation, a constraint condition is established, and the maximum number of non-conflict users is taken as the objective function, to obtain an optimization problem of the joint pilot-data transmission system model in the optimal configuration, and a successive approximation algorithm is used to convert the optimization problem in the optimal configuration into multiple geometric programming problems and solve them by using an iterative algorithm, to obtain the parameter configuration of the joint pilot-data transmission system model with the maximum number of users.

[0011] Further, the joint pilot-data transmission system model constructed in S1 includes one CPU, L one access point AP and N one single-antenna user, which communicate with each other in both directions; wherein each access point AP includes M a root antenna, M L >> N , M a root antenna uniform linear array, which is distributed in a distributed layout in geographical position, used to collect signals from users and send user data to the CPU through a backhaul network for data processing by the CPU; all users complete uplink transmission of one pilot and T data packets in a coherent time Q through the access point, the pilot length in the pilot pool is τ , and the orthogonal pilots are τ groups; in a pilot duration of τ , users try to activate according to the backoff probability broadcast by the access point at random access, and the activated users randomly select pilots, and users selecting the same pilot sequence are defined as conflict users, and users selecting different pilot sequences from other users are defined as non-conflict users;

[0012] The joint pilot-data transmission system model divides the time T into W sub-slots according to the pilot length, wherein S a user set will use its corresponding S sub-slots for pilot transmission, S = W - QIn the first sub-time slot, the AP broadcasts a random access backoff probability. All users attempt to select a pilot and send a pilot signal to the AP. After receiving the pilot signal, the AP sends feedback to silent users and conflicting users, instructing them to attempt piloting in the second sub-time slot. After pilot transmission in the first sub-time slot, the AP broadcasts a new random access backoff probability. Users entering the second sub-time slot repeat the same operation as in the first sub-time slot. All users, according to each... τ The backoff probability transmitted for each sub-time slot length is divided into different user sets, and users in different user sets select different sub-time slots for pilot transmission; if the CPU detects a user and the AP estimates the user's channel, the user transmits continuously and successfully in subsequent sub-time slots. Q There are 1 data packet, and the data packet length is 1. τ ;

[0013] In length of τ During the duration of the pilot signal, the access point (AP) l The expression for the receiving pilot matrix at the terminal is:

[0014]

[0015] in, and To normalize the pilot power and the data power, For users k and access point AP l Channel vectors between terminals, Indicates user t The pilot power control coefficient, Indicates user k exist s Data power control coefficients for sub-slots, For users t The transmitted orthogonal pilot sequence, For users k The transmitted orthogonal data sequence; For receiving the noise matrix, Is s The set of non-conflicting users in a sub-slot Represents the integration of sets, where Representative at s The number of data packets transmitted in a sub-time slot.

[0016] Furthermore, the joint pilot data transmission system model includes the Ricean channel model, which uses uplink orthogonal pilots to estimate the channel and derives the minimum mean square error channel estimation. The specific process is as follows:

[0017] user n To the access point l Channel vector at the end Represented as:

[0018]

[0019] in, For access point AP l End-to-user n The signal components between, For access point AP l End-to-user n Large-scale fading coefficient of the channel between them Rice K The power ratio of line-of-sight signals to non-line-of-sight signals is measured by the power factor. For line-of-sight channel vectors, This is a non-line-of-sight channel vector;

[0020] , ,

[0021] This represents the non-line-of-sight signal component, whose elements are independent and identically distributed Gaussian random variables with a mean of 0 and a variance of 1. Indicates the line-of-sight signal component;

[0022] Assuming that all users in the joint pilot data transmission system model are randomly distributed in space and have different angles of arrival, The m Each element is represented as:

[0023]

[0024] in, d It is the antenna spacing. It's the wavelength. User n Angle of arrival at access point AP1; It changes slowly over time and remains constant over a sufficient number of reception phases; Rice in the LOS component K The factor and angle of arrival are effectively estimated using channel feedback and sample mean estimation methods, while other parameters are known and accurate within the system. This refers to the known part in the system model;

[0025] According to the access point (AP) l From the received pilot matrix at the terminal, we can obtain the least squares estimated channel as follows:

[0026]

[0027] in, The least squares estimation channel error vector is expressed as follows:

[0028] ;

[0029] and, ;

[0030] Past sub-slot introduces channel estimation error, which will affect the current sub-slot channel estimation phase, assuming that the user k in the last sub-slot user set, the channel estimation error satisfies:

[0031] ,

[0032] where, denotes the variance of background noise and interference;

[0033] The least square estimation channel error expression of the non-line-of-sight part between the user n and the access point AP l in this sub-slot satisfies the following distribution;

[0034]

[0035] Where the users belonging to the same have the same distribution;

[0036] According to the approximate time series recursive relationship:

[0037]

[0038] Under the premise that is a stationary process of t , The sufficient condition is:

[0039]

[0040] And it satisfies

[0041]

[0042] Where is the expected operation, denotes the average value for all possible sub-slots s , the formula is: .

