URLLC-oriented large-scale MIMO open-loop communication power control and parameter optimization method

Abstract

The invention discloses a URLLC-oriented large-scale MIMO open-loop communication power control and parameter optimization method, which is applicable to the field of communication. The method comprises the following steps: constructing an open-loop communication framework comprising two layers of processing mechanisms based on an unlicensed uplink access framework; by establishing a joint channel estimation and competition detection model, an adaptive pilot frequency power control scheme based on a cell coverage radius is provided, and accurate user signal separation under the condition of no user prior information is realized; a global optimal parameter configuration algorithm is developed for microsecond-level delay constraints, and uplink bandwidth consumption minimization is realized through three-dimensional joint optimization (block length, pilot frequency sequence length and data packet repetition times) by taking the maximum available bandwidth, a block length upper limit and a packet loss probability threshold as inputs; in combination with the large-scale MIMO channel hardening characteristic, a load power dynamic adjustment mechanism is designed, and the transmission reliability equivalent to that of a closed-loop system is maintained while HARQ retransmission is eliminated.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a URLLC-oriented large-scale MIMO open-loop communication power control and parameter optimization method. Background Art

[0002] Ultra-Reliable and Low-Latency Communications (URLLC), one of the three core scenarios for fifth-generation mobile communications (5G), defines stringent technical specifications for one-way air interface latency ≤ 1ms and transmission reliability ≥ 99.999%. As sixth-generation mobile communications (6G) expands into vertical sectors such as Industry 4.0 and holographic communications, the technical requirements for URLLC scenarios are increasing exponentially: typical service latency thresholds are compressed to microseconds (50-200μs), and reliability requirements exceed 99.9999%. This technological leap from quantitative to qualitative change poses a fundamental restructuring challenge for existing 4G / 5G-based communication architectures. Current cellular communication systems generally utilize closed-loop transmission mechanisms to achieve reliable communication. These core technical approaches include: 1) the physical layer Hybrid Automatic Repeat Request (HARQ) retransmission mechanism; 2) segment reassembly at the radio link control layer; and 3) transport layer flow control. However, the inherent feedback latency, retransmission processing delay, and protocol stack layer-by-layer confirmation mechanisms inherent in closed-loop architectures make it difficult to break through the millisecond bottleneck of end-to-end latency. Especially under the microsecond latency constraints of 6G, traditional feedback mechanisms will incur a very high latency contribution.

[0003] Existing improvement plans focus on physical layer short packet transmission technology, reducing physical layer latency by compressing the transmission time interval. However, research shows that in typical 6G deployment scenarios, the proportion of wireless access network latency is very low, while queuing delay and protocol stack processing delay become the main bottlenecks. More seriously, the sharp decrease in channel coding redundancy caused by short packet transmission causes the bit error rate curve to collapse. The current technical system has three sets of irreconcilable contradictions: 1) The structural conflict between the microsecond latency requirement and the multi-level feedback mechanism; 2) The contradiction between the ultra-high reliability requirement and the Shannon limit of limited coding gain; 3) The contradiction between the need to improve spectrum efficiency and the engineering realization of large-bandwidth microsecond synchronization accuracy (when the carrier spacing in the Sub-6GHz band is ≤15kHz, the symbol duration is ≥66.7μs). These contradictions reveal that the traditional technical route based on incremental optimization has reached its theoretical ceiling, and it is urgent to build a new communication architecture that breaks through the existing communication protocol. Summary of the Invention

[0004] Purpose of the Invention: This invention aims to provide a method for power control and parameter optimization in massive MIMO open-loop communications that can achieve microsecond end-to-end latency for URLLC services. This method aims to achieve microsecond access latency while guaranteeing greater than 99.9999% transmission reliability.

[0005] Technical solution: To achieve this purpose, the present invention adopts the following technical solution:

[0006] The present invention provides a method for massive MIMO open-loop communication power control and parameter optimization for URLLC, comprising the following steps:

[0007] S1. Build a signal model and receive processing framework for massive MIMO uplink unlicensed access. Define the pilot signal matrix and data signal vector received by the base station for user contention detection, channel estimation, and signal decoding under non-feedback conditions.

