WO2023124453A1 - 一种去蜂窝大规模mimo网络低功耗大连接方法 - Google Patents

一种去蜂窝大规模mimo网络低功耗大连接方法 Download PDF

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WO2023124453A1
WO2023124453A1 PCT/CN2022/126961 CN2022126961W WO2023124453A1 WO 2023124453 A1 WO2023124453 A1 WO 2023124453A1 CN 2022126961 W CN2022126961 W CN 2022126961W WO 2023124453 A1 WO2023124453 A1 WO 2023124453A1
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network
served
nth
massive mimo
clustering
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French (fr)
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赵海涛
倪艺洋
夏文超
张晖
朱洪波
谈宇浩
刘琨
徐林林
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南京邮电大学
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0426—Power distribution
    • H04B7/043—Power distribution using best eigenmode, e.g. beam forming or beam steering
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0452—Multi-user MIMO systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/30—TPC using constraints in the total amount of available transmission power
    • H04W52/34—TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading
    • H04W52/346—TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading distributing total power among users or channels
    • 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 invention relates to a low-power large-scale connection technology, in particular to a low-power large-scale connection method for a cellular massive MIMO network, and belongs to the technical field of wireless communication.
  • Low power consumption and large connection technology is mainly for information collection services in mobile communication systems. It has the following application advantages: First, low power consumption and large connection technology can provide mobile devices with high coverage, wide connection, and sufficient security and reliability. . Secondly, the low-power consumption large connection technology reduces the energy consumption in the communication process of the equipment by allocating power to the equipment, so as to achieve the goal of reducing costs and improving system efficiency. Decellularized massive MIMO is an emerging mobile communication technology that can provide high spectrum utilization and high energy efficiency.
  • the present invention provides a low-power large-scale connection method for a cellular massive MIMO network.
  • the method allocates the transmission power of the equipment according to the interference situation in the system, reduces the power consumption of the equipment, and dynamically optimizes the AP clustering and beamforming matrix to improve the system capacity.
  • the present invention proposes a large-scale connection method with low power consumption in a cellular massive MIMO network, which specifically includes the following steps:
  • Step 1 Select K served devices, and determine the number of symbols ⁇ c for which the coherence time lasts;
  • Step 2 Determine the number of symbols ⁇ p that are used for channel estimation by the reference signal (RS) in ⁇ c according to the number of served devices K, and use the RS to obtain the uplink channel estimation value of each device
  • Step 3 Determine the power allocation rule according to the served device K, and determine the transmit power of the served device according to the rule, and then the CPU sends the result to each served device;
  • Step 4 Use the dual deep Q-learning (DDQL) algorithm to jointly optimize the beamforming matrix and the clustering scheme of the access point (AP) with the goal of maximizing the system capacity;
  • DQL dual deep Q-learning
  • Step 5 Every time the set time T hold passes, transmit power allocation and AP clustering are re-performed according to changes in the internal conditions of the system.
  • step 2 the number of symbols used for channel estimation by the reference signal (RS) is ⁇ p , which is used to transmit data
  • the symbol number of is ⁇ c - ⁇ p .
  • step 3 two power allocation rules ( ⁇ 1 and ⁇ 2 ) are defined, both of which are based on the system Determine the transmit power of each served device according to the interference situation.
  • p k represents the transmission power of the served device k.
  • I k,MMSE and I k,MR represent the interference situation at the served device k after MMSE (Minimum Root Mean Square Error) processing and MR (Maximum Ratio) processing respectively.
  • N k,noise represents the parameter related to the noise of the served device k, and ⁇ is an adjustable parameter.
  • step 4 the joint optimization problem of AP clustering and beamforming is expressed as follows:
  • the optimal solution to the joint optimization problem of AP clustering and beamforming is an AP clustering scheme that can maximize the uplink transmission rate And the corresponding beamforming matrix W * .
  • the dual deep Q learning (DDQL) algorithm for solving the above-mentioned joint optimization problem includes the training of two sub-networks, namely DDPG (Deep Deterministic Policy Gradient) networks and DDQL (Double Deep Q-Learning) networks.
  • the iterative steps of DDPG sub-network training in the DDQL algorithm are as follows:
  • Step 4-1-1 Utilize the target Q network to calculate the time sequence difference TD target of the Q network;
  • Step 4-1-2 Utilize the gradient of the minimum mean square error of the TD target loss function to update the comment parameters;
  • Step 4-1-3 Utilize gradient Monte Carlo estimation to update actor parameters
  • Step 4-1-4 After every P iterations, update the target comment critic and target policy network.
