WO2022262104A1 - 能效公平无蜂窝网络的资源分配及预编码方法与装置 - Google Patents

能效公平无蜂窝网络的资源分配及预编码方法与装置 Download PDF

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WO2022262104A1
WO2022262104A1 PCT/CN2021/112812 CN2021112812W WO2022262104A1 WO 2022262104 A1 WO2022262104 A1 WO 2022262104A1 CN 2021112812 W CN2021112812 W CN 2021112812W WO 2022262104 A1 WO2022262104 A1 WO 2022262104A1
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energy efficiency
network
resource allocation
optimal
precoding
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刘坤瓒
戴凌龙
张子健
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Tsinghua University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/53—Allocation or scheduling criteria for wireless resources based on regulatory allocation policies
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • the present application relates to the technical field of wireless mobile communication, and in particular to an energy-efficiency fair resource allocation and precoding method and device for a non-cellular network.
  • Ultra-Dense Network is one of the core technologies to improve wireless communication capacity. Compared with the 4G network, the 5G network capacity will reach about 1000 times of the original. As shown in FIG. 1 , in currently widely deployed cellular networks, a terminal in a cell is generally served by a base station, so a terminal at a cell boundary usually suffers severe inter-cell interference from neighboring cells. Ultra-Dense Network (UDN) is an alternative communication technology to further enhance network capacity. The core idea of the ultra-dense network is to increase the number of network base stations and reduce the cell size of the cellular network. However, as the cell density increases, the inter-cell interference will continue to increase.
  • UDN Ultra-Dense Network
  • the non-cellular network is a terminal-centric network, in which all base stations work together to serve all terminals without cell restrictions. Since there is no limitation of inter-cell interference in the non-cellular network, this is also considered as a potential technology for future communication, and it also attracts research on related resource allocation, precoding and channel estimation (refer to E.Nayebi, A.Ashikhmin , T.L.Marzetta, and H.Yang, "Cell-free massive MIMO systems," in Proc.2015 49th Asilomar Conf. Signals Sys.Comput.(ACSSC'15), Nov.2015, pp.695-699.).
  • This application provides an energy-efficient and fair resource allocation and precoding method and device for a non-cellular network, which is used to solve the problem of high power consumption in the non-cellular network in the prior art, and realize the low-cost and low-power operation of the non-cellular network. Addresses energy efficiency fairness in non-cellular networks.
  • the present application also provides a resource allocation and precoding method for an energy-efficient and fair non-cellular network, the method is applied to the non-cellular network, and the non-cellular network includes: multiple base stations, multiple terminals, and multiple smart metasurfaces , the base station allocates a carrier through an OFDMA mode; the method includes:
  • the Lagrange multiplier is obtained by optimizing the optimal energy efficiency model through the Lagrange dual method
  • Each variable in the optimal energy efficiency model is optimized sequentially by Lagrangian multipliers, so that the optimal energy efficiency model converges, and a resource allocation scheme with fair network energy efficiency and an intelligent metasurface precoding scheme are obtained.
  • the establishment of an optimal energy efficiency model for realizing network energy efficiency fairness includes: the non-cellular network is assumed to include B base stations and Q smart
  • the above formula represents the phase shift matrix of the qth smart metasurface, and ⁇ q represents the reflection coefficient, which can take values in continuous sets or discrete sets;
  • W k,p represents the transmission power of the k-th terminal on the p-th subcarrier
  • z b,p ⁇ C M ⁇ 1 represents the additive white Gaussian noise of the base station b on the sub-carrier p
  • ⁇ k,p represents an indicative variable indicating whether subcarrier p is allocated to terminal k; in order to achieve energy efficiency fairness, define the transmission power W k and the total power consumption of terminal k for:
  • the energy efficiency of terminal k can be expressed as
  • C1 means that the speed requirement of the terminal is met, Represents the required transmission rate, C2 represents the transmit power constraint, Indicates the maximum transmit power, C3 indicates the intelligent metasurface phase shift constraints, C4 and C5 indicate the OFDMA carrier allocation requirements.
  • the optimization of the optimal energy efficiency model through the fractional Dinkelbach algorithm includes: optimizing the optimal energy efficiency model according to the Dinkelbach method Fair energy efficiency ⁇ ⁇ Meet:
  • the Lagrange multiplier is obtained by optimizing the optimal energy efficiency model through the Lagrangian dual method, including: using the Lagrange dual method
  • the Lagrangian function to obtain the optimal energy efficiency model is:
  • ⁇ , ⁇ , ⁇ , and ⁇ are Lagrangian multipliers.
  • each variable in the optimal energy efficiency model is sequentially optimized through Lagrangian multipliers, so that the optimal energy efficiency model converges, and the network energy efficiency is obtained
  • Fair resource allocation scheme and intelligent metasurface precoding scheme including:
  • each variable in the optimal energy efficiency model is sequentially optimized through Lagrangian multipliers, so that the optimal energy efficiency model converges, and the network energy efficiency is obtained
  • Fair resource allocation scheme and intelligent metasurface precoding scheme including:
  • each variable in the optimal energy efficiency model is sequentially optimized through Lagrangian multipliers, so that the optimal energy efficiency model converges, and the network energy efficiency is obtained
  • a fair resource allocation scheme and an intelligent metasurface precoding scheme including: introducing auxiliary variables to transform the optimization problem of merging vector variables and the intelligent metasurface precoding problem in a resource allocation scheme with fair network energy efficiency into a convex optimization problem.
  • the closed-form optimal solution is obtained by processing the convex optimization problem.
  • the introduction of auxiliary variables converts the optimization problem of merging vector variables and the intelligent metasurface precoding problem in the resource allocation scheme of network energy efficiency fairness into Convex optimization problem, the closed optimal solution is obtained by processing the convex optimization problem, including:
  • the optimal w is obtained by deriving w, and the optimization result of the combined vector variable in the resource allocation scheme with fair network energy efficiency is:
  • the introduction of auxiliary variables converts the optimization problem of merging vector variables and the intelligent metasurface precoding problem in the resource allocation scheme of network energy efficiency fairness into Convex optimization problem, the closed optimal solution is obtained by processing the convex optimization problem, including: intelligent metasurface precoding is defined as:
  • the present application also provides an energy-efficient and fair resource allocation and precoding device for a non-cellular network, the device is applied to the non-cellular network, and the non-cellular network includes: multiple base stations, multiple terminals, and multiple smart metasurfaces , the base station assigns a carrier through an OFDMA mode; the device includes:
  • a model conversion unit configured to transform the optimization problem of the optimal energy efficiency model into a convex optimization problem
  • the processing unit is used to optimally process a convex optimization problem to obtain a resource allocation and precoding scheme for realizing network energy efficiency fairness.
  • the present application also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and operable on the processor.
  • the processor executes the program, any of the above-mentioned The steps of resource allocation and precoding method of energy efficiency fair non-cellular network are described.