[0008] S2. Based on the base station's known maximum cell coverage radius and noise power spectral density, an adaptive power control strategy is designed that is correlated with the coverage radius. This strategy dynamically sets the transmit power thresholds for user pilots and data payloads through system broadcasts, ensuring that the base station can complete competing user detection and signal processing without prior information.

[0009] S3. A frequency diversity-driven open-loop global parameter optimization algorithm is proposed. Taking the maximum available bandwidth, the upper limit of the block length, and the packet loss probability threshold as input, the algorithm jointly optimizes the data block length, pilot sequence length, and the number of packet repetitions to generate a parameter configuration that minimizes uplink bandwidth usage. The algorithm then broadcasts the preconfigured physical layer parameters, including the time-frequency resource pool, frame structure, and packet replication strategy, to users.

[0010] S4. Active users autonomously adjust the pilot and data transmission power based on the received broadcast information, randomly select resource units (RUs) and pilot sequences for unauthorized access, and determine the access status based on whether a response from the base station is received after the transmission is completed. If no response is detected, the access failure with microsecond-level latency is reported to the upper layer, triggering the same error reporting process as in the closed-loop mode.

[0011] Furthermore, the step S1 specifically includes the following sub-steps:

[0012] S11. Utilization represents the set of single-antenna users, Represents the channel vector between the base station and the kth user on a certain resource unit (RadioUnit, RU), where represents the complex field, Indicates that a vector obeys a complex Gaussian distribution with expectation x and variance y, α k =1 / (1+(r k / r0) α ) represents the large-scale fading coefficient of user k, r k represents the distance between user k and the base station, a and r0 are the fading index and reference distance respectively, I M Represents the identity matrix of size M×M; l is used to represent the RU index, then the set of active users on the l-th RU is expressed as Using τ p Represents the pilot length, then the number of orthogonal pilots is also τ p , then the orthogonal pilot set can be expressed as The orthogonal pilot matrix is And the column {φ i}satisfy i≠j and ||φ i || 2 =1; use represents the pilot index selected by user k;

[0013] S12. The pilot signal received by the base station side on the lth RU is expressed as:

[0014]

[0015] in, is the pilot transmission signal-to-noise ratio (SNR) of the kth user, represents a normalized complex Gaussian noise matrix;

[0016] S13. The data reception signal of the base station on the lth RU is expressed as

[0017]

[0018] in, is the data transmission SNR of the k′th user, s k′ Indicates that it obeys the complex Gaussian distribution The data signal,

[0019] represents a normalized additive white Gaussian noise vector.

[0020] Furthermore, the step S2 specifically includes the following sub-steps:

[0021] S21. Define the large-scale fading coefficient range under the effective coverage of the cell as [α min , α max ], where α min and α max are the minimum and maximum large-scale fading coefficients, respectively;

[0022] S22. The base station sets the minimum pilot transmission SNR threshold. Ability to achieve nearly negligible channel estimation error without pilot contamination; setting the Signal to Interference plus Noise Ratio (SINR) threshold It enables it to achieve extremely low physical layer decoding error probability in the absence of competition; set the maximum allowed active threshold to And broadcast the above parameters to all users in the cell;

[0023] S23. The kth active user has an active probability p k Activate and, based on the broadcast message, use the massive MIMO channel hardening characteristics to obtain the large-scale fading coefficient α k Then, parse the broadcast message and obtain the pilot transmission SNR threshold ζ ac and other pre-configured parameters; adaptively adjust the pilot transmission SNR:

[0024]

[0025] The user adaptively adjusts the data transmission SNR according to the SINR threshold: The user side randomly selects RUs and pilots for uplink open-loop communication;

[0026] S24. The base station side receives the pilot signal in turn through the pilot on any lth RU Decouple and obtain the decoupled received pilot signal:

[0027]

[0028] in, Indicates that pilot t is selected on the lth RU k The set of active users, represents the normalized complex Gaussian noise vector; according to the hardening characteristics of large-scale MIMO channels, when the number of antennas is extremely large, the decoupled signal variance is approximately expressed as

[0029]

[0030] Judging by fluctuation error Is it true? ∈ represents the fluctuation error, which is used to measure the approximate error caused by the channel hardening characteristics and is set by the base station side according to the coverage radius and channel fading. If it is true, it means that there is no competition and it is necessary to continue to perform channel estimation and signal detection under the condition of unknown user prior information. If it is not true, it means that the pilot t on the lth RU k There is contention and access fails.