[0043] Based on the above least square estimation channel, using the minimum mean square error estimation MMSE, the minimum mean square estimation channel vector of the non-line-of-sight part between the user n and the access point AP l is obtained, and its expression is:

[0044] ​

[0045] where, ,

[0046] for the access point AP l end-to-user n channel large-scale fading coefficient;

[0047] The integrated expression of the least square estimation channel and the minimum mean square error estimation channel is:

[0048]

[0049] where, n is the serial number of the served user, l is the end number of the access point AP, represents the estimation of the channel vector between the user n and the access point AP l end, represents the Rician K factor between the user n and the access point AP end, which measures the power ratio between the line-of-sight signal and the non-line-of-sight signal; is the line-of-sight channel vector, is the estimation of the non-line-of-sight channel vector.

[0050] Further, in the channel estimation stage of the joint pilot data transmission system model, in the first sub-slot, only the pilot vector is used for channel estimation, which is used for successfully decoding the data packet in the second sub-slot; after decoding, the data packet is removed, and the remaining signal is used to estimate the channel vector of the user sending the pilot in the second sub-slot;

[0051] The first S sub-slot decodes all user data packets step by step by repeating the operation in the first sub-slot;

[0052] Each user has one pilot signal and Q data packets, and the detection of all users and the completion of the uplink transmission of the data packets require S+Q sub-slots.

[0053] Further, in S2, based on the joint pilot data transmission system model, the process of deriving the closed-form expression of the user spectral efficiency is:

[0054] After all users finish sending the pilot, they immediately send data to the AP, and the uplink data symbol sent by the first n user is , which satisfies , and the system is in the first The first symbol duration in a sub-slot, the quantized received data vector at the l-port of the access point AP is denoted as:

[0055]

[0056] wherein, denotes the noise at the l-port of the access point AP in the sub-slot denotes the first element of denotes the conjugate transpose of denotes the pilot presence decision in the sub-slot

[0057] wherein,

[0058] denotes the sub-slot in which the pilot is missing

[0059] Based on the maximum ratio joint receiver, the l-port of the access point AP constructs a decoding matrix with the estimated channel, and sends to the CPU after the inner product of l The first symbol duration in the k-th sub-slot, the total demodulated signal of the user to n

[0060]

[0061] Using the worst non-coherent noise theory method, for the first symbol duration in the k-th sub-slot, the signal-to-noise ratio expression of the user n

[0062] wherein, denotes the data power control coefficient of the user t in the k-th sub-slot

[0063] denotes the data power control coefficient of the user n in the k-th sub-slot Based on the characteristics of large-scale MIMO, the closed-form expression is:

[0064] wherein,

[0065] , ,

[0066]

[0067] ​​​​​​​​​​​​ ;

[0068] denotes LS case, otherwise denotes MMSE case;

[0069] System throughput is affected by the signal-to-noise ratio, for the first symbol duration of the sub-slot, the spectral efficiency of the kth user is expressed as n

[0070] .

[0071] Further, in S2, based on the total spectral efficiency closed-form expression, the optimization problem of user power allocation is established as:

[0072]

[0073] Wherein, the constraint is the condition that the power control factor should satisfy.

[0074] Further, in S3, including:

[0075] S31, introduce slack variable , the objective function and constraint conditions in the user power allocation optimization problem in S2 are reconstructed, specifically:

[0076]

[0077] When is fixed, the objective function in S31 is quasi-convex, and is further optimized using the bisection method, specifically:

[0078] S321, set the upper and lower bounds of the objective function and , set , solve the feasibility problem in the above geometric programming problem, specifically ;

[0079] S322, if the problem is feasible, increase the lower bound, that is ;

[0080] S323, if the problem is not feasible, reduce the upper bound, that is , return to S33, re-set , judge the new feasibility problem again until end iteration, wherein represents the maximum error;

[0081] S324, the central processing unit gets the optimal parameter solution of inter-AP power allocation, and the output is and​​​ .

[0082] Further, the specific steps of the maximum minimum AP power control algorithm include:

[0083] L1, input τ, N, T, Q , ;

[0084] L2, initialization , , and >0;

[0085] L3, set , solve the feasibility problem and ;

[0086] L4, update using bisection method ;

[0087] L5, if the problem is feasible, set ;

[0088] L6, if the problem is not feasible, set ;

[0089] L7, while , output , .

