Hybrid user unlicensed random access method in cell-free massive MIMO

Abstract

The application provides a hybrid user unlicensed random access method in a cell-free massive MIMO, comprising the following steps: deploying a cell-free massive MIMO system and calculating an optimal backoff parameter; after obtaining the optimal backoff parameter, an access point broadcasts the optimal backoff parameter to all users; the users randomly activate according to the optimal backoff parameter broadcast by the access point; the users successfully activated randomly select a pilot from a predefined pilot pool and send the pilot to the access point; the access point judges whether the signal-to-interference ratio of the user with the maximum signal strength among the users selecting the same pilot is greater than a threshold value; if yes, the access point identifies the user with the maximum signal strength and accesses the user; otherwise, all the users in conflict are considered to have failed to access and wait for the next reinitiation of access. The application can significantly improve the problem of poor performance of edge users, improve the access efficiency of various types of users in an Internet of Things scene, has high feasibility and can be applied to an actual communication scene.

Description

Technical Field

[0001] This invention relates to a mixed-user unlicensed random access method, and more particularly to a mixed-user unlicensed random access method in cellular massive MIMO. Background Technology

[0002] In recent years, with the rapid development of wireless communication technology, cellular-free massive MIMO (Multiple-Input Multiple-Output) systems deployed in 6G have attracted widespread attention. Cellular-free massive MIMO systems are a new type of wireless communication system that combines the advantages of distributed massive MIMO and network MIMO, achieving higher spectral efficiency and better coverage. It introduces a "user-centric" approach, distributing access points over a large area, with each access point equipped with an antenna, and the access points connected to the central processing unit via backhaul links. Compared to centralized massive MIMO systems, cellular-free massive MIMO systems eliminate cell boundary limitations, reduce the difference between central and edge users, improve the uniformity of performance coverage, and ensure quality of service.

[0003] Unlicensed random access can reduce signaling overhead, but existing research on unlicensed random access mainly focuses on centralized massive MIMO, with relatively little research on unlicensed random access in cellular massive MIMO. Cellular massive MIMO has a wider coverage area, and users choosing the same pilot may be geographically distant. Access points can differentiate users based on signal strength differences, thus allowing even users choosing the same pilot to successfully access the system. Therefore, random access in cellular systems requires a different strategy than centralized massive MIMO. Furthermore, in the era of the Internet of Things (IoT), the types of users are increasing, and the configuration parameters of each user vary depending on their application scenario. Therefore, customized random access strategies are needed for IoT users.

[0004] In summary, how to apply unlicensed random access technology to non-cellular massive MIMO systems and optimize its design is a technical problem that urgently needs to be solved. Summary of the Invention

[0005] Purpose of the invention: The technical problem to be solved by the present invention is to provide a method for unlicensed random access of mixed users in non-cellular massive MIMO, which addresses the shortcomings of the prior art.

[0006] To address the aforementioned technical problems, this invention discloses a method for unlicensed random access of mixed users in cellular massive MIMO, comprising the following steps:

[0007] Step 1: In the non-cellular massive MIMO system, a central processing unit and one or more access points are deployed within a preset range. Each access point and the central processing unit are connected through a backhaul link. All users are evenly distributed within this area, and the users are divided into different types.

[0008] Step 2: Calculate the optimal backoff parameters, that is, calculate the optimal random access backoff probability for each type of user: Calculate the average spectral efficiency of the system based on the number of users, the number of access point antennas, and the probability of successful user access. Then, under the condition of time delay constraints, use the gradient descent algorithm to optimize the user backoff probability to maximize the average spectral efficiency of the system.

[0009] Step 3: After obtaining the optimal backoff parameters, the access point broadcasts the optimal backoff parameters to all users. Users are randomly activated according to the optimal backoff parameters broadcast by the access point. Users who are successfully activated randomly select pilots from the predefined pilot pool and send them to the access point.

