Time domain resource configuration method, device, electronic device and storage medium

Abstract

The present invention provides a time domain resource configuration method, device, electronic device and storage medium, wherein the method comprises: obtaining the error covariance of clock synchronization accuracy; constructing a time domain resource configuration optimization problem based on the error covariance of clock synchronization accuracy, the timing band length and the data packet arrival rate; wherein the time domain resource configuration optimization problem is a problem of maximizing the network throughput under the condition of ensuring the clock synchronization accuracy; solving the time domain resource configuration optimization problem to obtain the data packet arrival rate; determining the network throughput based on the data packet arrival rate and the timing band length. The time domain resource configuration method, device, electronic device and storage medium provided by the present invention calculate the maximized network throughput based on the timing band length and the data packet arrival rate obtained by solving the time domain resource configuration optimization problem, and since the maximized network throughput reflects the configuration of the time domain resources, the optimization of the time domain resource configuration is achieved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a time domain resource configuration method, device, electronic device and storage medium. Background Art

[0002] The fifth generation mobile network (5G) technology can provide low latency and ultra-reliable deterministic capabilities for industrial control systems and data traffic, and its wireless technology can provide system connectivity flexibility for the entire industrial manufacturing facilities and factories. Time Sensitive Network (TSN) is a series of IEEE 802 Ethernet standards developed by the IEEE802.1TSN Task Group, which can provide the network with low latency, low jitter and extremely low data loss rate capabilities, making Ethernet suitable for time-sensitive application scenarios with strict reliability and latency requirements. As a wireless communication, 5G can wirelessly connect industrial equipment such as sensors and actuators to the TSN network, which can give full play to the flexibility of 5G and the extremely low latency of TSN, and effectively reduce the laying of cables and the widespread use of mobile devices.

[0003] The coexistence of 5G and TSN networks places high demands on 5G deterministic communication capabilities. Not only does the 5G network need to provide Ultra Reliable Low Latency Communications (URLLC) capabilities, but it also needs to complete timing through the synchronous clock provided by the 5G air interface. Evaluating the determinism of latency in an uncertain wireless link environment, and further ensuring the accuracy of clock synchronization as a support for time domain resource allocation, is an issue that needs to be addressed urgently. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention provides a time domain resource configuration method, device, electronic device and storage medium.

[0005] The present invention provides a time domain resource configuration method, comprising:

[0006] Get the error covariance of clock synchronization accuracy;

[0007] A time domain resource configuration optimization problem is constructed based on the error covariance of the clock synchronization accuracy, the timing band length and the data packet arrival rate; wherein the time domain resource configuration optimization problem is a problem of maximizing the network throughput under the condition of ensuring the clock synchronization accuracy;

[0008] Solving the time domain resource configuration optimization problem to obtain the data packet arrival rate;

[0009] The network throughput is determined based on the packet arrival rate and the timing band length.

[0010] According to a time domain resource configuration method provided by the present invention, the error covariance of obtaining clock synchronization accuracy includes:

[0011] Construct a timestamp observation model based on timestamp information and clock state variables;

[0012] Evaluating the network delay determinism based on the timestamp observation model;

[0013] The error covariance of the clock synchronization accuracy is determined based on the evaluated timestamp observation model.

[0014] According to a time domain resource configuration method provided by the present invention, the timestamp observation model is constructed based on the timestamp information and the clock state variable, including:

[0015] Constructing the timestamp observation model based on formula (1);

[0016] z i,k =γ k [T i,1 +T i,2 -2t]=γ k [Cx i (k)+v i (k)] (1)

[0017] Among them, z i,k is the timestamp observation model, γ k is a binary Bernoulli random variable, i is the number of base station nodes, T i,1 Base station node gNB i To the timing terminal node UE j The moment when the time synchronization variable is sent, T i,2 Base station node gNB i Received from terminal node UE j The time to reply the time synchronization variable, j is the number of terminal nodes, t is the ideal reference time, C is the observation matrix, C = [0 2], x i (k) is the base station node gNB i The clock state variable, k is the kth cycle of the clock synchronization process, v i (k) is the observation noise, v i (k) = d i +Y i,k , v i (k) is a Gaussian random variable that follows N(0,R) distribution, d i is the fixed delay generated during synchronization, Y i,kis the random delay caused by the randomness of the wireless channel.

[0018] According to a time domain resource configuration method provided by the present invention, the network delay determinism is evaluated based on the timestamp observation model, including:

[0019] Solve the confidence level of the wireless network delay determinism in formula (2);

[0020] p d =p{t l <x<αt u}p{0 <w<(1-α)t u} (2)

[0021] Among them, p d is the confidence level of the wireless network delay determinism, α is the delay scaling factor, 0<α <t l / t u , t l is the lower bound of the sum of transmission delay and queuing delay, t u is the upper bound of the sum of transmission delay and queuing delay, x is the random variable of transmission delay, and w is the random variable of queuing delay of nodes in the wireless network; p{t l <x<αt u} is x falling on t l With αt u The probability value between z(t)=(2 L / (Wt) -1) / c, L is the length of the sent data packet, W is the bandwidth of the channel, and c is the constant coefficient of the received signal-to-noise ratio; p{0 <w<(1-α)t u} is w between 0 and (1-α)t u The probability value between θ satisfies the constraint E[e θA(1) ]E[e -θS(1) ]≤1, A(t) is the amount of data arriving in the time period (0, t), S(t) is the amount of data service in the time period (0, t); A(1) is the amount of data arriving per unit time, S(1) is the amount of data service per unit time, S[(1-α)t u ] is (0,(1-α)t u ) data service volume during the period;

[0022] Determine γ based on the confidence of the wireless network delay determinism k distribution.