[0090] Further, in S4, the steps for obtaining the parameter configuration of the joint pilot data transmission system model when the system is accessed by the maximum number of users are as follows:

[0091] S41, take the number of non-conflict users that the system can access as the objective function, and convert the user spectrum efficiency after power compensation into a constraint condition to obtain the optimization problem when the system is accessed by the maximum number of users, which is represented as:

[0092]

[0093] S42, considering the stability of the system, set the number of non-conflict users in each sub-slot to be equal, and set the system model to be stable. The number of non-conflict users in each set is controlled by the backoff probability, which satisfies the function:

[0094]

[0095] Since will decrease as s increases, consider Too low causes the backoff probability to be unable to obtain the specified number of non-collision users, and the number of non-collision users of the last user set is the minimum value of 0, which is continuous, and the system only considers the maximum value;

[0096] Without considering the constraint condition, the optimization problem of the optimal solution of the system configuration is:

[0097]

[0098] Wherein, represents the activation probability of the s sub-slot; represents the activation probability; represents the number of users in the

[0099] s sub-slot; represents the average number of non-collision users, represents the total number of non-collision users in the S sub-slots;

[0100] Wherein, for , the optimization problem is

[0101]

[0102] The problem is solved by using the idea of minimizing the continuous upper limit;

[0103] After the above problem is solved, the remaining system parameters and Q are integers with upper limits, which are obtained by traversal.

[0104] According to the technical solution, compared with the prior art, the present application has the following beneficial effects:

[0105] The present application proposes a new transmission model and allocation algorithm aiming at the shortcomings that user collision is difficult to solve and spectrum efficiency is difficult to evaluate in the existing unlicensed random access non-cell large-scale MIMO scene. The main parameter in the algorithm is the large-scale fading coefficient, which remains unchanged in many adjacent coherence time intervals, so that the optimization problem involved in the patent is simplified, and has good operability

[0106] (1) The application utilizes an access point to broadcast a backoff probability in different sub-slots to realize frequency division multiplexing of all users, the data uplink transmission method does not need communication handshake, is a grant-free random control user access mode, meets various scenes of large-scale random access of users, has the characteristics of low signal processing overhead and low delay compared with the grant random access mode, compared with the one-to-one correspondence mode of the pilot and the user in the traditional scheme, the application can use a small amount of pilot to meet the scene of a large number of users, and can obtain feasible transmission capacity after optimization.

[0107] (2) The traditional grant-free random access method mostly uses compression sensing and other methods, and there is a complex overhead caused by signal processing, and the large number of user data uplink transmission methods provided by the application remove the complexity of the receiver caused by compression sensing and other methods, and use a local orthogonal method for signal processing to reduce complexity, which is obviously lower in complexity than compression sensing and other methods; in addition, compared with the traditional system without joint pilot data transmission, the application uses an orthogonal method for signal processing to obtain higher spectral efficiency.

[0108] (3) The joint pilot data transmission grant-free random access method of the application can adapt to different system use environments and obtain complete system configuration schemes through specific optimization processing methods, and has good landing and good versatility compared with other joint pilot data transmission grant-free random access methods which only provide method descriptions and cannot provide effective system optimization configuration schemes. BRIEF DESCRIPTION OF DRAWINGS

[0109] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only embodiments of the application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of the provided drawings.

[0110] Figure 1 is the overall method flowchart of the joint pilot data transmission grant-free random access method of the embodiment of the application;

[0111] Figure 2 is the transmission flowchart of pilot data in the joint pilot data transmission grant-free random access method of the embodiment of the application;

[0112] Figure 3 is the algorithm flowchart of the joint pilot data transmission grant-free random access method of the embodiment of the application;

[0113] Figure 4 is the formula derivation verification of the joint pilot data transmission grant-free random access method of the present application and the comparison chart with the traditional method;

[0114] Figure 5 is the comparison table of the number of non-collision users and the number of required pilots in the algorithm simulation optimization of the embodiment of the present application and the traditional method;

[0115] Figure 6 is the comparison table of the minimum SE before optimization and the minimum SE after optimization in the algorithm simulation optimization of the embodiment of the present application and the traditional method. DETAILED DESCRIPTION

[0116] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0117] As shown in Figure 1 , the joint pilot data transmission grant-free random access method disclosed by the embodiment of the present application comprises the following steps:

[0118] Step 1, based on the cell-free massive MIMO system, combining the backoff mechanism and frequency division multiplexing, a joint pilot data transmission system model in the cell-free massive MIMO is constructed; it uses the uplink orthogonal pilot to estimate the channel error and deduces the minimum mean square error estimated channel;

[0119] Step 2, through the joint pilot data transmission system model in the cell-free massive MIMO, the expression structure of the user spectral efficiency is calculated, the closed-form expression of the user spectral efficiency under random access is obtained according to the characteristics of the cell-free massive MIMO, and the maximum-minimization user power allocation optimization problem is established;

[0120] Step 3, the objective function and the constraint condition in the user power allocation optimization problem are reconstructed, the power allocation optimization problem model between APs is constructed, the power allocation optimization problem model is converted into multiple equivalent convex feasibility problems by using the bisection method, and the optimal power allocation is performed;

[0121] Step 4, based on the user spectral efficiency after the optimal power allocation, the constraint condition is established, the maximum number of non-collision users is taken as the objective function, the optimization problem of the joint pilot data transmission system model under the optimal configuration is obtained, the successive approximation algorithm is used to convert it into multiple geometric programming problems, and the parameter configuration in the joint pilot data transmission system model when the user number access is maximum is obtained by using the iterative algorithm.