[0010] Step 4: The access point determines conflicting users by checking if the signal-to-interference ratio (SIR) of the user with the strongest signal among those using the same pilot signal is greater than a threshold. If so, the base station identifies the user with the strongest signal and allows that user to access the system. Otherwise, all conflicting users are considered to have failed to access the system and must wait for the next access attempt.

[0011] Furthermore, the cellular-free massive MIMO system described in step 1 specifically includes:

[0012] The total number of access points is L, and the number of antennas on each access point is M;

[0013] The total number of users is N, divided into K types; the number of users in the k-th (k = 1, 2, ..., K) type is N. k ,Right now

[0014] Furthermore, step 2, which involves calculating the optimal random access backoff probability for each type of user, specifically includes the following steps:

[0015] Step 2-1: Calculate the probability of a user's successful access;

[0016] Step 2-2: Calculate the system's average spectral efficiency using the access success probability of each user;

[0017] Steps 2-3 aim to maximize the average spectral efficiency of each cell within the coverage area of ​​this system. The gradient descent method is used to solve the optimization problem and calculate the optimal random access backoff probability for each type of user.

[0018] Furthermore, the calculation of the user's successful access probability in step 2-1 specifically includes:

[0019] The probability p of successful access for the m-th user of type k′ is... succ,lmk′ for:

[0020]

[0021] Where, p b,k′ Let τ represent the backoff probability of the k′-th type of user, τ be the pilot sequence, and p b,k Let represent the withdrawal probability of the k-th type of user; The probability that the signal-to-interference ratio (SIR) of the m-th user of the k′-th type is greater than the threshold is calculated as follows:

[0022]

[0023] Among them, X Th P represents the signal-to-interference ratio threshold. mk′ Let β be the transmit power of the m-th user of the k′-th type. mk′l This refers to the large-scale fading between the m-th user of the k′-th type and the l-th (l=1,2,...,L) access point;

[0024] P nk′ β represents the transmit power of the nth user of the k′th type. nk′l This represents the large-scale fading between the nth user of the k′th type and the lth access point;

[0025] P nk Let β represent the transmit power of the nth user of the kth type. nk This represents the large-scale fading between the nth user of the kth type and the lth access point.

[0026] Furthermore, the calculation of the system's average spectral efficiency described in step 2-2 specifically includes:

[0027] The system's average spectral efficiency S is:

[0028]

[0029] Among them, S mk′ The spectral efficiency of the m-th user of the k′-th type is expressed as follows:

[0030]

[0031] Where T represents the channel coherence time. The power of interference plus noise is expressed as follows:

[0032]

[0033]

[0034] Where Δ1 represents The first calculation factor, Δ2 table The second calculation factor.

[0035] Furthermore, the calculations described in steps 2-3 yield the optimal random access backoff probability for each type of user, specifically expressed as follows:

[0036]

[0037]

[0038] Where, p b,k Let k be the withdrawal probability of the k-th type of user. Let be the optimal backoff probability for the k-th type of user. This represents the upper limit of the latency constraint for the k-th type of user.

[0039] Furthermore, step 4 involves determining whether the signal-to-interference ratio (SIR) of the user with the highest signal strength among those using the same pilot signal is greater than a threshold, i.e., whether the following formula holds true:

[0040]

[0041] Among them, SIR mk′ G represents the signal-to-interference ratio (SIR) of the m-th user of the k′-th type. lm′ Let g be the channel vector between the m-th user of type k′ and the l-th access point. lnk′ Let g be the channel vector between the nth user of type k′ and the lth access point. lnk Let n be the channel vector between the nth user of the kth type and the lth access point. and This is an indicator function. It indicates whether the m-th user of type k′ and the n-th user of type k′ are conflicting users. This indicates whether the m-th user of type k′ and the n-th user of type k′ are conflicting users.

[0042] Furthermore, in step 4, a value of 1 for the indicator function indicates a conflict between the two, while a value of 0 indicates no conflict.