[0031] S25. Assign index k to the non-contention user detected on the lth RU, and decouple the received pilot signal to be expressed as

[0032]

[0033] in, Denotes the equivalent channel vector of the kth user; defines the equivalent channel fading coefficient ζ′=ζ ac -1 / τ p , using the known features on the base station side to perform k MMSE channel estimation and obtain the effective channel estimation vector and the estimated variance

[0034] S26. Using the estimated equivalent channel estimation Perform signal detection; define the normalized merge vector The decoupled data signal is expressed as

[0035]

[0036] in, represents the normalized effective noise; when MRC is used, When using a ZF receiver, d k =a k / ‖a k ‖, where a k is a matrix The kth column of Indicates all estimated values on the lth RU The equivalent channel matrix composed of .

[0037] Furthermore, the step S3 specifically includes the following sub-steps:

[0038] S31. Using URLLC end-to-end delay threshold T max , calculate the system's maximum uplink access delay T u =T max -T d -T p , and obtain the number of available time slots Where T0 represents the time length of each time slot; input bandwidth B max and the parameter set {b, τ max , ε max , K min , K max};

[0039] S32. Initialize bandwidth B temp =B max , uplink transmission block length τ u =b;

[0040] S33. If τ u ≤τ max , initialize the maximum number of active users K a =K min ; Otherwise, output the optimal solution {τ u , K a ,β};

[0041] S34. If K a ≤K max , initialize the number of packet repetitions β = 2; otherwise, let τ u =τ u +1, return to step S33;

[0042] S35. When satisfied When calculating the optimized total bandwidth

[0043]

[0044] Otherwise, let K a =K a +1, return to step S34;

[0045] S36. If B tot ≥B temp , then K a =K a +1, and return to step S34; otherwise, calculate the upper bound of the overall packet loss probability P according to the above parameters. ub , no competition probability and decoding error probability And judge whether it meets the reliability constraints; P ub The specific calculation method is as follows:

[0046]

[0047] in, And function

[0048]

[0049] for and The calculation is as follows:

[0050]

[0051] Where N = B tot n t ×n / (f s S) represents the number of RUs available on a given time-frequency resource block, n is the number of OFDM symbols in a TTI, and f sis the subcarrier spacing, S is the packet length of each user, and γ is the received SINR, all of which are set by the base station side;

[0052] S37. If Established, the iterative optimization solution {τ u , K a , β}, and set B temp =B tot ; Otherwise, no operation is performed;

[0053] S38. Set β=β+1 and return to step S35.

[0054] Furthermore, the step S4 specifically includes the following sub-steps:

[0055] S41. Each active user parses the periodic broadcast message from the base station and obtains N RUs from the available time-frequency resource pool. Each RU can accommodate a packet length of S symbols for transmitting pilot and data information. The active user randomly selects β RUs and selects them from τ p Select one of the pilot signals for access;

[0056] S42. Based on the selected pilot sequence, the user sends the same signal on the selected β RUs, and each RU is independent of each other;

[0057] S43. After transmission is complete, the active user waits for an access response within the time window specified by the base station. If successful, the user proceeds with normal scheduled transmission based on the message in the response. If reception fails, the user directly reports the microsecond-level access failure to the upper layer and triggers the same error reporting process as in the closed-loop mode.

[0058] Beneficial effects: The present invention discloses a method for power control and parameter optimization of massive MIMO open-loop communication for URLLC, and its technical solution includes three core innovations: first, constructing an open-loop communication architecture based on massive MIMO, and realizing microsecond-level end-to-end delay and ultra-high reliability transmission through low-frequency band physical layer design, breaking through the technical limitations of existing 5G protocols in delay control and reliability assurance; second, developing a system configuration algorithm based on multi-parameter joint optimization, and significantly reducing spectrum resource requirements by dynamically adjusting the coupling relationship between open-loop communication parameters and transmission bandwidth; finally, designing a power control strategy that does not require a priori channel state information, combined with the massive MIMO channel hardening characteristics, to achieve rapid and accurate detection of user equipment in the initial access scenario, greatly improving user access efficiency. This solution effectively solves the key technical challenges of latency, reliability and spectrum efficiency in URLLC scenarios through architectural innovation and algorithm collaborative design. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1Schematic diagram of an open-loop communication framework according to a specific embodiment of the present invention;

