A method for allocating resources in a delay-sensitive network based on truncated retransmission technique

Abstract

The application provides a time-sensitive network resource allocation method based on truncated retransmission technology, and belongs to the field of wireless communication.In the method, an end-to-end time-sensitive network transmission model is first constructed;secondly, the end-to-end time delay of the time-sensitive network is calculated according to an incremental redundancy-hybrid automatic repeat request (IR-HARQ) finite code length formula;finally, a code length and transmission power joint optimization problem with the minimum end-to-end time delay as the target is established, and a convex optimization method is used for efficient solution.The time-sensitive network resource optimization method based on the truncated retransmission technology can greatly guarantee the high reliability and low time delay requirement of the time-sensitive network.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of wireless communication, in particular to a time-sensitive network resource allocation method based on truncated retransmission technology. BACKGROUND

[0002] With the continuous growth of data flow in the power grid transmission line and the increasing real-time requirement of service control, the intelligent power grid has higher requirements for data transmission and collection. The data is divided into hard real-time, quasi real-time, non real-time and other types, and the corresponding transmission delay requirement is from milliseconds to seconds. Solving the data transmission delay problem is the key to building an intelligent power grid communication network, and is also the core foundation of ensuring the reliability, safety, efficiency and intelligence of future intelligent power grid communication network. The networking and sharing of data transmission platform are facing network congestion, increasing delay jitter and other problems at any time, which greatly affects the real-time and reliability of power grid line data collection and transmission, and even causes huge safety accidents to power grid equipment. The inventors found that for delay-limited power grid applications, it is more appropriate to use a finite code length mechanism for modeling. Under the finite code length mechanism, the reliability of communication becomes a probability function, because transmission errors can occur even if the coding rate is set below the Shannon capacity. The transmission error probability is often related to the code length and transmission power. Although the reliability can be improved by hybrid automatic repeat request, it will lead to retransmission attempts, which will cause the delay to increase. How to reasonably allocate the transmission code length and transmission power to achieve high-reliability and low-delay data transmission is still an open and difficult problem. SUMMARY

[0003] To achieve high-reliability and low-delay data transmission in time-sensitive networks, the present application provides a time-sensitive network resource allocation method based on truncated retransmission technology.

[0004] The technical solution adopted by the present application to solve its technical problems is: a time-sensitive network resource allocation method based on truncated retransmission technology, comprising the following steps:

[0005] Step 1: Construct an end-to-end time-sensitive network transmission model;

[0006] Step 2: Calculate the end-to-end delay of the time-sensitive network according to the incremental redundancy-hybrid automatic repeat request (IR-HARQ) finite code length formula;

[0007] Step 3: Establish a code length and transmission power joint optimization problem with the goal of minimizing the end-to-end delay and efficiently solve it using convex optimization methods to obtain the optimal allocation scheme.

[0008] The step 1 is specifically as follows:

[0009] The end-to-end time-sensitive network transmission model includes a sending end and a receiving end; the data packet of the sending end is sent to the receiving end through a wireless channel; a quasi-static Rayleigh fading model is adopted, so that the state of the wireless channel remains unchanged within each frame, but changes between different frames; each frame carries a primary transmission or retransmission of a data packet, and each retransmission is considered to have a length of m symbols; the channels of adjacent frames are correlated; the correlation between the channel of the kth frame and the channel of the previous frame is represented by adopting a Gaussian-Markov model:

[0010]

[0011] where h k is the channel gain of the kth frame, h k-1 is the channel gain of the (k-1)th frame, 0≤ρ 2 ≤1 is a channel correlation coefficient, Δh k ~CN(0,1) is a complex Gaussian random variable;

[0012] The signal-to-noise ratio of the receiving end at the ith time slot is represented as:

[0013]

[0014] where P is the transmission power, φ i is the path loss, is the noise power.

