Cf-ran pilot frequency allocation method and apparatus, equipment, medium and product

Abstract

Disclosed in the present application are a CF-RAN pilot frequency allocation method and apparatus, an equipment, a medium and a product. The method comprises: acquiring a channel estimation matrix between a user equipment and an access point, and a beamforming matrix of an edge distributed unit; on the basis of the channel estimation matrix and the beamforming matrix, determining the signal-to-noise ratio of the user equipment using a pilot signal for downlink transmission; on the basis of the signal-to-noise ratio, determining frequency spectrum efficiency; using a pilot frequency allocation principle as a constraint condition to construct a pilot frequency allocation problem with the purpose of maximizing the frequency spectrum efficiency; and solving the pilot frequency allocation problem to obtain an optimal pilot frequency allocation policy. For pilot frequency multiplexing problems, the present application achieves the optimization objective of maximizing the spectrum efficiency under appropriate pilot frequency allocation policies.

Description

Pilot allocation method, device, equipment, medium and product of CF-RAN

[0001] The present application claims priority to the Chinese patent application No. 202410445410.0, filed on April 12, 2024, to the Chinese Patent Office, the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] The present application relates to the technical field of wireless communication, in particular to a pilot allocation method, device, equipment, medium and product of CF-RAN. BACKGROUND

[0003] Cell-free massive MIMO technology is used to solve the serious inter-cell interference occurring in the ultra-dense network and the high complexity of massive MIMO in the traditional cellular network.

[0004] For the distributed architecture, user merging can be implemented in different baseband units with the support of pre-transmission network, so as to realize scalability. However, since only a single access point (AP) is used for multi-user detection, the distributed architecture shows poor performance compared with the implementation using small units, and less gain is obtained. For the centralized architecture, cooperative processing between multiple APs can be implemented to improve network performance. However, when the number of cooperative APs is too large, the centralized processing faces the problems of high computational complexity and poor scalability.

[0005] At present, a cell-free radio access network (CF-RAN) architecture combining the distributed and centralized methods is proposed to realize the scalability of cell-free. Under the CF-RAN architecture, the existing pilot allocation strategy cannot guarantee the maximum spectral efficiency.

[0006] SUMMARY

[0007] The present application provides a pilot allocation method, device, equipment, medium and product of CF-RAN to solve the problem that the existing pilot allocation strategy cannot guarantee the maximum spectral efficiency under the CF-RAN architecture, and gives a suitable pilot allocation method to maximize the spectral efficiency.

[0008] According to an aspect of the present application, a pilot allocation method of CF-RAN is provided, which is applied to a central processor of a cell-free radio access network (CF-RAN) system. The CF-RAN system further includes a user-centric distributed unit, an edge distributed unit and an access point configured on the edge distributed unit. The method includes:

[0009] obtaining a channel estimation matrix between a user equipment and the access point, and a beamforming matrix of the edge distributed unit;

[0010] determine a signal-to-noise ratio of downlink transmission of the user equipment using the pilot signal according to the channel estimation matrix and the beamforming matrix, and determine a spectral efficiency according to the signal-to-noise ratio;

[0011] construct a pilot allocation problem with the spectral efficiency maximization as an objective and pilot allocation principles as constraints;

[0012] solve the pilot allocation problem to obtain an optimal pilot allocation strategy.

[0013] According to another aspect of the present application, a pilot allocation device of a CF-RAN is provided, which is applied to a central processor of a CF-RAN system, the CF-RAN system further comprising: a user-centric distributed unit, an edge distributed unit, and an access point configured on the edge distributed unit; the device comprising:

[0014] an obtaining module, configured to obtain a channel estimation matrix between a user equipment and the access point and a beamforming matrix of the edge distributed unit;

[0015] a spectral efficiency calculation module, configured to determine a signal-to-noise ratio of downlink transmission of the user equipment using the pilot signal according to the channel estimation matrix and the beamforming matrix, and determine a spectral efficiency according to the signal-to-noise ratio;

[0016] a problem construction module, configured to construct a pilot allocation problem with the spectral efficiency maximization as an objective and pilot allocation principles as constraints;

[0017] a problem solving module, configured to solve the pilot allocation problem to obtain an optimal pilot allocation strategy.

[0018] According to another aspect of the present application, an electronic device is provided, the electronic device comprising:

[0019] at least one processor; and

[0020] a memory connected with the at least one processor in communication; wherein,

[0021] the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the pilot allocation method of the CF-RAN according to any of the embodiments of the present application.

[0022] According to another aspect of the present application, a computer readable storage medium is provided, the computer readable storage medium stores computer instructions for enabling a processor to perform the pilot allocation method of the CF-RAN according to any of the embodiments of the present application when executed.

[0023] According to another aspect of the present application, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the CF-RAN pilot allocation method described in any embodiment of the present application is implemented.

[0024] The technical solution of the embodiment of the present application obtains a channel estimation matrix between a user device and an access point, and a beamforming matrix of an edge distributed unit; determines the signal-to-noise ratio of a pilot signal used by the user device for downlink transmission based on the channel estimation matrix and the beamforming matrix, and determines the spectrum efficiency based on the signal-to-noise ratio; constructs a pilot allocation problem with the goal of maximizing spectrum efficiency and the pilot allocation principle as a constraint; solves the pilot allocation problem to obtain an optimal pilot allocation strategy; solves the problem that existing pilot allocation strategies cannot guarantee maximum spectrum efficiency under the CF-RAN architecture; and achieves the optimization goal of maximizing spectrum efficiency under a suitable pilot allocation strategy.

