An IRS-Assisted Uplink URLLC Resource Allocation Method

Abstract

The present invention discloses an IRS-assisted uplink URLLC resource allocation method. This method considers the uplink URLLC system with IRS assistance, and maximizes the sum rate of all uplink users under finite blocklength transmission by alternately optimizing the user transmit power, the receiver vector at the base station, and the IRS reflection phase shift. Compared with the existing resource allocation methods that consider maximizing the Shannon rate, the present invention can achieve a larger finite blocklength transmission rate under the same user transmit power budget, and the complexity of the present invention is relatively low, which is conducive to engineering implementation.

Description

Technical Field

[0001] The present invention belongs to the field of URLLC systems, and particularly relates to an IRS-assisted uplink URLLC resource allocation method. Background Art

[0002] An Intelligent Reflecting Surface (IRS) consists of a large number of passive reflecting units, and each unit can append an independent phase shift to the input signal, so that the system performance can be improved with low energy loss and hardware cost. With the development of communication systems, the requirements for latency and reliability are getting higher and higher, and Ultra Reliable and Low-Latency Communication (URLLC) technology has become a research hotspot for 5G and 6G.

[0003] URLLC considers finite blocklength transmission, in which case the Shannon formula is no longer applicable, and the URLLC rate formula needs to be used. Therefore, it is necessary to consider the resource allocation problem of the IRS-assisted uplink URLLC system to maximize the sum rate of users considering the influence of finite blocklength. Summary of the Invention

[0004] The purpose of the present invention is to provide an IRS-assisted uplink URLLC resource allocation method to solve the technical problem of maximizing the sum rate of users considering the influence of finite blocklength under the premise of satisfying the maximum transmit power limit of users and the unit modulus constraint of the IRS reflection phase shift.

[0005] To solve the above technical problem, the specific technical solution of the present invention is as follows:

[0006] An IRS-assisted uplink URLLC resource allocation method includes the following steps:

[0007] Step 1, set an iteration stop threshold, and initialize the user transmit power, the base station receiver vector, and the IRS reflection phase shift;

[0008] Step 2, aiming at maximizing the sum rate of finite blocklength users, alternately optimize the user transmit power, the base station receiver vector, and the IRS reflection phase shift;

[0009] Step 3, repeat Step 2 until the absolute value of the difference between the objective function of this iteration and that of the previous iteration is less than or equal to the iteration stop threshold set in Step 1, then end the iteration to obtain the optimized resource allocation result.

[0010] Furthermore, in Step 2, the user transmit power, the base station receiver vector, and the IRS reflection phase shift are optimized by constructing the following original optimization problem:

[0011] The optimization goal is to maximize

[0012] The constraints are:

[0013] |v i |=1,i=1,…,N

[0014] P k ≤P,k=1,…,K

[0015] in, represents the URLLC rate of the kth user, ln(·) represents the natural logarithm function, K is the total number of users, γ k is the signal-to-interference-and-noise ratio of the kth user, which is defined as where w k is the base station receiver vector of the kth user, (·) H means taking the conjugate transpose, represents the direct link channel between the base station and user k, represents the direct link channel between the base station and user i, is the reflection link cascade channel between the base station and user k, is the reflection link cascade channel between the base station and user i, where G is the channel matrix between the base station and IRS, is the channel vector between IRS and user k, is the channel vector between IRS and user i, diag( · ) represents a diagonal matrix with the input vector elements as diagonal elements, P k is the transmission power of the kth user, P i is the transmission power of the i-th user, v is the IRS reflection phase shift vector, ∈ represents the decoding error probability, Q -1 (·) represents the inverse function of the Q function, which is expressed as exp( · ) represents the natural exponential function, n represents the finite block length, v i represents the i-th IRS reflection phase shift, |·| is the modulus of the complex number, N is the number of IRS units, P is the maximum user transmission power, σ 2 Represents the noise power.