  • the iterative steps of DDQL sub-network training in the DDQL algorithm are as follows:
  • Step 4-2-1 Use the target Q network to select an action, namely Among them, a * is the optimal action, is the parameter of the target Q network;
  • Step 4-2-2 Q network parameters for DDQL to update
  • Step 4-2-3 After every P iterations, update the target Q network Q′ c .
  • the clustering scheme is obtained to cluster the APs, and each AP cluster is used as a virtual AP, Form a distributed antenna system DAS.
  • the method for large-scale connection with low power consumption and large-scale MIMO network without cellular provided by the present invention has the following advantages:
  • This method expands the scenario of de-cellular massive MIMO system to the scenario of large connection under normal circumstances, and combined with the transmission power allocation of devices, it can effectively support the access of more devices and significantly reduce the communication energy consumption of devices;
  • This method considers a decellularized massive MIMO system with AP dynamic clustering function, which can dynamically perform joint optimization of AP clustering scheme and beamforming matrix according to the channel state to improve system capacity;
  • the method can realize low-power consumption, high-speed, self-adaptive information transmission in the scenario of large-scale connection of mobile communication system equipment, and improves the resource utilization efficiency of the system.
  • FIG. 1 is a flow chart of an embodiment of a method for large connection with low power consumption in a cellular massive MIMO network according to the present invention.
  • the embodiment of the present invention provides a large-scale connection method with low power consumption for a decellularized massive MIMO network.
  • a decellularized massive MIMO system By extending the scenario of a decellularized massive MIMO system to a large connection scenario, combined with device transmission power allocation and AP dynamic clustering, Significantly reduces equipment communication energy consumption and improves system capacity.
  • FIG. 1 a flow chart of a method for large-scale connection with low power consumption in a cellular massive MIMO network provided by an embodiment of the present invention, the method includes the following steps:
  • Step 101 In a decellularized massive MIMO system consisting of L APs and a CPU, there are multiple user equipments. Select K served devices, and determine the symbol number ⁇ c of their coherence time duration.
  • Step 102 Determine the number of symbols ⁇ p in ⁇ c that are used for channel estimation by the reference signal (RS) according to the number of served devices K, and the number of symbols used for data transmission is ⁇ c - ⁇ p .
  • RS reference signal
  • the estimated value is used to estimate the interference situation at each device, so as to perform power allocation accordingly.
  • Step 103 According to the number of APs L and the served device K, if the number of devices is small (K ⁇ L), the power distribution rule ⁇ 1 is used for power distribution, and the rule ⁇ 1 is based on p k ⁇ N k, noise (I k, MMSE ) -1 determines the transmission power of the served device k; if the number of devices is large (K>L), then the power allocation rule ⁇ 2 is used for power allocation, and the rule ⁇ 2 is based on p k ⁇ N k, noise (I k, MR ) -1 determines the transmit power of the served device k.
  • we set the adjustable parameter ⁇ 10. After determining the transmission power of each device, the CPU sends the distribution result to all served devices, and the served device k will adjust its transmission power according to the distribution result.
  • Step 104 Use the dual deep Q-learning algorithm to solve the joint optimization problem of AP clustering and beamforming:
  • the DDPG sub-network and DDQL sub-network training steps are as follows:
  • Step 104-1 Use the target Q network to calculate the temporal difference target of the Q network; use the gradient of the TD target loss function minimum mean square error to update the comment parameters, and use the gradient Monte Carlo estimation to update the actor parameters.
  • Step 104-2 After every P iterations, update the target comment and target policy network.
  • Step 104-3 Select an action using the target Q-network, namely Q network parameters for DDQL to update.
  • Step 104-4 Update the target Q-network Q′ c every time P iterations are passed.
  • Step 105 re-perform transmit power allocation and AP clustering every time T hold passes.
  • the optimal solution to the joint optimization problem of AP clustering and beamforming is an AP clustering scheme that can maximize the uplink transmission rate And the corresponding beamforming matrix W * .
  • the APs are clustered according to the clustering results, and each AP cluster is used as a virtual AP to form a distributed antenna system (DAS).