[0023] According to a time domain resource configuration method provided by the present invention, the error covariance of clock synchronization accuracy is determined based on the evaluated timestamp observation model, including:

[0024] Determining the error covariance of the clock synchronization accuracy based on formula (3);

[0025] P k =E[e k e k T ] (3)

[0026] Among them, P k is the error covariance of the clock synchronization accuracy, e k is the estimated error of the clock state variable, e k T for e k The transposed matrix of x k is the clock state variable, is the value of x under the condition that the observed value is known k The estimate,

[0027] According to a time domain resource configuration method provided by the present invention, the time domain resource configuration optimization problem constructed based on the error covariance of the clock synchronization accuracy, the timing band length and the data packet arrival rate is expressed as:

[0028] P1: max{U(B,λ)};

[0029]

[0030] C2:Tr(E[P k ])+ΔE k ≤B <T S ;

[0031] C3: 0<λ<1;

[0032] Wherein, P1 is the objective function of the time domain resource allocation optimization problem, C1 to C3 are the constraints of the objective function P1, and U(B,λ)=R b , B is the length of the timing band, λ is the packet arrival rate, R b is the network throughput, P k is the error covariance of the clock synchronization accuracy, E[P k ] is P k The expected value, E[P k ]=AXA T +Q-λAXC T (CXC T +R) -1 CXA T , X=E[P k-1 ],λ=E[γ k ]=p d , ΔE kis the fixed error in the synchronization process, Tr(E[P k ]) is E[P k ], T S is the packet time slot length, A is the coefficient matrix, A T is the transposed matrix of the coefficient matrix, Q is the covariance matrix of the process noise, R is the covariance of the observation noise, P k-1 is the error covariance matrix at time k-1.

[0033] According to a time domain resource configuration method provided by the present invention, solving the time domain resource configuration optimization problem to obtain the data packet arrival rate includes:

[0034] The time domain resource allocation optimization problem is converted into a convex optimization problem:

[0035] P2:min{Tr(P k )E[a k |P k ]-VU}

[0036] stC4:Tr(E[P k ])+ΔE k ≤B <T S ;

[0037] C5: 0<λ<1;

[0038] Where P2 is the objective function of the convex optimization problem, C4 and C5 are the constraints of the objective function P2, Tr(P k ) is P k traces, a k is the random arrival process of the virtual queue, E[a k |P k ] is P k In the known case k The expected value of , V is the control coefficient, P k+1 =max{P k -b k ,0}+a k , b k is the leaving process of the virtual queue, b k is a constant, and b k ≥E[a k ];

[0039] The convex optimization problem is solved to obtain the data packet arrival rate.

[0040] According to a time domain resource configuration method provided by the present invention, the determining of the network throughput based on the data packet arrival rate and the timing band length comprises:

[0041] Determining the network throughput based on formula (4);

[0042]

[0043] Among them, W e To preserve the equivalent bandwidth of the time-band length, is the inverse function of Q(x), 1-λ is the probability of packet loss, and t is the integral variable.

[0044] The present invention also provides a time domain resource configuration device, comprising:

[0045] An acquisition unit, used for acquiring the error covariance of clock synchronization accuracy;

[0046] A construction unit is used to construct a time domain resource configuration optimization problem based on the error covariance of the clock synchronization accuracy, the timing band length and the data packet arrival rate; wherein the time domain resource configuration optimization problem is a problem of maximizing the network throughput under the condition of ensuring the clock synchronization accuracy;

[0047] A solving unit, used for solving the time domain resource configuration optimization problem to obtain the data packet arrival rate;

[0048] A determination unit is used to determine the network throughput based on the data packet arrival rate and the timing band length.

[0049] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of any of the above-mentioned time domain resource configuration methods are implemented.

[0050] The present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned time domain resource configuration methods are implemented.

[0051] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of any of the above-mentioned time domain resource configuration methods are implemented.

[0052] The present invention provides a time domain resource configuration method, device, electronic device and storage medium, which constructs a time domain resource configuration optimization problem based on the error covariance of clock synchronization accuracy, the time-preserving band length and the data packet arrival rate, and calculates the maximized network throughput based on the time-preserving band length and the data packet arrival rate obtained by solving the time domain resource configuration optimization problem. Since the maximized network throughput reflects the configuration of the time domain resources, the optimization of the time domain resource configuration is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 It is one of the flow charts of the time domain resource configuration method provided by the present invention;

[0055] Figure 2 This is the second flow chart of the time domain resource configuration method provided by the present invention;

[0056] Figure 3 is a schematic diagram of a timestamp observation model of a two-way information exchange mechanism provided by the present invention;

[0057] Figure 4 It is a structural schematic diagram of the time domain resource configuration device provided by the present invention;

[0058] Figure 5 It is a schematic diagram of the physical structure of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Combine the following Figures 1 to 3 The time domain resource configuration method of the present invention is described.

[0061] Figure 1 is one of the flow charts of the time domain resource configuration method provided by the present invention, such as Figure 1 As shown, the time domain resource configuration method includes:

[0062] Step 101: Obtain error covariance of clock synchronization accuracy.

[0063] Step 102: construct a time domain resource configuration optimization problem based on the error covariance of the clock synchronization accuracy, the timing band length and the data packet arrival rate.

[0064] The time domain resource configuration optimization problem is to maximize the network throughput under the condition of ensuring clock synchronization accuracy.

[0065] Step 103: Solve the time domain resource configuration optimization problem to obtain the data packet arrival rate.

[0066] Step 104: Determine the network throughput based on the data packet arrival rate and the timing band length.

[0067] The present invention provides a time domain resource configuration method, which constructs a time domain resource configuration optimization problem based on the error covariance of clock synchronization accuracy, the time-preserving band length and the data packet arrival rate, and calculates the maximized network throughput based on the time-preserving band length and the data packet arrival rate obtained by solving the time domain resource configuration optimization problem. Since the maximized network throughput reflects the configuration of the time domain resources, the optimization of the time domain resource configuration is achieved.

[0068] Optionally, Figure 2 This is a second flow chart of the time domain resource configuration method provided by the present invention, such as Figure 2 As shown, Figure 1 Step 101 in the embodiment can be specifically implemented by the following steps:

[0069] Step 1011: construct a timestamp observation model based on the timestamp information and the clock state variables.

[0070] Optionally, the timestamp observation model is constructed based on formula (1):

[0071] z i,k =γ k [T i,1 +T i,2 -2t]=γ k [Cx i (k)+v i (k)] (1)

[0072] Among them, z i,k is the timestamp observation model, γ k is a binary Bernoulli random variable, which can also be called a time observation variable value, i is the number of base station nodes, T i,1 Base station node gNB i To the timing terminal node UE j The moment when the time synchronization variable is sent, T i,2 Base station node gNB i Received from terminal node UE j The time to reply the time synchronization variable, j is the number of terminal nodes, t is the ideal reference time, C is the observation matrix, C = [0 2], x i (k) is the base station node gNBi The clock state variable, k is the kth cycle of the clock synchronization process, v i (k) is the observation noise, v i (k) = d i +Y i,k , v i (k) is a Gaussian random variable that follows N(0,R) distribution, d i is the fixed delay generated during synchronization, Y i,k is the random delay caused by the randomness of the wireless channel.