[0016] Furthermore, the original optimization problem constructed in step 2 is converted into the following optimization problem for solution when alternately optimizing the user transmit power, the base station receiver vector, and the IRS reflection phase shift:

[0017] The optimization goal is to maximize

[0018] The constraints are:

[0019] |v i | = 1, i = 1, …, N

[0020] P k ≤ P, k = 1, …, K

[0021] where represents a lower bound concave approximation of r k with the expression:[[]] where (·) * represents taking the conjugate

[0022]

[0023] where represents the base station receiver vector of the latest updated user k represents the latest updated IRS reflection phase shift represents the transmit power of the latest updated user k represents the transmit power of the latest updated user i represents taking the real part

[0024]

[0025] Furthermore, when updating the user transmit power P k , k = 1, …, K in step 2, the base station receiver vector and the IRS reflection phase shift need to be fixed at the latest updated values, and its expression is:[[]] where min{·} represents taking the minimum value of the elements in the brackets where and the expressions are respectively the same as the above and only the subscripts need to be changed

[0026] Furthermore, when updating the base station receiver vector w k , k = 1, …, K in step 2, the user transmit power and the IRS reflection phase shift need to be fixed at the latest updated values, and its expression is:[[]] where (·) -1 represents matrix inversion, and I M represents the identity matrix of dimension M × M

[0027] Furthermore, when updating the IRS reflection phase shift in step 2, the user transmit power and the base station receiver vector need to be fixed at the latest updated values, and its expression is:[[]] where arg( · ) represents taking the complex argument, and M = λmax (U)I N ,λ max (U) represents the maximum eigenvalue of U.

[0028] An IRS-assisted uplink URLLC resource allocation method of the present invention has the following advantages:

[0029] 1. For the IRS-assisted uplink URLLC system, by alternately optimizing the user transmit power, the base station receiver vector, and the IRS reflection phase shift, the finite-blocklength user sum rate is maximized under the premise of satisfying the user maximum transmit power limit and the IRS reflection phase shift unit modulus constraint.

[0030] 2. Compared with the traditional resource allocation method based on Shannon transmission rate, the present invention can achieve a higher finite-blocklength user sum rate under the same user transmit power budget.

[0031] 3. The present invention uses a fast-converging iterative algorithm with low computational complexity, which is conducive to engineering implementation. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is the algorithm flowchart of the present invention;

[0033] Figure 2 is the simulation experiment result diagram of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0034] In order to better understand the purpose, structure, and function of the present invention, the following further describes in detail an IRS-assisted uplink URLLC resource allocation method of the present invention with reference to the drawings.

[0035] The typical application scenario of the present invention is an IRS-assisted uplink URLLC system. By alternately optimizing the user transmit power, the base station receiver vector, and the IRS reflection phase shift, the finite-blocklength user sum rate is maximized under the premise of satisfying the user maximum transmit power limit and the IRS reflection phase shift unit modulus constraint. As Figure 1 shown, an IRS-assisted uplink URLLC resource allocation method disclosed in an embodiment of the present invention specifically includes the following steps:

[0036] Step 1. Set an iteration stop threshold and initialize the user transmit power, the base station receiver vector, and the IRS reflection phase shift.

[0037] In this step, the user transmit power, the base station receiver vector, and the IRS transmit phase shift can be initialized to the user transmit power, the base station receiver vector, and the IRS transmit phase shift obtained by optimizing the user sum rate problem under maximizing the Shannon rate.

[0038] Step 2: With the goal of maximizing the sum rate of finite blocklength users, alternately optimize the user transmit power, the base station receiver vector, and the IRS reflection phase shifts.