  • the present application also provides a non-transitory computer-readable storage medium on which a computer program is stored, wherein, when the computer program is executed by a processor, energy efficiency fairness as described in any one of the above-mentioned resources without a cellular network is implemented. Steps of the allocation and precoding method.
  • the energy-efficiency fair non-cellular network and its resource allocation and precoding methods provided by this application add smart metasurfaces to the traditional non-cellular networks, and use the smart metasurfaces as communication relays to regulate the channel environment, thereby achieving a low-cost, Low power consumption improves the energy efficiency of non-cellular networks; in addition, this application proposes the optimal energy efficiency problem in broadband networks assisted by smart metasurfaces, aiming to improve the performance of terminals with the worst energy efficiency in uplink networks.
  • the resource allocation and precoding scheme designed by this application jointly optimizes terminal power allocation, intelligent metasurface precoding, base station signal combination and network carrier allocation through fractional planning, Lagrangian dual and other methods. And other network variables, so as to effectively improve the performance of the worst energy efficiency terminal in the uplink network, and realize the energy efficiency fairness of the terminal in the non-cellular network.
  • FIG. 1 is a schematic structural diagram of a cellular network in the prior art
  • Fig. 2 is a schematic diagram of the non-cellular network structure assisted by the intelligent metasurface provided by the application;
  • Fig. 3 is a schematic structural diagram of an energy-efficiency fair network model assisted by an intelligent metasurface provided by the present application
  • Fig. 4 is a flow chart of the resource allocation and precoding method of the energy-efficiency fair non-cellular network provided by the present application
  • Fig. 5 is the simulation result schematic diagram of the non-cellular network assisted by the intelligent metasurface provided by the application;
  • FIG. 6 is a structural block diagram of an energy-efficiency fair non-cellular network resource allocation and precoding device provided by the present application.
  • FIG. 7 is a schematic structural diagram of an electronic device provided by the present application.
  • An energy-efficiency fair non-cellular network provided by an embodiment of the present application is described below with reference to FIG. 2 , including: multiple base stations, multiple terminals, and multiple smart metasurfaces.
  • the base stations allocate carriers through OFDMA.
  • the base station may select a single-antenna base station or a multi-antenna base station, and the carrier allocated by way of Orthogonal Frequency Division Multiple Access (OFDMA) may be a single carrier or multiple carriers.
  • OFDMA Orthogonal Frequency Division Multiple Access
  • the energy efficiency fairness referred to in the embodiment of this application is also called the optimal energy efficiency fairness, which is to maximize the energy efficiency of the worst terminal.
  • the calculation method of energy efficiency can be selected according to different networks or requirements, including but not limited to terminal rate and The ratio of the sum of the static power consumption to the dynamic power consumption, the ratio of the weighted terminal speed to the sum of the static power consumption and the dynamic power consumption, etc. can be transformed into each other.
  • the smart metasurface can adjust the phase of the signal, and there are various hardware implementation methods.
  • the phase adjustment precision includes but is not limited to continuous adjustable, 1-bit discrete adjustable and other limited precision phase adjustment.
  • a terminal in a cell is generally served by a base station, so a terminal at a cell boundary usually suffers severe inter-cell interference from neighboring cells.
  • RIS Reconfigurable Intelligent Surface
  • the smart metasurface is composed of electromagnetic metamaterials. Its appearance provides the possibility to improve spectral efficiency at low cost and low power consumption. As a large-scale array composed of many units, a smart metasurface can passively reflect incident electromagnetic waves to any direction with high gain by properly adjusting the phase shift of each unit. There is still no research on how the smart metasurface-assisted cellular-free network can improve the energy efficiency of the network, which is the core indicator in the network.
  • the following describes an energy-efficiency fair non-cellular network resource allocation and precoding method provided by the embodiment of the present application in conjunction with Fig. 3-Fig. 5.
  • the method in the embodiment of the present application is applied to the smart metasurface assisted energy efficiency fairness of no honeycombs.
  • the embodiment of the present application models the problem of energy efficiency fairness under the non-cellular network assisted by the smart metasurface, and designs a corresponding resource allocation scheme and precoding scheme under the network.
  • Step 101 Establishing an optimal energy efficiency model to realize network energy efficiency fairness
  • the established optimal energy efficiency model is used to solve the optimal energy efficiency problem of the network.
  • the non-cellular network includes B base stations, Q smart metasurfaces (RIS) and K single-antenna terminals, M b antennas are arranged on the b base station, and the qth smart metasurface (RIS) ) includes N q units, adopts orthogonal frequency division multiple access (OFDMA) technology to realize network uplink transmission, and includes P subcarriers.
  • RIS smart metasurfaces
  • OFDMA orthogonal frequency division multiple access
  • the channel model h b,k,p ⁇ C M ⁇ 1 , f q,k,p ⁇ C N ⁇ 1 , G b,q,b ⁇ C N ⁇ M means that for all k ⁇ [K], b ⁇ [ B], p ⁇ [P], q ⁇ [Q], the frequency domain channel on subcarrier p from terminal k to base station b, assuming that all channel state information is known at the base station (can be obtained by channel estimation method ), for all q ⁇ [Q], let
  • ⁇ q represents the phase shift matrix of the qth intelligent metasurface (RIS), and ⁇ q represents the reflection coefficient, which can take values in a continuous set or in a discrete set;
  • W k,p represents the transmission power of the k-th terminal on the p-th subcarrier
  • z b,p ⁇ C M ⁇ 1 represents the additive white Gaussian noise (AWGN) of the base station b on the sub-carrier p
  • H b, k, p represent the equivalent channel of terminal k and base station b on subcarrier p, including a direct channel and Q reflection channels, after combining at the base station, it can be obtained by combining vector w b ,p ⁇ C M ⁇ 1 represents, and The decoding signal-to-noise ratio of
  • ⁇ k,p represents an indicative variable indicating whether subcarrier p is allocated to terminal k; in order to achieve energy efficiency fairness, define the transmission power W k and the total power consumption of terminal k for
  • C1 means meeting the rate requirement of the terminal, Represents the required transmission rate, C2 represents the transmit power constraint, Indicates the maximum transmit power, C3 indicates the intelligent metasurface phase shift constraints, C4 and C5 indicate the OFDMA carrier allocation requirements.
  • Step 102 Optimizing the optimal energy efficiency model through the fractional Dinkelbach method
  • step 101 the optimal energy efficiency problem in step 101 is transformed into an integral programming problem.
  • the parametric relaxation method is first used to relax ⁇ k,p from ⁇ 0,1 ⁇ to [0,1], introducing a new variable
  • ⁇ ⁇ can be reformulated as
  • Step 103 Optimizing the optimal energy efficiency model through the Lagrangian dual method to obtain the Lagrangian multiplier
  • step 102 the integral programming problem in step 102 is transformed into a convex optimization problem.
  • the Lagrange function of the optimal energy efficiency model obtained by the Lagrange dual method is:
  • ⁇ , ⁇ , ⁇ , and ⁇ represent the Lagrangian multipliers introduced in the Lagrangian function respectively.
