Resource Allocation Optimization Method, Controller and Communication System for Multi-Power Terminal Communication

Abstract

The present invention provides a resource allocation optimization method, a controller and a communication system for multi-power terminal communication. Multiple power terminals are grouped according to their own required power data information; the power terminals estimate the channel state information according to the signaling signals sent by the central controller, and send their own channel state information to the central controller; the central controller completes the allocation of the transmission power and the block length under the given delay and reliability performance index requirements according to the energy consumption minimization optimization result, and determines the intelligent reflecting surface (IRS) and the reflection phase matrix associated with each power terminal group to assist the central controller to complete information transmission. With the assistance of the IRS, each power terminal can, while meeting its own delay and reliability index requirements, reasonably allocate resources through the central controller, ensuring the minimization of system energy consumption. It conforms to the actual power Internet of Things scenario, and through the optimal allocation of resources, the system energy consumption is greatly reduced.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and in particular, to a method for optimizing resource allocation, a controller, and a communication system for multi-power terminal communication. Background Art

[0002] Currently, with the development of power Internet of Things technologies, the types of terminal devices in the power Internet of Things are complex and diverse, the quantity is growing rapidly, and the distribution range is wide. Under this background, reasonable allocation of resources and reduction of energy consumption have become important issues, which can avoid problems such as resource waste, shortage of device own resources, and inability of terminals to respond due to unreasonable resource allocation, and improve the concurrency of the terminal cluster. At the same time, wired transmission has been replaced by wireless transmission to enhance flexibility and reduce infrastructure costs, but this change poses challenging requirements for wireless transmission in terms of latency and reliability. And lower latency and higher reliability can achieve real-time and stable control of power terminals. Therefore, Ultra-Reliable and Low Latency Communications (URLLC) has become a basic indicator requirement for the power Internet of Things.

[0003] It is difficult to meet the above strict requirements, which has promoted the development of the concept of Intelligent Reflecting Surface (IRS). As a low-power and high-energy-efficiency emerging technology, IRS can overcome the above obstacles. Specifically, IRS is a uniform planar array integrated by a large number of passive reflecting elements, and each of its reflecting elements can independently adjust the phase and amplitude of the received signal, thereby changing the transmission direction of the reflected signal. IRS increases the received signal strength at the receiving end by creating a virtual line-of-sight link from the transmitting end to the receiving end, reduces the power consumption of the transmitting end, and improves energy efficiency.

[0004] Currently, the research on IRS-assisted wireless communication systems based on URLLC does not fully consider resource allocation and energy consumption reduction. Resource allocation and energy consumption reduction are important issues in the development of the power Internet of Things. Unreasonable resource allocation will cause problems such as resource waste and inability of terminals to respond; failure to reduce energy consumption will cause shortage of device own resources and ultimately result in the inability to successfully transmit the power data information required by power terminals.

[0005] Moreover, current research on IRS-assisted wireless communication systems based on URLLC only considers a single user or a relatively concentrated group of multiple users. However, in actual power IoT scenarios, a central controller needs to control multiple power terminals, and the types of power terminals are complex and diverse, with a wide distribution range. If only one IRS is deployed, due to the large number of power terminals, one IRS cannot serve all power terminals well; and due to the wide distribution range of power terminals, deploying only one IRS will result in most power terminals being relatively far from the IRS, and the IRS cannot reflect signals to them, and can only serve the power terminals close to it.

[0006] The scenario of multiple IRSs serving multiple power terminals, compared with the scenario of a single IRS serving a small number of concentrated power terminals, will inevitably lead to more power terminals and more IRSs, and thus the problem of reasonable matching between power terminal groups and IRSs needs to be considered. And how to formulate a reasonable association strategy between power terminal groups and IRSs is a problem. Secondly, when considering resource allocation and reducing energy consumption, the transmit power allocation and block length allocation of the central controller need to be considered, as well as the trade-off between energy consumption, delay, and reliability, which is also a difficulty in the present invention. Summary of the Invention

[0007] The present invention aims to at least solve one of the technical problems in the prior art that the IRS-assisted wireless communication system based on URLLC does not fully consider resource allocation and reducing energy consumption, and only considers a single user or a relatively concentrated group of multiple users.

[0008] To this end, the first aspect of the present invention provides a resource allocation optimization method for multi-power-terminal communication.

[0009] The second aspect of the present invention provides a controller.

[0010] The third aspect of the present invention provides a communication system.