[0060] Figure 2 Flowchart of an open-loop communication parameter optimization algorithm according to a specific embodiment of the present invention;

[0061] Figure 3 This is a performance comparison diagram of the method according to a specific embodiment of the present invention and the traditional closed-loop communication mode with two retransmissions, including end-to-end delay and transmission reliability, where each bandwidth value on the horizontal axis is obtained by optimizing the proposed algorithm. DETAILED DESCRIPTION

[0062] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention.

[0063] The present invention considers a large-scale MIMO system, using represents the set of single-antenna users, Represents the channel vector between the base station and the kth user on a certain RU, where represents the complex field, Indicates that a vector obeys a complex Gaussian distribution with expectation x and variance y, α k =1 / (1+(r k / r0) a ) represents the large-scale fading coefficient of user k, r k represents the distance between user k and the base station, a and r0 are the fading index and reference distance respectively; l is used to represent the RU index, and the set of active users on the lth RU is expressed as Using τ p Represents the pilot length, then the number of orthogonal pilots is also τ p , then the orthogonal pilot set can be expressed as pilot matrix And the column {φ i}satisfy i≠j and ||φ i || 2 =1; use represents the pilot index selected by user k;

[0064] The pilot signal received by the base station side at the lth RU is expressed as:

[0065]

[0066] in, is the pilot transmission SNR of the kth user, represents a normalized complex Gaussian noise matrix;

[0067] The data reception signal of the base station on the lth RU is expressed as

[0068]

[0069] in, is the data transmission SNR of the k′th user, s k′ Indicates that it obeys the complex Gaussian distribution The data signal, represents a normalized additive white Gaussian noise vector.

[0070] The large-scale MIMO open-loop communication power control and parameter optimization method designed by the present invention is shown in the following schematic diagram: Figure 1 As shown, it is implemented by the base station and user-side related operations. Specifically, it includes:

[0071] S1. The base station calculates the large-scale fading coefficient interval [α min , α max ];

[0072] S2. The base station sets the minimum pilot transmission power that satisfies the approximately negligible channel estimation error. And calculate the maximum running activity threshold Setting SINR threshold This enables it to achieve extremely low physical layer decoding error probability in the absence of contention;

[0073] S3. The base station calculates the optimal parameter configuration {τ u , K a ,β}, the algorithm flow is as follows Figure 2 As shown, specifically including:

[0074] S31. Using URLLC end-to-end delay threshold T max , calculate the system's maximum uplink access delay T u =T max -T d -T p , and obtain the number of available time slots Where T0 represents the time length of each time slot; input bandwidth B max and the parameter set {b, τ max , ε max , K min , K max};

[0075] S32. Initialize bandwidth B temp =B max , uplink transmission block length τ u =b;

[0076] S33. If τ u ≤τ max , initialize the maximum number of active users K a =K min ; Otherwise, output the optimal solution {τ u , K a ,β};

[0077] S34. If K a ≤K max , initialize the number of packet repetitions β = 2; otherwise, let τ u =τ u +1, return to step S33;

[0078] S35. When satisfied When calculating the optimized total bandwidth

[0079]

[0080] Otherwise, let K a =K a +1, return to step S34;

[0081] S36. If B tot ≥B temp , then K a =K a +1, and return to step S34; otherwise, calculate the upper bound of the overall packet loss probability P according to the above parameters. ub , no competition probability and decoding error probability And judge whether it meets the reliability constraints; P ub The specific calculation method is as follows:

[0082]

[0083] in, And function

[0084]

[0085] for and The calculation is as follows:

[0086]

[0087] Where N = B tot n t ×n / (f s S) represents the number of RUs available on a given time-frequency resource block, n is the number of OFDM symbols in a TTI, and f sis the subcarrier spacing, S is the packet length of each user, and γ is the received SINR, all of which are set by the base station side;

[0088] S37. If Established, the iterative optimization solution {τ u , K a , β}, and set B temp =B tot ; Otherwise, no operation is performed;

[0089] S38. Set β=β+1 and return to step S35.