[0015] The step 2 is specifically as follows: in order to meet the reliability requirement of the transmission data packet, the decoding error probability of the receiving end is considered; according to the finite code length formula based on IR-HARQ, the decoding error probability of the ith transmission of the data packet is represented as:

[0016]

[0017] where Q is a Q function, m i represents the code length of the data packet transmission, L represents the number of bits of the data packet, and V represents the channel dispersion; in order to meet the low latency requirement, the data packet cannot be retransmitted for an infinite number of times; given the maximum number of transmissions N max , the probability of successful transmission of the data packet after N transmissions is:

[0018]

[0019] At this time, the end-to-end latency of the data packet can be represented as T=NmT s , T s is the time for transmitting one code; if the data packet is not successfully transmitted within the maximum number of times N max , the data packet is discarded, and a new data packet is transmitted instead, and the packet loss probability can be represented as:

[0020]

[0021] Finally, the expectation of end-to-end delay E[T] = E[N]mT is calculated s where the expectation of transmission times can be expressed as:

[0022]

[0023] The joint optimization problem of code length and transmit power in step 3 is to minimize the end-to-end delay, which is specifically:

[0024]

[0025] s.t.: P min ≤ P ≤ P max ,

[0026] 0 ≤ m ≤ m max ,

[0027] where the optimization objective means that by optimizing the data packet transmission code length m and the transmit power P, the mean of the end-to-end delay is minimized; the constraint P min ≤ P ≤ P max means that the minimum value of the transmit power is P min , and the maximum value is P max ; the constraint 0 ≤ m ≤ m max means that the code length of a single transmission is not greater than the given threshold m max .

[0028] The efficient solution of step 3 using convex optimization method includes:

[0029] First, the constraint is relaxed to m ≥ 0; then, the feasible block length of m is divided into N intervals, i.e.:

[0030]

[0031] where m n is the code length of the nth transmission, ε -1 (x) is the inverse function of ε i , and ε max is the error probability threshold of a single transmission; then the original problem can be converted to the following sub-problems:

[0032]

[0033] s.t.: P min ≤ P ≤ P max ,

[0034] m N-n ≤ m ≤ m N-n+1 ,

[0035] The above sub-problems are joint convex problems, which can be efficiently solved in each sub-interval by convex optimization methods such as ellipsoid method, so as to obtain an optimal allocation scheme.

[0036] Compared with the prior art, the beneficial effects of the present application are that the present application proposes a time delay sensitive network resource allocation method based on truncated retransmission technology, in order to meet the high reliability and low delay requirement of data packets, the truncated retransmission technology is considered, by jointly optimizing the transmission code length and the transmission power, a non-convex and non-concave optimization problem is converted into a convex sub-problem, which greatly guarantees the high reliability and low delay requirement of time sensitive network. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 The allocation method flowchart of the present application;

[0038] Figure 2 The constructed end-to-end time sensitive network transmission model diagram;

[0039] Figure 3 The average delay curve with the change of data packet code length and transmission power. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. It should be noted that the same reference numerals and letters represent the same items in the following description, so once an item is defined in one formula, it does not need to be further defined and explained in subsequent formulas. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the present application.

[0041] Reference Figure 1 A time delay sensitive network resource allocation method based on truncated retransmission technology mainly includes constructing an end-to-end time sensitive network transmission model, calculating the end-to-end time delay of the time sensitive network according to the IR-HARQ finite code length formula, establishing a code length and transmission power joint optimization problem with the goal of minimizing the end-to-end time delay and efficiently solving it by using convex optimization method, and realizing the reliability and low delay requirement of the time sensitive network. Specifically:

[0042] Step 1: The implementation method of the present application first constructs Figure 2An end-to-end time-sensitive network transmission model is shown. The model contains a sender and a receiver. The sender's data packet is transmitted to the receiver through a wireless channel. A quasi-static Rayleigh fading model is adopted, which makes the state of the wireless channel remain unchanged within each frame but changes between different frames. Each frame carries a data packet's initial transmission or retransmission, while each retransmission is considered to have a length of m symbols. In addition, a short packet scenario is considered, in which the block length of retransmission is more likely to be shorter than the channel coherence time. Therefore, the channels of adjacent frames are correlated. The correlation between the kth frame channel and the previous frame channel is represented by adopting a Gaussian-Markov model

