Self-adaptive state updating method based on information urgency degree

Abstract

The invention discloses a dynamic state updating scheduling method in a multi-terminal wireless communication system. The method comprises the following steps of: aiming at the problem that uplink resources are limited, constructing a virtual queue to represent the historical resource use condition of a terminal; dynamic updating indexes are designed to quantify urgency of state updating; high-value terminal screening and an optimal scheduling decision are executed in each time slot; and a Lyapunov optimization framework is creatively combined with a dynamic threshold mechanism. By adopting the method provided by the invention, the average information urgency can be minimized, and the information transmission performance of the system is effectively improved.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, specifically relating to a multi-terminal state update scheduling method. Background Technology

[0002] With the deep application of 5G and IoT technologies in industrial control, remote monitoring, and other fields, multi-terminal systems are placing higher demands on the timeliness and resource efficiency of real-time status updates. Traditional scheduling methods have significant limitations. Existing solutions typically only optimize instantaneous resource allocation and lack mechanisms to guarantee the fairness of long-term terminal updates, easily leading to some terminals experiencing "update starvation" due to channel conditions or priority differences. Indicators such as Age of Information (AoI) only reflect the linear accumulation over time and cannot characterize the context-dependent nature of state information value. Therefore, a low-complexity scheduling method is urgently needed to dynamically quantify and minimize the system's average Urgency of Information (UoI) while ensuring dual constraints. Summary of the Invention

[0003] This invention aims to provide a multi-terminal state update scheduling method. By introducing a virtual queue mechanism and dynamic update indicators, it minimizes the average UoI of the system while satisfying the resource constraint (K-constraint) of updating at most K terminals per time slot and the long-term average update frequency constraint (ρ-constraint).

[0004] The technical solution to achieve the objective of this invention is: a multi-terminal state update scheduling method, the specific steps of which include:

[0005] S1: Establish virtual queue H i,t To track the historical update frequency of each terminal, where H i,t+1 =[H i,t -ρ+U i,t ] + ρ∈(0,1) is the preset average update constraint, U i,t ∈{0,1} represents the update decision of terminal i in time slot t;

[0006] S2: Calculate the update metrics for each terminal i. Where ω i,t+1 As a weighting factor, Let Q be its mean, p be the probability of successful transmission through the channel, and Q be its mean. i,t This represents the state estimation error;

[0007] S3: Filtering the candidate set Where V is an adjustable parameter;

[0008] S4: From Select J i,tThe top K terminals with the largest values ​​are updated in their states, while the remaining terminals are not updated.

[0009] S5: Update the estimation error Q for the next time step. i,t+1 Update the virtual queue H for the next time step. i,t+1 ;

[0010] S6: Repeat S2-S5 until the system runtime requirement is met.

[0011] Preferably, the initialization of the virtual queue satisfies And its average rate stability condition is:

[0012]

[0013] Preferably, the update index J i,t The parameter configuration in the file satisfies:

[0014]

[0015] Where θ is the weight adjustment factor.

[0016] Preferably, the estimation error Q i,t The update rules are as follows:

[0017] Q i,t+1 =(1-U i,t S i,t )Q i,t +A i,t (3)

[0018] Where S i,t ~Bernoulli(p) represents the channel transmission result, A i,t The variance of the error increment for the independent and identically distributed zero-mean estimate is σ. 2 .

[0019] Preferably, the value of parameter V affects the upper bound of the average UoI:

[0020]

[0021] Preferably, the weighting factor ω i,t+1 With state error Q i,t Statistical independence.

[0022] Compared with existing technologies, the significant advantages of this invention are: it adopts dual constraint guarantees, simultaneously satisfying the resource limit per time slot (K-constraint) and long-term update fairness (ρ-constraint), thus avoiding terminal resource starvation; it uses dynamic optimization indicators to better quantify update urgency; and it achieves lower complexity and improves computational efficiency based on the sorted K-terminal selection mechanism, making it suitable for large-scale networks. Attached Figure Description

[0023] Figure 1 This is a flowchart of the scheduling method of the present invention.

[0024] Figure 2 This is a schematic diagram of the average UoI performance of two scheduling schemes in Embodiment 1 of the present invention under the same constraints, as the maximum number of transmitting terminals K in a single time slot changes.

[0025] Figure 3 This is a schematic diagram of the average UoI performance under different V conditions as the average update frequency constraint changes in Embodiment 2 of the present invention. Detailed Implementation

[0026] This invention applies to a multi-terminal state update system consisting of N terminals and 1 base station. In this system, terminals transmit state information (such as sensor readings, device status, etc.) to the base station via a shared wireless channel. The system objective is to minimize the average UoI while satisfying the following dual constraints:

[0027] 1. Resource constraints: A maximum of K terminals can be scheduled per time slot (K≤N);

[0028] 2. Fairness constraint: The long-term average update frequency of each terminal shall not be less than ρ (0 < ρ ≤ 1).