[0090] S4. The base station sets the parameters Send it to all users in the cell through a predetermined broadcast channel;

[0091] S5. The kth active user has an active probability p k Activate and, based on the broadcast message, use the massive MIMO channel hardening characteristics to obtain the large-scale fading coefficient α k Then, parse the broadcast message and obtain the pilot transmission SNR threshold ζ ac and other pre-configured parameters; adaptively adjust the pilot transmission SNR:

[0092]

[0093] The user adaptively adjusts the data transmission SNR according to the SINR threshold: Active users randomly select β RUs and select them from τ p One pilot is selected for access. Based on the selected pilot sequence, the user sends the same signal on the selected β RUs, and each RU is independent of each other. After the transmission is completed, the active user waits for the access response within the time window specified by the base station. If the access response is received successfully, the subsequent normal scheduling transmission is carried out according to the message in the response. If the reception fails, the microsecond-level delay access failure is directly reported to the upper layer and the same error reporting process as the closed-loop mode is triggered.

[0094] S6. The base station receives the pilot signal (1) on any e-th RU in turn through the pilot signal Decouple and obtain the decoupled received pilot signal:

[0095]

[0096] in, Indicates that pilot t is selected on the e-th RU k The set of active users, represents the normalized complex Gaussian noise vector; according to the hardening characteristics of large-scale MIMO channels, when the number of antennas is extremely large, the decoupled signal variance is approximately expressed as

[0097]

[0098] Judging by fluctuation error Is it true? ∈ represents the fluctuation error, which is used to measure the approximate error caused by the channel hardening characteristics and is set by the base station side according to the coverage radius and channel fading. If it is true, it means that there is no competition and it is necessary to continue to perform channel estimation and signal detection under the condition of unknown user prior information. If it is not true, it means that the pilot t on the lth RU k There is contention and access fails.

[0099] S7. Assign index k to the non-contention user detected on the e-th RU, and decouple the received pilot signal to be expressed as

[0100]

[0101] in, Denotes the equivalent channel vector of the kth user; defines the equivalent channel fading coefficient ζ′=ζ ac -1 / τ p , using the known features on the base station side to perform k MMSE channel estimation and obtain the effective channel estimation vector and the estimated variance

[0102] S8. Using the estimated equivalent channel estimation Perform signal detection; define the normalized merge vector The decoupled data signal is expressed as

[0103]

[0104] in, represents the normalized effective noise; when MRC is used, When using a ZF receiver, d k =a k / ||a k ||, where a k is a matrix The kth column of Indicates all estimated values on the e-th RU The equivalent channel matrix composed of .

[0105] Figure 3 The cell radius is 500m, there are a maximum of 1000 users in the cell, and the probability of each user being active is p k =10 -3When activating and launching random access, the large-scale MIMO open-loop communication power control and parameter optimization method for URLLC traffic in this specific embodiment is adopted to achieve end-to-end latency (E2E latencyT) in open-loop mode and double retransmission-closed-loop mode. e for OlC vs E2E latency for closed-loop mode) and transmission reliability (Packet lossprobability and vs Packet loss probability for OLC) performance comparison chart. The frame structure, bandwidth, resource selection, and transmit power of the closed-loop mode are identical to those of open-loop communication. The difference is that the closed-loop mode is subject to feedback waiting and reserved retransmission resources, which limits the number of available time slots. This can serve as a comparison scheme for the implementation scheme proposed in this invention. Figure 3 It can be seen that the end-to-end delay obtained by adopting this specific implementation method can be reduced to 0.467ms at most, and the reliability can be no less than 1-10 -9 , while the latency of the closed-loop mode is maintained at a minimum of 1ms when using basically the same configuration, demonstrating the effectiveness of the proposed implementation plan.