[0011] The present invention provides a resource allocation optimization method for multi-power-terminal communication, including the following steps:

[0012] S1. Group multiple power terminals. According to the power data required by the power terminals, divide KI single-antenna power terminals into K different power terminal groups. Each of the I power terminals in each group performs the same task, that is, the central controller transmits the same power data information to the terminals in the same group;

[0013] S2. The central controller sends a signaling signal to the KI power terminals through the direct link with the power terminals and the reflection links of the M IRSs. After receiving the signaling sent by the central controller, the power terminals estimate the channel state information and send it to the central controller through the uplink;

[0014] S3. Develop an optimization strategy for minimizing the energy consumption of multiple power terminals assisted by multiple IRSs based on URLLC. The developed strategy realizes energy consumption minimization through the joint optimization of the discrete variable correlation matrix, continuous variable transmission power, channel block length, and IRS reflection phase matrix, while ensuring reliability and delay constraints; by sequentially solving the transmission power, channel block length, IRS reflection phase matrix, and correlation matrix, the resource allocation results and minimum energy consumption results of multiple power terminals assisted by multiple IRSs based on URLLC are obtained;

[0015] S4. The central controller and IRS set the transmission power, block length allocation, and reflection phase according to the calculation results at the optimal solution of the optimization problem to perform power data signal transmission. Under the premise of meeting reliability and delay, the total system energy consumption is minimized;

[0016] According to the resource allocation optimization method for multi-power terminal communication of the above technical solution of the present invention, it may also have the following additional technical features:

[0017] Further, S3 includes the following steps:

[0018] S31. Solve the transmission power and channel block length. First, fix the correlation matrix and IRS reflection phase matrix, and then transform the developed optimization strategy into a convex programming problem for minimizing energy consumption by introducing slack variables, using arithmetic-geometric mean, and continuous convex approximation. By solving the convex programming problem, the transmission power and channel block length are obtained;

[0019] S32. Solve the IRS reflection phase matrix. According to the transmission power and channel block length obtained in step S31, transform the optimization strategy into a feasible solution problem, and then obtain the IRS reflection phase matrix by solving the convex programming problem of the feasible solution;

[0020] S33. Solve the correlation matrix, transform it into a bilateral matching problem, and then use the minimum energy consumption obtained in steps S31 and S32 as the utility value of the matching problem. Finally, obtain the correlation matrix and the results of the original optimization strategy by solving the stable matching of this matching problem.

[0021] Further, S2 includes the following steps:

[0022] S21. The central controller sends the signaling signal \(x\) to \(KI\) power terminals through the direct link with the power terminals and the reflection link of the IRS k , where \(x\) k is the signaling signal from the central controller to the \(k\)th group of terminals, satisfying \(E\{|x\) k |\) 2 \}=1;

[0023] S22. Calculate the superimposed signal of the direct link and the IRS reflection link received by the $i$-th power terminal in the $k$-th group:

[0024]

[0025] where $x$ k,m $\in \{0, 1\}$ ($1\leq k\leq K$, $1\leq m\leq M$) is the association coefficient between the $k$-th group of terminals and the $m$-th IRS; is the channel matrix between the $m$-th IRS and the $i$-th terminal in the $k$-th group; is the channel coefficient between the central controller and the $i$-th terminal in the $k$-th group, is the channel matrix between the central controller and the $m$-th IRS; $p$ k is the transmission power of the base station to send the signal $x$ k ; is the additive white Gaussian noise at the $i$-th terminal in the $k$-th group; is the reflection matrix of the $m$-th IRS, where $\theta$ m,n $\in [0, 2\pi]$, $1\leq m\leq M$, $1\leq n\leq N$;

[0026] S23. Calculate the pre - estimated received signal - to - noise ratio SNR of the $i$-th power terminal in the $k$-th group:

[0027]

[0028] Calculate the decoding error probability $\varepsilon$ corresponding to the received signal - to - noise ratio SNR k,i :

[0029]

[0030] where $V$ k,i $= 1-(1 + \gamma$ k,i ) -2 , assuming $\gamma$ k,i is large enough, approximate $V$ k,i as 1; $D$ is the size of the transmission data packet; $m$ k is the channel block length of the $k$-th group of terminals,

[0031] S24. The power terminal pre - estimates the channel state information according to the received signal and sends it to the central controller.

[0032] Furthermore, model the optimization strategy in S3 as:

[0033]

[0034] where $M$ 0 is the total channel block length of the system; The objective function of the resource allocation model representing the minimization of the total system energy consumption, i.e., the total system energy consumption; C1 represents that all power terminals within each group meet the reliability requirements; C2 represents that the system should meet the latency requirements; C3 represents that if a group of power terminals is matched with the IRS, the matching coefficient is 1, otherwise 0; C4 represents that each group of power terminals can only select one IRS; C5 represents that each IRS can only be selected by one group of power terminals; C6 represents the continuous phase constraint of each reflection unit of each IRS.

[0035] Furthermore, S31 includes the following steps:

[0036] S311. Fix the association matrix X between the discrete power terminal groups and the IRS, as well as the IRS reflection phase matrix, transform the original optimization problem into a joint optimization, and minimize the energy efficiency on the premise of meeting the reliability and latency requirements;

[0037] S312. For the non-convex objective function, first introduce slack variables, and then transform the non-convex objective function into a convex objective function and convex constraint C1' through the arithmetic-geometric mean calculation for each term;

[0038] S313. For the non-convex inequality constraint C1, convert the non-convex constraint into convex constraints C3' and C4' by introducing slack variables and continuous convex approximation;

[0039] S314. According to S312 and S313, represent the problem OP1 as:

[0040] OP2:

[0041] s.t.C1':

[0042] C2:

[0043] C3':

[0044] C4':

[0045] where l is the number of iterations; φ and γ' k,i are slack variables; OP2 is a convex optimization problem, and the corresponding solution is obtained through the convex optimization toolbox; update the results obtained by solving OP2 each time and and update through the obtained p k and m k update Based on the idea of continuous convex approximation, repeatedly update γ k,i '(l-1) and The value until the p in two adjacent iterations k and m k The difference is less than a certain precision, that is, the algorithm is considered to converge, and the latest p k and m k values are the solutions to the optimization problem.