  • DAS distributed antenna system
  • the present invention proposes a large-scale connection method with low power consumption and large-scale MIMO network without cellular.
  • large device connections we have achieved reliable access to a large number of terminal devices by combining de-cellular massive MIMO technology with device power allocation; and in terms of low power consumption, we have designed device power allocation methods to reduce the power consumption during communication. energy consumption.
  • APs mobile access points

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  • Signal Processing (AREA)
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Abstract

本发明公开了一种去蜂窝大规模MIMO网络低功耗大连接方法，该方法按照以下步骤进行：1）选择被服务设备，确定相干时间；2）利用参考信号得到设备上行信道估计值；3）根据设备数确定功率分配法则，依据该法则确定各设备发射功率；4）对波束形成矩阵以及接入点的分簇方案进行联合优化；5）每经过时间T hold，重新进行发射功率分配和AP分簇。本发明在降低设备能耗的同时，提高了系统容量。

Description

一种去蜂窝大规模MIMO网络低功耗大连接方法 技术领域
本发明涉及低功耗大连接技术，特别涉及一种去蜂窝大规模MIMO网络低功耗大连接方法，属于无线通信技术领域。
背景技术
随着移动通信技术的发展，工业、农业以及交通运输等行业正在加速推进设备通信无线化，智能化。同时，由于移动通信服务领域的不断拓展，用户终端设备的数量正在迅速增长，分布也更加密集。然而，大量移动设备的接入给移动通信系统的安全、稳定运行提出巨大的挑战。
低功耗大连接技术主要面向移动通信系统中的信息采集类业务，其具有如下应用优势：首先，低功耗大连接技术能够为移动设备提供高覆盖、广连接以及足够的安全性和可靠性。其次，低功耗大连接技术通过对设备进行功率分配，降低设备通信过程中的能耗，从而达到降低成本，提高系统效率的目标。去蜂窝大规模MIMO是一种能够提供高频谱利用率以及高能效的新兴移动通信技术。
发明内容
发明目的：针对现有技术中存在的问题与不足，本发明提供一种去蜂窝大规模MIMO网络低功耗大连接方法。该方法根据系统中的干扰情况进行设备的发射功率分配，降低设备功耗，并且动态地优化AP分簇和波束成形矩阵，提高系统容量。
技术方案：本发明为实现以上要点，提出一种去蜂窝大规模MIMO网络低功耗大连接方法，具体包括以下步骤：
步骤一：选择K个被服务设备，确定其相干时间持续的符号数τ c；
步骤二：根据被服务设备数量K确定τ c中被参考信号(RS)用于信道估计的符号数τ p，并利用RS得到各设备上行信道估计值
Figure PCTCN2022126961-appb-000001
步骤三：根据被服务设备K确定功率分配法则，并根据该法则确定被服务设备的发射功率，随后CPU将结果发送至各被服务设备；
步骤四：利用双重深度Q学习(DDQL)算法，以最大化系统容量为目标，对波束成形矩阵以及接入点(AP)的分簇方案进行联合优化；
步骤五：每经过设定时间T hold，根据系统内部条件变化，重新进行发射功率分配和AP分簇。
作为本发明所述的去蜂窝大规模MIMO网络低功耗大连接方法的进一步优化方案，步骤 二中，被参考信号(RS)用于信道估计的符号数为τ p，则被用于传输数据的符号数为τ c-τ p。