[0122] In step 1 of the embodiment, the joint pilot data transmission system model in the constructed cell-free massive MIMO includes one CPU, L one access point AP and N one single-antenna user, which communicate with each other; each access point AP contains M a root antenna, M L >> N , M a root antenna uniform linear array, which is distributed in geographical position and used to collect signals from users and send user data to the CPU through a backhaul network for data processing; all users complete uplink transmission of one pilot and Q one data packet in a coherent time T through the access point, the pilot length in the pilot pool is τ , and the orthogonal pilots are τ groups; in the pilot duration of τ , the user tries to activate according to the backoff probability broadcasted by the access point at random access, the activated user randomly selects a pilot, and the users selecting the same pilot sequence are defined as collision users, and the users selecting different pilot sequences from other users are defined as non-collision users;

[0123] As shown in Figure 2 , in the joint pilot data transmission method, the joint pilot data transmission system model divides the coherent time T into τ sub-slots with a length of W , wherein S a user set will use its corresponding S sub-slots for pilot transmission, S = W - Q ; in the first sub-slot, the AP broadcasts a random access backoff probability, all users try to select a pilot and send a pilot signal to the AP, and the AP feeds back information after receiving the pilot signal, so that the silent users and the collision users wait for the second sub-slot to try the pilot; after the pilot transmission in the first sub-slot, the AP broadcasts a new random access backoff probability, and the users entering the second sub-slot repeat the same operation as in the first sub-slot; finally, all users are divided into different user sets according to the backoff probability issued by each sub-slot length; the users in different user sets transmit pilots according to the sub-slot in the pilot time, and if the CPU can detect the users and the AP can estimate the channel of the users, these users can successfully transmit data packets in subsequent sub-slots, assuming that each user fixedly transmits Q one data packet with a length of τ .

[0124] In the pilot duration of l , the access point APl The receiving pilot matrix expression of the end is:

[0125]

[0126] wherein, and are normalized pilot power and normalized data power, is the channel vector between user k and the access point AP, l end, represents the pilot power control coefficient of user t , represents the data power control coefficient of user k in s sub-time slot, is the orthogonal pilot sequence sent by user t , is the orthogonal data sequence sent by user k ; in addition, is the receiving noise matrix, is the non-conflict user set in s sub-time slot, represents the integration of the set, wherein represents the number of data packets transmitted at s sub-time slot;

[0127] In order to fit the actual application, the Rician channel model is considered in channel processing. Since the line-of-sight part is known in large-scale MIMO, the known part is removed and the least square method is used. It is assumed that the channel estimation error of user k in the last user set satisfies . At this time, the least square estimation channel expression of the non-line-of-sight part between user n and the l end of the access point AP is:

[0128]

[0129] wherein, is the least square estimation channel error vector, and its expression is as follows:

[0130] ;

[0131] and, ;

[0132] For , in order to avoid the estimation error variance from increasing arbitrarily with time, resulting in data transmission failure, therefore, we need to effectively control the joint pilot data transmission system model. The specific control constraints are as follows:

[0133]

[0134] Based on the least squares estimation channel expression for the non-line-of-sight portion, and using the minimum mean square error criterion, the user... n To the access point (AP) l The minimum mean square estimation channel expression for the non-line-of-sight portion between ends is:

[0135]

[0136] in , , For access point AP l End-to-user n Large-scale fading coefficient of the channel between them.

[0137] Finally, the integrated users n To the access point (AP) l The least squares and least mean square estimation channel expressions between terminals are:

[0138]

[0139] Furthermore, in step 2, the specific process of deriving the closed-form expression for the user spectral efficiency is as follows:

[0140] All users immediately transmit data to the AP after the pilot signal is sent, regardless of whether a collision occurs. The uplink data symbol sent by the nth user is set to... ,satisfy Considering generality, let's take the first... The duration of the first symbol in the sub-slot, the access point (AP) l The received data vector after end-quantization is represented as:

[0141]

[0142] in, Indicates in Noise at the L end of the sub-time slot of the access point (AP); express The first element; express The conjugate transpose of; Is The pilots defined in the sub-slot contain a set of decision-making liveness parameters, specifically defined as:

[0143]

[0144] This indicates a sub-slot where pilot signals are lost or missing;

[0145] Based on the maximum ratio joint receiver, the access point (AP) l The end uses the estimated channel to construct the decoding matrix, and and After performing the inner product, the data is sent to the CPU via the forward link. Therefore, for the ... The duration of the first symbol in the sub-slot, when the CPU receives user input... n The total demodulated signal is:

[0146]

[0147] Using the worst-case incoherent noise theory method, for the th The duration of the first symbol in the sub-slot, user n The signal-to-noise ratio expression is:

[0148]

[0149] in, Indicates user t in Data power control coefficients for sub-slots; Indicates that user n is in Data power control coefficients for sub-slots;

[0150] Based on the characteristics of large-scale MIMO, its closed-form expression is:

[0151]

[0152] in, , ,

[0153] ;

[0154] Indicates the LS case, otherwise Indicates the MMSE situation;

[0155] System throughput is affected by signal-to-noise ratio, for the first... The duration of the first symbol in the sub-slot, the n The closed-loop expression for the spectral efficiency of a single user is expressed as follows:

[0156]

[0157] Furthermore, in step 2 of this invention, the optimization problem for user power allocation, based on the closed-form expression for total spectral efficiency, is as follows:

[0158]

[0159] Here, the constraints are the conditions that the power control factor should satisfy;

[0160] Obviously, the optimization problem of user power allocation is not a standard optimization problem, and it is difficult to determine its concavity and convexity, so it is difficult to obtain its optimal solution; in order to effectively solve the optimization problem of user power allocation, a slack variable is introduced The objective function and the constraint condition in the optimization problem of user power allocation are reconstructed, and the reconstructed optimization problem is:

[0161]

[0162] When , the objective function of the reconstructed optimization problem model is quasi-convex, so the bisection method is used to solve the model optimization problem.

[0163] The upper and lower bounds of in the objective function of the problem are set as and , and is set, the above feasibility problem is solved ; if the problem is feasible, the lower bound is increased, that is , if the problem is not feasible, the upper bound is reduced, that is , and the feasibility is judged again ; until the iteration is ended, wherein represents the maximum error.

[0164] The specific steps of the maximum minimum AP power control algorithm are shown in sub-algorithm 1:

[0165]

[0166] Further, in step 4 of the present application, the configuration parameters of the user access maximum model in the joint pilot data transmission system model are obtained, which specifically include:

[0167] The number of users that can be accessed in the joint pilot data transmission system model is taken as the objective function, and the optimal solution of the joint pilot data transmission system model configuration is obtained, and the optimization problem is represented as follows:

[0168]

[0169] Considering the stability of the joint pilot data transmission system model, the number of non-conflict users in each sub-slot should be roughly equal. Since the set joint pilot data transmission system model should be more stable, we can assume that the number of non-conflict users in each set should be roughly equal. In addition, since the number of non-conflict users in each set can be controlled by the backoff probability, it satisfies the function:

[0170]

[0171] As will decrease with the increase of s , considering too low leads to the backoff probability unable to obtain the specified number of non-conflict users, so the range of the number of non-conflict users of the last user set can meet the range of the number of non-conflict users of each set of the joint pilot data transmission system model, considering that the minimum of the range is 0 and continuous, so only the maximum value needs to be considered. Without considering the constraint condition of , the optimization problem of the optimal solution of the joint pilot data transmission system model configuration can be optimized as:

[0172]

[0173] wherein for , the optimization problem is

[0174]

[0175] The problem can be solved using the idea of minimizing the continuous upper limit.

[0176] The specific steps are shown in Sub-algorithm 2:

[0177]

[0178] As shown in Figure 3 , considering τ and Q are integers with upper bounds, for this purpose, the required original data is obtained by traversing the feasible configurations of the joint pilot data transmission system model. Specifically, the upper limit of τ and Q is obtained by traversing , and the number of users of the last sub-slot is obtained by traversing , which is input into Sub-algorithm 2 to obtain and store it in . If saves the corresponding parameter information of the joint pilot data transmission system model; otherwise, it is discarded. Based on , the stored information is sorted from large to small, and the largest data is input into Sub-algorithm 1 to obtain and , and it is judged whether the joint pilot data transmission system model is feasible and is established. If it is established, the final output of the optimization parameters of the joint pilot data transmission system model , , , ; otherwise, the next data is transmitted.

[0179] The above iterations can be represented using Synthesis Algorithm 1:

[0180]

[0181] Figure 5 and Figure 6 The two tables show the optimal number of non-collision users and their corresponding pilot requirements for the new and traditional schemes under LS and MMSE conditions, respectively.