[0025] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0027] FIG1 is a flowchart of a CF-RAN pilot allocation method provided in Example 1 of the present application;

[0028] FIG2 is a schematic diagram of the structure of the CF-RAN system;

[0029] FIG3 is a flowchart of a CF-RAN pilot allocation method provided in Embodiment 2 of the present application;

[0030] FIG4 is a schematic diagram showing simulation results and theoretical results of pilot number-spectral efficiency;

[0031] FIG5 is a schematic diagram showing the simulation results and theoretical results of the number of access points-spectrum efficiency;

[0032] FIG6 is a schematic diagram showing the simulation results and theoretical results of the number of antennas of an access point versus spectrum efficiency;

[0033] FIG7 is a schematic diagram of the convergence of the pilot allocation algorithm based on QGA;

[0034] Figure 8 is a schematic diagram of the relationship between the number of user equipment and the spectral efficiency;

[0035] Figure 9 is a schematic diagram of the structure of a pilot allocation device of a CF-RAN according to Embodiment Three of the application;

[0036] Figure 10 is a schematic diagram of the structure of an electronic device implementing a pilot allocation method of a CF-RAN according to the application. DETAILED DESCRIPTION

[0037] For those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of the present application.

[0038] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification and claims of the present application and the above-described drawings are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device containing a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product or device.

[0039] Embodiment One

[0040] Figure 1 is a flowchart of a pilot allocation method of a CF-RAN according to Embodiment One of the present application. The present embodiment can be applied to a case where the pilot allocation of a CF-RAN system is implemented to maximize the spectral efficiency. The method can be performed by a pilot allocation device of a CF-RAN, which can be implemented in the form of hardware and / or software, and can be configured in an electronic device.

[0041] Figure 2 is a schematic diagram of the structure of a CF-RAN system. As shown in Figure 2, the CF-RAN system includes a central processor CPU, a user-centric distributed unit UCDU, an edge distributed unit EDU, and an access point AP configured on the edge distributed unit. The UCDU mainly implements the distribution and combination of data; the EDU mainly implements the functions of channel estimation, multi-user / multi-stream detection and multi-user / multi-stream beamforming; and the AP mainly acts as a radio frequency transceiver and performs digital-analog / analog-digital conversion.

[0042] In the CF-RAN system, one user equipment UE can be associated with multiple EDUs, but only one UCDU. In the uplink direction, the EDU estimates the uplink channel matrix between the AP and the user equipment, and selects the UE connected by the EDU according to the number of data streams supported by the system and the uplink demodulation reference signal. After the EDU detects the multi-user data stream, it is sent to the UCDU, which combines the same data stream from different user equipment UEs. In the downlink direction, the beamforming matrix is calculated by the EDU, and the transmission data stream is beamformed.

[0043] As shown in FIG. 1, the method comprises:

[0044] S110, obtaining a channel estimation matrix between the user equipment and the access point, and a beamforming matrix of the edge distributed unit.

[0045] The channel estimation matrix is a matrix obtained by estimating the noise of the channel properties. The beamforming (BF) matrix can be calculated by the edge distributed unit EDU in the downlink direction. The goal of beamforming is to form the best combination or distribution of baseband (intermediate frequency) signals according to system performance indicators. Specifically, its main task is to compensate for signal fading and distortion introduced by spatial loss, multipath effects and other factors during wireless transmission, while reducing interference between co-channel users.

[0046] In this embodiment, the CF-RAN system receives the signal sent by the user equipment UE through the receiving antenna of the access point AP of the edge distributed unit EDU, obtains the signal received by the edge distributed unit EDU through the central processor CPU, and obtains the channel estimation matrix by performing uplink channel estimation on the signal between the user equipment UE and the access point AP. And in the downlink direction, the beamforming matrix is calculated by the edge distributed unit EDU, and the beamforming matrix is sent to the central processor, so that the central processor receives the beamforming matrix of the edge distributed unit EDU. The method of calculating the beamforming matrix can use the beamforming method based on interference suppression, that is, the zero-forcing algorithm (ZF). The main idea of the beamforming method based on interference suppression is to find a beamforming vector under the condition that all user equipment real-time channel information is known, so that the target user equipment has zero interference to all other user equipment. This embodiment of the application will not be repeated.

[0047] S120, determining the signal-to-noise ratio of the user equipment using the pilot signal downlink transmission according to the channel estimation matrix and the beamforming matrix, and determining the spectral efficiency according to the signal-to-noise ratio.

[0048] The signal-to-noise ratio (SINR) is the ratio of the strength of the received useful signal to the strength of the received interference signal (noise and interference).

[0049] In this embodiment, based on the definition of the signal-to-noise ratio, the signal-to-noise ratio of each user equipment (UE) using the pilot signal for downlink transmission is calculated according to the channel estimation matrix and the beamforming matrix, and the spectral efficiency is calculated according to the signal-to-noise ratio.

[0050] S130, a pilot allocation problem is constructed with the maximum spectral efficiency as the target and the pilot allocation principle as the constraint condition.

[0051] The pilot allocation principle is a principle for allocating pilot signals to user equipment in a CF-RAN system. For example, the pilot allocation principle can include allocating all pilot signals to each user equipment according to a preset strategy, and the number of pilot signals allocated to each user equipment is equal.

[0052] In this embodiment, for the pilot allocation requirement, in order to obtain good system performance, the maximum spectral efficiency is taken as the target, and the pilot allocation principle is taken as the constraint condition to construct the pilot allocation problem.

[0053] S140, the pilot allocation problem is solved to obtain an optimal pilot allocation strategy.

[0054] The optimal pilot allocation strategy is a way of allocating pilots to each user equipment when the spectral efficiency reaches the maximum value, which can specifically include the number of pilot signals allocated to each user equipment.