[0073] For example, the present invention is applied to a wireless communication environment formed by a 5G base station and a timing terminal, and the 5G base station and the timing terminal form a clock synchronization system through two-way information exchange measurement. The base station node gNB i The recursive equation of the clock state variable can be expressed by the following formula (5):

[0074] x i (k) = Ax i (k-1)+w i (k)+b (5)

[0075] in, β i (k) is gNB i The instantaneous clock skew, that is, the frequency offset of the clock synchronization signal; For gNB i The accumulated clock offset, i.e., phase offset, x i (k-1) is the base station node gNB at time k-1 i The clock state variable; A is the coefficient matrix, τ0 is the sampling period; w i (k) is the process noise, w i (k) = [μ i (k)τ0μ i (k)] T ; p i is the phase noise parameter; B i (k) is the standard Wiener process; B i ′(k) is B i (k) Difference with respect to k; B i ′(k-1) is B i (k-1) Difference with respect to k-1; B i (k-1) is the standard Wiener process of the k-1th period; μ i (k) has a mean of 0 and a variance of 2p i Gaussian process; b is a constant matrix, b = [0-τ0] T ;wi (k) satisfies the following formula (6) and formula (7):

[0076] E[w i (k)]=0 (6)

[0077] E[w i (k)w i T (k)]=Q (7)

[0078] Among them, E[w i (k)] is w i The expected value of (k), E[w i (k)w i T (k)] is w i (k)w i T The expected value of (k).

[0079] Based on the above definition of clock state variables, a timestamp observation model based on a two-way information exchange mechanism is established. Figure 3 is a schematic diagram of the timestamp observation model of the two-way information exchange mechanism provided by the present invention, such as Figure 3 As shown, assume that the base station node gNB i The timestamp set in a two-way clock synchronization process is {T i,1 ,T i,2}, where T i,1 Base station node gNB i To the timing terminal node UE j The moment when the time synchronization variable is sent, T i,1 The following formula (8) is used to express: T i,2 Base station node gNB i Received from terminal node UE j The time to reply to the time synchronization variable, T i,2 It is expressed by the following formula (9):

[0080]

[0081]

[0082] in, For gNB i The accumulated clock offset, d i is the fixed delay generated during synchronization, Y i,kis the random delay caused by the randomness of the wireless channel, t=kτ0 represents the ideal reference time. Since the time occupied by two-way information exchange is relatively short compared with the time required for the entire clock synchronization algorithm, the ideal reference time t can be considered to remain unchanged in a round of two-way information exchange; studies have shown that such time delays can be modeled as independent and identically distributed Gaussian random processes.

[0083] It should be noted that Figure 3 The request packet in is the base station node gNB i To the timing terminal node UE j The data packet sent and the reply packet are the terminal node UE j To base station node gNB i Reply packet.

[0084] Adding formula (8) and formula (9) and transforming them, we can get the 5G base station node gNB i The initial observation model is expressed by the following formula (10):

[0085]

[0086] Among them, y i,k is the initial observation model.

[0087] Combined with the above The initial observation model can be expressed by the following formula (11):

[0088] y i,k =Cx i (k)+v i (k) (11)

[0089] Among them, the measurement matrix C = [0 2], the observation noise v i (k) = d i +Y i,k is a Gaussian random variable that follows N(0,R) distribution.

[0090] In 5G networks, due to the influence of random variables of wireless channel fading, the transmission of links is no longer reliable. In other words, in the process of bidirectional information exchange of clock synchronization, the gNB i Send to UE j Timestamp information and UE j Reply to gNB i The timestamp information cannot be guaranteed to arrive. Therefore, it is considered to modify the initial observation model of formula (11) to make it suitable for the modeling of time synchronization mechanism in the wireless industrial network scenario. The modified initial observation model is the timestamp observation model expressed by the above formula (1).

[0091] Step 1012: Evaluate the network delay determinism based on the timestamp observation model.

[0092] Optionally, solve the confidence level of the wireless network delay determinism in formula (2); determine γ based on the confidence level of the wireless network delay determinism. k distribution.

[0093] p d =p{t l <x<αt u}p{0 <w<(1-α)t u} (2)

[0094] Among them, p d is the confidence level of the wireless network delay determinism, α is the delay scaling factor, 0<α <t l / t u , t l is the lower bound of the sum of transmission delay and queuing delay, t u is the upper bound of the sum of transmission delay and queuing delay, x is the random variable of transmission delay, and w is the random variable of queuing delay of nodes in the wireless network; p{t l <x<αt u} is x falling on t l With αt u The probability value between z(t)=(2 L / (Wt) -1) / c, L is the length of the transmitted data packet, W is the bandwidth of the channel, and c is the constant coefficient of the received signal-to-noise ratio; p{0 <w<(1-α)t u} is w between 0 and (1-α)t u The probability value between θ satisfies the constraint E[e θA(1) ]E[e -θS(1) ]≤1, A(t) is the amount of data arriving in the time period (0, t), S(t) is the amount of data service in the time period (0, t), for The expected value, E[e θA(1) ] is e θA(1) The expected value, E[e -θs(1) ] is e -θS(1) expected value.

[0095] For example, based on the binary Bernoulli random variable γ k The deterministic descriptive indicators of 5G wireless networks are discussed. For a binary Bernoulli random variable γ k , the probability distribution of its value is reflected by the probability that the delay in the wireless network falls within the specified interval, that is, γ k ~b(1,p d ), pd It is expressed by the following formula (12):

[0096] p d =p{t l <x+w<t u},t l <t u (12)

[0097] Where b is the Bernoulli distribution, x is the random variable of transmission delay, whose randomness is mainly caused by the fading variable of the wireless channel; w is the random variable of the queuing delay of the node in the wireless network, which is a joint random variable determined by the random process of the arrival of the data packet and the channel fading; t l is the lower bound of the delay, t u is the upper bound of the delay; p d The value of x+w is equal to the value of x+w falling within the interval (t l ,t u ). Generally speaking, the transmission delay and queuing delay of data packets are the main components of the node delay of the communication system, so p d The value of can accurately characterize the confidence of the deterministic estimation of delay in wireless deterministic networks. Since w is a joint random variable, it is difficult to directly solve formula (12), so p d The calculation formula of is updated to the above formula (2). Obviously, the delay boundary condition of formula (2) is a sufficient but not necessary condition of formula (12), that is, when the delay random variable satisfies the probability condition of formula (2), it must satisfy the delay boundary of formula (12), otherwise it cannot. Therefore, using formula (2) as p d The value of reflects the degree of certainty of the delay estimation, and then characterizes the binary Bernoulli random variable γ k distribution.