作为本发明所述的去蜂窝大规模MIMO网络低功耗大连接方法的进一步优化方案，步骤三中，共定义了两种功率分配法则(Ξ 1以及Ξ 2)，两种法则均根据系统中的干扰情况对各被服务设备的发射功率进行确定。其中，法则Ξ 1依据p k←ζN k,noise(I k,MMSE) -1,k＝{1,...,K}对被服务设备k的发射功率进行确定；法则Ξ 2依据p k←ζN k,noise(I k,MR) -1,k＝{1,...,K}对被服务设备k的发射功率进行确定。其中，p k表示被服务设备k的发射功率。I k,MMSE以及I k,MR分别表示经过MMSE(最小均方根误差)处理以及MR(最大比)处理后，被服务设备k处的干扰情况。N k,noise表示被服务设备k与噪声相关的参量，ζ为可调参量。
作为本发明所述的去蜂窝大规模MIMO网络低功耗大连接方法的进一步优化方案，对于具有L个AP和K个被服务设备的系统，若设备数量较少(K≤L)，则采用功率分配法则Ξ 1进行功率分配；若设备数量较多(K＞L)，则采用功率分配法则Ξ 2进行功率分配。
作为本发明所述的去蜂窝大规模MIMO网络低功耗大连接方法的进一步优化方案，步骤四中，AP分簇与波束成形联合优化问题表示如下：
Figure PCTCN2022126961-appb-000002
其中，C j表示AP分簇方案集合C中序号为j的分簇方案，C＝{C 1,...,C S}，j＝1,2,…,S，S表示AP分簇方案数量；W为波束成形矩阵，其第k列元素构成的向量w k＝[w 1k,...,w Nk]，w nk表示被服务设备k与C j中第n个AP簇之间的波束成形参量；
Figure PCTCN2022126961-appb-000003
表示当采用分簇方案C j时，被服务设备k与AP通信时的信干噪比(SINR)，其根据下式进行确定：
Figure PCTCN2022126961-appb-000004
其中，
Figure PCTCN2022126961-appb-000005
表示被服务设备k与C j中第n个AP簇内第m n个AP之间的信道增益，
Figure PCTCN2022126961-appb-000006
表示被服务设备l与C j中第n个AP簇内第m n个AP之间的信道增益，
Figure PCTCN2022126961-appb-000007
表示被服务设备v与C j中第n个AP簇内第m n个AP之间的信道增益，
Figure PCTCN2022126961-appb-000008
表示被服务设备u与C j中第n个AP簇内第m n个AP之间的信道增益，D n表示C j中第n个AP簇内包含的AP数量。
作为本发明所述的去蜂窝大规模MIMO网络低功耗大连接方法的进一步优化方案，AP分簇与波束成形联合优化问题的最优解是能够最大化上行链路传输速率的AP分簇方案
Figure PCTCN2022126961-appb-000009
以及与之对应的波束成形矩阵W *。
作为本发明所述的去蜂窝大规模MIMO网络低功耗大连接方法的进一步优化方案，用于 求解上述联合优化问题的双重深度Q学习(DDQL)算法包括了对两个子网络的训练，即DDPG(深度确定性策略梯度)网络和DDQL(双重深度Q学习)网络。其中状态空间s为各被服务设备与AP通信时的SINR所构成的矩阵，记为s＝[s 1,...,s K]，其中，
Figure PCTCN2022126961-appb-000010
动作空间a＝(ω,C j)，其中，ω表示DDPG的连续动作。
作为本发明所述的去蜂窝大规模MIMO网络低功耗大连接方法的进一步优化方案，DDQL算法中DDPG子网络训练的迭代步骤如下：
步骤4-1-1.利用目标Q网络计算Q网络的时序差分TD目标；
步骤4-1-2.利用TD目标损失函数最小均方误差的梯度更新评论参数；
步骤4-1-3.利用梯度蒙特卡罗估计更新演员actor参数；
步骤4-1-4.每经过P次迭代，更新一次目标评论critic和目标策略policy网络。
作为本发明所述的去蜂窝大规模MIMO网络低功耗大连接方法的进一步优化方案，DDQL算法中DDQL子网络训练的迭代步骤如下：
步骤4-2-1.利用目标Q网络选择动作，即
Figure PCTCN2022126961-appb-000011
其中，a *为最优动作，
Figure PCTCN2022126961-appb-000012
为目标Q网络的参数；
步骤4-2-2.对DDQL的Q网络参数
Figure PCTCN2022126961-appb-000013
进行更新；
步骤4-2-3.每经过P次迭代，更新一次目标Q网络Q′ c。
作为本发明所述的去蜂窝大规模MIMO网络低功耗大连接方法的进一步优化方案，根据步骤四中联合优化得到分簇方案对AP进行分簇，并将每个AP簇作为一个虚拟AP，组成一个分布式天线系统DAS。
有益效果：本发明提供的去蜂窝大规模MIMO网络低功耗大连接方法，具有如下优点：