  • Step 104 Optimizing each variable in the optimal energy efficiency model sequentially through Lagrangian multipliers, so that the optimal energy efficiency model converges, and obtaining a resource allocation scheme with fair network energy efficiency and an intelligent metasurface precoding scheme.
  • the smart metasurface precoding is defined as
  • the projection method is used to optimize each Lagrangian multiplier in sequence, and then the optimized Lagrangian multiplier is used to optimize Therefore, the process of optimizing each variable through Lagrangian multipliers, optimizing Lagrangian multipliers, and optimizing each variable with optimized Lagrangian multipliers is a repeated cyclic process until the final
  • the convergence of the optimal energy efficiency model is also the convergence of the integral programming problem, so as to obtain the corresponding resource allocation scheme and precoding scheme.
  • the optimal value of each variable is obtained through step 104, and the optimal value of each variable constitutes a set of resource allocation scheme and precoding scheme.
  • the energy efficiency of the embodiment of the present application is fair.
  • the worst energy consumption of the non-cellular network is 20% higher than the worst energy consumption of the traditional non-smart metasurface network, which comprehensively improves the energy efficiency of the terminals in the network.
  • an embodiment of the present application provides an energy-efficient and fair resource allocation and precoding device for a non-cellular network.
  • the device is applied to the non-cellular network, and the non-cellular network includes: multiple base stations, multiple A terminal and a plurality of smart metasurfaces, the base station allocates carriers through OFDMA; the device includes:
  • the modeling unit 601 is configured to construct an optimal energy efficiency model for realizing network energy efficiency fairness.
  • the application modeling unit 601 establishes an optimal energy efficiency model of an energy-efficiency fair non-cellular network through the method of step 101 in the embodiment of the previous invention, as shown in formula (9) in the embodiment of the previous invention.
  • a model transformation unit 602 configured to transform the optimization problem of the optimal energy efficiency model into a convex optimization problem.
  • the application model transformation unit 602 transforms formula (9) into problems of formulas (12) and (13) through the method of step 102 in the embodiment of the previous invention.
  • the processing unit 603 is configured to optimally process a convex optimization problem to obtain a resource allocation and precoding scheme that realizes network energy efficiency fairness.
  • the application processing unit 603 obtains the optimal solutions of the resource allocation and precoding schemes to achieve network energy efficiency fairness through the steps 103 and 104 in the previous embodiment of the invention as formulas (15), (16), (24), ( 30).
  • FIG. 730 A schematic diagram of the physical structure of an electronic device provided by an embodiment of the present application is described below in conjunction with FIG. 7.
  • FIG. 730 and a communication bus 740, wherein, the processor 710, the communication interface 720, and the memory 730 complete mutual communication through the communication bus 740.
  • the processor 710 can call the logic instructions in the memory 630 to execute the resource allocation and precoding method of the energy efficiency fair non-cellular network.
  • the method includes: establishing an optimal energy efficiency model for realizing network energy efficiency fairness; Optimize the optimal energy efficiency model; optimize the optimal energy efficiency model through the Lagrangian dual method to obtain the Lagrangian multiplier; optimize each variable in the optimal energy efficiency model sequentially through the Lagrange multiplier, so that the optimal energy efficiency model converges , to obtain a resource allocation scheme with fair network energy efficiency and an intelligent metasurface precoding scheme.
  • the above-mentioned logic instructions in the memory 730 may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as an independent product.
  • the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , including several instructions to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disc, etc., which can store program codes. .
  • the embodiment of the present application also provides a computer program product
  • the computer program product includes a computer program stored on a non-transitory computer-readable storage medium
  • the computer program includes program instructions, when the program instructions When being executed by a computer, the computer can execute the resource allocation and precoding methods of the energy-efficiency fair non-cellular network provided by the above-mentioned methods.
  • the method includes: establishing an optimal energy efficiency model for realizing network energy efficiency fairness; Optimize the optimal energy efficiency model; optimize the optimal energy efficiency model through the Lagrangian dual method to obtain the Lagrangian multiplier; optimize each variable in the optimal energy efficiency model sequentially through the Lagrange multiplier, so that the optimal energy efficiency model converges , to obtain a resource allocation scheme with fair network energy efficiency and an intelligent metasurface precoding scheme.
  • the embodiment of the present application also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it is implemented to perform the energy-efficiency fairness and resource-free cellular network provided above.
  • Each variable in the optimal energy efficiency model is optimized sequentially by Lagrangian multipliers, so that the optimal energy efficiency model converges, and a resource allocation scheme with fair network energy efficiency and an intelligent metasurface precoding scheme are obtained.
  • the device embodiments described above are only illustrative, and the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in One place, or it can be distributed to multiple network elements. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present application. It can be understood and implemented by those skilled in the art without any creative effort.
  • each implementation can be implemented by means of software plus a necessary general hardware platform, and of course also by hardware.
  • the essence of the above technical solution or the part that contributes to the prior art can be embodied in the form of software products, and the computer software products can be stored in computer-readable storage media, such as ROM/RAM, magnetic discs, optical discs, etc., including several instructions to make a computer device (which may be a personal computer, server, or network device, etc.) execute the methods described in various embodiments or some parts of the embodiments.