[0182] In the joint pilot data transmission system model, we assume a minimum rate =5 bit / s / Hz, number of antennas M=100, and considering five cases, namely N=10, 20, 30, 50, 80, the maximum number of pilots allowed by the joint pilot data transmission system model is set to 30.

[0183] Figure 5 The table compares the number of non-collision users and their corresponding pilot requirements for the traditional method and the PDSDI method under LS and MMSE scenarios. The results show that the PDSDI method outperforms the traditional method in increasing the number of non-collision users and reducing pilot requirements, especially in user-dense scenarios. Under both LS and MMSE scenarios, the PDSDI method outperforms the traditional method under various configurations, demonstrating higher efficiency and robustness.

[0184] Figure 6 The table compares the optimized (Opt.) and unoptimized (Unopt.) results of the minimum SE value obtained by the user under the traditional method and the PDSDI method, and analyzes the LS and MMSE cases separately. Figure 6 The table shows that the optimized PDSDI results significantly outperform the unoptimized results in both LS and MMSE scenarios, providing higher SE values. This indicates that the optimized PDSDI method improves the user's SE value under both LS and MMSE configurations, demonstrating its optimization performance comparable to traditional methods.

[0185] In this embodiment, during the estimation phase of the joint pilot data transmission channel, channel estimation is performed using only the pilot vector in the first sub-time slot, thus enabling successful decoding of data packets in the second sub-time slot. After decoding, the data packets are removed, and the remaining signal can be used to estimate the channel vector of the user transmitting the pilot in the second sub-time slot. In this way, all data packets can be decoded step by step.

[0186] The embodiment is a transmission model suitable for the unlicensed random access non-cellular massive MIMO scene, and a system parameter optimization strategy is designed for the model to meet different application scenarios. The main points include: transmitting pilot data with frame transmission and automatically dividing users to obtain pilot from the same pilot pool multiple times without conflict, which greatly reduces the system conflict rate; since the overall system spectrum efficiency is difficult to optimize, consider the threshold of the spectrum efficiency of a single user, compensate through power optimization, and meet the minimum requirement of the spectrum efficiency of all users, at this time, the optimization target is changed to the number of non-conflict users of the system, so as to indirectly obtain a relatively good system spectrum efficiency.

[0187] The embodiment proposes a new transmission model and allocation algorithm to solve the problems of user conflict and spectrum efficiency evaluation in the existing unlicensed random access non-cellular massive MIMO scene. The main parameter in the algorithm is the large-scale fading coefficient, which remains unchanged in many adjacent coherent time intervals, so the optimization problem involved in the embodiment is simplified, and has good operability.

[0188] The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the related parts can be referred to the method part.

[0189] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein,

[0190] but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for unlicensed random access to joint pilot data transmission, characterized in that, It comprises the following steps: S1, based on a cell-free massive MIMO system, combining the backoff mechanism and frequency division multiplexing, a joint pilot data transmission system model is constructed; The joint pilot data transmission system model constructed by S1 includes: a CPU, L Access Points (APs) and N Each user has a single antenna, and the three communicate bidirectionally; each access point (AP) contains... M Root antenna, M L >> N, M A uniform linear array of antennas, geographically distributed, is used to collect signals from users and transmit user data to the CPU via a backhaul network for data processing. All users access the network at the coherence time point. T Complete one pilot and Q The uplink transmission of data packets has a pilot length of [number] in the pilot pool. τ Orthogonal pilot frequency τ Group; in a length of τ During the pilot duration, users attempt to activate based on the backoff probability broadcast by the access point during random access. Activated users randomly select pilots. Users who select the same pilot sequence are defined as conflicting users, while users who select different pilot sequences from other users are defined as non-conflicting users. The joint pilot data transmission system model divides the coherence time into T sub-slots according to the pilot length, wherein W a set of users will transmit pilots in their corresponding S sub-slots, and S the first sub-slot is used for pilot transmission, S = W - Q in the first sub-slot, the AP broadcasts a random access backoff probability, all users try to select pilots and send pilot signals to the AP, after receiving the pilot signals, the AP feeds back information to inform the silent users and the conflict users to wait for the second sub-slot to try the pilot, after the pilot transmission in the first sub-slot, the AP broadcasts a new random access backoff probability, the users entering the second sub-slot repeat the same operation as in the first sub-slot, and all users are divided into different user sets according to the backoff probability sent by each τ sub-slot length, the users in different user sets select different sub-slots for pilot transmission, and if the CPU detects the user and the AP estimates the channel of the user, the user continuously and successfully transmits Q data packets in the subsequent sub-slots, and the data packet length is τ ; The access point AP's (AP) receive pilot matrix expression is: τ l The access point AP's (AP) receive pilot matrix expression is:​ in, and To normalize the pilot power and the data power, For users k and access point AP l Channel vectors between terminals, Indicates user t The pilot power control coefficient, Indicates user k exist s Data power control coefficients for sub-slots, For users t The transmitted orthogonal pilot sequence, For users k The transmitted orthogonal data sequence; For receiving the noise matrix, Is s The set of non-conflicting users in a sub-slot Represents the integration of sets, where Representative at s The number of data packets transmitted in a sub-time slot; This represents the channel vector between user t and the l-end of the access point AP; S2, through the joint pilot data transmission system model, a closed-form expression of user spectral efficiency is derived, and a maximum-minimization user power allocation optimization problem is established; S3, the objective function and constraint conditions in the user power allocation optimization problem are reconstructed, a power allocation optimization problem model between APs is constructed, the power allocation optimization problem model is converted into multiple equivalent convex feasibility problems by using the bisection method, and optimal power allocation is performed; S4, based on the user spectral efficiency after optimal power allocation, the constraint condition is established, the maximum number of non-conflict users is taken as the objective function, the optimization problem of the joint pilot data transmission system model in the optimal configuration is obtained, the successive approximation algorithm is used to convert the optimization problem in the optimal configuration into multiple geometric programming problems, and the parameter configuration of the joint pilot data transmission system model when the number of users accessed is maximum is obtained by using the iterative algorithm.