[0046] Furthermore, in S32, the p k and m k values obtained in S31 are used to solve the feasible solution problem, thereby determining the reflection phase Θ m of each IRS, that is, to solve the feasible solution problem represented by OP1:

[0047] OP3: Find U m

[0048] s.t. C1”:

[0049] C2”:

[0050] C3”: U m,n,n ≤1, 1≤n≤N, U m,N+1,N+1 = 1

[0051] C4”: rank(U m ) = 1, (1≤m≤M)

[0052] where OP3 obtains the corresponding solution Θ m .

[0053] Furthermore, S33 includes the following steps:

[0054] S331. Convert the association matrix X between the discrete power terminal group and the IRS into a feasible matching Φ, that is, if x k,m = 1, it represents that the power terminal group k is matched with the IRS m, denoted as if x k,m = 0, it represents that the power terminal group k is not matched with the IRS m;

[0055] S332. Regard the minimized energy consumption obtained in steps S31 and S32 as the utility value U of the matching problem, that is, the utility value corresponding to the current matching Φ is U(Φ);

[0056] S333. For each power terminal group k, find another power terminal group k′(k′≠k), and exchange the current matching with k′ to generate a new matching;

[0057]

[0058] The corresponding utility value is U(Φ k,k′ );

[0059] S334. If U(Φ k,k′ ) < U(Φ), then update the current match Φ = Φ k,k′ ; otherwise, do not update;

[0060] S335. Repeat S333 and S334 until there is no new match Φ that can reduce the utility value k,k′ , at this time, a stable match Φ * is obtained, which is the optimal solution to the original problem. The utility value U(Φ * ) corresponding to this match is the optimal value of the original problem.

[0061] Furthermore, S4 includes the following steps:

[0062] S41. When the optimal solution of the optimization model is obtained, the central controller broadcasts the execution command information to the IRS and the power terminals according to the calculated p k and m k ;

[0063] S42. Each IRS sets the corresponding reflection phase according to the calculation result and reflects the central controller signal to the associated and matched power terminal group;

[0064] S43. Each power terminal in the power terminal group receives the required power data information by receiving the superimposed signal of the direct link of the controller and the reflected link of the IRS matched with it, and minimizes the energy consumption on the premise of ensuring reliability and delay, thus completing the whole process.

[0065] The present invention provides a controller, including a central controller, and the central controller executes the following steps: First, the central controller sends a signaling signal to the power terminal; the power terminal pre-estimates the channel state information according to the received signal and sends it to the central controller; the central controller formulates a resource allocation optimization strategy for multiple power terminals assisted by multiple IRSs based on URLLC according to the channel state information estimated by the power terminal, and determines the transmission power, the allocated resource block length, the association matrix, and the IRS reflection phase matrix; then sends the phase matrix information to the IRS to adjust it to the corresponding phase; finally, sends the required power data information through the direct link with the power terminal and the reflected link of the IRS matched with it, and minimizes the energy consumption on the premise of ensuring reliability and delay, thus completing the whole transmission process.

[0066] The present invention provides a communication system that performs communication resource allocation using the resource allocation optimization method for multi-power-terminal communication described in the above technical solution. The system includes a power terminal grouping module, a channel estimation module, a resource allocation module, an IRS reflection communication module, an association matching module, and a calculation result output module, where:

[0067] The power terminal grouping module is used to group power terminals according to the power data required by the power terminals.

[0068] The channel estimation module is used to enable the central controller to transmit a signaling signal to the power terminal, and then the power terminal determines the channel state information according to the received signaling signal.

[0069] The resource allocation module is used to perform transmission power allocation and block length resource allocation when the central controller transmits information to multiple power terminal groups.

[0070] The IRS reflection communication module is used to reflect the signal transmitted by the central controller to the power terminal and determine the IRS reflection phase matrix to ensure the delay and reliability performance index requirements of the power terminal.

[0071] The association matching module is used to achieve the association matching between the power terminal group and multiple IRSs, determine the association matrix, and ensure the minimum system energy consumption.

[0072] The calculation result output module is used for the central controller to allocate the transmission power and block length based on the optimization result, and for the IRS to set its own reflection phase matrix based on the optimization result.

[0073] In summary, due to the adoption of the above technical features, the beneficial effects of the present invention are as follows:

[0074] The present invention utilizes the great potential of low energy consumption and high energy efficiency brought by the intelligent reflecting surface (IRS) technology. By deploying multiple IRSs to serve a large number of power terminals with various types and wide distributions, it can increase the transmission link from the central controller to the power terminal, reasonably allocate resources, and meet the index requirements of the power terminal for delay and reliability, thereby achieving the minimum system energy consumption.