[0055] In this embodiment, the pilot allocation problem is solved by using a mathematical algorithm to obtain an optimal solution, so as to determine the optimal pilot allocation strategy when the spectral efficiency reaches the maximum value.

[0056] The technical scheme of the embodiment of the present application comprises the following steps: obtaining a channel estimation matrix between user equipment and an access point, and a beamforming matrix of an edge distributed unit; determining the signal-to-noise ratio (SINR) of the user equipment using the pilot signal for downlink transmission according to the channel estimation matrix and the beamforming matrix, and determining the spectral efficiency according to the signal-to-noise ratio; constructing a pilot allocation problem with the maximum spectral efficiency as the target and the pilot allocation principle as the constraint condition; solving the pilot allocation problem to obtain an optimal pilot allocation strategy; and for the pilot multiplexing problem, the optimization target of maximizing the spectral efficiency under the appropriate pilot allocation strategy is realized.

[0057] Embodiment two

[0058] FIG. 3 is a flow chart of a pilot allocation method of a CF-RAN according to Embodiment Two of the present application. Embodiment Two is a further refinement of the calculation of the signal-to-noise ratio and the pilot allocation problem constructed based on Embodiment One. In Embodiment Two, the CF-RAN system includes Z edge distributed units (EDUs), N access points (APs), and K user equipments (UEs). The N APs are distributed to the Z EDUs, and each EDU is connected to L APs. Each AP is equipped with M antennas, and each UE is equipped with one antenna.

[0059] As shown in FIG. 3, the method includes the following steps.

[0060] S210, obtaining a channel estimation matrix between the UEs and the APs, and a beamforming matrix of the EDUs.

[0061] In an optional embodiment, the channel estimation matrix between the K UEs and the N APs connected to the Z EDUs is:

[0062] wherein, is an estimated channel matrix between the zth EDU and the K UEs; is an estimated channel between the zth EDU and the kth UE, z∈[1, Z], k∈[1, K];

[0063] In the case where the qth UE using the tth pilot signal represents the kth UE, the estimated channel between the qth UE using the tth pilot signal and all APs of the zth EDU is is:

[0064] The signal y received by the L APs of the zth EDU using the tth pilot signal through the receiving antennas is z,t is:

[0065] The channel h between the qth UE using the tth pilot signal and the zth EDU is z,t,q is:

[0066] Λ z,t,q = diag(λ z,t,q,1 ,…,λ z,t,q,L ) represents a large-scale channel fading matrix, λ z,t,q,Ldenotes the large-scale fading factor from the qth user equipment using the tth pilot signal to the Lth access point of the zth edge distributed unit; g z,t,q ~CN(0, I LM ) denotes the small-scale channel fading matrix; n z,t ~CN(0, εI LM ) is the noise vector; I M is an M x M identity matrix, I N is an N x N identity matrix, I LM is an LM x LM identity matrix; p denotes the pilot signal power; t denotes the total number of pilot signals; ε is the noise power, S t denotes the set of user equipments using the tth pilot signal.

[0067] In this embodiment, assume that t pilot signals are assigned to K UEs, the channel between the qth UE using the tth pilot signal and all EDUs is:

[0068] For uplink channel estimation, the signal received by the AP of the zth EDU using the tth pilot signal through the receive antenna is:

[0069] According to the minimum mean square error (MMSE) channel estimation method, the estimated channel from the qth UE using the tth pilot signal to all EDUs is: wherein, and define the estimated channel is obtained as: The channel estimation error is defined as The channel estimation error covariance matrix is The channel estimation matrix between the L access points connected by all UEs (K user equipments) and all EDUs (Z edge distributed units) is: wherein is the estimated channel between the zth EDU and the kth UE. Λ t,q denotes the large-scale fading factor from the qth user equipment using the tth pilot signal to all access points, n t denotes the noise vector.

[0070] In another optional embodiment, the beamforming matrix of the zth edge distributed unit is:

[0071] wherein, W z is the beamforming matrix of the zth edge distributed unit; is the estimated channel matrix between the zth edge distributed unit and K user equipments; the upper index H is the matrix transpose symbol.

[0072] In the embodiment, for downlink channel transmission, it is assumed that all users are served by the zth EDU, and the estimated channel matrix between the zth EDU and the K UEs is: The beamforming matrix of the zth EDU is:

[0073] S220, determining the signal-to-noise ratio of the kth user equipment using the pilot signal for downlink transmission according to the channel estimation matrix and the beamforming matrix.

[0074] In an optional embodiment, in the case that the qth user equipment using the tth pilot signal represents the kth user equipment, the signal-to-noise ratio of the kth user equipment is:

[0075] wherein γ k represents the signal-to-noise ratio of the kth user equipment, γ t,q represents the signal-to-noise ratio of the qth user equipment using the tth pilot signal; γ k = γ t,q ; w z,i,j represents the beamforming vector between the jth user equipment using the ith pilot signal and the zth edge distributed unit; represents the beamforming vector between the qth user equipment using the tth pilot signal and the zth edge distributed unit; represents the estimated channel between the qth user equipment using the tth pilot signal and all access points of the zth edge distributed unit; h z,t,q represents the channel between the qth user equipment using the tth pilot signal and the zth edge distributed unit; P z = diag(p z,1 ,…,p z,K ) represents the power coefficient matrix of the zth edge distributed unit, p z,k represents the power coefficient of the zth edge distributed unit to the kth user equipment; p z,i,j represents the power coefficient of the zth edge distributed unit to the jth user equipment using the ith pilot signal; p z,t,q represents the power coefficient of the zth edge distributed unit to the qth user equipment using the tth pilot signal; is the downlink compression noise of the zth edge distributed unit, q z,l ~ CN(0, μ z,l I M ) is the downlink compression noise of the lth access point at the zth edge distributed unit, μ z,l is the downlink compression noise power, l ∈ [1, L]. is the noise variance for the qth user equipment using the tth pilot signal; the upper index H is the matrix transpose symbol.