[0029] The state update process is as follows Figure 1 As shown, the specific implementation steps are as follows:

[0030] S1: Establish virtual queue H i,t To track the historical update frequency of each terminal, where H i,t+1 =[H i,t -ρ+U i,t ] + ρ∈(0,1) is the preset average update constraint, U i,t ∈{0,1} represents the update decision of terminal i in time slot t;

[0031] S2: Calculate the update metrics for each terminal i. Where ω i,t+1 As a weighting factor, Let Q be its mean, p be the probability of successful transmission through the channel, and Q be its mean. i,t This represents the state estimation error;

[0032] S3: Filtering the candidate set Where V is an adjustable parameter;

[0033] S4: From Select J i,t The top K terminals with the largest values ​​are updated in their states, while the remaining terminals are not updated.

[0034] S5: Update the estimation error Q for the next time step. i,t+1 Update the virtual queue H for the next time step. i,t+1 ;

[0035] S6: Repeat S2-S5 until the system runtime requirement is met.

[0036] The value of parameter V affects the upper bound of the average UoI:

[0037] A smaller V reduces UoI but slows down convergence; a trade-off needs to be struck depending on the scenario.

[0038] Example 1

[0039] This invention uses MATLAB software to implement the method, and the system parameters are set as follows:

[0040] Network size: N = 10 terminals, simulation duration T = 3 × 10 5 The statistical window starts from the 10,000th time slot;

[0041] Channel model: Successful transmission probability p = 0.8, state estimation error increment

[0042] Business model: Weighting factor ω i,t+1 The value can be 1 (probability 0.95) or 100 (probability 0.05);

[0043] Comparative approach: Only constrains that a maximum of K terminals are updated per time slot (without long-term average update frequency constraints), and takes the update index. Maximum of K terminals can send;

[0044] The present invention's solution simultaneously satisfies the K constraint and the long-term average update frequency constraint ρ=1, with parameter V=1, and takes the desired update index. Send to a maximum of K terminals.

[0045] like Figure 2 As shown in the figure, the results indicate that the average UoI of the present invention is lower under different constraint K conditions; this phenomenon shows that the communication scenario provided by the present invention can achieve a smaller average UoI.

[0046] Example 2

[0047] This invention uses MATLAB software to implement the method, and the system parameters are set as follows:

[0048] Network size: N = 10 terminals, simulation duration T = 3 × 10 5 The statistical window starts from the 10,000th time slot;

[0049] Channel model: Successful transmission probability p = 0.8, state estimation error increment

[0050] Business model: Weighting factor ω i,t+1 The value can be 1 (probability 0.95) or 100 (probability 0.05);

[0051] The present invention's solution: simultaneously satisfies the K constraint and the long-term average update frequency constraint ρ, with parameter V = 1, and takes the value that satisfies... A maximum of K terminals can send the message.

[0052] like Figure 3 As shown in the figure, the results indicate that the average UoI of the present invention varies with the average update frequency constraint under condition V; this phenomenon shows that the present invention can obtain a smaller average UoI in the communication scenario.

Claims

1. A multi-terminal state update scheduling method, characterized in that, Includes the following steps: S1: Establish virtual queue H i,t To track the historical update frequency of each terminal, where H i,t+1 =[H i,t -ρ+U i,t ] + ρ∈(0,1) is the preset average update constraint, U i,t ∈{0,1} represents the update decision of terminal i in time slot t; S2: Calculate the update metrics for each terminal i. Where ω i,t+1 As a weighting factor, Let Q be its mean, p be the probability of successful transmission through the channel, and Q be its mean. i,t This represents the state estimation error; S3: Filtering the candidate set Where V is an adjustable parameter; S4: From Select J i,t The top K terminals with the largest values ​​are updated in their states, while the remaining terminals are not updated. S5: Update the estimation error Q for the next time step. i,t+1 Update the virtual queue H for the next time step. i,t+1 ; S6: Repeat S2-S5 until the system runtime requirement is met.

2. The multi-terminal state update scheduling method according to claim 1, characterized in that, The initialization of the virtual queue satisfies And its average rate stability condition is:

3. The multi-terminal state update scheduling method according to claim 1, characterized in that, The update index J i,t The parameter configuration in the file satisfies: Where θ is the weight adjustment factor.

4. The multi-terminal state update scheduling method according to claim 1, characterized in that, The estimation error Q i,t The update rules are as follows: Q i,t+1 =(1-U i,t S i,t )Q i,t +A i,t (3) Where S i,t ~Bernoulli(p) represents the channel transmission result, A i,t The variance of the error increment for the independent and identically distributed zero-mean estimate is σ. 2 .

5. The multi-terminal state update scheduling method according to claim 1, characterized in that, The value of parameter V affects the upper bound of the average Urgency of Information (UoI):

6. The multi-terminal state update scheduling method according to claim 1, characterized in that, The weighting factor ω i,t+1 With state error Q i,t Statistical independence.

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