[0075] The present invention specifically describes the application of IRS technology in actual power Internet of Things scenarios and wireless communication scenarios. Through the reasonable association matching between the power terminal group and the IRS, power resources, block length resources are jointly allocated, and the reflection function of the IRS is fully utilized to ensure the delay and reliability performance index requirements of each power terminal, and the minimum system energy consumption is achieved.

[0076] The present invention proposes a resource allocation optimization method for high-reliability and low-latency communication of multiple power terminals. The method specifically describes the application of IRS technology in actual power Internet of Things scenarios and wireless communication scenarios. Through reasonable association and matching between power terminal groups and IRS, power resources, block length resources are jointly allocated, and the reflection effect of IRS is fully utilized to ensure that each power terminal meets the requirements of latency and reliability performance indicators, and the system energy consumption is minimized.

[0077] The present invention proposes a resource allocation optimization method, device and system for high-reliability and low-latency communication of multiple power terminals. The method takes into account that in the actual power Internet of Things scenario, a central controller needs to control multiple power terminals, and the types of power terminals are complex and diverse, and the distribution range is wide. It also considers the actual resource allocation problems and energy consumption problems in the power Internet of Things scenario. In addition, IRS can reflect the signals sent by the central controller, thereby improving the signal strength received by the power terminal, further meeting the requirements of the power terminal for latency and reliability performance indicators, and reducing the system energy consumption.

[0078] The present invention adopts IRS technology. By reflecting the signals sent by the central controller, it assists the central controller to transmit signals to the power terminals. In this context, the power terminal groups and IRS are reasonably associated and matched, power resources, block length resources are jointly allocated, and the reflection phase of IRS is set to ensure that each power terminal meets the requirements of latency and reliability performance indicators, and the system energy consumption is minimized.

[0079] The additional aspects and advantages of the present invention will become obvious in the following description part, or will be understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] The above and / or additional aspects and advantages of the present invention will become obvious and easy to understand from the description of the embodiments in conjunction with the following drawings, wherein:

[0081] Figure 1 is a flowchart of a resource allocation optimization method for multi-power terminal communication according to an embodiment of the present invention;

[0082] Figure 2 is a schematic structural diagram of a communication system according to an embodiment of the present invention;

[0083] Figure 3 is a multi-IRS assisted system model diagram for multi-power terminal URLLC in a power Internet of Things scenario according to an embodiment of the present invention;

[0084] Figure 4 is a program block diagram of a resource allocation optimization method for multi-power terminal communication according to an embodiment of the present invention;

[0085] Figure 5 It is an example diagram for comparing the performance of a resource allocation optimization method for multi-power terminal communication in an embodiment of the present invention with traditional strategies;

[0086] Figure 6 It is an example diagram of the system performance of a resource allocation optimization method for multi-power terminal communication in an embodiment of the present invention under different numbers of reflection units. Detailed implementation manners

[0087] In order to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation manners. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.

[0088] Many specific details are set forth in the following description in order to fully understand the present invention. However, the present invention may be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0089] The following refers to Figures 1 to 6 to describe a resource allocation optimization method for multi-power terminal communication provided according to some embodiments of the present invention.

[0090] Some embodiments of the present application provide a resource allocation optimization method for multi-power terminal communication.

[0091] As Figures 1 to 6 shown, the first embodiment of the present invention proposes a resource allocation optimization method for multi-power terminal communication, including the following steps:

[0092] S1. Group multiple power terminals. According to the power data required by the power terminals, divide KI single-antenna power terminals into K different power terminal groups. The I power terminals in each group perform the same task, that is, the central controller transmits the same power data information to the terminals in the same group;

[0093] S2. The central controller sends a signaling signal to KI power terminals through the direct link with the power terminals and the reflection links of M IRSs. After receiving the signaling sent by the central controller, the power terminals estimate the channel state information and send it to the central controller through the uplink;

[0094] Specifically, S2 includes the following steps:

[0095] S21. The central controller sends a signaling signal x k to KI power terminals through the direct link with the power terminals and the reflection links of the IRSs, where x kis the signaling signal sent by the central controller to the k-th group of terminals, satisfying E{|x k | 2} = 1;

[0096] S22. Calculate the superimposed signal received by the i-th power terminal in the k-th group from the direct link and the IRS reflection link:

[0097]

[0098] where x k,m ∈ {0, 1} (1 ≤ k ≤ K, 1 ≤ m ≤ M) is the association coefficient between the k-th group of terminals and the m-th IRS; is the channel matrix between the m-th IRS and the i-th terminal in the k-th group; is the channel coefficient between the central controller and the i-th terminal in the k-th group, is the channel matrix between the central controller and the m-th IRS; p k is the transmit power of the base station sending the signal x k ; is the additive white Gaussian noise at the i-th terminal in the k-th group; is the reflection matrix of the m-th IRS, where θ m,n ∈ [0, 2π], 1 ≤ m ≤ M, 1 ≤ n ≤ N;

[0099] S23. Calculate the pre-estimated received signal-to-noise ratio SNR of the i-th power terminal in the k-th group:

[0100]

[0101] Calculate the decoding error probability ε k,i corresponding to the received signal-to-noise ratio SNR:

[0102]

[0103] where V k,i = 1 - (1 + γ k,i ) -2 , assuming γ k,i is large enough, approximate V k,i as 1; D is the size of the transmitted data packet; m k is the channel block length of the k-th group of terminals,

[0104] S24. The power terminal pre-estimates the channel state information based on the received signal, specifically pre-estimates the channel state information using the minimum mean square error (MMSE) method according to the calculation results of S22 and S23, and sends the estimated channel state information to the central controller.