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Abstract

本申请提供一种能效公平无蜂窝网络的资源分配及预编码方法与装置，包括：多个基站、多个终端以及多个智能超表面，所述基站通过正交频分多址方式分配载波。在传统的无蜂窝网络中引入智能超表面，使用智能超表面做通信中继对信道环境进行调控，从而以一种低成本方式提高网络能效；针对该网络提出一套资源分配方案与预编码方案，通过对网络能效公平的最优能效模型的建立以及通过分式型规划、拉格朗日对偶等方法联合优化了网络模型中的各变量，从而有效提升上行网络中最差能效终端的性能，在所提出的资源分配方案及预编码方案下可获得相比传统网络更好的性能。

Description

能效公平无蜂窝网络的资源分配及预编码方法与装置
相关申请的交叉引用
本申请要求于2021年06月17日提交的申请号为202110673957.2，发明名称为“能效公平无蜂窝网络的资源分配及预编码方法与装置”的中国专利申请的优先权，其通过引用方式全部并入本文。
技术领域
本申请涉及无线移动通信技术领域，尤其涉及一种能效公平无蜂窝网络的资源分配及预编码方法与装置。
背景技术
网络技术是提高无线通信容量的核心技术之一。相比于4G网络，5G网络容量将会达到原来的1000倍左右。如图1所示，在目前广泛布置的蜂窝网络中，在小区中的终端一般是被一个基站来服务，因此处于小区边界处的终端通常会受到来自旁边小区的严重的小区间干扰。超密集网络(Ultra-Dense Network,UDN)是一种进一步提升网络容量的通信备选技术。超密集网络的核心思想是增大网络基站数以及减小蜂窝网络的小区大小。然而，随着小区密度的增加，小区间干扰会不断增大。协作理论已经证明，网络性能上界受小区间干扰影响，即说明小区间干扰已经成为限制超密集网络容量的瓶颈。这个内在的问题是由蜂窝网络的特点所决定的，难以找到方法解决。
为了解决这一问题，一种新的称为无蜂窝网络的概念被提出。相比传统以小区为中心的网络，无蜂窝网络是一种以终端为中心的网络，其中所有基站共同协作服务所有终端而不存在小区限制。由于无蜂窝网络中不存在小区间干扰的限制，这也被认为是未来通信的潜在技术，也吸引了相关的资源分配、预编码和信道估计等方面的研究(参考 E.Nayebi,A.Ashikhmin,T.L.Marzetta,and H.Yang,“Cell-free massive MIMO systems,”in Proc.2015 49th Asilomar Conf.Signals Sys.Comput.(ACSSC’15),Nov.2015,pp.695-699.)。
然而，在无蜂窝网络中部署大量基站会导致难以接受的高功耗问题，因此无蜂窝网络的高功耗问题是目前业界亟待解决的重要课题。发明内容
本申请提供一种能效公平无蜂窝网络的资源分配及预编码方法与装置，用以解决现有技术中无蜂窝网络中高功耗问题，实现无蜂窝网络以低成本、低功耗方式运行，同时解决了无蜂窝网络中的能效公平问题。
本申请还提供一种能效公平无蜂窝网络的资源分配及预编码方法，所述方法应用于所述无蜂窝网络，所述无蜂窝网络包括：多个基站、多个终端以及多个智能超表面，所述基站通过正交频分多址方式分配载波；所述方法包括：
建立实现网络能效公平的最优能效模型；
通过分式型丁克尔巴赫方法优化最优能效模型；
通过拉格朗日对偶法优化最优能效模型得到拉格朗日乘子；
通过拉格朗日乘子依次优化最优能效模型中的各个变量，使得最优能效模型收敛，得到网络能效公平的资源分配方案及智能超表面预编码方案。
根据本申请提供的一种能效公平无蜂窝网络的资源分配及预编码方法，所述将建立实现网络能效公平的最优能效模型，包括：所述无蜂窝网络假设包括B个基站、Q个智能超表面以及K个终端，在第b个基站上布置M个天线，第q个智能超表面包含N个单元，采用正交频分多址实现网络上行传输，其中包含有P个子载波；信道模型h b,k,p∈C M×1，f q,k,p∈C N×1，G b,q,b∈C N×M表示对于所有的k∈[K]，b∈[B],p∈[P],q∈[Q]的从终端k到基站b的子载波p上的频域信道， 假设所有信道状态信息在基站处是已知的，对于所有q∈[Q]，令
Figure PCTCN2021112812-appb-000001
上式表示第q个智能超表面的相移矩阵，θ q表示反射系数，其可以取值于连续集合也可以取值于离散集合；
对所有k和p，
Figure PCTCN2021112812-appb-000002
表示终端k在子载波p上传输的数据，并假设k和p均为具有零均值和单位方差的独立同分布的高斯随机变量，令yb,k,p∈C M×1表示终端k发出、经过子载波p而到达第b个基站的信号，可以被写为
Figure PCTCN2021112812-appb-000003
上式中，W k,p表示第k个终端在第p个子载波上的传输功率，z b,p∈C M×1表示基站b在子载波p上的加性高斯白噪声，其中
Figure PCTCN2021112812-appb-000004
表示终端k和基站b在子载波p上的等效信道，包括一个直射信道和Q个反射信道，在基站处组合后，它可以由合并向量w b,p∈C M×1表示，而
Figure PCTCN2021112812-appb-000005
的解码信噪比为
Figure PCTCN2021112812-appb-000006
定义
Figure PCTCN2021112812-appb-000007
则和速率R k可表示为
Figure PCTCN2021112812-appb-000008
其中ρ k,p表示一个指示子载波p是否被分配给终端k的示性变量；为了实现能效公平，定义发送功率W k和终端k的总功耗
Figure PCTCN2021112812-appb-000009
为：
Figure PCTCN2021112812-appb-000010
Figure PCTCN2021112812-appb-000011
其中因数A k,
Figure PCTCN2021112812-appb-000012
分别表示能量转换效率和终端k的静态功耗，因此，终端k的能效可表示为
Figure PCTCN2021112812-appb-000013
因此，实现网络能效公平的最优能效模型形式化地描述为：
Figure PCTCN2021112812-appb-000014
Figure PCTCN2021112812-appb-000015
Figure PCTCN2021112812-appb-000016
Figure PCTCN2021112812-appb-000017
Figure PCTCN2021112812-appb-000018
Figure PCTCN2021112812-appb-000019
其中C1表示满足终端的速率要求，
Figure PCTCN2021112812-appb-000020
表示所需的传输速率，C2表示发射功率约束，
Figure PCTCN2021112812-appb-000021
表示最大发射功率，C3表示智能超表面相移 约束条件，C4和C5表示正交频分多址载波分配要求。
根据本申请提供的一种能效公平无蜂窝网络的资源分配及预编码方法，所述通过分式型丁克尔巴赫算法优化最优能效模型，包括：根据丁克尔巴赫方法最优能效模型的最优公平能效η ★满足：
Figure PCTCN2021112812-appb-000022
通过交替优化求解最优的η ★，即：
Figure PCTCN2021112812-appb-000023
s.t.C1,C2,C3,C4,C5。
根据本申请提供的一种能效公平无蜂窝网络的资源分配及预编码方法，所述通过拉格朗日对偶法优化最优能效模型得到拉格朗日乘子，包括：通过拉格朗日对偶法获得最优能效模型的拉格朗日函数为：
Figure PCTCN2021112812-appb-000024
其中α、β、γ、κ分别为拉格朗日乘子。
根据本申请提供的一种能效公平无蜂窝网络的资源分配及预编码方法，所述通过拉格朗日乘子依次优化最优能效模型中的各个变量，使得最优能效模型收敛，得到网络能效公平的资源分配方案及智能超表面预编码方案，包括：
通过应用关于W k,p的Karush-Kuhn-Tucker条件得到网络能效公平的资源分配方案中的功率分配变量的优化结果