[0043] Furthermore, the calculation of the optimal random access backoff probability for each type of user described in steps 2-3 specifically includes the following steps:

[0044] Step 2-3-1: Randomly generate the initial backoff probability for all types of users, i.e. Set step sizes v1 and v2, error Ξ, and iteration variable t;

[0045] Step 2-3-2: Calculate the gradient vector at the t-th time step. Let be the backoff probability of the k-th type of user in the t-th iteration. The system average spectral efficiency is calculated in the t-th iteration.

[0046] Step 2-3-3: Calculate the backoff probability of the k-th type of user in the (t+1)-th iteration. like The constraints are not met. Then let Then proceed to the next step;

[0047] Steps 2-3-4: Recalculate the system average spectral efficiency for the k-th type of user in the (t+1)-th iteration. judge If the condition is not met, then set t = t + 1 and repeat steps 2-3-2 to 2-3-4 until... If true, then the optimal backoff probability for the k-th type of user is:

[0048] Step 2-3-5: Execute steps 2-3-2 to 2-3-4 once for each increment of k, thereby obtaining the optimal backoff probability for all types of users.

[0049] Furthermore, the step sizes v1 and v2, the error Ξ, and the iteration variable t mentioned in step 2-3-1 are randomly generated.

[0050] Beneficial effects:

[0051] The method proposed in this invention can identify conflicting users in a non-cellular massive MIMO system by utilizing the signal-to-noise ratio, thereby increasing the likelihood of conflicting users accessing the network and improving user access efficiency. Furthermore, the method of this invention comprehensively considers real-world non-cellular systems in its design and adopts a standardized random access standard, making the final method highly feasible and practically applicable to real-world communication scenarios.

[0052] Furthermore, this invention provides a novel approach to the research and application of cellular massive MIMO and unlicensed random access technologies, and offers a reference for other related issues in the field of wireless communication. It can serve as a basis for further and in-depth research, and has a very broad application prospect. Attached Figure Description

[0053] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0054] Figure 1 This is a flowchart illustrating the method of the present invention. Detailed Implementation

[0055] A method for unlicensed random access of mixed users in cellular massive MIMO, such as Figure 1 As shown, it includes the following steps:

[0056] Step 1: In the non-cellular massive MIMO system, a central processing unit and one or more access points are deployed within a preset range. Each access point and the central processing unit are connected through a backhaul link. All users are evenly distributed within this area, and the users are divided into different types.

[0057] The aforementioned cellular-free massive MIMO system specifically includes:

[0058] The total number of access points is L, and the number of antennas on each access point is M;

[0059] The total number of users is N, divided into K types; the number of users in the k-th (k = 1, 2, ..., K) type is N. k ,Right now

[0060] Step 2: Calculate the optimal backoff parameters, that is, calculate the optimal random access backoff probability for each type of user: Calculate the average spectral efficiency of the system based on the number of users, the number of access point antennas, and the probability of successful user access. Then, under the condition of time delay constraints, use the gradient descent algorithm to optimize the user backoff probability to maximize the average spectral efficiency of the system.

[0061] The calculation of the optimal random access backoff probability for each type of user specifically includes the following steps:

[0062] Step 2-1, calculate the probability of successful user access, specifically including:

[0063] The probability p of successful access for the m-th user of type k′ is... succ,lm′ for:

[0064]

[0065] Where, p b,k′ Let τ represent the backoff probability of the k′-th type of user, τ be the pilot sequence, and p b,k Let represent the withdrawal probability of the k-th type of user; The probability that the signal-to-interference ratio (SIR) of the m-th user of the k′-th type is greater than the threshold is calculated as follows:

[0066]

[0067] Among them, X ThP represents the signal-to-interference ratio threshold. mk′ Let β be the transmit power of the m-th user of the k′-th type. mk′l This refers to the large-scale fading between the m-th user of the k′-th type and the l-th (l=1,2,...,L) access point;

[0068] P nk′ β represents the transmit power of the nth user of the k′th type. nk′l This represents the large-scale fading between the nth user of the k′th type and the lth access point;

[0069] P nk Let β represent the transmit power of the nth user of the kth type. nkl This represents the large-scale fading between the nth user of the kth type and the lth access point.