[0105] S3. Develop an optimization strategy for minimizing the energy consumption of multiple groups of power terminals assisted by multiple IRSs based on URLLC. The developed strategy realizes the minimization of energy consumption through the joint optimization of the discrete variable correlation matrix, continuous variable transmit power, channel block length, and IRS reflection phase matrix, while ensuring reliability and latency constraints; by successively solving the transmit power, channel block length, IRS reflection phase matrix, and correlation matrix, the resource allocation results and the minimum energy consumption results of multiple power terminals assisted by multiple IRSs based on URLLC are obtained;

[0106] Specifically, model the optimization strategy in S3 as:

[0107]

[0108] where M 0 is the total channel block length of the system; represents the objective function of the resource allocation model for minimizing the total energy consumption of the system, that is, the total energy consumption of the system; C1 represents that all power terminals within each group meet the reliability requirements; C2 represents that the system should meet the latency requirements; C3 represents that if a group of power terminals is matched with an IRS, the matching coefficient is 1, otherwise it is 0; C4 represents that each group of power terminals can only select one IRS; C5 represents that each IRS can only be selected by one group of power terminals; C6 represents the continuous phase constraint of each reflection unit of each IRS.

[0109] Specifically, S3 includes the following steps:

[0110] S31. Solve the transmit power and channel block length. First, fix the correlation matrix and the IRS reflection phase matrix, and then transform the developed optimization strategy into a convex programming problem for minimizing energy consumption by introducing slack variables, using the arithmetic-geometric mean, and continuous convex approximation. By solving the convex programming problem, the transmit power and the channel block length are obtained;

[0111] S31 includes the following steps:

[0112] S311. Fix the discrete power terminal group - IRS correlation matrix X and the IRS reflection phase matrix, and transform the original optimization problem into a joint optimization to minimize the energy efficiency under the premise of meeting reliability and latency requirements;

[0113] S312. For the non - convex objective function, first introduce slack variables, and then transform the non - convex objective function into a convex objective function and convex constraint C1' through arithmetic - geometric mean calculation for each term;

[0114] S313. For the non - convex inequality constraint C1, transform the non - convex constraint into convex constraints C3' and C4' by introducing slack variables and continuous convex approximation;

[0115] S314. Represent the problem OP1 according to S312 and S313 as:

[0116] OP2:

[0117] s.t. C1':

[0118] C2:

[0119] C3':

[0120]

[0121] C4':

[0122] where l is the number of iterations; φ and γ' k,i are slack variables; OP2 is a convex optimization problem, and the corresponding solution is obtained through a convex optimization toolbox; the results obtained by solving OP2 each time are used to update and and by the obtained p k and m k update Based on the idea of successive convex approximation, repeatedly update γ k,i '(l-1) and until the difference between the p k and m k in two adjacent iterations is less than a certain precision (such as 0.01), that is, the algorithm is considered to converge, and the latest obtained p k and m k values are the solutions to the optimization problem.

[0123] S32. Solve the IRS reflection phase matrix. According to the transmit power and channel block length obtained in step S31, transform the optimization strategy into a feasible solution problem, and then solve the feasible solution convex programming problem to obtain the IRS reflection phase matrix;

[0124] In S32, use the p k and m k values obtained in S31 to solve the feasible solution problem, thereby determining the reflection phase Θ m of each IRS, that is, solve the feasible solution problem represented by OP1:

[0125] OP3: Find U m

[0126] s.t. C1”:

[0127] C2”:

[0128] C3”: U m,n,n ≤ 1, 1 ≤ n ≤ N, U m,N+1,N+1 = 1

[0129] C4”: rank(U m ) = 1, (1 ≤ m ≤ M)

[0130] Wherein, OP3 obtains the corresponding solution Θ through the convex optimization toolbox m .

[0131] S33. Solve the association matrix, transform it into a bilateral matching problem, then use the minimum energy consumption obtained in steps S31 and S32 as the utility value of the matching problem, and finally obtain the association matrix and the result of the original optimization strategy by solving the stable matching of this matching problem.

[0132] S33 includes the following steps:

[0133] S331. Transform the association matrix X of the discrete power terminal group and the IRS into a feasible matching Φ, that is, if x k,m = 1, it means that the power terminal group k is matched with IRS m, expressed as If x k,m = 0, it means that the power terminal group k is not matched with IRS m;

[0134] S332. Regard the minimized energy consumption solved in steps S31 and S32 as the utility value U of the matching problem, that is, the utility value corresponding to the current matching Φ is U(Φ);

[0135] S333. For each power terminal group k, find other power terminal groups k' (k' ≠ k), and exchange the current matching with k' to generate a new matching;

[0136]

[0137] The corresponding utility value is U(Φ k,k′ );

[0138] S334. If U(Φ k,k′ ) < U(Φ), then update the current matching Φ = Φ k,k′ ; Otherwise, do not update;

[0139] S335. Repeat S333 and S334 until there is no new matching Φ that can reduce the utility value k,k′ , at this time, a stable matching Φ * is obtained, which is the optimal solution of the original problem, and the utility value U(Φ * ) corresponding to this matching is the optimal value of the original problem.