[0043]

[0044] where h k is the channel gain of the kth frame, h k-1 is the channel gain of the k-1th frame, 0≤ρ 2 ≤1 is the channel correlation coefficient, Δh k ~CN(0, 1) is a complex Gaussian random variable. The signal-to-noise ratio of the receiver at the ith time slot can be represented as

[0045]

[0046] where P is the transmit power, φ i is the path loss, is the noise power.

[0047] Step 2: The implementation method of the present application calculates the end-to-end delay of the time-sensitive network according to the IR-HARQ finite code length formula. In order to meet the reliability requirements of the transmitted data packet, the decoding error probability of the receiver is considered. According to the finite code length formula based on IR-HARQ, the decoding error probability of the ith transmission of the data packet can be represented as,

[0048]

[0049] where Q is the Q function, m i represents the code length of the data packet transmission, and L represents the number of bits of the data packet. In order to meet the low delay requirement, the data packet cannot be retransmitted for an infinite number of times. Given the maximum number of transmissions N max , the probability of successful transmission of the data packet after N transmissions is

[0050]

[0051] At this time, the end-to-end delay of the data packet can be represented as T = NmT s , and T s is the time of transmitting one symbol. If the maximum number of transmissions N maxIf the packet is not successfully transmitted, it is dropped and a new packet is transmitted instead, and the probability of packet dropping can be expressed as

[0052]

[0053] Finally, the expectation of end-to-end delay E[T] = E[N]mT is calculated s where the expectation of the number of transmissions can be expressed as

[0054]

[0055] Step 3: The implementation method of the present application finally establishes a joint optimization problem of code length and transmission power aiming at minimizing the end-to-end delay, and efficiently solves it by using convex optimization method:

[0056]

[0057] s.t.: P min ≤ P ≤ P max ,

[0058] 0 ≤ m ≤ m max ,

[0059] The meaning of the above objective is to minimize the mean of end-to-end delay by optimizing the packet transmission code length m and the transmission power P.

[0060] The transmission power P constraint is defined as:

[0061] P min ≤ P ≤ P max

[0062] The meaning of the above constraint is that the minimum value of the transmission power is P min , and the maximum value is P max .

[0063] The packet transmission code length m constraint is defined as:

[0064] 0 ≤ m ≤ m max

[0065] The meaning of the above constraint is that the code length of a single transmission is not greater than the given threshold m max .

[0066] Obviously, the objective problem is a non-convex and non-concave function about m. In order to solve this problem, the constraint is first relaxed to m ≥ 0. Then, the feasible block length of m is divided into N intervals, i.e.

[0067]

[0068] where m n is the code length of the nth transmission, and ε -1 (x) is εi the inverse function of ε max is the error probability threshold of single transmission. Then the original problem can be converted into the following sub-problems

[0069]

[0070] s.t.:P min ≤P≤P max ,

[0071] m N-n ≤m≤m N-n+1 ,

[0072] The above sub-problems are joint convex problems, which can be efficiently solved in each sub-interval by convex optimization methods such as ellipsoid method, to guarantee the resource allocation of time-sensitive network with high reliability and low latency requirements.

[0073] Figure 3 The transformation curve of the average latency of the time-sensitive network optimized by the specific embodiment with the data packet code length and the transmission power is given, it can be seen that with the increase of the code length, the average latency is piecewise convex, and the greater the transmission power, the smaller the average latency. At the same time, when the code length takes a relatively large value, the average latency value tends to be equal.

[0074] It should be understood that the parts not elaborated in the specification are all prior art.