[0039] In this step, the constructed original optimization problem is described as:

[0040] The optimization goal is: to maximize

[0041] The constraint conditions are:

[0042] |v i | = 1, i = 1, …, N

[0043] P k ≤P, k = 1, …, K

[0044] where represents the URLLC rate of the k-th user, ln(·) represents the natural logarithm function, K is the total number of users, γ k is the signal-to-interference-plus-noise ratio of the k-th user, which is defined as where w k is the base station receiver vector of the k-th user, (·) H represents the conjugate transpose, represents the direct link channel between the base station and user k, represents the direct link channel between the base station and user i, is the cascaded reflection link channel between the base station and user k, is the cascaded reflection link channel between the base station and user i, where G is the channel matrix between the base station and the IRS, is the channel vector between the IRS and user k, is the channel vector between the IRS and user i, diag(·) represents the diagonal matrix with the elements of the input vector as the diagonal elements, P k is the transmit power of the k-th user, P i is the transmit power of the i-th user, v is the IRS reflection phase shift vector, ∈ represents the decoding error probability, Q -1 (·) represents the inverse function of the Q function, and the Q function is expressed as exp( · ) represents the natural exponential function, n represents the finite blocklength, v i represents the i-th IRS reflection phase shift, |·| is the modulus of a complex number, N is the number of IRS elements, P is the maximum value of the user transmit power, σ 2 represents the noise power.

[0045] In this step, for the convenience of solving, the above-mentioned original optimization problem can be converted into the following optimization problem when alternately optimizing the user transmit power, the base station receiver vector, and the IRS reflection phase shift for solution:

[0046] The optimization objective is: to maximize

[0047] The constraint conditions are:

[0048] |v i | = 1, i = 1, …, N

[0049] P k ≤ P, k = 1, …, K

[0050] where, represents a lower bound concave approximation of r k , and the expression is: where, (·) * represents taking the conjugate,

[0051]

[0052] where, represents the latest updated base station receiver vector of user k, represents the latest updated IRS reflection phase shift, represents the latest updated transmit power of user k, represents the latest updated transmit power of user i, represents taking the real part,

[0053]

[0054]

[0055] In this step, when updating the user transmit power P k , k = 1, …, K, the base station receiver vector and the IRS reflection phase shift need to be fixed at the latest updated values, and its expression is: where, min{·} represents taking the minimum value of the elements in the brackets, where and have the same expressions as the above and respectively, only the subscripts need to be changed.

[0056] In this step, when updating the base station receiver vector w k , k = 1, …, K, the user transmit power and the IRS reflection phase shift need to be fixed at the latest updated values, and its expression is: -Among them (·) -1 represents matrix inversion, and I M represents the identity matrix of dimension M×M.

[0057] In this step, when updating the IRS reflection phase shift, the user transmit power and the base station receiver vector should be fixed to the latest updated values, and its expression is: where arg( · ) represents taking the complex argument, M = λ max (U)I N , λ max (U) represents the maximum eigenvalue of U.

[0058] Step 3: Repeat Step 2 until the absolute value of the difference between the objective function of this iteration and that of the previous iteration is less than or equal to the iteration stop threshold set in Step 1, and then end the iteration to obtain the optimized resource allocation result.

[0059] To verify the effectiveness of the present invention, simulation experiments were carried out. The parameters involved in the simulation experiments are shown in the following table, where the users are uniformly distributed in a circular area.

[0060] Table 1 Simulation Experiment Parameter Table

[0061] Parameter Value Number of base station antennas 4 Number of intelligent reflecting surface units 35 Number of user antennas 1 Number of users 3 Noise power of the uplink -90 dBm Block length 32 Decoding error probability <![CDATA[10 -6 > Iteration stop threshold <![CDATA[10 -3 > Rice factor 10 Path loss of the direct link channel 32.6 + 36.7lg(d) Path loss of the cascaded reflection link channel 22 + 22lg(d) Base station location (0 m, 0 m) Intelligent reflecting surface location (150 m, 0 m) User center location (150 m, 30 m) Radius of the circular area where users are distributed 10m

[0062] Figure 2 As the comparison result of the simulation experiment, where the "maximizing finite blocklength rate scheme" is the method proposed in the present invention. The simulation results show that: under the same maximum transmit power limit of users, the method proposed in the present invention can achieve a higher finite blocklength sum rate compared with the traditional Shannon rate-based method.