[0076] wherein the power coefficient can be a precoding power allocation factor.

[0077] In this embodiment, for downlink channel transmission, each AP needs to satisfy the power constraint, and the front link rate between EDUs and EDUs is limited, the signal processed by the zth EDU is z z P z s+q z , wherein s = [s1, …, s K ] T = [s t,1 ,s t,2 ,…,s t,q ] T is the downlink transmission symbol of the K UEs (the qth UE using the tth pilot signal), and s k is a random variable with zero mean and zero unit variance, P z = diag(p z,1 ,…,p z,K ) is the power coefficient matrix of the zth EDU, is the downlink compressed noise of the zth EDU, q z,l ~ CN(0, μ z,l I M ) is the downlink compressed noise of the zth EDU of the lth access point. The signal received by the qth UE using the tth pilot signal is:

[0078] wherein, is the additive white Gaussian noise of the qth UE using the tth pilot signal.

[0079] Definition is the beamforming vector between the qth UE using the tth pilot signal and the zth EDU, is the signal required by the qth UE using the tth pilot signal, s t,q indicates the signal sent to the qth user equipment using the tth pilot signal; in the case of the qth UE using the tth pilot signal, the SINR of the kth UE is:

[0080] wherein, is the power of the useful signal, is the interference term of other UEs to the kth UE, is the channel estimation error interference term,​ to compress the noise interference term.

[0081] On the basis of the above-mentioned embodiments, in the case of using the zero-forcing algorithm to calculate the beamforming matrix of the zth edge distributed unit, the spectral efficiency of the kth user equipment is:

[0082] R k = log2(1+γ k );

[0083] wherein R k is the spectral efficiency of the kth user equipment, and γ k is the signal-to-noise ratio of the kth user equipment;

[0084] The asymptotic expression of the signal-to-noise ratio is:

[0085]

[0086] [Ξ z,t ] q,j is the element in the qth row and jth column of the matrix [Ξ z,t ]; is the element in the mth row and nth column of the matrix ; represents the channel estimation error interference matrix between all access points in the zth edge distributed unit for the qth user equipment using the tth pilot signal and the user equipment using the ith pilot signal; Λ z,t,q = diag(λ z,t,q,1 ,…,λ z,t,q,L ) represents the large-scale channel fading matrix, λ z,t,q,L is the large-scale fading factor from the qth user equipment using the tth pilot signal to the Lth access point of the zth edge distributed unit; I N is an N×N unit matrix; ρ represents the pilot signal power; τ is the total number of pilot signals; ε represents the noise power, S t represents the set of user equipment using the tth pilot signal; Λ z,k represents the large-scale channel fading matrix corresponding to the channel between the kth user equipment and the zth edge distributed unit; represents the noise variance of the kth user equipment; Tr(·) represents the trace of a matrix.

[0087] In this embodiment, by using the theory of large-dimensional random matrix, it can be obtained that:

[0088] ​ where a.s. means almost sure;

[0089] Let For It can be obtained that:

[0090] where:

[0091] Based on the property of the zero-forcing algorithm precoding, it can be obtained that:

[0092] where (t, q)≠(i, j).

[0093] For the channel estimation error interference term in the denominator, it can be rewritten as:

[0094] It can be obtained that:

[0095] where:

[0096] For the compression noise interference term, it can be rewritten as:

[0097] The spectral efficiency of the kth UE based on the zero-forcing algorithm beamforming is denoted as R k = log2(1+γ k ), and the asymptotic form of γ k is denoted as:

[0098] When equal power allocation is used using the statistical power normalization factor,

[0099] The asymptotic form of γ k is rewritten as: where denotes the sum of the compression noise interference and the Gaussian noise power; denotes the compression noise interference, denotes the Gaussian noise.

[0100] S230, determining the spectral efficiency of the kth user equipment according to the signal-to-noise ratio of the kth user equipment.

[0101] In this embodiment, the spectral efficiency of the k user equipments is: R k = log2(1+γ k ); where R kSpectral efficiency for the kth user equipment, γk k Signal-to-noise ratio for the kth user equipment. In the asymptotic form based on γk k The expression of spectral efficiency obtained in the asymptotic form based on γk is called the closed-form asymptotic spectral efficiency expression.

[0102] S240, constructing a pilot allocation problem with the pilot allocation principle as a constraint and the maximum spectral efficiency as an objective.

[0103] In the embodiment, there are τ pilot signals in the CF-RAN system, and the τ pilot signals are mutually orthogonal pilot signals. The pilot allocation principle is to allocate the τ pilot signals to the K user equipments, and each pilot signal can be shared by multiple user equipments, that is, pilot multiplexing can be achieved.

[0104] When p z,k and μ z,l are fixed, the spectral efficiency of all UEs is The pilot allocation problem constructed with the pilot allocation principle as a constraint and the maximum spectral efficiency as an objective is expressed as:

[0105] s.t.φ={φ1,…,φ K},

[0106] Wherein, φ is a pilot allocation vector, f(φ) is a pilot allocation function, R k is the spectral efficiency of the kth user equipment; τ is the total number of pilot signals.

[0107] S250, using a quantum genetic algorithm to solve the pilot signal allocation problem and obtaining an optimal pilot allocation strategy.