Figure PCTCN2021112812-appb-000025
Figure PCTCN2021112812-appb-000026
根据本申请提供的一种能效公平无蜂窝网络的资源分配及预编码方法，所述通过拉格朗日乘子依次优化最优能效模型中的各个变量，使得最优能效模型收敛，得到网络能效公平的资源分配方案及智能超表面预编码方案，包括：
通过应用关于ρ k,p的Karush-Kuhn-Tucker条件得到网络能效公平的资源分配方案中的子载波分配变量的优化结果
Figure PCTCN2021112812-appb-000027
Figure PCTCN2021112812-appb-000028
其中
Figure PCTCN2021112812-appb-000029
根据本申请提供的一种能效公平无蜂窝网络的资源分配及预编码方法，所述通过拉格朗日乘子依次优化最优能效模型中的各个变量， 使得最优能效模型收敛，得到网络能效公平的资源分配方案及智能超表面预编码方案，包括：引入辅助变量将网络能效公平的资源分配方案中的合并向量变量的优化问题及智能超表面预编码问题转化为凸优化问题，对所述凸优化问题进行处理得到闭式最优解。
根据本申请提供的一种能效公平无蜂窝网络的资源分配及预编码方法，所述引入辅助变量将网络能效公平的资源分配方案中的合并向量变量的优化问题及智能超表面预编码问题转化为凸优化问题，对所述凸优化问题进行处理得到闭式最优解，包括：
引入一个辅助变量μ∈C K×P得到：
Figure PCTCN2021112812-appb-000030
Figure PCTCN2021112812-appb-000031
其中
Figure PCTCN2021112812-appb-000032
关于μ k,p求导得到最优μ k,p为：
Figure PCTCN2021112812-appb-000033
进一步引入辅助变量ν∈C K×P，得到：
Figure PCTCN2021112812-appb-000034
Figure PCTCN2021112812-appb-000035
其中
Figure PCTCN2021112812-appb-000036
关于ν k,p求导得到最优ν k,p为：
Figure PCTCN2021112812-appb-000037
关于w求导得到最优w，得到网络能效公平的资源分配方案中的合并向量变量的优化结果为：
Figure PCTCN2021112812-appb-000038
根据本申请提供的一种能效公平无蜂窝网络的资源分配及预编码方法，所述引入辅助变量将网络能效公平的资源分配方案中的合并向量变量的优化问题及智能超表面预编码问题转化为凸优化问题，对所述凸优化问题进行处理得到闭式最优解，包括：智能超表面预编码定义为：
Figure PCTCN2021112812-appb-000039
其中
Figure PCTCN2021112812-appb-000040
Figure PCTCN2021112812-appb-000041
为：
Figure PCTCN2021112812-appb-000042
相应地，智能超表面预编码的子问题为：
Figure PCTCN2021112812-appb-000043
Figure PCTCN2021112812-appb-000044
其中
Figure PCTCN2021112812-appb-000045
其中
Figure PCTCN2021112812-appb-000046
Figure PCTCN2021112812-appb-000047
Figure PCTCN2021112812-appb-000048
Figure PCTCN2021112812-appb-000049
Figure PCTCN2021112812-appb-000050
通过标准优化工具求解智能超表面预编码的子问题，
通过使用投影法解得到
Figure PCTCN2021112812-appb-000051
即为网络能效公平的智能超表面预编码方案的有限精度预编码结果：
Figure PCTCN2021112812-appb-000052
本申请还提供一种能效公平无蜂窝网络的资源分配及预编码装置，所述装置应用于所述无蜂窝网络，所述无蜂窝网络包括：多个基 站、多个终端以及多个智能超表面，所述基站通过正交频分多址方式分配载波；所述装置包括：
建模单元，用于构建实现网络能效公平的最优能效模型；
模型变换单元，用于将所述最优能效模型的优化问题转化为凸优化问题；
处理单元，用于优化处理凸优化问题得出实现网络能效公平的资源分配及预编码方案。
本申请还提供一种电子设备，包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述处理器执行所述程序时实现如上述任一种所述能效公平无蜂窝网络的资源分配及预编码方法的步骤。
本申请还提供一种非暂态计算机可读存储介质，其上存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现如上述任一种所述能效公平无蜂窝网络的资源分配及预编码方法的步骤。
本申请提供的能效公平无蜂窝网络及其资源分配及预编码方法，在传统无蜂窝网络中加入智能超表面，通过智能超表面做通信中继对信道环境进行调控，从而以一种低成本、低功耗的方式提高无蜂窝网络的能效；另外，本申请提出了智能超表面辅助的宽带网络中的最优能效问题，旨在提升上行网络中最差能效终端的性能。针对该问题，本申请设计的资源分配与预编码方案，该方案通过分式型规划、拉格朗日对偶等方法联合优化了终端功率分配、智能超表面预编码、基站信号合并以及网络载波分配等网络变量，从而有效提升上行网络中最差能效终端的性能，实现无蜂窝网络中终端的能效公平。
附图说明
为了更清楚地说明本申请或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作一简单地介绍，显而易见地，下面描述中的附图是本申请的一些实施例，对于本领域普通技术 人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是现有技术中蜂窝网络的结构示意图；
图2是本申请提供的智能超表面辅助的无蜂窝网络结构示意图；
图3是本申请提供的智能超表面辅助的能效公平网络模型结构示意图；
图4是本申请提供的能效公平无蜂窝网络的资源分配及预编码方法的流程图；
图5是本申请提供的智能超表面辅助的无蜂窝网络的仿真结果示意图；
图6为本申请提供的能效公平无蜂窝网络的资源分配及预编码装置的结构框图；
图7是本申请提供的电子设备的结构示意图。
具体实施方式
为使本申请的目的、技术方案和优点更加清楚，下面将结合本申请中的附图，对本申请中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
下面结合图2描述本申请实施例提供的一种能效公平无蜂窝网络，包括：多个基站、多个终端以及多个智能超表面，所述基站通过正交频分多址方式分配载波。
本申请实施例中，基站可选择单天线基站或多天线基站，正交频分多址(OFDMA)方式分配的载波可为单载波或多载波。本申请实施例中所指的能效公平也称最优能效公平，是为最大化最差终端的能效，能效的计算方式可根据不同的网络或者需求选择不同的方式，包括但不限于终端速率与静态功耗和动态功耗之和的比值、带权终端速 率与静态功耗和动态功耗之和的比值等可以相互转化的形式。本申请实施例中智能超表面对信号可以进行相位调整，其硬件实现方式可有多种，相位调整精度包括但不限于连续可调、1比特离散可调等有限精度相位调整。
如图1所示，在目前广泛布置的蜂窝网络中，在小区中的终端一般是被一个基站来服务，因此处于小区边界处的终端通常会受到来自旁边小区的严重的小区间干扰。
如图2所示，在一以终端为中心的无蜂窝网络中，所有基站共同协作服务所有终端用户而不存在小区限制，因此无蜂窝网络中不存在小区间干扰的限制。称为智能超表面(Reconfigurable Intelligent Surface,RIS)的新技术被刚刚提出，智能超表面由电磁超材料构成，它的出现为低成本和低功耗地提升频谱效率提供了可能。智能超表面作为一个由众多单元构成的大规模阵列，通过合适地调整各个单元的相位移位，它可以无源地将入射电磁波以高增益反射到任意方向上。智能超表面辅助的无蜂窝网络如何提升这个网络的能量效率这一网络中的核心指标，尚无此方面的研究。