[0070] Step 2-2: Calculate the system's average spectral efficiency using the access success probability of each user. This includes:

[0071] The system's average spectral efficiency S is:

[0072]

[0073] Among them, S mk′ The spectral efficiency of the m-th user of the k′-th type is expressed as follows:

[0074]

[0075] Where T represents the channel coherence time. The power of interference plus noise is expressed as follows:

[0076]

[0077] Where Δ1 represents The first calculation factor, Δ2 table The second calculation factor.

[0078] Steps 2-3, with the objective of maximizing the average spectral efficiency of each cell within the system's coverage area, use the gradient descent method to solve the optimization problem, calculating the optimal random access backoff probability for each type of user, as specifically expressed below:

[0079]

[0080]

[0081] Where, p b,k Let k be the withdrawal probability of the k-th type of user. Let be the optimal backoff probability for the k-th type of user. This represents the upper limit of the latency constraint for the k-th type of user.

[0082] The calculation of the optimal random access backoff probability for each type of user includes the following steps:

[0083] Step 2-3-1: Randomly generate the initial backoff probability for all types of users, i.e. Set step sizes v1 and v2, error Ξ, and iteration variable t;

[0084] The step sizes v1 and v2, the error Ξ, and the iteration variable t are randomly generated.

[0085] Step 2-3-2: Calculate the gradient vector at the t-th time step. Let be the backoff probability of the k-th type of user in the t-th iteration. The system average spectral efficiency is calculated in the t-th iteration.

[0086] Step 2-3-3: Calculate the backoff probability of the k-th type of user in the (t+1)-th iteration. like The constraints are not met. Then let Then proceed to the next step;

[0087] Steps 2-3-4: Recalculate the system average spectral efficiency for the k-th type of user in the (t+1)-th iteration. judge If the condition is not met, then set t = t + 1 and repeat steps 2-3-2 to 2-3-4 until... If true, then the optimal backoff probability for the k-th type of user is:

[0088] Step 2-3-5: Execute steps 2-3-2 to 2-3-4 once for each increment of k, thereby obtaining the optimal backoff probability for all types of users.

[0089] Step 3: After obtaining the optimal backoff parameters, the access point broadcasts the optimal backoff parameters to all users. Users are randomly activated according to the optimal backoff parameters broadcast by the access point. Users who are successfully activated randomly select pilots from the predefined pilot pool and send them to the access point.

[0090] Step 4: The access point determines conflicting users by checking if the signal-to-interference ratio (SIR) of the user with the strongest signal among those using the same pilot signal is greater than a threshold. If so, the base station identifies the user with the strongest signal and allows that user to access the system. Otherwise, all conflicting users are considered to have failed to access the system and must wait for the next access attempt.

[0091] The determination of conflicting users involves selecting users with the highest signal strength among those using the same pilot signal and checking if their signal-to-interference ratio (SIR) is greater than a threshold, i.e., checking if the following formula holds true:

[0092]

[0093] Among them, SIR mk′ G represents the signal-to-interference ratio (SIR) of the m-th user of the k′-th type. lmk′ Let g be the channel vector between the m-th user of type k′ and the l-th access point. lnk′ Let g be the channel vector between the nth user of type k′ and the lth access point. lnk Let n be the channel vector between the nth user of the kth type and the lth access point. and This is an indicator function. It indicates whether the m-th user of type k′ and the n-th user of type k′ are conflicting users. This indicates whether the m-th user of type k′ and the n-th user of type k′ are conflicting users.

[0094] The value of the indicator function is 1, which indicates a conflict between the two, and vice versa.