[0098] In formula (2), p d The solution of can be divided into two parts: x and w. Regarding the x part, since x is a function of the random variable of channel fading, for the Rayleigh random channel with parameter σ, the interval probability of x in formula (2) can be obtained by the Shannon channel capacity formula and the cumulative distribution function of the channel random variable, expressed by formula (13):

[0099]

[0100] Where z(t)=(2 L / (Wt) -1) / c, L is the length of the sent data packet, W is the bandwidth of the channel, and c is the constant coefficient of the received signal-to-noise ratio;

[0101] Regarding the w part, the random network calculus theory based on the moment generating function (MGF) is used to solve the interval probability of w in formula (2). For the arrival and service processes that are independent of each other and have independent and identically distributed increments and finite MGF, the upper bound of w satisfies the following formula (14):

[0102]

[0103] Among them, θ satisfies the constraint condition E[e θA(1) ]E[e -θS(1) ]≤1, A(t) is the amount of data arriving in the time period (0, t), S(t) is the amount of data service in the time period (0, t); A(1) is the amount of data arriving per unit time, S(1) is the amount of data service per unit time, S[(1-α)t u ] is (0,(1-α)t u ) data service volume during the period; when the value of θ makes the formula E[e θA(1) ]E[e -θs(1) ]≤1 is approximately 1, then the equality of formula (14) is approximately established, from which the interval probability of w in formula (2) can be obtained, and then p d The value of , completes the binary Bernoulli random variable γ k Determination of distribution.

[0104] Step 1013: Determine the error covariance of the clock synchronization accuracy based on the evaluated timestamp observation model.

[0105] Optionally, the time domain resource allocation optimization problem is expressed as:

[0106] P1: max{U(B,λ)};

[0107]

[0108] C2:Tr(E[P k ])+ΔE k ≤B <T S ;

[0109] C3: 0<λ<1;

[0110] Wherein, P1 is the objective function of the time domain resource allocation optimization problem, C1 to C3 are the constraints of the objective function P1, and U(B,λ)=R b , B is the time-guaranteed band length, λ is the packet arrival rate, R b is the network throughput, P k is the error covariance of the clock synchronization accuracy, E[P k ] is P kThe expected value, E[P k ]=AXA T +Q-λAXC T (CXC T +R) -1 CXA T , X=E[P k-1 ],λ=E[γ k ]=p d , ΔE k is the fixed error in the synchronization process, E[P k-1 ] is P k-1 The expected value of E[γ k ] is γ k The expected value of Tr(E[P k ]) is E[P k ], T S is the packet time slot length, A is the coefficient matrix, A T is the transposed matrix of the coefficient matrix, Q is the covariance matrix of the process noise, R is the covariance of the observation noise, P k-1 is the error covariance matrix at time k-1.

[0111] Optionally, based on the above definition of the timestamp observation model, the error covariance of the clock synchronization accuracy is determined using the following formula (3):

[0112] P k =E[e k e k T ] (3)

[0113] Among them, P k is the error covariance of the clock synchronization accuracy, e k is the estimated error of the clock state variable, e k T for e k The transposed matrix of x k is the clock state variable, is the value of x under the condition that the observed value is known k The estimate,

[0114] Based on the Kalman filter algorithm, when the time observation variable value is lost, the error covariance P of the clock synchronization accuracy is calculated using the following formulas (15) to (19) in each cycle of clock synchronization: k To update:

[0115]

[0116] P k ′=APk A T +Q (16)

[0117]

[0118] P k+1 =P k ′-γ k+1 K k+1 CP k ′ (18)

[0119] K k+1 =P k ′C T (CP k ′C T +R) -1 (19)

[0120] in, is the x predicted by the Kalman filter algorithm k , P k ′ is the P predicted by the Kalman filter algorithm k , For the updated x k+1 The estimate of γ k+1 is a binary Bernoulli variable, K k+1 is the Kalman filter gain of the k+1th cycle, z k+1 is the clock observation variable value of the k+1th cycle.

[0121] Substituting formula (16) and formula (19) into formula (18), we can obtain P expressed by the following formula (20): k The recursive formula is:

[0122] P k+1 =AP k A T +Q-γ k AP k C T (CP k C T +R) -1 CP k A T (20)

[0123] It can be seen from formula (20) that P k is affected by k The random variable affected by k The analysis in the statistical sense is very necessary. Under the premise of running the Kalman filter algorithm a finite number of times, the E[P k ]The recursive formula in steady state:

[0124] E[P k ]=AXA T +Q-λAXC T (CXC T +R) -1 CXA T (twenty one)

[0125] Where X = E[P k-1 ],λ=E[γ k ]=p d .

[0126] Since high-precision timing services are sensitive to wireless communication delays, in order to prevent clock synchronization errors from causing data packet collisions during data transmission, a timing band is added to the 5G Orthogonal Frequency Division Multiple Access (OFDMA) time domain to protect each transmission time slot, that is, a portion is left between every two adjacent transmission time slots as a protection interval. Obviously, on the one hand, the longer the timing band, the less data can be transmitted in each transmission time slot; on the other hand, a timing band that is too small will increase the probability of data collision. Therefore, the length of the timing band needs to be set reasonably, while taking into account the effectiveness and reliability of communication. Here, the following constraints can be set for the timing band length B:

[0127] B ≥ ΔE k +{E[P k ]} (twenty two)

[0128] Where, ΔE k is the fixed error in the synchronization process, {E[P k ]} is the incremental synchronization variance. To simplify the calculation, E[P k ] replace {E[P k ]}, that is, the constraint condition of the timing belt length B is expressed by the following formula (23):

[0129] B ≥ ΔE k +Tr(E[P k ]) (twenty three)

[0130] Based on the above analysis of deterministic networks with clock synchronization accuracy requirements, it is necessary to ensure that the overhead of time domain resources is balanced under the constraints of clock synchronization accuracy and to maximize network throughput. Therefore, this problem is determined as a time domain resource configuration optimization problem, which is expressed as:

[0131] P1: max{U(B,λ)};

[0132]

[0133] C2:Tr(E[P k ])+ΔE k ≤B <T S ;

[0134] C3:0<λ<1.