下面结合图3-图5描述本申请实施例提供的一种能效公平无蜂窝网络的资源分配及预编码方法，本申请实施例中的方法应用于上一实施例的智能超表面辅助的能效公平的无蜂窝中。
如图3所示，本申请实施例建模了智能超表面辅助的无蜂窝网络下的能效公平问题，并在该网络下设计了相应的资源分配方案与预编码方案。
如图4所示为本实施的方法的流程图，本申请实施例的方法包括：
步骤101：建立实现网络能效公平的最优能效模型；
本步骤中，建立的最优能效模型是用于解决网络的最优能效问题。
本申请实施例中，无蜂窝网络假设包括B个基站、Q个智能超表面(RIS)以及K个单天线终端，在第b个基站上布置M b个天线，第q个智能超表面(RIS)包含N q个单元，采用正交频分多址(orthogonal frequency division multiple access，OFDMA)技术实现网络上行传输， 其中包含有P个子载波。
信道模型h b,k,p∈C M×1，f q,k,p∈C N×1，G b,q,b∈C N×M表示对于所有的k∈[K]，b∈[B],p∈[P],q∈[Q]的从终端k到基站b的子载波p上的频域信道，假设所有信道状态信息在基站处是已知的(可通过信道估计方法获得)，对于所有q∈[Q]，令
Figure PCTCN2021112812-appb-000053
(1)式中Θ q表示第q个智能超表面(RIS)的相移矩阵，θ q表示反射系数，其可以取值于连续集合也可以取值于离散集合；
对所有k和p，
Figure PCTCN2021112812-appb-000054
表示终端k在子载波p上传输的数据，并假设k和p均为具有零均值和单位方差的独立同分布的高斯随机变量，令yb,k,p∈C M×1表示终端k发出、经过子载波p而到达b基站的信号，可以被写为
Figure PCTCN2021112812-appb-000055
(2)式中W k,p表示第k个终端在第p个子载波上的传输功率，z b,p∈C M×1表示基站b在子载波p上的加性高斯白噪声(AWGN)，其中
Figure PCTCN2021112812-appb-000056
(3)式中H b,k,p表示终端k和基站b在子载波p上的等效信道，包括一个直射信道和Q个反射信道，在基站处组合后，它可以由合并向量w b,p∈C M×1表示，而
Figure PCTCN2021112812-appb-000057
的解码信噪比为
Figure PCTCN2021112812-appb-000058
定义
Figure PCTCN2021112812-appb-000059
则和速率R k可表示为
Figure PCTCN2021112812-appb-000060
(5)式中ρ k,p表示指示子载波p是否被分配给终端k的示性变量；为了实现能效公平，定义发送功率W k和终端k的总功耗
Figure PCTCN2021112812-appb-000061
为
Figure PCTCN2021112812-appb-000062
Figure PCTCN2021112812-appb-000063
(7)式中因数A k,
Figure PCTCN2021112812-appb-000064
分别表示能量转换效率和终端k的静态功耗，因此，终端k的能效η k(ρ,W,w,θ)可表示为
Figure PCTCN2021112812-appb-000065
因此，实现网络能效公平的最优能效模型形式化地描述为
Figure PCTCN2021112812-appb-000066
(9)式中C1表示满足终端的速率要求，
Figure PCTCN2021112812-appb-000067
表示所需的传输速率，C2表示发射功率约束，
Figure PCTCN2021112812-appb-000068
表示最大发射功率，C3表示智能超表面相移约束条件，C4和C5表示正交频分多址载波分配要求。
步骤102：通过分式型丁克尔巴赫方法优化最优能效模型；
通过本步骤将步骤101中的最优能效问题转化为整式规划问题。
由于智能超表面辅助的无蜂窝网络中的能效公平问题具有非凸性，因此需要解耦问题。
本步骤中，根据丁克尔巴赫方法(Dinkelbach)使得最优能效模型的最优公平能效η ★满足
Figure PCTCN2021112812-appb-000069
通过交替优化求解最优的η ★，即
Figure PCTCN2021112812-appb-000070
s.t.C1,C2,C3,C4,C5
在本步骤之后，优选地，为了解决混合整数问题，首先使用参量松弛方法将ρ k,p从{0,1}放松到[0,1]，引入新的变量
Figure PCTCN2021112812-appb-000071
来解决最优能效模型的的非光滑性，η ★可以被重新表述为
Figure PCTCN2021112812-appb-000072
定义
Figure PCTCN2021112812-appb-000073
用户信噪比可以被重新写为
Figure PCTCN2021112812-appb-000074
步骤103：通过拉格朗日对偶法优化最优能效模型得到拉格朗日乘子；
通过本步骤，将步骤102的整式规划问题转化为凸优化问题。
通过拉格朗日对偶法获得最优能效模型的拉格朗日函数为:
Figure PCTCN2021112812-appb-000075
(14)式中α、β、γ、κ分别表示拉格朗日函数中引入的拉格朗日乘子。
步骤104：通过拉格朗日乘子依次优化最优能效模型中的各个变量，使得最优能效模型收敛，得到网络能效公平的资源分配方案及智能超表面预编码方案。
本步骤中，通过应用关于W k,p的Karush-Kuhn-Tucker(KKT)条件得到网络能效公平的资源分配方案中的功率分配变量的优化结果
Figure PCTCN2021112812-appb-000076
Figure PCTCN2021112812-appb-000077
本步骤中，通过应用关于ρ k,p的Karush-Kuhn-Tucker(KKT)条件得到网络能效公平的资源分配方案中的子载波分配变量的优化结果
Figure PCTCN2021112812-appb-000078
Figure PCTCN2021112812-appb-000079
(16)式中
Figure PCTCN2021112812-appb-000080
本步骤中在解决资源分配方案中的合并向量进行优化以及获取智能超表面预编码的问题时，由于问题具有非常强的非凸性，故无法直接获得各个最优解的闭式表达式。因此本步骤中在对以上问题进行处理时引入辅助变量来改写问题，将网络能效公平的资源分配方案中的合并向量变量的优化问题及智能超表面预编码问题转化为可以获得闭式最优解的凸优化问题，对所述凸优化问题进行处理得到闭式最优解。
具体地，引入一个辅助变量μ∈C K×P来解耦上述问题，得到
Figure PCTCN2021112812-appb-000081
(18)式中
Figure PCTCN2021112812-appb-000082
关于μ k,p求导得到最优μ k,p为
Figure PCTCN2021112812-appb-000083
进一步引入辅助变量ν∈C K×P，得到
Figure PCTCN2021112812-appb-000084
(21)式中
Figure PCTCN2021112812-appb-000085
关于ν k,p求导得到最优ν k,p为
Figure PCTCN2021112812-appb-000086
关于w求导得到最优w，得到网络能效公平的资源分配方案中的合并向量变量的优化结果为
Figure PCTCN2021112812-appb-000087
具体地，智能超表面预编码定义为
Figure PCTCN2021112812-appb-000088
(25)式中
Figure PCTCN2021112812-appb-000089
Figure PCTCN2021112812-appb-000090
为
Figure PCTCN2021112812-appb-000091
相应地，智能超表面预编码的子问题为
Figure PCTCN2021112812-appb-000092
(27)式中
Figure PCTCN2021112812-appb-000093
