CN-116406007-B - Data transmission optimization method, device, terminal and medium of communication system

CN116406007BCN 116406007 BCN116406007 BCN 116406007BCN-116406007-B

Abstract

The invention discloses a data transmission optimization method, a device, a terminal and a medium of a communication system, which comprise the steps of modeling an IRS auxiliary channel according to a cascade channel model, obtaining AoI violation probability of each device according to AoI definition, constructing an effective capacity maximization problem of information timeliness guarantee, converting a long-term random optimization problem into a deterministic optimization problem of each time slot according to a Lyapunov optimization algorithm, converting the original effective capacity optimization problem into a Markov decision process, and carrying out joint optimization on IRS phase shift and device transmission power according to a SAC algorithm to obtain an optimized data transmission strategy. The invention solves the problem of maximizing the effective capacity of information timeliness guarantee in an IRS-assisted NOMA communication system with imperfect CSI.

Inventors

LIU LONG
XU XIAODONG
CHEN HAO
CHEN JIANQIAO
MA NAN
ZHANG PING

Assignees

鹏城实验室

Dates

Publication Date: 20260508
Application Date: 20221220

Claims (11)

1. A method for optimizing data transmission in a communication system, comprising: modeling an IRS-assisted channel according to the cascade channel model; obtaining AoI violation probability of each device according to the definition of AoI; the problem of maximizing the effective capacity of the information timeliness guarantee is constructed, which comprises the following steps: determining the effective capacity of the device k related to time delay: Wherein, the Representing the quality of service factor (qos) and, Indicating the length of the time slot, Representing the rate of device k; The effective capacity maximization problem of the information timeliness guarantee is expressed as: s.t. information timeliness limit: instantaneous power limit: average power limit: Phase decision limit: Wherein, the Representing the power values of K devices throughout the T slot, Representing the phase shift values of all subunits of the IRS throughout the T slot, A threshold value representing the instantaneous power value of device k, A threshold value representing the average power value of device k; The long-term random optimization problem is converted into a deterministic optimization problem per time slot according to a Lyapunov optimization algorithm, which comprises the following steps: Converting the information timeliness limiting condition and the average power limiting condition into a queue stability problem by using a virtual queue model: Determining a lyapunov offset penalty function: Wherein, the Variable values expressed as decision variable independence; the problem of maximizing the effective capacity of information timeliness guarantee is converted into the problem of deterministic optimization per time slot: s.t. instantaneous power limit conditions: phase decision constraint: ; converting the original effective capacity optimization problem into a Markov decision process; And carrying out joint optimization on the IRS phase shift and the equipment transmission power according to the SAC algorithm to obtain an optimized data transmission strategy.
2. The method for optimizing data transmission in a communication system according to claim 1, wherein modeling IRS-assisted channels according to a concatenated channel model previously comprises: dividing time according to time slots to obtain a plurality of time slots; in time slot At this time, the signal of device k is acquired: Wherein, the Representing a channel state matrix between the AP and the IRS at time slot t; representing a channel state matrix between the IRS and device k at time slot t; a signal representing device k at time slot t; representing the power of device k at time slot t; representing noise of the equipment k at the base station end; the phase matrix representing all subunits of the IRS at time slot t, 。
3. The method for optimizing data transmission in a communication system according to claim 2, wherein modeling IRS-assisted channels according to a concatenated channel model comprises: modeling the IRS-assisted communication channel using the cascading channel model: Wherein, the Represented as an estimated concatenated channel, A cascading channel matrix from the AP to the device through the IRS; Representing the corresponding channel estimation error of the device k concatenated channel at time slot t.
4. A method of optimizing data transmission in a communication system according to claim 3, wherein said deriving AoI violation probability for each device according to the definition of AoI comprises: Determine AoI values for device k at time slot t: Wherein, the The AoI value representing device k at time slot t, Indicating the time of arrival of the u-th packet at device k, Indicating device k transmits the th Time of each data packet; calculating AoI violation probabilities for each device k: Wherein, the The threshold value of the device k AoI is indicated, Representing events The probability of the establishment of the two-dimensional model, Representing the maximum value of the probability of a violation by the device kAoI.
5. The method for optimizing data transmission in a communication system according to claim 4, wherein said deriving AoI violation probability for each device according to the definition of AoI further comprises: acquiring the relation between the violation probability of the equipment AoI aiming at any task reaching mode and parameters such as a real-time task queue, the number of processing tasks and the like: Wherein, the Expressed in time The number of packets is reached at the internal device k, Representing the number of transmission status update packets; calculating AoI violation probability of each device k according to the relation between the device AoI violation probability and the parameters such as the real-time task queue, the processing task number and the like: Wherein, the Indicating the total number of slots.
6. The method for optimizing data transmission in a communication system according to claim 5, wherein said converting the original capacity optimization problem into a markov decision process comprises: defining an action space, a decision space and a reward function of the Markov decision process; And constructing a discrete Markov decision process according to the action space, the decision space and the rewarding function.
7. The method of optimizing data transmission in a communication system according to claim 6, wherein said defining an action space, a decision space and a reward function of said markov decision process comprises: State space defining the action space of an IRS-assisted NOMA communication system for imperfect CSI as: Wherein, the Representing all queue lengths; Action space defining the action space as: Wherein, the 、 Respectively representing power values and phase values of all devices and IRS subunits in time slot t; bonus function defining an objective function of deterministic optimization problem per slot as a bonus obtained by executing a decision per slot t: 。
8. The method for optimizing data transmission in a communication system according to claim 7, wherein the performing joint optimization on IRS phase shift and device transmission power according to SAC algorithm to obtain an optimized data transmission policy comprises: Outputting by decision distribution for current state information Obtaining the current decision ; Obtaining rewards based on current status information and actions And next state information And will Logging into experience playback; Updating parameters of the Q1 network and the Q2 network based on minimizing the gradient of the loss function and the mean square error And Taking the minimum value of the two Q function values as the updated Q function value of each Q function; updating parameters of the policy network based on minimizing the KL divergence function and the gradient of KL divergence ; Updating weight values of entropy functions using gradient descent minimizing weighted sums of bonus functions and entropy functions ; Updating parameters of target Q1 function and target Q2 function by using soft updating method And 。
9. A data transmission optimizing apparatus of a communication system for realizing the data transmission optimizing method of a communication system according to any one of claims 1 to 8, comprising: The channel modeling module is used for modeling the IRS-assisted channel according to the cascade channel model; The violation probability calculation module is used for obtaining AoI violation probability of each device according to the definition of AoI; the effective capacity module is used for constructing the problem of effective capacity maximization of information timeliness guarantee; The first optimization module is used for converting the long-term random optimization problem into a deterministic optimization problem per time slot according to a Lyapunov optimization algorithm; The second optimization module is used for converting the original effective capacity optimization problem into a Markov decision process; And the joint optimization module is used for performing joint optimization on the IRS phase shift and the equipment transmission power according to the SAC algorithm to obtain an optimized data transmission strategy.
10. A terminal comprising a processor and a memory, the memory storing a data transmission optimization program of a communication system, which when executed by the processor is adapted to carry out the operations of the data transmission optimization method of a communication system according to any one of claims 1-8.
11. A medium, characterized in that the medium is a computer-readable storage medium storing a data transmission optimization program of a communication system, which when executed by a processor is adapted to carry out the operations of the data transmission optimization method of a communication system according to any one of claims 1-8.