[0140] S4, the central controller, and the IRS set the transmission power, block length allocation, and reflection phase according to the calculation results when the optimal solution of the optimization problem is obtained to perform power data signal transmission. Under the premise of meeting reliability and delay, the total system energy consumption is minimized;

[0141] Specifically, S4 includes the following steps:

[0142] S41. When the optimal solution of the optimization model is obtained, the central controller broadcasts the execution command information to the IRS and the power terminal according to the calculated p k and m k ;

[0143] S42. Each IRS sets the corresponding reflection phase according to the calculation results and reflects the central controller signal to the associated and matched power terminal group;

[0144] S43. Each power terminal in the power terminal group receives the required power data information by receiving the superimposed signal of the direct link of the controller and the reflection link of the IRS matched with it, and minimizes the energy consumption under the premise of ensuring reliability and delay, thus completing the whole process.

[0145] As Figure 5 shown, a performance comparison example diagram of the method adopted in this embodiment and the traditional strategy is given. The abscissa of the legend is the total available channel block length. The minimum system energy consumption under no IRS, single IRS, and multi-IRS of the present invention is compared in the figure. It can be seen from the figure that as the available channel block length increases, the minimum system energy consumption also decreases. Compared with the traditional single IRS system, this embodiment can achieve lower system energy consumption.

[0146] As Figure 6 shown, a system performance example diagram of this embodiment under different numbers of reflection units is given. It can be seen from the figure that with the increase in the number of IRS reflection units, lower system energy consumption can be achieved. This is because the increase in the number of reflection units can increase more reflection links, thereby improving the signal strength received by the power terminal, reducing the transmission power of the central controller, and thus reducing the overall system energy consumption.

[0147] Some embodiments of this application provide a controller.

[0148] The second embodiment of the present invention proposes a controller, and on the basis of the first embodiment, as Figures 1 to 6As shown in the figure, it includes a central controller, and the central controller performs the following steps: First, the central controller sends a signaling signal to the power terminal; the power terminal pre-estimates the channel state information according to the received signal and sends it to the central controller; the central controller formulates an optimized resource allocation strategy for multiple power terminals assisted by multiple IRSs based on URLLC according to the channel state information estimated by the power terminal, and determines the transmission power, the allocated resource block length, the association matrix, and the IRS reflection phase matrix; then sends the phase matrix information to the IRS to adjust it to the corresponding phase; finally, sends the required power data information through the direct link with the power terminal and the reflection link of the IRS matched with it, so as to minimize the energy consumption on the premise of ensuring reliability and delay, thus completing the entire transmission process.

[0149] As Figure 3 shown in the figure, a multi-IRS assisted system model diagram for URLLC facing multiple power terminals in the power Internet of Things scenario is provided. Among them, the central controller sends the required power data information to each power terminal group, and each IRS reflects the power data information sent by the central controller to the power terminal group matched with it. Among them, the central controller transmits information to each power terminal group in the OFDMA manner.

[0150] Some embodiments of the present application provide a communication system.

[0151] The third embodiment of the present invention proposes a communication system, and on the basis of any of the above embodiments, as Figures 1 to 6 shown in the figure, the resource allocation optimization method for communication facing multiple power terminals described in the above embodiments is adopted for communication resource allocation, including a power terminal grouping module, a channel estimation module, a resource allocation module, an IRS reflection communication module, an association matching module, and a calculation result output module connected in sequence, where:

[0152] The power terminal grouping module is used to group power terminals according to the power data required by the power terminals;

[0153] The channel estimation module is used to realize that after the central controller transmits a signaling signal to the power terminal, the power terminal determines the channel state information according to the received signaling signal;

[0154] The resource allocation module is used to realize the transmission power allocation and block length resource allocation when the central controller transmits information to multiple power terminal groups;

[0155] The IRS reflection communication module is used to realize reflecting the signal transmitted by the central controller to the power terminal, and determining the IRS reflection phase matrix to ensure the delay and reliability performance index requirements of the power terminal;

[0156] The association matching module is used to implement the association matching of power terminal groups and multiple IRSs, determine the association matrix, and ensure the minimum system energy consumption;

[0157] The calculation result output module is used for the central controller to allocate the transmission power and block length based on the optimization result, and for the IRS to set its own reflection phase matrix based on the optimization result.

[0158] The above communication system can be applied to the power Internet of Things system or the wireless communication system, and combines the method described in the first embodiment to realize the resource allocation optimization of multiple power terminals assisted by multiple IRSs based on URLLC in the power Internet of Things scenario.