[0108] In the embodiment of the application, a quantum genetic algorithm (Genetic Quantum Algorithm, GQA) is used to solve the pilot allocation problem. The steps of the pilot allocation scheme based on QGA are as follows:

[0109] (1) Initialize a quantum population consisting of D X-dimensional quantum individuals:

[0110] Wherein, i=1, 2, …, D and X are related to the dimension of the problem.

[0111] (2) Measure the quantum population once to obtain the measurement state:

[0112] Wherein, The following rules are followed: rand is a random variable between 0 and 1.

[0113] (3) by binary conversion, is mapped to the pilot allocation vector φ i The fitness of each quantum individual is calculated according to the formula, and according to the calculation result, the optimal measurement state with the highest fitness value is identified.

[0114] (4) A new quantum population is generated based on the quantum rotation gate. The quantum rotation strategy is shown in Table 1, from which it can be found that the quantum individual evolves in a more favorable direction.

[0115] Table 1

[0116] (5) If the termination condition is not met, return to S2.

[0117] An exemplary algorithm for the pilot allocation process based on QGA is shown in Algorithm 1.

[0118] Algorithm 1 Pilot allocation algorithm based on QGA

[0119] Input:

[0120] Initialize the quantum population V 0 , for example |a| 2 = |b| 2 = 0.5;

[0121] Set r = 0;

[0122] 1: Repeat;

[0123] 2: Perform a measurement on the quantum population to obtain a measurement state

[0124] 3: Map to the pilot allocation vector and calculate the fitness of each quantum individual. Find the highest fitness value;

[0125] 4: Update the quantum population based on the quantum rotation gate;

[0126] 5: r = r + 1

[0127] 6: Until r = maxIter, where maxIter is the maximum number of iterations of the pilot;

[0128] Output: Optimal pilot allocation φ.

[0129] The technical scheme of the embodiment of the application comprises the following steps: obtaining a channel estimation matrix between a user equipment and an access point and a beamforming matrix of the edge distributed unit; determining a signal-to-noise ratio of downlink transmission of a pilot signal used by the user equipment according to the channel estimation matrix and the beamforming matrix; determining a spectral efficiency of the kth user equipment according to the signal-to-noise ratio of the kth user equipment; obtaining a pilot allocation principle, the pilot allocation principle being to allocate τ pilot signals to K user equipments; constructing a pilot allocation problem with the maximum spectral efficiency as a target and the pilot allocation principle as a constraint condition; solving the pilot signal allocation problem by using a quantum genetic algorithm to obtain an optimal pilot allocation strategy. In consideration of power coefficients and compression noise, the spectral efficiency performance of the CF-RAN under the condition of pilot multiplexing and zero-forcing beamforming downlink is determined; for the pilot multiplexing problem, a pilot allocation algorithm based on the quantum genetic algorithm is proposed to determine the optimal pilot allocation strategy, and the optimization target of maximizing the spectral efficiency is achieved.

[0130] Further, the advantages of the pilot allocation method provided by the embodiment of the application are specifically described in the following multiple performance comparison experiments. In the embodiment, all access points APs are randomly associated with edge distributed units EDUs, and each edge distributed unit EDU processes L APs. The channel model simulation parameters are shown in Table 2.

[0131] Table 2

[0132] Figure 4 is a diagram of simulation results and theoretical results of pilot quantity-spectral efficiency. Set M = 8, N = 48, K = 10, the number of edge distributed units Z = 1, Z = 2, Z = 4. Figure 4 shows that the spectral efficiency increases with the increase of the pilot quantity based on the random pilot strategy. The theoretical results are basically consistent with the simulation results, thereby proving the accuracy of the asymptotic expression of the signal-to-noise ratio. Figure 5 is a diagram of simulation results and theoretical results of access point quantity-spectral efficiency. Set τ = 4, K = 6, M = 8. Figure 5 shows that the spectral efficiency increases with the increase of the AP quantity. And due to the use of large-scale random matrix theory, the gap between the theoretical value and the simulation value decreases with the increase of the AP quantity. Figure 6 is a diagram of simulation results and theoretical results of the number of antennas of access points-spectral efficiency. Set N = 48, K = 6, τ = 4. Z = 1 represents the case of complete concentration, and Z = 48 represents the case of complete distribution. Since the assumption of large-scale random matrix theory is not met in the case of complete distribution, only the corresponding simulation curve is given. It can be seen from Figure 6 that the performance of CF-RAN (Z = 2, Z = 4, Z = 8) is between the case of complete concentration and the case of complete distribution, which can be attributed to the better coordination ability of CF-RAN compared to the case of complete distribution. In addition, the spectral efficiency of the system increases with the increase of the number of antennas. According to Figures 3 and 6, the accuracy of the closed asymptotic spectral efficiency expression is proved. Therefore, according to Figures 4 to 6, the accuracy of the closed asymptotic spectral efficiency expression is proved.

[0133] Figure 7 is a diagram of the convergence of the pilot allocation algorithm based on QGA. Set N = 40, K = 10, τ = 4, Z = 2. As shown in Figure 7, the more the number of antennas, the higher the spectral efficiency of the system, the larger the population size, the faster the convergence speed of the algorithm. Regardless of the population size, for the same number of antennas, the algorithm converges to the same result.