Description

Data transmission optimization method, device, terminal and medium of communication system Technical Field The present invention relates to the field of NOMA communication technologies, and in particular, to a method, an apparatus, a terminal, and a medium for optimizing data transmission in a communication system. Background Compared to the orthogonal multiple access technique, NOMA (Non-Orthogonal Multiple Access, i.e., non-orthogonal multiple access) is one of the key wireless accesses that improves the spectral efficiency. Also, a new IRS-assisted NOMA communication system can be constructed using an IRS (INTELLIGENT REFLECTING Surface, i.e., smart reflection plane) capable of changing the wireless channel environment. Because of the passive nature of IRSs, it is challenging to obtain accurate CSI (CHANNEL STATE Information). Aiming at the traditional problem of maximizing the effective capacity, the existing scheme only considers the problem of optimizing the channel capacity under the limitation of communication realization, but the problem of optimizing the channel capacity also needs to consider the information timeliness, wherein the information timeliness is a performance index different from the communication time delay and is measured by using the information ages (Age of Information, aoI). Because the effect of limitations on the timeliness of the information on the effective capacity is a matter of considerable investigation. Finally, considering that interference exists between NOMA systems, and IRS has a large number of subunits, the existing channel capacity optimization scheme cannot achieve maximization of effective capacity while information timeliness is guaranteed. Accordingly, there is a need in the art for improvement. Disclosure of Invention The invention aims to solve the technical problem that the invention provides a data transmission optimization method, a device, a terminal and a medium of a communication system aiming at the defects of the prior art, so as to solve the problem of maximizing the effective capacity of information timeliness guarantee in an IRS-assisted NOMA communication system with imperfect CSI. The technical scheme adopted for solving the technical problems is as follows: In a first aspect, the present invention provides a data transmission optimization method for a communication system, including: modeling an IRS-assisted channel according to the cascade channel model; obtaining AoI violation probability of each device according to the definition of AoI; the problem of maximizing the effective capacity of the information timeliness guarantee is solved; according to the Lyapunov optimization algorithm, the long-term random optimization problem is converted into a deterministic optimization problem per time slot; converting the original effective capacity optimization problem into a Markov decision process; And carrying out joint optimization on the IRS phase shift and the equipment transmission power according to the SAC algorithm to obtain an optimized data transmission strategy. In one implementation, modeling IRS-assisted channels according to a cascading channel model includes, before: dividing time according to time slots to obtain a plurality of time slots; in time slot At this time, the signal of device k is acquired: Wherein, the Representing a channel state matrix between the AP and the IRS at time slot t; representing a channel state matrix between the IRS and device k at time slot t; a signal representing device k at time slot t; representing the power of device k at time slot t; representing noise of the equipment k at the base station end; the phase matrix representing all subunits of the IRS at time slot t, 。 In one implementation, the modeling IRS-assisted channels according to a cascading channel model includes: modeling the IRS-assisted communication channel using the cascading channel model: Wherein, the Represented as an estimated concatenated channel,A cascading channel matrix from the AP to the device through the IRS; Representing the corresponding channel estimation error of the device k concatenated channel at time slot t. In one implementation, the deriving AoI violation probability for each device according to the definition of AoI includes: Determine AoI values for device k at time slot t: Wherein, the The AoI value representing device k at time slot t,Indicating the time of arrival of the u-th packet at device k,Indicating device k transmits the thTime of each data packet; calculating AoI violation probabilities for each device k: Wherein, the The threshold value of the device k AoI is indicated,Representing eventsThe probability of the establishment of the two-dimensional model,Representing the maximum value of the probability of a violation by the device kAoI. In one implementation, the deriving AoI violation probability for each device according to the definition of AoI further includes: acquiring the relation between the vio