[0159] In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or instance. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0160] Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An optimization method for resource allocation in multi-power terminal communication, characterized in that, it includes the following steps: S1. Group multiple power terminals. According to the power data required by the power terminals, divide KI single-antenna power terminals into K different power terminal groups. The I power terminals in each group perform the same task, that is, the central controller transmits the same power data information to the terminals in the same group; S2. The central controller sends a signaling signal to the KI power terminals through the direct link with the power terminals and the reflection links of M IRSs. After receiving the signaling sent by the central controller, the power terminals estimate the channel state information and send it to the central controller through the uplink; S3. Develop an optimization strategy for minimizing the energy consumption of multiple groups of power terminals assisted by multiple IRSs based on URLLC. The developed strategy realizes the minimization of energy consumption through the joint optimization of the discrete variable correlation matrix, the continuous variable transmit power, the channel block length, and the IRS reflection phase matrix while ensuring reliability and delay constraints; obtain the resource allocation results and the minimum energy consumption results of multiple power terminals assisted by multiple IRSs based on URLLC by sequentially solving the transmit power, the channel block length, the IRS reflection phase matrix, and the correlation matrix; S4. The central controller and the IRS set the transmit power, block length allocation, and reflection phase according to the calculation results when the optimization problem has an optimal solution to perform power data signal transmission. Under the premise of meeting reliability and delay, the total system energy consumption is minimized; S3 includes the following steps: S31. Solve the transmit power and the channel block length. First, fix the correlation matrix and the IRS reflection phase matrix, and then transform the developed optimization strategy into a convex programming problem for minimizing energy consumption by introducing slack variables, using the arithmetic-geometric mean, and continuous convex approximation. Obtain the transmit power and the channel block length by solving the convex programming problem; S32. Solve the IRS reflection phase matrix. Transform the optimization strategy into a feasible solution problem according to the transmit power and the channel block length obtained in step S31, and then obtain the IRS reflection phase matrix by solving the convex programming problem of the feasible solution; S33. Solve the correlation matrix. Transform it into a bilateral matching problem, and then use the minimum energy consumption obtained in steps S31 and S32 as the utility value of the matching problem. Finally, obtain the correlation matrix and the results of the original optimization strategy by solving the stable matching of this matching problem.

2. The optimization method for resource allocation in multi-power terminal communication according to claim 1, characterized in that, S2 includes the following steps: S21. The central controller sends a signaling signal x to KI power terminals through a direct link with the power terminal and a reflection link of the IRS k , where x k is the signaling signal sent by the central controller to the k-th group of terminals and satisfies Ε{|x k | 2} = 1; S22. Calculate the superimposed signal received by the i-th power terminal in the k-th group from the direct link and the IRS reflection link: where \(x\) k,m \(\in \{0, 1\}\) is the association coefficient between the \(k\)-th group of terminals and the \(m\)-th IRS, where \(1\leq k\leq K\) and \(1\leq m\leq M\); is the channel matrix between the \(m\)-th IRS and the \(i\)-th terminal in the \(k\)-th group, where \(1\leq i\leq I\); is the channel coefficient between the central controller and the \(i\)-th terminal in the \(k\)-th group, is the channel matrix between the central controller and the \(m\)-th IRS; \(p\) k is the signal \(x\) transmitted by the base station k 's transmit power; is the additive white Gaussian noise at the \(i\)-th terminal in the \(k\)-th group, where \(1\leq k\leq K\); is the reflection matrix of the \(m\)-th IRS, where \(\theta\) m,n \(\in [0, 2\pi]\), \(1\leq n\leq N\); S23. Calculate the pre-estimated received signal-to-noise ratio SNR of the i-th power terminal in the k-th group: Calculate the decoding error probability ε corresponding to the received signal-to-noise ratio SNR k,i : Among them, V k,i = 1 - (1 + γ k,i ) -2 , assuming that γ k,i is large enough, approximate V k,i to 1; D is the size of the transmitted data packet; m k is the channel block length of the k-th group of terminals, S24. The power terminal pre-estimates the channel state information according to the received signal and sends it to the central controller.

3. The optimization method for resource allocation in multi-power terminal communication according to claim 2, characterized in that, model the optimization strategy in S3 as: Among them, M 0 is the total channel block length of the system; represents the objective function of the resource allocation model for minimizing the total system energy consumption, that is, the total system energy consumption; C1 means that all power terminals in each group meet the reliability requirements; C2 means that the system should meet the delay requirements; C3 represents that if a group of power terminals is matched with the IRS, the matching coefficient is 1, otherwise it is 0; C4 represents that each group of power terminals can only select one IRS; C5 represents that each IRS can only be selected by one group of power terminals; C6 represents the continuous phase constraint of each reflection unit of each IRS.