[0095] Example:

[0096] In a specific embodiment of the present invention, a method for unlicensed random access of hybrid users applicable to cellular massive MIMO is disclosed in detail. The method specifically includes the following steps:

[0097] S1. Deploy a central processing unit and multiple access points over a large area. Each access point and the central processing unit are connected via a backhaul link. Various types of mixed users are evenly distributed within this area.

[0098] Furthermore, the steps in S1 can be further refined as follows:

[0099] S11. The total number of access points is L = 10, and the number of antennas on each access point is M = 200.

[0100] S12. The total number of users is N = 180, divided into K = 2 types. The number of users in the kth (k = 1, 2) type is N. k ,Right now Where N1 = 120, N2 = 50.

[0101] S2. Set pilot length τ = 90, and conflict user signal-to-noise ratio identification threshold X. Th =0.001, the transmit power P of the m-th user of the k′-th type. mk′=0dB. The system's average spectral efficiency is calculated based on the number of users, the number of access point antennas, and the probability of successful user access. Then, under time delay constraints, the gradient descent algorithm is used to optimize the user backoff parameters to maximize the system's average spectral efficiency. The specific steps include:

[0102] S21, The probability of successful access for the m-th user of the k′-th type is:

[0103]

[0104] in

[0105]

[0106] p b,k′ Let X represent the backoff probability of the k′-th type of user, τ be the pilot sequence, and X be the backoff probability. Th The signal-to-noise ratio difference between conflicting users is represented by P. mk′ Let β be the transmit power of the m-th user of the k′-th type. mk′l This refers to the large-scale fading between the m-th user of the k′-th type and the l-th (l=1,2,...,L) access point.

[0107] S22. Using the access success probability of each user, the average spectral efficiency of the system can be calculated as follows:

[0108]

[0109] in

[0110] Where T represents the channel coherence time. The power of interference plus noise is expressed as follows:

[0111]

[0112]

[0113] T is the channel coherence time;

[0114] S23. With the goal of maximizing the average spectral efficiency S of each cell, the gradient descent algorithm is used to solve the optimization problem, and the optimal random access backoff probability for each type of user can be obtained.

[0115]

[0116]

[0117] Where, p b,k Let k be the withdrawal probability of the k-th type of user. Let be the optimal backoff probability for the k-th type of user. This represents the upper limit of the latency constraint for the k-th type of user.

[0118] Furthermore, the steps in S23 can be further refined as follows.

[0119] S231. Let the initial backoff probability of all types of users be generated randomly, that is... Step size v1 = 0.1, v2 = 1, error Ξ = 10 -6 The iteration variable t = 0;

[0120] S232. Calculate the gradient vector at the t-th iteration. Let be the backoff probability of the k-th type of user in the t-th iteration;

[0121] S233. Calculate the backoff probability of the k-th type of user in the (t+1)-th iteration. like The constraints are not met. Then let Then proceed to the next step; S234, recalculate the system average spectral efficiency for the k-th type of user in the (t+1)-th iteration. judge If the condition is not met, then set t = t + 1 and repeat steps S232-S234 until... If true, then the optimal backoff probability for the k-th type of user is: S235. Let k increase once and execute steps S232-S234 once to obtain the optimal backoff probability for all types of users.

[0122] S3. After obtaining the optimal backoff probability, the access point broadcasts it to all users. Users are randomly activated based on the backoff parameters broadcast by the access point. Users who are successfully activated randomly select a pilot from the predefined pilot pool and send it to the access point.

[0123] S4. The access point determines whether the signal-to-interference-plus-noise ratio (SIR) of the user with the strongest signal strength among conflicting users (users with the same pilot signal are called conflicting users) is greater than a threshold, i.e., whether the following formula holds true.