[0135] Where U(B,λ)=R b , constraint C1 means E[P k ] tends to be stable under long-term conditions, that is, the accuracy of clock synchronization is guaranteed; constraint C2 represents the design criterion for the length of the timing band, that is, it should not be less than the critical value that causes data collision, and should not exceed the length of the transmission time slot; constraint C3 means limiting the arrival rate of data packets to between (0,1).

[0136] Furthermore, the time domain resource configuration optimization problem is converted into a convex optimization problem: the convex optimization problem is solved to obtain the data packet arrival rate.

[0137] P2:min{Tr(P k )E[a k |P k ]-VU}

[0138] stC4:Tr(E[P k ])+ΔE k ≤B <T S ;

[0139] C5: 0<λ<1;

[0140] Where P2 is the objective function of the convex optimization problem, C4 and C5 are the constraints of the objective function P2, Tr(P k ) is P k traces, a k is the random arrival process of the virtual queue, E[a k |P k ] is P k In the known case k The expected value of , V is the control coefficient, P k+1 =max{P k -b k ,0}+a k , b k is the leaving process of the virtual queue, b k is a constant, and b k ≥E[a k ].

[0141] For example, since the constraints of problem P1 contain nonlinear constraints, and it can be seen from the analysis that its feasible set and objective function are non-convex, it is difficult to solve P1 directly. The present invention adopts the Lyapunov optimization method based on deep reinforcement learning to analyze and solve problem P1.

[0142] First, based on the Lyapunov optimization theory, problem P1 is transformed into a joint optimization problem of the error covariance of clock synchronization accuracy and throughput. Specifically, for the error covariance P k , based on the recursive definition of formula (20), a virtual queue is expressed by the following formula (24):

[0143] P k+1 =max{P k -b k ,0}+a k (twenty four)

[0144] Among them, P k+1 is a virtual queue, b k is the leaving process of the virtual queue. For the convenience of calculation, it is assumed that b k is a constant and satisfies b k ≥E[a k ], to ensure the stability of the queue, E[a k ] is a k The expected value of k is the random arrival process of the virtual queue, and its randomness is due to the random variable γ in (20) k The introduced k It can be calculated by formula (20), k It is expressed by the following formula (25):

[0145]

[0146] Based on the definition of virtual queue in formula (24), about P k The Lyapunov function L(P k ) can be expressed by the following formula (26):

[0147] L(P k )=P k 2 / 2 (26)

[0148] Then the Lyapunov drift function Δ(P k ) is expressed by the following formula (27):

[0149] Δ(P k )=L(P k+1 )-L(P k)≤D+P k E[a k |P k ] (27)

[0150] Among them, L(P k+1 ) is about P k+1 Lyapunov function, D is the boundary value, D=(E[a k 2 |P k ]+b k 2 ) / 2, E[a k 2 |P k ] is P k Given the known random variable a k The expected value of the second moment of .

[0151] The drift penalty function is expressed by the following formula (28):

[0152] Δ(P k )-VU≤D+P k E[a k |P k ]-VU (28)

[0153] Among them, V is the control coefficient, which is used to adjust the influence of the drift penalty function on the overall problem, so that problem P1 can be transformed into a Lyapunov optimization problem, that is, into a convex optimization problem represented by problem P2:

[0154] P2: min{Tr(P k )E[a k |P k ]-VU}

[0155] stC4:Tr(E[P k ])+ΔE k ≤B <T S ;

[0156] C5:0<λ<1.

[0157] Furthermore, it can be seen from problem P2 that although the constraints of problem P2 have been simplified, the constraints in C4 and C5 are not independent, that is, the change of λ will affect E[P k ], and thus affects the constraint condition of B. However, it is not difficult to see that the change of the time-keeping belt length B only directly affects U in the objective function of problem P2, and U is a monotonically decreasing function of B. Therefore, the value of B only needs to be set to the minimum value that satisfies the constraint condition C2, that is, B = Tr(E[P k ])+ΔE k. Then problem P2 can be simplified to:

[0158] P3: min{Tr(P k )E[a k |P k ]-VU}

[0159] stC6: 0<λ<1

[0160] Among them, P3 is the objective function of the convex optimization problem, and C6 is the constraint condition of the objective function P3.

[0161] For example, in B = Tr (E [P k ])+ΔE k In the case of , solve problem P3 and get the data packet arrival rate.

[0162] The present invention solves problem P3 based on an improved algorithm of deep Q-learning (DQN), namely Dueling-DDQN (hereinafter referred to as 3DQN). The 3DQN algorithm mainly consists of three parts: state, action and reward, where the state space S = {P k ,U,B,λ}, reflects the value of each parameter of the environment in the 3DQN algorithm at the current step; the action space a={x inc ,x de}, a value of {1,0} indicates that λ increases in steps, and a value of {0,1} indicates that λ decreases in steps; reward function r = Lya pre -Lya cur , among which, Lya pre represents the value of the objective function of problem P3 before each round of iteration update. cur It represents the value of the objective function of problem P3 after each round of iteration. That is, r reflects the reduction value of the objective function of the algorithm in one round of iteration. When the algorithm obtains the optimal solution, the value of r tends to be stable and the system converges to the optimal state.

[0163] Specifically, in B=Tr(E[P k ])+ΔE k In the case of , the process of solving problem P3 is as follows:

[0164] Obtaining initial state variables and initial function values; wherein the initial state variables include initial error covariance, initial data packet arrival rate, initial timing band length, and initial network throughput;

[0165] Select the target action in the action space according to the initial state variables;

[0166] Execute the target action, calculate the cost reward of taking the target action under the initial state variable according to the reward function, and generate the next state variable;

[0167] Store each state variable, the corresponding target action, and the cost reward in the experience pool;

[0168] When it is determined that the current number of iterations is greater than or equal to a preset threshold, a data sample is obtained from the experience pool for training to determine an evaluation value of the state;

[0169] When the training process is executed for a preset number of times, the parameters of the current Q network are copied to the target Q network until the convergence condition is reached and the optimal packet arrival rate is output.

[0170] The simulation results show that the algorithm proposed in the present invention has certain advantages in the joint optimization of clock synchronization accuracy and throughput compared with the existing strategies.

[0171] Optionally, Figure 1 Step 104 in the embodiment can be specifically implemented in the following manner:

[0172] The network throughput is determined based on formula (4):

[0173]

[0174] Among them, W e To preserve the equivalent bandwidth of the time-band length, is the inverse function of Q(x), 1-λ is the probability of packet loss, that is, the transmission error rate, and t is the integral variable.