[0075] It should be understood that the above description of the embodiments is more detailed, and therefore should not be considered as a limitation on the scope of patent protection of the present application. Ordinary skilled in the art can make substitutions or modifications without departing from the scope of the claims, which shall fall within the scope of protection of the present application. The scope of protection of the present application shall be subject to the appended claims.

Claims

1. A method for resource allocation in a delay-sensitive network based on truncated retransmission technique, characterized in that: The method comprises the following steps: Step 1: constructing an end-to-end time-sensitive network transmission model; Step 2: calculating an end-to-end time delay of the time-sensitive network according to an incremental redundancy-hybrid automatic repeat request (IR-HARQ) finite code length formula; The step 2 is specifically as follows: in order to meet the reliability requirement of a transmission data packet, a decoding error probability of a receiving end is considered; according to the IR-HARQ-based finite code length formula, the decoding error probability of the nth transmission of the data packet is represented as: wherein is a Q function, m i denotes the code length of data packet transmission, γ i is the signal-to-noise ratio of the receiving end in the ith time slot, L denotes the bit number of the data packet, and V denotes channel dispersion; in order to meet the requirement of low latency, the data packet cannot be retransmitted for an infinite number of times; given the maximum number of transmissions N max then the probability of successful transmission of the data packet after N transmissions is: The data packet end-to-end time delay can be expressed as T= NmT s , m is the data packet transmission code length, T s is the time for transmitting one code; If the maximum number of transmissions N max is not successful, the packet is dropped and a new packet is transmitted instead, and the probability of dropping the packet can be expressed as: Finally, the expectation of the end-to-end delay E[T] = E[N]mT is calculated s where the expectation of the number of transmissions can be expressed as: Step 3: establishing a code length and transmission power joint optimization problem with the minimum end-to-end time delay as an objective and efficiently solving the problem by using a convex optimization method, so as to obtain an optimal allocation scheme.

2. The method of claim 1, wherein: The step 1 is specifically as follows: The end-to-end time-sensitive network transmission model comprises a sending end and a receiving end; the data packet of the sending end is sent to the receiving end through a wireless channel; a quasi-static Rayleigh fading model is adopted, so that the state of the wireless channel remains unchanged within each frame but changes between different frames; each frame carries a first transmission or retransmission of a data packet, and each retransmission is considered to have a length of m symbols; adjacent frames are correlated; the correlation between the kth frame channel and the previous frame channel is represented by using a Gaussian-Markov model: where h k is the channel gain of the kth frame, h k-1 is the channel gain of the k-1th frame, 0≤ρ 2 is the channel correlation coefficient, is a complex Gaussian random variable; The signal-to-noise ratio of the receiving end at the ith time slot is represented as: where P is the transmit power, φ i is the path loss, is the noise power.

3. The method of claim 1, wherein: The step 3 of establishing the code length and transmission power joint optimization problem with the minimum end-to-end time delay as the objective is specifically as follows: s.t.: P min ≤ P ≤ P max , 0 < m < m max , where the optimization objective means minimizing the mean of the end-to-end delay by optimizing the code length m and the transmit power P of the data packet transmission; constraint P min ≤ P ≤ P max means the minimum value of the transmit power is P min and the maximum value is P max ; constraint 0 ≤ m ≤ m max means the code length of a single transmission is not greater than a given threshold m max .

4. The method of claim 3, wherein: The efficient solving of the step 3 by using the convex optimization method comprises: First, the constraint is relaxed as m>0; then, the feasible block length of m is divided into N intervals, that is: where m n is the code length for the nth transmission, ε -1 is the error probability threshold for a single transmission; then the original problem can be converted into the following sub-problems: i (x) is the inverse function of ε max is the error probability threshold for a single transmission; then the original problem can be converted into the following sub-problems: s.t.: P min ≤ P ≤ P max , m N-n ≤m≤m N-n+1 , The above sub-problem is a joint convex problem, which can be efficiently solved in each sub-interval by using the convex optimization method, so as to obtain the optimal allocation scheme.

Images