[0124]

[0125] Where g lmk′ Let m be the channel vector between the m-th user of type k′ and the l-th access point, and let the indicator function be... This indicates whether the m-th user of type k′ and the n-th user of type k′ are conflicting users, i.e. A 0 indicates a conflict between the two, and a 0 indicates no conflict. If the above formula is true, the base station can identify the m-th user of the k′-th type. If it is not true, all conflicting users are considered to have failed to access and must wait for the next attempt to access.

[0126] In this embodiment, the impact of time delay constraints on the optimal backoff probability is shown in Table 1:

[0127] Table 1. Experimental data on the optimal backoff probability under different time delay constraints.

[0128] Table 1 illustrates the impact of delay constraints on the optimal backoff probability in a cellular-free massive MIMO system. As can be seen from the table, when... fixed When, the best p b,1 along with The value decreases as the delay increases, but there is a time delay constraint threshold. Once this threshold is exceeded, the optimal p... b,1 The value remains essentially unchanged. This section refers to this delay constraint threshold as the minimum delay easing boundary value. This indicates that when the user's delay constraint reaches the minimum delay easing boundary value, its impact on the user's optimal backoff probability is relatively weak. When the number of users 1 remains constant and the number of users 2 decreases, the optimal p... b,2 Increase, meaning an increase in the number of connected users, while simultaneously optimizing p b,1 This means that the backoff parameters of different types of users are highly coupled.

[0129] In its specific implementation, this application provides a computer storage medium and a corresponding data processing unit. The computer storage medium is capable of storing a computer program, which, when executed by the data processing unit, can run the invention's content regarding a method for unlicensed random access to mixed users in cellular-free massive MIMO, as well as some or all of the steps in various embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0130] Those skilled in the art will clearly understand that the technical solutions in the embodiments of the present invention can be implemented using computer programs and their corresponding general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of computer programs, i.e., software products. These computer program software products can be stored in a storage medium and include several instructions to cause a device containing a data processing unit (which may be a personal computer, server, microcontroller, MUU, or network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of the present invention.

[0131] This invention provides a concept and method for unlicensed random access of mixed users in cellular massive MIMO. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment of the invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. A method for unlicensed random access of mixed users in cellular-free massive MIMO, characterized in that, Includes the following steps: Step 1: In the non-cellular massive MIMO system, a central processing unit and one or more access points are deployed within a preset range. Each access point and the central processing unit are connected through a backhaul link. All users are evenly distributed within this area, and the users are divided into different types. Step 2: Calculate the optimal backoff parameters, that is, calculate the optimal random access backoff probability for each type of user: Calculate the average spectral efficiency of the system based on the number of users, the number of access point antennas, and the probability of successful user access. Then, under the condition of time delay constraints, use the gradient descent algorithm to optimize the user backoff probability to maximize the average spectral efficiency of the system. Step 3: After obtaining the optimal backoff parameters, the access point broadcasts the optimal backoff parameters to all users. Users are randomly activated according to the optimal backoff parameters broadcast by the access point. Users who are successfully activated randomly select pilots from the predefined pilot pool and send them to the access point. Step 4: The access point determines conflicting users by checking whether the signal-to-interference ratio (SIR) of the user with the strongest signal among the users with the same pilot signal is greater than the threshold. If so, the base station identifies the user with the strongest signal and allows that user to access the system. Otherwise, all conflicting users are considered to have failed to access the system and must wait for the next access attempt. Step 2 includes step 2-1, calculating the probability of a user's successful access, specifically including: No. Type 1 Probability of successful access for each user for: ； in, Indicates the first The probability of withdrawal for each type of user It is a pilot sequence. Indicates the first The probability of withdrawal for different types of users; Indicates the first Type 1 The probability that a user's signal-to-interference ratio (SINR) is greater than a threshold is calculated as follows: ； in, Indicates the signal-to-interference ratio threshold. For the first Type 1 Transmit power of each user For the first Type 1 The user and the first Large-scale fading between access points; Indicates the first Type 1 Transmit power of each user Indicates the first Type 1 The user and the first Large-scale fading between access points; Indicates the first Type 1 Transmit power of each user Indicates the first Type 1 The user and the first Large-scale fading between access points; Step 2-2, calculate the system's average spectral efficiency, specifically including: System average spectral efficiency for: ； in, Indicates the first Type 1 The spectral efficiency for an individual user is calculated as follows: ； in, Indicates the channel coherence time. The power of interference plus noise is expressed as follows: ； in, express The first calculation factor, surface The second calculation factor.