[0175] For example, in a power grid deterministic communication scenario, the impact of reliability on network performance is greater than that of a network in a general scenario, which is reflected in the throughput mainly due to the impact of the error rate of data packet transmission. The present invention adopts a modified channel capacity calculation method that takes into account the transmission error rate to obtain a high-reliability network throughput, and the calculation of the network throughput is expressed by the above formula (4). When the timing band length B is determined and the optimal data packet arrival rate λ is calculated, the timing band length B and the data packet arrival rate λ are substituted into formula (4) to obtain the maximized network throughput.

[0176] The present invention provides a time domain resource configuration method, which constructs a time domain resource configuration optimization problem based on the error covariance of clock synchronization accuracy, the time-preserving band length and the data packet arrival rate, and calculates the maximized network throughput based on the time-preserving band length and the data packet arrival rate obtained by solving the time domain resource configuration optimization problem. Since the maximized network throughput reflects the configuration of the time domain resources, the optimization of the time domain resource configuration is achieved.

[0177] The time domain resource configuration device provided by the present invention is described below. The time domain resource configuration device described below and the time domain resource configuration method described above can be referenced to each other.

[0178] Figure 4 is a schematic diagram of the structure of the time domain resource configuration device provided by the present invention, such as Figure 4 As shown, the time domain resource configuration device includes an acquisition unit 4014, a construction unit 402, a solution unit 4034 and a determination unit 404; wherein:

[0179] An acquisition unit 401 is used to acquire the error covariance of clock synchronization accuracy;

[0180] A construction unit 402 is used to construct a time domain resource configuration optimization problem based on the error covariance of the clock synchronization accuracy, the timing band length and the data packet arrival rate; wherein the time domain resource configuration optimization problem is a problem of maximizing the network throughput under the condition of ensuring the clock synchronization accuracy;

[0181] A solving unit 403, configured to solve the time domain resource configuration optimization problem to obtain the data packet arrival rate;

[0182] The determination unit 404 is configured to determine the network throughput based on the data packet arrival rate and the timing band length.

[0183] The present invention provides a time domain resource configuration device, which constructs a time domain resource configuration optimization problem based on the error covariance of clock synchronization accuracy, the time-preserving band length and the data packet arrival rate, and calculates the maximized network throughput based on the time-preserving band length and the data packet arrival rate obtained by solving the time domain resource configuration optimization problem. Since the maximized network throughput reflects the configuration of the time domain resources, the optimization of the time domain resource configuration is achieved.

[0184] Based on any of the above embodiments, the acquiring unit 401 is specifically used for:

[0185] Construct a timestamp observation model based on timestamp information and clock state variables;

[0186] Evaluating the network delay determinism based on the timestamp observation model;

[0187] The error covariance of the clock synchronization accuracy is determined based on the evaluated timestamp observation model.

[0188] Based on any of the above embodiments, the acquiring unit 401 is further specifically configured to:

[0189] Constructing the timestamp observation model based on formula (1);

[0190] z i,k =γk [T i,1 +T i,2 -2t]=γ k [Cx i (k)+v i (k)] (1)

[0191] Among them, z i,k is the timestamp observation model, γ k is a binary Bernoulli random variable, i is the number of base station nodes, T i,1 Base station node gNB i To the timing terminal node UE j The moment when the time synchronization variable is sent, T i,2 Base station node gNB i Received from terminal node UE j The time to reply the time synchronization variable, j is the number of terminal nodes, t is the ideal reference time, C is the observation matrix, C = [0 2], x i (k) is the base station node gNB i The clock state variable, k is the kth cycle of the clock synchronization process, v i (k) is the observation noise, v i (k) = d i +Y i,k , v i (k) is a Gaussian random variable that follows N(0,R) distribution, d i is the fixed delay generated during synchronization, Y i,k is the random delay caused by the randomness of the wireless channel.

[0192] Based on any of the above embodiments, the acquiring unit 401 is further specifically configured to:

[0193] Solve the confidence level of the wireless network delay determinism in formula (2);

[0194] p d =p{t l <x<αt u}p{0 <w<(1-α)t u} (2)

[0195] Among them, p d is the confidence level of the wireless network delay determinism, α is the delay scaling factor, 0<α <t l / t u , t l is the lower bound of the sum of transmission delay and queuing delay, t u is the upper bound of the sum of transmission delay and queuing delay, x is the random variable of transmission delay, and w is the random variable of queuing delay of nodes in the wireless network; p{tl <x<αt u} is x falling on t l With αt u The probability value between z(t)=(2 L / (Wt) -1) / c, L is the length of the transmitted data packet, W is the bandwidth of the channel, and c is the constant coefficient of the received signal-to-noise ratio; p{0 <w<(1-α)t u} is w between 0 and (1-α)t u The probability value between θ satisfies the constraint E[e θA(1) ]E[e -θS(1) ]≤1, A(1) is the amount of data arriving per unit time, S(1) is the amount of data service per unit time, S[(1-α)t u ] is (0,(1-α)t u ) data service volume during the period;

[0196] Determine γ based on the confidence of the wireless network delay determinism k distribution.

[0197] Based on any of the above embodiments, the acquiring unit 401 is further specifically configured to:

[0198] Determining the error covariance of the clock synchronization accuracy based on formula (3);

[0199] P k =E[e k e k T ] (3)

[0200] Among them, P k is the error covariance of the clock synchronization accuracy, e k is the estimated error of the clock state variable, e k T for e k The transposed matrix of x k is the clock state variable, is the value of x under the condition that the observed value is known k The estimate,

[0201] Based on any of the above embodiments, the time domain resource configuration optimization problem is expressed as:

[0202] P1: max{U(B,λ)};

[0203]

[0204] C2:Tr(E[Pk ])+ΔE k ≤B <T S ;

[0205] C3: 0<λ<1;

[0206] Wherein, P1 is the objective function of the time domain resource allocation optimization problem, C1 to C3 are the constraints of the objective function P1, and U(B,λ)=R b , B is the length of the timing band, λ is the packet arrival rate, R b is the network throughput, P k is the error covariance of the clock synchronization accuracy, E[P k ] is P k The expected value, E[P k ]=AXA T +Q-λAXC T (CXC T +R) -1 CXA T , X=E[P k-1 ],λ=E[γ k ]=p d , ΔE k is the fixed error in the synchronization process, Tr(E[P k ]) is E[P k ], T S is the packet time slot length, A is the coefficient matrix, A T is the transposed matrix of the coefficient matrix, Q is the covariance matrix of the process noise, R is the covariance of the observation noise, P k-1 is the error covariance matrix at time k-1.