4. The optimization method for resource allocation in multi-power terminal communication according to claim 3, It is characterized in that S31 includes the following steps: S311. Fix the discrete power terminal group and the IRS association matrix X, as well as the IRS reflection phase matrix, transform the original optimization problem into a joint optimization, and minimize the energy efficiency on the premise of meeting the reliability and delay requirements; S312. For the non-convex objective function, first introduce slack variables, and then transform the non-convex objective function into a convex objective function and convex constraint C1' by calculating each term through the arithmetic-geometric mean; S313. For the non-convex inequality constraint C1, convert the non-convex constraint into convex constraints C3' and C4' by introducing slack variables and continuous convex approximation; S314. According to S312 and S313, represent the problem OP1 as: where \(l\) is the number of iterations; \(\varphi\) and \(\gamma'\) k,i are slack variables; OP2 is a convex optimization problem, and the corresponding solution is obtained through the convex optimization toolbox; update and \(\gamma\) k,i ' (l-1) using the result obtained by solving OP2 each time, and update k and \(m\) k using the obtained \(p\) Based on the idea of successive convex approximation, repeatedly update \(\gamma\) k,i ' (l-1) and until the difference between \(p\) k and \(m\) k in two adjacent iterations is less than a certain precision, that is, the algorithm is considered to converge, and the latest \(p\) k and \(m\) k values are the solutions to the optimization problem.

5. A resource allocation optimization method for multi-power-terminal communication according to claim 4, It is characterized in that In S32, the feasible solution problem is solved using the p k and m k values to determine the reflection phase Θ of each IRS m , that is, to solve the feasible solution problem represented by OP1: OP3: Find U m C2”: U m ±0.1 ≤ m ≤ M "C3": U m,n,n ≤ 1, 1 ≤ n ≤ N, U m,N+1,N+1 = 1 C4”: rank(U m ) == 1, 1 ≤ m ≤ M Among them, OP3 obtains the corresponding solution Θ through the convex optimization toolbox m .

6. A resource allocation optimization method for multi-power-terminal communication according to claim 5, It is characterized in that S33 includes the following steps: S331. Convert the association matrix X between the discrete power terminal groups and the IRS into a feasible matching Φ, that is, if x k,m = 1, it represents that the power terminal group k is matched with the IRS m, denoted as If x k,m = 0, it represents that the power terminal group k is not matched with the IRS m; S332. Regard the minimized energy consumption solved in steps S31 and S32 as the utility value U of the matching problem, that is, the utility value corresponding to the current matching Φ is U(Φ); S333. For each power terminal group k, find other power terminal groups k', k'≠k, and exchange the current matching with k' to generate a new matching; The corresponding utility value is U(Φ k,k′ ); S334. If U(Φ k,k′ ) < U(Φ), then update the current match Φ = Φ k,k′ ; otherwise, do not update; S335. Repeat S333 and S334 until there is no new match Φ that can reduce the utility value k,k′ , at this time, a stable match Φ is obtained * , which is the optimal solution to the original problem. The utility value U(Φ * ) is the optimal value of the original problem.

7. A resource allocation optimization method for multi-power-terminal communication according to any one of claims 1 to 6, It is characterized in that S4 includes the following steps: S41. When the optimal solution of the optimization model is obtained, the central controller broadcasts the execution command information to the IRS and the power terminal according to the calculated p k and m k . S42. Each IRS sets the corresponding reflection phase according to the calculation result, and reflects the central controller signal to the associated and matched power terminal group; S43. Each power terminal in the power terminal group receives the required power data information by receiving the superimposed signal of the direct link of the controller and the reflection link of the IRS matched with it, and minimizes the energy consumption on the premise of ensuring reliability and delay, thereby completing the whole process.

8. A controller, It is characterized in that Applied to a resource allocation optimization method for multi-power-terminal communication according to any one of claims 1 to 7, the controller includes a central controller, and the central controller executes the following steps: First, the central controller sends a signaling signal to the power terminal; the power terminal pre-estimates the channel state information according to the received signal and sends it to the central controller; the central controller formulates a resource allocation optimization strategy for multiple IRS-assisted multiple power terminals based on URLLC according to the channel state information estimated by the power terminal, and determines the transmission power, the allocated resource block length, the association matrix, and the IRS reflection phase matrix; then sends the phase matrix information to the IRS to adjust it to the corresponding phase; finally, sends the required power data information through the direct link with the power terminal and the reflection link of the IRS matched with it, and minimizes the energy consumption on the premise of ensuring reliability and delay, thereby completing the whole transmission process.

9. A communication system, It is characterized in that Applied to an optimization method for resource allocation in multi-power-terminal communication as described in any one of claims 1 to 7, the communication system includes a power terminal grouping module, a channel estimation module, a resource allocation module, an IRS reflection communication module, an association matching module, and a calculation result output module, where: The power terminal grouping module is used to group power terminals according to the power data required by the power terminals; The channel estimation module is used to determine the channel state information by the power terminal according to the received signaling signal after the central controller transmits the signaling signal to the power terminal; The resource allocation module is used to realize the transmission power allocation and block length resource allocation when the central controller transmits information to multiple power terminal groups; The IRS reflection communication module is used to reflect the signal transmitted by the central controller to the power terminal and determine the IRS reflection phase matrix to ensure the delay and reliability performance index requirements of the power terminal; The association matching module is used to realize the association matching between the power terminal group and multiple IRSs, determine the association matrix, and ensure the minimum system energy consumption; The calculation result output module is used for the central controller to allocate the transmission power and block length based on the optimization result, and for the IRS to set its own reflection phase matrix based on the optimization result.

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