(28)式中
Figure PCTCN2021112812-appb-000094
由于上述智能超表面预编码的子问题具有凸限制条件和凸目标函数，因此可以通过标准优化工具求解。
对于具有离散相移的智能超表面，通过使用投影法可解得
Figure PCTCN2021112812-appb-000095
即为网络能效公平的智能超表面预编码方案的有限精度预编码结果
Figure PCTCN2021112812-appb-000096
本申请实施例中，通过拉格朗日乘子优化各变量的过程中，具体地，使用投影方法按顺序依次优化各个拉格朗日乘子，再用优化后的拉格朗日乘子优化各个变量，因此上述通过拉格朗日乘子优化各变量，以及优化拉格朗日乘子，再用优化后的拉格朗日乘子优化各变量的过程是重复进行的循环过程，直至最优能效模型收敛也即整式规划问题收敛，从而获得相应的资源分配方案及预编码方案。
通过步骤104得到了各变量的最优取值，各变量的最优取值构成 了一套资源分配方案与预编码方案。如图5所示的仿真实验结果可看出，通过将本申请实施例的方法获得的资源分配方案与预编码方案配置在智能超表面辅助的无蜂窝网络中，使得本申请实施例的能效公平无蜂窝网络的最差能耗相较于传统没有配置智能超表面网络的最差能耗提升20％，全面提升了网络中终端的能效。
如图6所示，本申请实施例提供一种能效公平无蜂窝网络的资源分配及预编码装置，所述装置应用于所述无蜂窝网络，所述无蜂窝网络包括：多个基站、多个终端以及多个智能超表面，所述基站通过正交频分多址方式分配载波；所述装置包括：
建模单元601，用于构建实现网络能效公平的最优能效模型。应用建模单元601通过上一发明实施例中的步骤101的方法建立能效公平无蜂窝网络的最优能效模型如上一发明实施例的公式(9)所示。
模型变换单元602，用于将所述最优能效模型的优化问题转化为凸优化问题。应用模型变换单元602通过上一发明实施例中的步骤102的方法将公式(9)变换为公式(12)及(13)的问题。
处理单元603，用于优化处理凸优化问题得出实现网络能效公平的资源分配及预编码方案。应用处理单元603通过上一发明实施例中的步骤103及步骤104得出实现网络能效公平的资源分配及预编码方案的最优解分别为公式(15)、(16)、(24)、(30)。
下面结合图7描述本申请实施例提供的一种电子设备的实体结构示意图，如图7所示，该电子设备可以包括：处理器(processor)710、通信接口(Communications Interface)720、存储器(memory)730和通信总线740，其中，处理器710，通信接口720，存储器730通过通信总线740完成相互间的通信。处理器710可以调用存储器630中的逻辑指令，以执行能效公平无蜂窝网络的资源分配及预编码方法，该方法包括：建立实现网络能效公平的最优能效模型；通过分式型丁克尔巴赫方法优化最优能效模型；通过拉格朗日对偶法优化最优能效模型得到拉格朗日乘子；通过拉格朗日乘子依次优化最优能效模型中的各个变量，使得最优能效模型收敛，得到网络能效公平的资源分配方案 及智能超表面预编码方案。
此外，上述的存储器730中的逻辑指令可以通过软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请实施例的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
另一方面，本申请实施例还提供一种计算机程序产品，所述计算机程序产品包括存储在非暂态计算机可读存储介质上的计算机程序，所述计算机程序包括程序指令，当所述程序指令被计算机执行时，计算机能够执行上述各方法所提供的能效公平无蜂窝网络的资源分配及预编码方法，该方法包括：建立实现网络能效公平的最优能效模型；通过分式型丁克尔巴赫方法优化最优能效模型；通过拉格朗日对偶法优化最优能效模型得到拉格朗日乘子；通过拉格朗日乘子依次优化最优能效模型中的各个变量，使得最优能效模型收敛，得到网络能效公平的资源分配方案及智能超表面预编码方案。
又一方面，本申请实施例还提供一种非暂态计算机可读存储介质，其上存储有计算机程序，该计算机程序被处理器执行时实现以执行上述各提供的能效公平无蜂窝网络的资源分配及预编码方法，建立实现网络能效公平的最优能效模型；通过分式型丁克尔巴赫方法优化最优能效模型；通过拉格朗日对偶法优化最优能效模型得到拉格朗日乘子；通过拉格朗日乘子依次优化最优能效模型中的各个变量，使得最优能效模型收敛，得到网络能效公平的资源分配方案及智能超表面预编码方案。
以上所描述的装置实施例仅仅是示意性的，其中所述作为分离部 件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本申请实施例方案的目的。本领域普通技术人员在不付出创造性的劳动的情况下，即可以理解并实施。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到各实施方式可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件。基于这样的理解，上述技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品可以存储在计算机可读存储介质中，如ROM/RAM、磁碟、光盘等，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行各个实施例或者实施例的某些部分所述的方法。
最后应说明的是：以上实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的精神和范围。

Claims (10)

  1. 一种能效公平无蜂窝网络的资源分配及预编码方法，所述方法应用于所述无蜂窝网络，其特征在于，所述无蜂窝网络包括：多个基站、多个终端以及多个智能超表面，所述基站通过正交频分多址方式分配载波；所述方法包括：
    建立实现网络能效公平的最优能效模型；
    通过分式型丁克尔巴赫方法优化最优能效模型；
    通过拉格朗日对偶法优化最优能效模型得到拉格朗日乘子；
    通过拉格朗日乘子依次优化最优能效模型中的各个变量，使得最优能效模型收敛，得到网络能效公平的资源分配方案及智能超表面预编码方案。
  2. 根据权利要求1所述的能效公平无蜂窝网络的资源分配及预编码方法，其特征在于，所述将建立实现网络能效公平的最优能效模型，包括：所述无蜂窝网络假设包括B个基站、Q个智能超表面以及K个终端，在第b个基站上布置M个天线，第q个智能超表面包含N个单元，采用正交频分多址实现网络上行传输，其中包含有P个子载波；信道模型h b,k,p∈C M×1，f q,k,p∈C N×1，G b,q,b∈C N×M表示对于所有的k∈[K]，b∈[B],p∈[P],q∈[Q]的从终端k到基站b的子载波p上的频域信道，假设所有信道状态信息在基站处是已知的，对于所有q∈[Q]，令
    Θ q：＝diag(θ q)＝diag([θ q,1,…,θ q,N] T)
    上式表示第q个智能超表面的相移矩阵，θ q表示反射系数，其可以取值于连续集合也可以取值于离散集合；
    对所有k和p，
    Figure PCTCN2021112812-appb-100001
    表示终端k在子载波p上传输的数据，并假设k和p均为具有零均值和单位方差的独立同分布的高斯随机变量，令y b,k,p∈C M×1表示终端k发出、经过子载波p而到达第b个基站的信号，可以 被写为
    Figure PCTCN2021112812-appb-100002
    上式中，W k,p表示第k个终端在第p个子载波上的传输功率，z b,p∈C M×1表示基站b在子载波p上的加性高斯白噪声，其中
    Figure PCTCN2021112812-appb-100003
    表示终端k和基站b在子载波p上的等效信道，包括一个直射信道和Q个反射信道，在基站处组合后，它可以由合并向量w b,p∈C M×1表示，而
    Figure PCTCN2021112812-appb-100004
    的解码信噪比为
    Figure PCTCN2021112812-appb-100005
    定义
    Figure PCTCN2021112812-appb-100006