[0207] Based on any of the above embodiments, the solving unit 403 is specifically used for:

[0208] The time domain resource allocation optimization problem is converted into a convex optimization problem:

[0209] P2:min{Tr(P k )E[a k |P k ]-VU}

[0210] stC4:Tr(E[P k ])+ΔE k ≤B <T S ;

[0211] C5: 0<λ<1;

[0212] Where P2 is the objective function of the convex optimization problem, C4 and C5 are the constraints of the objective function P2, Tr(Pk ) is P k traces, a k is the random arrival process of the virtual queue, E[a k |P k ] is P k In the known case k The expected value of , V is the control coefficient, P k+1 =max{P k -b k ,0}+a k , b k is the leaving process of the virtual queue, b k is a constant, and b k ≥E[a k ];

[0213] The convex optimization problem is solved to obtain the data packet arrival rate.

[0214] Based on any of the above embodiments, the determining unit 404 is specifically configured to:

[0215] Determining the network throughput based on formula (4);

[0216]

[0217] Among them, W e To preserve the equivalent bandwidth of the time-band length, is the inverse function of Q(x), 1-λ is the probability of packet loss, and t is the integral variable.

[0218] Figure 5 is a schematic diagram of the physical structure of the electronic device provided by the present invention, such as Figure 5 As shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530 and a communication bus 540, wherein the processor 510, the communication interface 520 and the memory 530 communicate with each other through the communication bus 540. The processor 510 may call the logic instructions in the memory 530 to execute the time domain resource configuration method, which includes: obtaining the error covariance of the clock synchronization accuracy;

[0219] A time domain resource configuration optimization problem is constructed based on the error covariance of the clock synchronization accuracy, the timing band length and the data packet arrival rate; wherein the time domain resource configuration optimization problem is a problem of maximizing the network throughput under the condition of ensuring the clock synchronization accuracy;

[0220] Solving the time domain resource configuration optimization problem to obtain the data packet arrival rate;

[0221] The network throughput is determined based on the packet arrival rate and the timing band length.

[0222] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0223] On the other hand, the present invention further provides a computer program product, the computer program product includes a computer program, the computer program can be stored in a non-transitory computer-readable storage medium, when the computer program is executed by a processor, the computer can execute the time domain resource configuration method provided by the above methods, the method includes: obtaining the error covariance of the clock synchronization accuracy;

[0224] A time domain resource configuration optimization problem is constructed based on the error covariance of the clock synchronization accuracy, the timing band length and the data packet arrival rate; wherein the time domain resource configuration optimization problem is a problem of maximizing the network throughput under the condition of ensuring the clock synchronization accuracy;

[0225] Solving the time domain resource configuration optimization problem to obtain the data packet arrival rate;

[0226] The network throughput is determined based on the packet arrival rate and the timing band length.

[0227] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, the computer program being implemented when executed by a processor to execute the time domain resource configuration method provided by the above methods, the method comprising: obtaining an error covariance of clock synchronization accuracy;

[0228] A time domain resource configuration optimization problem is constructed based on the error covariance of the clock synchronization accuracy, the timing band length and the data packet arrival rate; wherein the time domain resource configuration optimization problem is a problem of maximizing the network throughput under the condition of ensuring the clock synchronization accuracy;

[0229] Solving the time domain resource configuration optimization problem to obtain the data packet arrival rate;

[0230] The network throughput is determined based on the packet arrival rate and the timing band length.

[0231] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0232] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0233] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A time domain resource configuration method, characterized in that: include: Get the error covariance of clock synchronization accuracy; A time domain resource configuration optimization problem is constructed based on the error covariance of the clock synchronization accuracy, the timing band length and the data packet arrival rate; wherein the time domain resource configuration optimization problem is a problem of maximizing the network throughput under the condition of ensuring the clock synchronization accuracy; Solving the time domain resource configuration optimization problem to obtain the data packet arrival rate; Determining the network throughput based on the packet arrival rate and the timing band length; The error covariance of obtaining clock synchronization accuracy includes: Construct a timestamp observation model based on timestamp information and clock state variables; Evaluating the network delay determinism based on the timestamp observation model; Determining the error covariance of clock synchronization accuracy based on the evaluated timestamp observation model; The constructing of a timestamp observation model based on timestamp information and clock state variables includes: Constructing the timestamp observation model based on formula (1); z i,k =γ k [T i,1 +T i,2 -2t]=γ k [Cx i (k)+v i (k)] (1) Among them, z i,k is the timestamp observation model, γ k is a binary Bernoulli random variable, i is the number of base station nodes, T i,1 Base station node gNB i To the timing terminal node UE j The moment when the time synchronization variable is sent, T i,2 Base station node gNB i Received from terminal node UE j The time to reply the time synchronization variable, j is the number of terminal nodes, t is the ideal reference time, C is the observation matrix, C = [0 2], x i (k) is the base station node gNB i The clock state variable, k is the kth cycle of the clock synchronization process, v i (k) is the observation noise, v i (k) = d i +Y i,k , v i (k) is a Gaussian random variable that follows N(0,R) distribution, d i is the fixed delay generated during synchronization, Y i,k is the random delay caused by the randomness of the wireless channel; The evaluating the network delay determinism based on the timestamp observation model includes: Solve the confidence level of the wireless network delay determinism in formula (2); p d =p{t l <x<αt u } p{0<w<(1-α)t u } (2) Among them, p d is the confidence level of the wireless network delay determinism, α is the delay scaling factor, 0<α <t l / t u , t l is the lower bound of the sum of transmission delay and queuing delay, t u is the upper bound of the sum of transmission delay and queuing delay, x is the random variable of transmission delay, and w is the random variable of queuing delay of nodes in the wireless network; p{t l <x<αt u } is x falling on t l With αt u The probability value between z(t)=(2 L / (Wt) -1) / c, L is the length of the transmitted data packet, W is the bandwidth of the channel, and c is the constant coefficient of the received signal-to-noise ratio; p{0 <w<(1-α)t u } is w between 0 and (1-α)t u The probability value between; p{w>(1-α)t u }≤E[e -θS[(1-α)tu] ], θ satisfies the constraint E[e θA(1) ]E[e -θS(1) ]≤1, A(1) is the amount of data arriving per unit time, S(1) is the amount of data service per unit time, S[(1-α)t u ] is (0,(1-α)t u ) data service volume during the period; Determine γ based on the confidence of the wireless network delay determinism k The distribution of , where γ k ~b(1,p d ), b is Bernoulli distribution; The step of determining the error covariance of clock synchronization accuracy based on the evaluated timestamp observation model includes: Determining the error covariance of the clock synchronization accuracy based on formula (3); P k =E[e k And k T ] (3) Among them, P k is the error covariance of the clock synchronization accuracy, e k is the estimated error of the clock state variable, e k T for e k The transposed matrix of x k is the clock state variable, is the value of x under the condition that the observed value is known k The estimate, 2. The time domain resource configuration method according to claim 1, characterized in that: The time domain resource allocation optimization problem is expressed as: P1: max{U(B,λ)}; C2:Tr(E[P k ])+ΔE k ≤B<T S ; C3: 0<λ<1; Wherein, P1 is the objective function of the time domain resource allocation optimization problem, C1 to C3 are the constraints of the objective function P1, and U(B,λ)=R b , B is the length of the timing band, λ is the packet arrival rate, R b is the network throughput, P k is the error covariance of the clock synchronization accuracy, E[P k ] is P k The expected value, E[P k ]=AXA T +Q-λAXC T (CXC T +R) -1 CXA T , X=E[P k-1 ],λ=E[γ k ]=p d , ΔE k is the fixed error in the synchronization process, Tr(E[P k ]) is E[P k ], T S is the packet time slot length, A is the coefficient matrix, A T is the transposed matrix of the coefficient matrix, Q is the covariance matrix of the process noise, R is the covariance of the observation noise, P k-1 is the error covariance matrix at time k-1.