2. The method for unlicensed random access of mixed users in cellular-free massive MIMO according to claim 1, characterized in that, The cellular-free massive MIMO system described in step 1 specifically includes: The total number of access points is The number of antennas on each access point is ; The total number of users is It is divided into 100 parts. Type; First The number of users of each type is ,Right now .

3. The method for unlicensed random access of mixed users in non-cellular massive MIMO according to claim 2, characterized in that, Step 2, which involves calculating the optimal random access backoff probability for each type of user, specifically includes the following steps: Step 2-1: Calculate the probability of a user's successful access; Step 2-2: Calculate the system's average spectral efficiency using the access success probability of each user; Steps 2-3 aim to maximize the average spectral efficiency of each cell within the coverage area of ​​this system. The gradient descent method is used to solve the optimization problem and calculate the optimal random access backoff probability for each type of user.

4. The method for unlicensed random access of mixed users in non-cellular massive MIMO according to claim 3, characterized in that, The calculations described in steps 2-3 yield the optimal random access backoff probability for each type of user, as specifically expressed below: ； in, For the first The probability of withdrawal for each type of user For the first The optimal backoff probability for each type of user. For the first The upper limit of latency constraints for each type of user.

5. The method for unlicensed random access of mixed users in non-cellular massive MIMO according to claim 4, characterized in that, Step 4, which involves determining conflicting users, specifically whether the signal-to-interference ratio (SIR) of the user with the highest signal strength among those using the same pilot signal is greater than a threshold, is based on whether the following formula holds true: ； in, Indicates the first Type 1 The ratio of individual user's creditworthiness For the first Type 1 The user and the first Channel vectors between access points For the first Type 1 The user and the first Channel vectors between access points For the first Type 1 The user and the first Channel vectors between access points and For indicator functions; indicating Type 1 The user and the first Type 1 Whether a user is a conflicting user, an indicator function. express Type 1 The user and the first Type 1 Is the user a conflicting user? 6. The method for unlicensed random access of mixed users in non-cellular massive MIMO according to claim 5, characterized in that, In step 4, a value of 1 for the indicator function indicates a conflict between the two, while a value of 0 indicates no conflict.

7. The method for unlicensed random access of mixed users in cellular-free massive MIMO according to claim 6, characterized in that, Steps 2-3 describe the calculation of the optimal random access backoff probability for each type of user, which specifically includes the following steps: Step 2-3-1: Randomly generate the initial backoff probability for all types of users, i.e. Set step size and ,error Iteration variables ; Step 2-3-2, calculate the first... gradient vector of the first order , For the first The iteration of the ... The probability of withdrawal for each type of user For the first The system average spectral efficiency obtained from the next iteration; Step 2-3-3, calculate the first... The iteration of the ... The probability of withdrawal for different types of users ;like The constraints are not met. Then let Then proceed to the next step; Steps 2-3-4: Recalculate the first... The iteration of the ... The average spectral efficiency of the system for each type of user ,judge Whether it is true or not, if it is not true, then let And repeat steps 2-3-2 to 2-3-4 until... If true, then the first... The optimal backoff probability for this type of user is ; Steps 2-3-5, let Each time an additional step is added, steps 2-3-2 to 2-3-4 are executed once to obtain the optimal backoff probability for all types of users.

8. The method for unlicensed random access of mixed users in non-cellular massive MIMO according to claim 7, characterized in that, Step size described in step 2-3-1 and ,error and iteration variables , is generated randomly.

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