[0106] The technical means disclosed in the solutions of the present invention are not limited to those disclosed in the above-mentioned embodiments, but also include technical solutions composed of any combination of the above-mentioned technical features. It should be noted that those skilled in the art may make various improvements and modifications without departing from the principles of the present invention, and such improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for massive MIMO open-loop communication power control and parameter optimization for URLLC, characterized in that: The method is based on a communication system including an M-antenna base station and K single-antenna users, wherein the channels between all users and the base station are quasi-static block fading channels, and the base station has no prior information on user identities, channel coefficients, and activity states. The method comprises the following steps: S1. Build a signal model and receive processing framework for massive MIMO uplink unlicensed access. Define the pilot signal matrix and data signal vector received by the base station for user contention detection, channel estimation, and signal decoding under non-feedback conditions. S2. Based on the base station's known maximum cell coverage radius and noise power spectral density, an adaptive power control strategy is designed that is correlated with the coverage radius. This strategy dynamically sets the transmit power thresholds for user pilots and data payloads through system broadcasts, ensuring that the base station can complete competing user detection and signal processing without prior information. S3. A frequency diversity-driven open-loop global parameter optimization algorithm is proposed. Taking the maximum available bandwidth, the upper limit of the block length, and the packet loss probability threshold as input, the algorithm jointly optimizes the data block length, pilot sequence length, and the number of packet repetitions to generate a parameter configuration that minimizes uplink bandwidth usage. The algorithm then broadcasts the preconfigured physical layer parameters, including the time-frequency resource pool, frame structure, and packet replication strategy, to users. S4. Active users autonomously adjust the pilot and data transmission power based on the received broadcast information, randomly select resource units (RUs) and pilot sequences for unauthorized access, and determine the access status based on whether a response from the base station is received after the transmission is completed. If no response is detected, the access failure with microsecond-level latency is reported to the upper layer, triggering the same error reporting process as in the closed-loop mode.

2. The method for massive MIMO open-loop communication power control and parameter optimization for URLLC according to claim 1, wherein: Step S1 includes: use represents the set of single-antenna users, Represents the channel vector between the base station and the kth user on a certain RU, where represents the complex field, Indicates that a vector obeys a complex Gaussian distribution with expectation x and variance y, α k =1 / (1+(r k / r0) a ) represents the large-scale fading coefficient of user k, I M represents the identity matrix of size M×M, r k represents the distance between user k and the base station, a and r0 are the fading index and reference distance respectively; l is used to represent the RU index, and the set of active users on the lth RU is expressed as ; Using τ p Represents the pilot length, then the number of orthogonal pilots is also τ p , then the orthogonal pilot set is expressed as , the orthogonal pilot matrix is , and the column {φ i }satisfy , i≠j and ||φ i || 2 =1; use represents the pilot index selected by user k; The pilot signal received by the base station side at the lth RU is expressed as: in, is the pilot transmission SNR of the kth user, represents a normalized complex Gaussian noise matrix; The data reception signal of the base station on the lth RU is expressed as in, is the data transmission SNR of the k′th user, s k′ Indicates that it obeys the complex Gaussian distribution The data signal, represents a normalized additive white Gaussian noise vector.

3. The method for massive MIMO open-loop communication power control and parameter optimization for URLLC according to claim 2, characterized in that: The large-scale fading coefficient interval under the condition of effective cell coverage defined in step S2 is [α min , α max ], where α min and α max are the minimum and maximum large-scale fading coefficients, respectively; Adaptive power control strategy and uplink contention detection specifically include: S31. The base station sets the minimum pilot transmission SNR threshold Ability to achieve nearly negligible channel estimation error in the absence of pilot contamination; setting SINR threshold Enables it to achieve extremely low physical layer decoding error probability in the absence of contention; calculates the maximum allowed active threshold And broadcast the above parameters to all users in the cell; S32. The kth active user has an active probability p k Activate and, based on the broadcast message, use the massive MIMO channel hardening characteristics to obtain the large-scale fading coefficient α k Then, parse the broadcast message and obtain the pilot transmission SNR threshold ζ ac and other pre-configured parameters; adaptively adjust the pilot transmission SNR: The user adaptively adjusts the data transmission SNR according to the SINR threshold: The user side randomly selects RUs and pilots for uplink open-loop communication. S33. The base station side receives the pilot signal in turn through the pilot on any lth RU Decouple and obtain the decoupled received pilot signal: in, Indicates that pilot t is selected on the lth RU k The set of active users, represents the normalized complex Gaussian noise vector; according to the hardening characteristics of large-scale MIMO channels, when the number of antennas is extremely large, the decoupled signal variance is approximately expressed as Judging by fluctuation error Is it true? ∈ represents the fluctuation error, which is used to measure the approximate error caused by the channel hardening characteristics and is set by the base station side according to the coverage radius and channel fading. If it is true, it means that there is no competition and it is necessary to continue to perform channel estimation and signal detection under the condition of unknown user prior information. If it is not true, it means that the pilot t on the lth RU k There is contention and access fails.