[0063] It can be understood that the present invention is described through some embodiments. Those skilled in the art know that, without departing from the spirit and scope of the present invention, various changes or equivalent replacements can be made to these features and embodiments. In addition, under the teaching of the present invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application belong to the scope protected by the present invention.

Claims

1. An IRS-assisted uplink URLLC resource allocation method, characterized in that, It includes the following steps: Step 1: Set the iteration stop threshold, and initialize the user transmit power, the base station receiver vector, and the IRS reflection phase shift; Step 2: With the goal of maximizing the finite blocklength user sum rate, alternately optimize the user transmit power, the base station receiver vector, and the IRS reflection phase shift; Step 3: Repeat Step 2 until the absolute value of the difference between the objective function of the current iteration and that of the previous iteration is less than or equal to the iteration stop threshold set in Step 1, and then end the iteration to obtain the optimized resource allocation result; In Step 2, the user transmit power, the base station receiver vector, and the IRS reflection phase shift are optimized by constructing the following original optimization problem: The optimization objective is: to maximize The constraint conditions are: |v i | = 1, i = 1, …, N P k ≤P, k = 1, …, K Among them, denotes the URLLC rate of the \(k\)-th user, \(\ln(\cdot)\) denotes the natural logarithm function, \(K\) is the total number of users, and \(\gamma\) k is the signal-to-interference-plus-noise ratio (SINR) of the \(k\)-th user, which is defined as where \(w\) k is the base station receiver vector of the \(k\)-th user, \((\cdot)^\dagger\) H denotes the conjugate transpose, denotes the direct link channel between the base station and user \(k\), denotes the direct link channel between the base station and user \(i\), is the cascaded reflection link channel between the base station and user \(k\), is the cascaded reflection link channel between the base station and user \(i\), where \(G\) is the channel matrix between the base station and the IRS, is the channel vector between the IRS and user \(k\), is the channel vector between the IRS and user \(i\), \(\text{diag}(\cdot)\) denotes the diagonal matrix with the elements of the input vector as the diagonal elements, \(P\) k is the transmit power of the \(k\)-th user, \(P\) i is the transmit power of the \(i\)-th user, \(v\) is the IRS reflection phase shift vector, \(\epsilon\) represents the decoding error probability, \(Q\) -1 \((\cdot)\) denotes the inverse function of the \(Q\)-function, and the \(Q\)-function is expressed as \(\exp(\cdot)\) denotes the natural exponential function, \(n\) represents the finite block length, \(v\) i denotes the \(i\)-th IRS reflection phase shift, \(|\cdot|\) is the modulus of a complex number, \(N\) is the number of IRS elements, \(P\) is the maximum value of the user transmit power, and \(\sigma\) 2 denotes the noise power; In Step 2, when alternately optimizing the user transmit power, the base station receiver vector, and the IRS reflection phase shift, the above constructed original optimization problem is converted into the following optimization problem for solution: The optimization objective is: to maximize The constraint conditions are: |v i | = 1, i = 1, …, N P k ≤ P, k = 1, …, K Among them, represents a lower bound concave approximation of r k with the expression: where (·) * denotes taking the conjugate Among them, represents the base station receiver vector of the latest updated user k, represents the IRS reflection phase shift of the latest update, represents the transmit power of the latest updated user k, represents the transmit power of the latest updated user i, represents taking the real part, Update the user transmit power P in step 2 k , when k = 1, …, K, the base station receiver vector and the IRS reflection phase shift should be fixed to the latest updated values, and their expressions are: where min{·} represents taking the minimum value of the elements in the brackets, Update the base station receiver vector w in Step 2 k , when k = 1, …, K, fix the user transmit power and IRS reflection phase shift to the newly updated values, and its expression is: Among them (·) -1 represents matrix inversion, and I M represents the identity matrix of dimension M×M; When updating the IRS reflection phase shift in step 2, the user transmission power and the base station receiver vector should be fixed to the latest updated values, and its expression is: where arg(·) represents taking the complex argument, M = λ max (U)I N , λ max (U) represents the maximum eigenvalue of U,

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