2. The joint pilot data transmission grant-free random access method of claim 1, wherein, The joint pilot data transmission system model comprises a Rician channel model, which estimates the channel by using uplink orthogonal pilots and derives a minimum mean square error estimation channel, and the specific process is as follows: User n To access point l Channel vector of the end Is expressed as: wherein, is an access point, AP, l end-to-user n between signal components, is an access point, AP, l end-to-user n between channels large-scale fading coefficients, denotes the Rician K-factor between user n and the l-th access point, AP, and measures the power ratio between line-of-sight and non-line-of-sight signals; is a line-of-sight channel vector, is a non-line-of-sight channel vector; ， ， denotes the non-line-of-sight signal component whose elements are independent and identically distributed Gaussian random variables with mean 0 and variance 1 ; denotes the line-of-sight signal component; Assume that all users in the joint pilot data transmission system model are randomly distributed in space and have different angles of arrival, the first m element is represented as: in, d It is the antenna spacing. It's the wavelength. User n To the access point l Angle of arrival; It changes slowly over time and remains constant over a sufficient number of reception phases; Rice in the LOS component K The factor and angle of arrival are effectively estimated using channel feedback and sample mean estimation methods, while other parameters are known and accurate within the system. This refers to the known part in the system model; According to the access point (AP) l From the expression for the received pilot matrix at the receiving end, the least-squares estimation channel expression for the non-line-of-sight portion is derived as follows: wherein is the least squares estimate of the channel error vector, which is expressed as follows: ； And, ; denotes the Rice K-factor between user k and the l-th port of access point AP; denotes the noise of user n and the l-th port of access point AP in the s-th sub-slot; denotes the orthogonal pilot sequence sent by user n; denotes the pilot power control coefficient of user n; M denotes the total number of antennas; The channel estimation error introduced by past sub-slots will affect the channel estimation stage of the current sub-slot. Assuming that in the user set of the previous sub-slot, users... k The channel estimation error satisfies: ， wherein denotes the variance of the background noise and interference; then the sub-slot user n to an access point (AP) l least square estimate channel error expression for non-line-of-sight portions between the ends satisfies the following distribution; Wherein, same as the user, its distribution is consistent; According to the approximate time sequence recursive relationship: In is t the premise that is a sufficient condition for And it satisfies wherein is the desired operation, denotes the average over all possible sub-slots s of formula Based on the least square estimate channel, using minimum mean square error estimate (MMSE), the user n to an access point (AP) and l the least square estimate channel vector of the non-line-of-sight part between the end and the access point (AP) is expressed as wherein , for an access point, AP l end-to-user n between channels; a large-scale fading coefficient The integrated expression of the least square estimation channel and the minimum mean square error estimation channel is: wherein, l is the port number of the access point AP, denotes the user n to the access point AP l an estimate of the channel vector between the ports, denotes the Rician K-factor between the user n and the l port of the access point AP, measuring the power ratio between line-of-sight and non-line-of-sight signals; is the line-of-sight channel vector, is an estimate of the non-line-of-sight channel vector.

3. The joint pilot data transmission grant-free random access method of claim 1, wherein, In the channel estimation stage of the joint pilot data transmission system model, in the first sub-time slot, only the pilot vector is used for channel estimation, which is used for successfully decoding the data packet in the second sub-time slot; After decoding, the data packet is removed, and the remaining signal in the second sub-time slot is used to estimate the channel vector of the user sending the pilot in the second sub-time slot; Pre S Each sub-slot decodes the data packets of all users step by step by repeating the operation in the first sub-slot. Each user has one pilot signal and Q packets, the users to be detected and the uplink transmission of the packets to be completed need S+Q subslots.