3. The time domain resource configuration method according to claim 2, characterized in that: The step of solving the time domain resource configuration optimization problem to obtain the data packet arrival rate includes: The time domain resource allocation optimization problem is converted into a convex optimization problem: P2:min{Tr(P k )E[a k |P k ]-VU} s.t.C4:Tr(E[P k ])+ΔE k ≤B<T S ; C5: 0<λ<1; Where P2 is the objective function of the convex optimization problem, C4 and C5 are the constraints of the objective function P2, Tr(P k ) is P k traces, a k is the random arrival process of the virtual queue, E[a k |P k ] is P k In the known case k The expected value of , V is the control coefficient, is the leaving process of the virtual queue, b k is a constant, and b k ≥E[a k ]; The convex optimization problem is solved to obtain the data packet arrival rate.

4. The time domain resource configuration method according to claim 3, characterized in that: The determining the network throughput based on the data packet arrival rate and the timing band length comprises: Determining the network throughput based on formula (4); Among them, W e To preserve the equivalent bandwidth of the time-band length, is the inverse function of Q(x), 1-λ is the probability of packet loss, and t is the integral variable.

5. A time domain resource configuration device, characterized in that: An acquisition unit, used for acquiring the error covariance of clock synchronization accuracy; A construction unit, configured to construct a time domain resource configuration optimization problem based on the error covariance of the clock synchronization accuracy, the timing band length, and the data packet arrival rate; wherein the time domain resource configuration optimization problem is a problem of maximizing the network throughput under the condition of ensuring the clock synchronization accuracy; A solving unit, used for solving the time domain resource configuration optimization problem to obtain the data packet arrival rate; A determination unit, configured to determine the network throughput based on the data packet arrival rate and the timing band length; The acquisition unit is specifically used for: Construct a timestamp observation model based on timestamp information and clock state variables; Evaluating the network delay determinism based on the timestamp observation model; Determining the error covariance of clock synchronization accuracy based on the evaluated timestamp observation model; The acquisition unit is further specifically used for: Constructing the timestamp observation model based on formula (1); z i,k =γ k [T i,1 +T i,2 -2t]=γ k [Cx i (k)+v i (k)] (1) Among them, z i,k is the timestamp observation model, γ k is a binary Bernoulli random variable, i is the number of base station nodes, T i,1 Base station node gNB i To the timing terminal node UE j The moment when the time synchronization variable is sent, T i,2 Base station node gNB i Received from terminal node UE j The time to reply the time synchronization variable, j is the number of terminal nodes, t is the ideal reference time, C is the observation matrix, C = [0 2], x i (k) is the base station node gNB i The clock state variable, k is the kth cycle of the clock synchronization process, v i (k) is the observation noise, v i (k) = d i +Y i,k , v i (k) is a Gaussian random variable that follows N(0,R) distribution, d i is the fixed delay generated during synchronization, Y i,k is the random delay caused by the randomness of the wireless channel; Solve the confidence level of the wireless network delay determinism in formula (2); p d =p{t l <x<αt u } p{0<w<(1-α)t u } (2) Among them, p d is the confidence level of the wireless network delay determinism, α is the delay scaling factor, 0<α <t l / t u , t l is the lower bound of the sum of transmission delay and queuing delay, t u is the upper bound of the sum of transmission delay and queuing delay, x is the random variable of transmission delay, and w is the random variable of queuing delay of nodes in the wireless network; p{t l <x<αt u } is x falling on t l With αt u The probability value between z(t)=(2 L / (Wt) -1) / c, L is the length of the transmitted data packet, W is the bandwidth of the channel, and c is the constant coefficient of the received signal-to-noise ratio; p{0 <w<(1-α)t u } is w between 0 and (1-α)t u The probability value between θ satisfies the constraint E[e θA(1) ]E[e -θS(1) ]≤1, A(1) is the amount of data arriving per unit time, S(1) is the amount of data service per unit time, S[(1-α)t u ] is (0,(1-α)t u ) data service volume during the period; Determine γ based on the confidence of the wireless network delay determinism k The distribution of , where γ k ~b(1,p d ), b is Bernoulli distribution; Determining the error covariance of the clock synchronization accuracy based on formula (3); P k =E[e k And k T ] (3) Among them, P k is the error covariance of the clock synchronization accuracy, e k is the estimated error of the clock state variable, e k T for e k The transposed matrix of x k is the clock state variable, is the value of x under the condition that the observed value is known k The estimate, 6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the time domain resource configuration method according to any one of claims 1 to 4 are implemented.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the time domain resource configuration method according to any one of claims 1 to 4 are implemented.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the time domain resource configuration method according to any one of claims 1 to 4 are implemented.

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