    则和速率R k可表示为
    Figure PCTCN2021112812-appb-100007
    其中ρ k,p表示一个指示子载波p是否被分配给终端k的示性变量；为了实现能效公平，定义发送功率W k和终端k的总功耗
    Figure PCTCN2021112812-appb-100008
    为：
    Figure PCTCN2021112812-appb-100009
    Figure PCTCN2021112812-appb-100010
    其中因数A k,
    Figure PCTCN2021112812-appb-100011
    分别表示能量转换效率和终端k的静态功耗，因此，终端k的能效可表示为
    Figure PCTCN2021112812-appb-100012
    因此，实现网络能效公平的最优能效模型形式化地描述为：
    Figure PCTCN2021112812-appb-100013
    Figure PCTCN2021112812-appb-100014
    Figure PCTCN2021112812-appb-100015
    Figure PCTCN2021112812-appb-100016
    Figure PCTCN2021112812-appb-100017
    Figure PCTCN2021112812-appb-100018
    其中C1表示满足终端的速率要求，
    Figure PCTCN2021112812-appb-100019
    表示所需的传输速率，C2表示发射功率约束，
    Figure PCTCN2021112812-appb-100020
    表示最大发射功率，C3表示智能超表面相移约束条件，C4和C5表示正交频分多址载波分配要求。
  3. 根据权利要求2所述的能效公平无蜂窝网络的资源分配及预编码方法，其特征在于，所述通过分式型丁克尔巴赫算法优化最优能效模型，包括：根据丁克尔巴赫方法最优能效模型的最优公平能效η ★满足：
    Figure PCTCN2021112812-appb-100021
    通过交替优化求解最优的η ★，即：
    Figure PCTCN2021112812-appb-100022
    s.t.C1,C2,C3,C4,C5。
  4. 权利要求3所述的能效公平无蜂窝网络的资源分配及预编码 方法，其特征在于，所述通过拉格朗日对偶法优化最优能效模型得到拉格朗日乘子，包括：通过拉格朗日对偶法获得最优能效模型的拉格朗日函数为：
    Figure PCTCN2021112812-appb-100023
    其中α、β、γ、κ分别为拉格朗日乘子。
  5. 根据权利要求4所述的能效公平无蜂窝网络的资源分配及预编码方法，其特征在于，所述通过拉格朗日乘子依次优化最优能效模型中的各个变量，使得最优能效模型收敛，得到网络能效公平的资源分配方案及智能超表面预编码方案，包括：
    通过应用关于W k,p的Karush-Kuhn-Tucker条件得到网络能效公平的资源分配方案中的功率分配变量的优化结果
    Figure PCTCN2021112812-appb-100024
    Figure PCTCN2021112812-appb-100025
  6. 根据权利要求4所述的能效公平无蜂窝网络的资源分配及预编码方法，其特征在于，所述通过拉格朗日乘子依次优化最优能效模型中的各个变量，使得最优能效模型收敛，得到网络能效公平的资源 分配方案及智能超表面预编码方案，包括：
    通过应用关于ρ k,p的Karush-Kuhn-Tucker条件得到网络能效公平的资源分配方案中的子载波分配变量的优化结果
    Figure PCTCN2021112812-appb-100026
    Figure PCTCN2021112812-appb-100027
    其中
    Figure PCTCN2021112812-appb-100028
  7. 根据权利要求4所述的能效公平无蜂窝网络的资源分配及预编码方法，其特征在于，所述通过拉格朗日乘子依次优化最优能效模型中的各个变量，使得最优能效模型收敛，得到网络能效公平的资源分配方案及智能超表面预编码方案，包括：引入辅助变量将网络能效公平的资源分配方案中的合并向量变量的优化问题及智能超表面预编码问题转化为凸优化问题，对所述凸优化问题进行处理得到闭式最优解。
  8. 根据权利要求7所述的能效公平无蜂窝网络的资源分配及预编码方法，其特征在于，所述引入辅助变量将网络能效公平的资源分配方案中的合并向量变量的优化问题及智能超表面预编码问题转化为凸优化问题，对所述凸优化问题进行处理得到闭式最优解，包括：
    引入一个辅助变量μ∈C K×P得到：
    Figure PCTCN2021112812-appb-100029
    Figure PCTCN2021112812-appb-100030
    其中
    Figure PCTCN2021112812-appb-100031
    关于μ k,p求导得到最优μ k,p为：
    Figure PCTCN2021112812-appb-100032
    进一步引入辅助变量ν∈C K×P，得到：
    Figure PCTCN2021112812-appb-100033
    Figure PCTCN2021112812-appb-100034
    其中
    Figure PCTCN2021112812-appb-100035
    关于ν k,p求导得到最优ν k,p为：
    Figure PCTCN2021112812-appb-100036
    关于w求导得到最优w，得到网络能效公平的资源分配方案中的合并向量变量的优化结果为：
    Figure PCTCN2021112812-appb-100037
  9. 根据权利要求7所述的能效公平无蜂窝网络的资源分配及预编码方法，其特征在于，所述引入辅助变量将网络能效公平的资源分配方案中的合并向量变量的优化问题及智能超表面预编码问题转化为凸优化问题，对所述凸优化问题进行处理得到闭式最优解，包括：智能超表面预编码定义为：
    Figure PCTCN2021112812-appb-100038
    其中
    Figure PCTCN2021112812-appb-100039
    Figure PCTCN2021112812-appb-100040
    为：
    Figure PCTCN2021112812-appb-100041
    相应地，智能超表面预编码的子问题为：
    Figure PCTCN2021112812-appb-100042
    Figure PCTCN2021112812-appb-100043
    其中
    Figure PCTCN2021112812-appb-100044
    其中
    Figure PCTCN2021112812-appb-100045
    Figure PCTCN2021112812-appb-100046
    Figure PCTCN2021112812-appb-100047
    通过标准优化工具求解智能超表面预编码的子问题，
    通过使用投影法解得到
    Figure PCTCN2021112812-appb-100048
    即为网络能效公平的智能超表面预编码方案的有限精度预编码结果：
    Figure PCTCN2021112812-appb-100049
  10. 一种能效公平无蜂窝网络的资源分配及预编码装置，所述装置应用于所述无蜂窝网络，其特征在于，所述无蜂窝网络包括：多个基站、多个终端以及多个智能超表面，所述基站通过正交频分多址方式分配载波；所述装置包括：
    建模单元，用于构建实现网络能效公平的最优能效模型；
    模型变换单元，用于将所述最优能效模型的优化问题转化为凸优化问题；
    处理单元，用于优化处理凸优化问题得出实现网络能效公平的资源分配及预编码方案。
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