[0134] Figure 8 is a diagram of the relationship between the number of user equipment (UE) and spectral efficiency, where RANDOM represents random pilot allocation and GD represents greedy pilot allocation. Set τ = 4, N = 40, M = 8. As shown in Figure 8, the difference in spectral performance between systems with different numbers of EDUs becomes larger and larger as the number of users increases, and even when Z = 4, the spectral efficiency decreases with the increase of the number of users. The performance of the random pilot allocation strategy tends to be worse with the increase of the EDU, because as the number of UEs increases, the greedy algorithm tends to fall into a local optimal solution. The performance of the pilot allocation algorithm based on QGA is always the best, and the gap between the QGA algorithm and the greedy algorithm gradually becomes larger with the increase of the number of UEs. According to Figures 7 and 8, the performance superiority of the pilot allocation algorithm based on QGA is proved.

[0135] Embodiment Three

[0136] Fig. 9 is a structural schematic diagram of a pilot allocation device of a CF-RAN according to Embodiment Three of the present application. The device is applied to a central processor of a CF-RAN system, which further comprises a user-centric distributed unit, an edge distributed unit and an access point configured on the edge distributed unit. As shown in Fig. 9, the device comprises an acquisition module 310, a spectrum efficiency calculation module 320, a problem construction module 330 and a problem solving module 340; wherein,

[0137] The acquisition module 310 is configured to acquire a channel estimation matrix between user equipment and the access point and a beamforming matrix of the edge distributed unit.

[0138] The spectrum efficiency calculation module 320 is configured to determine a signal-to-noise ratio of downlink transmission of user equipment using a pilot signal according to the channel estimation matrix and the beamforming matrix, and determine a spectrum efficiency according to the signal-to-noise ratio.

[0139] The problem construction module 330 is configured to construct a pilot allocation problem with the maximum spectrum efficiency as a target and a pilot allocation principle as a constraint condition.

[0140] The problem solving module 340 is configured to solve the pilot allocation problem to obtain an optimal pilot allocation strategy.

[0141] Optionally, the CF-RAN system comprises Z edge distributed units, N access points and K user equipment; the N access points are allocated to the Z edge distributed units, and each edge distributed unit is connected to L access points; each access point is equipped with M antennas, and each user equipment is equipped with one antenna.

[0142] Optionally, the channel estimation matrix between the K user equipment and the N access points connected with the Z edge distributed units is:

[0143] wherein, is an estimated channel matrix between the zth edge distributed unit and the K user equipment; is an estimated channel between the zth edge distributed unit and the kth user equipment, z∈[1, Z], k∈[1, K];

[0144] In the case that the qth user equipment using the tth pilot signal represents the kth user equipment, the estimated channel between the qth user equipment using the tth pilot signal and all access points of the zth edge distributed unit is ​

[0145] L access points of the zth edge distribution unit using the tth pilot signal received by the receiving antenna z,t

[0146] channel between the qth user equipment using the tth pilot signal and the zth edge distribution unit z,t,q

[0147] Λ z,t,q = diag(λ z,t,q,1 ,…,λ z,t,q,L ) represents a large-scale channel fading matrix, λ z,t,q,L represents a large-scale fading factor of the qth user equipment using the tth pilot signal to the Lth access point of the zth edge distribution unit; g z,t,q ~ CN(0, I LM ) represents a small-scale channel fading matrix; n z,t ~ CN(0, εI LM ) is a noise vector; I M is an MxM identity matrix, I N is an NxN identity matrix, I LM is an LMxLM identity matrix; p represents pilot signal power; t represents the total number of pilot signals; ε is noise power, S t represents a set of user equipment using the tth pilot signal.

[0148] Optionally, the beamforming matrix of the zth edge distribution unit is:

[0149] wherein W z is the beamforming matrix of the zth edge distribution unit; is the estimated channel matrix between the zth edge distribution unit and the K user equipments; the upper index H is the matrix transpose symbol.

[0150] Optionally, in the case that the qth user equipment using the tth pilot signal represents the kth user equipment, the signal-to-noise ratio of the kth user equipment is:

[0151] wherein γ k represents the signal-to-noise ratio of the kth user equipment, γ t,q represents the signal-to-noise ratio of the qth user equipment using the tth pilot signal; γ k = γ t,q ; W z represents the beamforming matrix of the zth edge distribution unit; w z,i,j ​​denotes the beamforming vector between the jth user equipment using the ith pilot signal and the zth edge distributed unit; denotes the beamforming vector between the qth user equipment using the tth pilot signal and the zth edge distributed unit; denotes the estimated channel between the qth user equipment using the tth pilot signal and all access points of the zth edge distributed unit; h z,t,q denotes the channel between the qth user equipment using the tth pilot signal and the zth edge distributed unit; P z = diag(p z ,1,…,p z,K ) denotes the power coefficient matrix of the zth edge distributed unit, p z,k denotes the power coefficient of the zth edge distributed unit to the kth user equipment; p z,i,j denotes the power coefficient of the zth edge distributed unit to the jth user equipment using the ith pilot signal; p z,t,q denotes the power coefficient of the zth edge distributed unit to the qth user equipment using the tth pilot signal; denotes the downlink compression noise of the zth edge distributed unit, q z,l ~ CN(0, μ z,l I M ) denotes the downlink compression noise of the lth access point at the zth edge distributed unit, μ z,l denotes the downlink compression noise power, l ∈ [1, L]; is the noise variance of the qth user equipment using the tth pilot signal; z ∈ [1, Z], k ∈ [1, K]; the upper index H is the matrix transpose symbol;

[0152] Optionally, in the case of calculating the beamforming matrix of the zth edge distributed unit by using the zero-forcing algorithm, the spectral efficiency R k of the kth user equipment is:

[0153] R k = log2(1 + γ k );

[0154] The asymptotic expression of the signal-to-noise ratio is:

[0155] wherein,

[0156] [Ξ z,t ] q,j is the matrix [Ξz,t the element in the qth row and jth column of matrix is the matrix is the element in the mth row and nth column of matrix denotes the channel estimation error interference matrix between all access points in the zth edge distributed unit for the qth user equipment using the tth pilot signal and the user equipment using the ith pilot signal; Λ z,t,q = diag(λ z,t,q,1 ,…,λ z,t,q,L ) denotes a large-scale channel fading matrix, λ z,t,q,L is a large-scale fading factor from the qth user equipment using the tth pilot signal to the Lth access point of the zth edge distributed unit; I N is an N*N identity matrix; ρ denotes pilot signal power; τ is the total number of pilot signals; ε denotes noise power, S t denotes a set of user equipment using the tth pilot signal; Λ z,k denotes a large-scale channel fading matrix corresponding to the channel between the kth user equipment and the zth edge distributed unit; denotes the noise variance of the kth user equipment; Tr(·) denotes the trace of a matrix.