4. The joint pilot data transmission grant-free random access method of claim 2, wherein, In S2, based on the joint pilot data transmission system model, the process of deriving the closed-form expression of user spectral efficiency is as follows: All users immediately send data to the AP after the pilot signal transmission is completed, setting the [number]th [function]. n The uplink data symbols sent by each user are ,satisfy The system in the The duration of the first symbol in the sub-slot, the access point (AP) l The received data vector after end-quantization is represented as: wherein, denotes the noise at the l-port of the access point AP; denotes the noise at the l-port of the access point AP; denotes the first element of denotes the first element of denotes the conjugate transpose of denotes the set of surviving pilot existence decisions in the sub-slot denotes the set of surviving pilot existence decisions in the sub-slot​ subslot representing a pilot loss loss; Based on the maximum ratio joint receiver, the access point AP's l terminal constructs a decoding matrix using the estimated channel, and performs inner product on the decoding matrix and the total demodulation signal of the user to obtain the first symbol duration of the first sub-slot and sends it to the CPU through the front link. Then, for the first symbol duration of the first sub-slot, the CPU receives the total demodulation signal of the user to the access point AP as follows: n ​ Using the worst non-coherent noise theory approach, the signal-to-noise ratio expression for the first symbol duration of the sub-slot is: n The signal-to-noise ratio expression for the first symbol duration of the sub-slot is:​ wherein, denotes the data power control coefficient of user t in subslot; denotes the data power control coefficient of user n in subslot; Based on the characteristics of massive MIMO, the closed-form expression is: wherein , , ； represents the LS case, otherwise represents the MMSE case; The system throughput is affected by the signal-to-noise ratio, for the first sub-slot, the spectral efficiency of the first user is expressed in closed form as n the second user is expressed in closed form as 。 5. The joint pilot data transmission grant-free random access method of claim 4, wherein, In S2, based on the total spectral efficiency closed-form expression, the optimization problem of user power allocation is established as: Wherein, the constraint is the condition that the power control factor should satisfy.

6. The joint pilot data transmission grant-free random access method of claim 5, wherein, In S3, it comprises: S31, introducing slack variables reconstruct the objective function and the constraint conditions in the user power allocation optimization problem in S2, specifically: 。 7. The joint pilot data transmission grant-free random access method of claim 6, wherein, When The objective function in S31 is quasi-convex when fixed, and is further optimized using bisection method, specifically: S321, set the target function upper and lower bounds and , set , solve the feasibility problem in the above geometric programming problem, specifically ; S322、if the problem is feasible, then raise the lower bound, i.e. ; S323, if the problem is not feasible, then reduce the upper bound, i.e. , return to S33, re-set , judge the new feasibility problem again, until end the iteration, where represents the maximum error; S324, the central processing unit obtains the optimal parameter solution of the inter-AP power allocation, and outputs and .

8. The joint pilot data transmission grant-free random access method of claim 1, wherein, The power allocation optimization problem model between APs comprises a maximum-minimum AP power control algorithm, and the specific steps of the maximum-minimum AP power control algorithm comprise: L1, input τ, N, T, Q, ; L2, initialization , , and > 0; L3, set , solve feasibility problem and ; L4, updating using bisection ; L5, if the problem is feasible, set ; L6, if the problem is not feasible, set ; L7, while , output , .

9. The joint pilot data transmission grant-free random access method according to claim 7 or 8, characterized in that, In S4, the steps of obtaining the system configuration with maximum system access are as follows: S41, taking the number of non-conflict users that the system can access as the objective function, and converting the user spectral efficiency after successful power compensation into a constraint condition, the optimization problem when the system accesses maximum is obtained, which is expressed as: S42, considering the stability of the system, the number of non-conflict users in each sub-time slot is set to be equal, the system model set tends to be stable, the number of non-conflict users in each set is controlled by the backoff probability, and it satisfies the function: because Will follow s The increase and decrease, take into consideration If the probability of backoff is too low, the specified number of non-conflicting users cannot be obtained. The minimum number of non-conflicting users in the last user set is 0, and its value is continuous. The system only considers the maximum value. Without considering the constraints, the optimization problem for system configuration optimal solution is: wherein denotes the activation probability of a sub-slot; s denotes the activation probability of a sub-slot; denotes the activation probability of a sub-slot; denotes the activation probability of a sub-slot; s the number of users per sub-slot; an average number of non-conflicting users, the total number of non-conflicting users in S sub-slots; where, for the optimization problem is The problem is solved using the idea of minimizing a continuous upper bound; After the above problems are solved, the remaining system parameters and Q are all integers with upper bounds, which are obtained by traversal.

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