4. The method for massive MIMO open-loop communication power control and parameter optimization for URLLC according to claim 3, wherein: The channel estimation and signal detection method in step S33 under the condition of unknown user prior information specifically includes: Assign index k to the non-contention user detected on the lth RU, and decouple the received pilot signal to be expressed as in, Denotes the equivalent channel vector of the kth user; defines the equivalent channel fading coefficient ζ′=ζ ac -1 / τ p , using the known features on the base station side to perform k MMSE channel estimation and obtain the effective channel estimation vector and the estimated variance Using the estimated equivalent channel Perform signal detection; define the normalized merge vector The decoupled data signal is expressed as in, represents the normalized effective noise; when MRC is used, When using a ZF receiver, d k =a k / ‖a k ‖, where a k is a matrix The kth column of Indicates all estimated values on the e-th RU The equivalent channel matrix composed of .

5. The method for massive MIMO open-loop communication power control and parameter optimization for URLLC according to claim 1, characterized in that: In step S3, the URLLC end-to-end delay threshold T is used. max , calculate the system's maximum uplink access delay T u =T max -T d -T p , and obtain the number of available time slots Where T0 represents the time length of each time slot; input bandwidth B max and parameter set {b,τ max ,ε max ,K min ,K max }, where b and τ max Represent the number of information bits and the maximum block length, ε max is the maximum allowed packet loss probability threshold, K min and K max Represent the minimum and maximum thresholds of the number of active users respectively; Under the condition of satisfying the packet loss probability constraint and block length constraint, calculate B tot The minimum global optimization value of the open-loop parameter is {τ u , K a , β}, where K a is the maximum number of active users, and β is the number of packet repetitions. The specific steps of the frequency diversity-driven open-loop parameter global optimization algorithm include: S51. Initialize bandwidth B temp =B max , uplink transmission block length τ u =b; S52. If τ u ≤τ max , initialize the maximum number of active users K a =K min ; Otherwise, output the optimal solution {τ u ,K a ,β}; S53. If K a ≤K max , initialize the number of packet repetitions β = 2; otherwise, let τ u =τ u +1, return to step S52; S54. When satisfied When calculating the optimized total bandwidth Otherwise, let K a =K a +1, return to step S53; S55. If B tot ≥B temp , then K a =K a +1, and return to step S53; otherwise, calculate the upper bound of the overall packet loss probability P according to the above parameters. ub , no competition probability and decoding error probability , and judge whether it meets the reliability constraints; P ub The specific calculation method is as follows: in, , , and the function for and , calculated as follows: Where N = B tot n t ×n / (f s S) represents the number of RUs available on a given time-frequency resource block, n is the number of OFDM symbols in a TTI, and f s is the subcarrier spacing, S is the packet length of each user, and γ is the received SINR, all of which are set by the base station side; S56. If Established, the iterative optimization solution {τ u , K a , β}, and set B temp =B tot ; Otherwise, no operation is performed; S57. Set β=β+1 and return to step S54.

6. The method for massive MIMO open-loop communication power control and parameter optimization for URLLC according to claim 1, characterized in that: The step of the active user performing open-loop communication in step S4 specifically includes: Each active user parses the periodic broadcast message from the base station and obtains N RUs in the available time-frequency resource pool. Each RU can accommodate a packet length of S symbols for transmitting pilot and data information. The active user randomly selects β RUs and selects them from τ p One pilot is selected for access. Based on the selected pilot sequence, the user sends the same signal on the selected β RUs, and each RU is independent of each other. After the transmission is completed, the active user waits for the access response within the time window specified by the base station. If the access response is received successfully, the subsequent normal scheduling transmission is carried out according to the message in the response. If the reception fails, the microsecond-level delay access failure is directly reported to the upper layer and the same error reporting process as the closed-loop mode is triggered.

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