[0157] Optionally, a pilot allocation problem is constructed with the objective of maximizing the spectral efficiency and with the pilot allocation principle as a constraint, and the pilot allocation problem is expressed as:

[0158] s.t. φ = {φ K 1,…,φ

[0159] wherein φ is a pilot allocation vector, f(φ) is a pilot allocation function, R k is the spectral efficiency of the kth user equipment; τ is the total number of pilot signals.

[0160] Optionally, the problem solving module 340 is specifically configured to:

[0161] use a quantum genetic algorithm to solve the pilot signal allocation problem, and obtain an optimal pilot allocation strategy.

[0162] The pilot allocation device of the CF-RAN provided in the embodiments of the present application can perform the pilot allocation method of the CF-RAN provided in any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of performing the method.

[0163] Embodiment Four

[0164] FIG. 10 shows a structural diagram of an electronic device 10 that can be used to implement embodiments of the present application. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application as described and / or claimed in this document.

[0165] As shown in FIG. 10, the electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., connected to the at least one processor 11 in communication, where the memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer programs stored in the read-only memory (ROM) 12 or loaded into the random access memory (RAM) 13 from the storage unit 18. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0166] Various components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc., an output unit 17, such as various types of displays, speakers, etc., a storage unit 18, such as a magnetic disk, an optical disk, etc., and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0167] The processor 11 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the pilot allocation method of the CF-RAN.

[0168] In some embodiments, the pilot allocation method of a CF-RAN can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as storage unit 18. In some embodiments, portions of the computer program, or all of the computer program, can be loaded onto the electronic device 10 via, for example, ROM 12 and / or communication unit 19. When the computer program is loaded onto RAM 13 and executed by processor 11, one or more steps of the pilot allocation method of a CF-RAN as described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the pilot allocation method of a CF-RAN by any other suitable means, such as by way of firmware.

[0169] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, specially designed application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0170] In some embodiments, the pilot allocation method of a CF-RAN can be implemented as a computer program tangibly embodied in a computer program product, the computer program implementing the pilot allocation method of a CF-RAN of the present application when executed by a processor, the computer program product can be understood as a software product mainly realizing the solution thereof by means of the computer program. The computer program for implementing the method of the present application can be written in any combination of one or more programming languages. The computer program can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed by the processor, implements the functions / operations specified in the flow charts and / or block diagrams. The computer program can execute entirely on a machine, partly on a machine, partly on a machine and partly on a remote machine or entirely on a remote machine or server.

[0171] In the context of this application, a computer readable storage medium can be a tangible medium that can include or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer readable storage medium can be a machine readable signal medium. More specific examples of a machine readable storage medium will include one or more lines of a program of instructions in a transitory signal form, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0172] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0173] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), blockchain networks, and the Internet.

[0174] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0175] It should be understood that the various forms of flow shown above can be reordered, added to, or have steps deleted. For example, the steps described in this application can be performed in parallel, in series, or in a different order, as long as the desired results of the technical solutions of this application can be achieved, and this application does not limit herein.

[0176] The above detailed description does not constitute a limitation on the scope of protection of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A CF-RAN pilot allocation method, applied to a central processor of a cellular-free radio access network (CF-RAN) system, wherein the CF-RAN system further comprises: A user-centric distributed unit, an edge distributed unit, and an access point configured on the edge distributed unit; the method includes: Obtaining a channel estimation matrix between the user equipment and the access point, and a beamforming matrix of the edge distributed unit; Determining a signal-to-noise ratio of a pilot signal for downlink transmission by a user equipment based on the channel estimation matrix and the beamforming matrix, and determining a spectrum efficiency based on the signal-to-noise ratio; Constructing a pilot allocation problem with the goal of maximizing the spectrum efficiency and with the pilot allocation principle as a constraint condition; The pilot allocation problem is solved to obtain an optimal pilot allocation strategy.

2. The method according to claim 1, wherein The CF-RAN system includes: Z edge distributed units, N access points and K user devices; N access points are allocated to the Z edge distributed units, each edge distributed unit is connected to L access points; each access point is equipped with M antennas, and each user device is equipped with 1 antenna.

3. The method according to claim 2, wherein: The channel estimation matrix between K user devices and N access points connected to Z edge distributed units is: in, represents the estimated channel matrix between the zth edge distributed unit and K user equipments; = represents the estimated channel between the z-th edge distributed unit and the k-th user equipment, z∈[1,Z],k∈[1,K]; In the case where the qth user equipment using the tth pilot signal represents the kth user equipment, the estimated channels between the qth user equipment using the tth pilot signal and all access points of the zth edge distributed unit are for: The signal y received by the L access points of the zth edge distributed unit using the tth pilot signal through the receiving antenna z,t for: The channel h between the qth user equipment and the zth edge distributed unit using the tth pilot signal z,t,q for: Λ z,t,q =diag(λ z,t,q,1 ,…,λ z,t,q,L ) represents the large-scale channel fading matrix, λ z,t,q,L represents the large-scale fading factor from the qth user equipment to the Lth access point of the zth edge distributed unit using the tth pilot signal; g z,t,q ~CN(0,I LM ) represents the small-scale channel fading matrix; n z,t ~CN(0,εI LM ) is the noise vector; I M is the M×M identity matrix, I N is the N×N identity matrix, I LM is the LM×LM unit matrix; ρ represents the pilot signal power; t represents the total number of pilot signals; ε is the noise power, S t represents the set of user equipments using the tth pilot signal.

4. The method according to claim 2, wherein: The beamforming matrix of the zth edge distributed unit is: Among them, W z is the beamforming matrix of the zth edge distributed unit; is the estimated channel matrix between the zth edge distributed unit and K user equipments; the superscript H is the matrix transpose symbol.

5. The method according to claim 2, wherein: When the qth user equipment using the tth pilot signal represents the kth user equipment, the signal-to-noise ratio of the kth user equipment is: Among them, γ k represents the signal-to-noise ratio of the kth user equipment, γ t,q represents the signal-to-noise ratio of the qth user equipment of the tth pilot signal; γ k =γ t,q ;W z represents the beamforming matrix of the zth edge distributed unit; w z,i,j is the beamforming vector between the j-th user equipment and the z-th edge distributed unit using the i-th pilot signal; represents the beamforming vector between the qth user equipment and the zth edge distributed unit using the tth pilot signal; represents the estimated channel between the qth user equipment and all access points of the zth edge distributed unit using the tth pilot signal; h z,t,q represents the channel between the qth user equipment and the zth edge distributed unit using the tth pilot signal; P z =diag(p z,1 ,…,p z,K ) represents the power coefficient matrix of the zth edge distributed unit, p z,k Indicates the zth The power coefficient of the edge distributed unit to the kth user equipment; p z,i,j represents the power coefficient of the zth edge distributed unit to the jth user equipment using the i-th pilot signal; p z,t,q represents the power coefficient of the zth edge distributed unit to the qth user equipment using the tth pilot signal; represents the downlink compression noise of the zth edge distributed unit, q z,l ~CN(0,μ z,l I M ) is the downlink compression noise of the lth access point at the zth edge distributed unit, μ z,l is the downlink compression noise power, l∈[1,L]; represents the noise variance of the qth user equipment using the tth pilot signal; z∈[1,Z],k∈[1,K]; the superscript H is the matrix transpose symbol.

6. The method according to claim 5, wherein: When the zero-forcing algorithm is used to calculate the beamforming matrix of the z-th edge distributed unit, the spectrum efficiency R of the k-th user equipment is k is: R k =log2(1+γ k ); The asymptotic expression of the signal-to-noise ratio is: in, [Ξ z,t ] q,j is the matrix [Ξ z,t ]’s element in the qth row and jth column; is a matrix The element at row m and column n in ; represents the channel estimation error interference matrix between the qth user equipment using the tth pilot signal and the user equipment using the ith pilot signal and all access points in the zth edge distributed unit; Λ z,t,q =diag(λ z,t,q,1 ,…,λ z,t,q,L ) represents the large-scale channel fading matrix, λ z,t,q,L is the large-scale fading factor from the qth user equipment to the Lth access point of the zth edge distributed unit using the tth pilot signal; I N is an N×N unit matrix; ρ represents the pilot signal power; t is the total number of pilot signals; ε represents the noise power, S t represents the set of user equipment using the t-th pilot signal; z,k represents the large-scale channel fading matrix corresponding to the channel between the k-th user equipment and the z-th edge distributed unit; represents the noise variance of the kth user equipment; Tr(·) represents the trace of the matrix.

7. The method according to claim 1, wherein The pilot allocation problem is expressed as: stφ={φ1,…,φ K }, Where φ is the pilot allocation vector, f(φ) is the pilot allocation function, R k is the spectrum efficiency of the kth user equipment, k∈[1,K], K is the total number of user equipment; t is the total number of pilot signals.

8. The method according to claim 1 or 7, wherein Solving the pilot signal allocation problem to obtain an optimal pilot allocation strategy includes: A quantum genetic algorithm is used to solve the pilot signal allocation problem and obtain an optimal pilot allocation strategy.

9. A CF-RAN pilot allocation device, applied to a central processor of a non-cellular radio access network (CF-RAN) system, wherein the CF-RAN system further comprises: A user-centric distributed unit, an edge distributed unit, and an access point configured on the edge distributed unit; the device includes: An acquisition module, configured to acquire a channel estimation matrix between the user equipment and the access point and a beamforming matrix of the edge distributed unit; a spectrum efficiency calculation module, configured to determine a signal-to-noise ratio of a pilot signal used by a user equipment for downlink transmission based on the channel estimation matrix and the beamforming matrix, and determine a spectrum efficiency based on the signal-to-noise ratio; A problem construction module, configured to construct a pilot allocation problem with the maximization of the spectrum efficiency as a goal and the pilot allocation principle as a constraint condition; The problem solving module is used to solve the pilot allocation problem and obtain the optimal pilot allocation strategy. slightly.

10. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the pilot allocation method of the CF-RAN according to any one of claims 1 to 8.

11. A computer-readable storage medium storing computer instructions, wherein the computer instructions are configured to cause a processor to implement the CF-RAN pilot allocation method according to any one of claims 1 to 8 when executed.

12. A computer program product, comprising a computer program, wherein when executed by a processor, the computer program implements the pilot allocation method of the CF-RAN according to any one of claims 1 to 8.

Images