1.本方法将一般情况下去蜂窝大规模MIMO系统的场景拓展到大连接场景下，并结合设备发射功率分配，能够有效支持更多设备的接入，并显著降低设备通信能耗；
2.本方法考虑了一个具备AP动态分簇功能的去蜂窝大规模MIMO系统，能够根据信道状态动态地进行AP分簇方案与波束成形矩阵联合优化，提高系统容量；
3.本方法能够在移动通信系统设备大连接的场景下，实现低功耗、高速率、自适应的信息传输，提高了系统的资源利用效率。
附图说明
图1为本发明去蜂窝大规模MIMO网络低功耗大连接方法的一个实施例的流程图。
具体实施方法
下面结合具体实施例，进一步阐明本发明，应理解这些实施例仅用于说明本发明而不用于限制本发明的范围，在阅读了本发明之后，本领域技术人员对本发明的各种等价形式的修 改均落于本申请所附权利要求所限定的范围。
本发明实施例提供的一种去蜂窝大规模MIMO网络低功耗大连接方法，通过将去蜂窝大规模MIMO系统的场景拓展到大连接场景下，并结合设备发射功率分配以及AP动态分簇，显著降低了设备通信能耗，并提高系统容量。
如图1所示，本发明实施例提供的一种去蜂窝大规模MIMO网络低功耗大连接方法的流程图，该方法包括以下步骤：
步骤101：在一个由L个AP以及一个CPU组成的去蜂窝大规模MIMO系统中，有多个用户设备。选择K个被服务设备，确定其相干时间持续的符号数τ c。
基于3GPP模型，我们可以得到在相干时间T c＝1ms内，共有τ c＝12÷14＝168个符号。
步骤102：根据被服务设备数量K确定τ c中被参考信号(RS)用于信道估计的符号数τ p，则被用于传输数据的符号数为τ c-τ p。其中，我们规定τ p的值最大不超过τ c的一半。此外，利用参考信号RS得到设备i上行信道估计值
Figure PCTCN2022126961-appb-000014
该估计值被用于对各设备处干扰情况进行估计，以便据此进行功率分配。
步骤103：根据AP数量L以及被服务设备K，若设备数量较少(K≤L)，则采用功率分配法则Ξ 1进行功率分配，法则Ξ 1依据p k←ζN k,noise(I k,MMSE) -1对被服务设备k的发射功率进行确定；若设备数量较多(K＞L)，则采用功率分配法则Ξ 2进行功率分配，法则Ξ 2依据p k←ζN k,noise(I k,MR) -1对被服务设备k的发射功率进行确定。在本实施例中，我们令可调参量ζ＝10。在确定各设备发射功率之后，CPU将分配结果发送至所有被服务设备，被服务设备k将按照分配结果调整其发射功率。
步骤104：利用双重深度Q学习算法求解AP分簇与波束成形联合优化问题：
Figure PCTCN2022126961-appb-000015
其中DDPG子网络以及DDQL子网络训练步骤如下：
步骤104-1.利用目标Q网络计算Q网络的时序差分目标；利用TD目标损失函数最小均方误差的梯度更新评论参数，并利用梯度蒙特卡罗估计更新演员参数。
步骤104-2.每经过P次迭代，更新一次目标评论和目标策略网络。
步骤104-3.利用目标Q网络选择动作，即
Figure PCTCN2022126961-appb-000016
对DDQL的Q网络参数
Figure PCTCN2022126961-appb-000017
进行更新。
步骤104-4.每经过P次迭代，更新一次目标Q网络Q′ c。
步骤105：每经过一定时间T hold，重新进行发射功率分配和AP分簇。
AP分簇与波束成形联合优化问题的最优解，是能够最大化上行链路传输速率的AP分簇方案
Figure PCTCN2022126961-appb-000018
以及与之对应的波束成形矩阵W *。根据分簇结果对AP进行分簇，并将每个AP簇作为一个虚拟AP，组成一个分布式天线系统(DAS)。
本发明针对移动通信相关应用场景，提出了一种去蜂窝大规模MIMO网络低功耗大连接方法。在设备大连接方面，我们通过结合去蜂窝大规模MIMO技术与设备功率分配，从而实现大量终端设备的可靠接入；而在低功耗方面，通过设计设备功率分配方法，降低其通信过程中的能量消耗。此外，考虑到设备与移动接入点(AP)通信的效率，我们考虑了去蜂窝大规模MIMO系统中AP的动态分簇，从而进一步提高设备的上行传输速率，降低传输能耗。
以上所述，仅为本发明的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，可轻易想到的变化或替换，都应涵盖在本发明的保护范围内。

Claims (9)

  1. 一种去蜂窝大规模MIMO网络低功耗大连接方法，其特征在于，该方法具体包括以下步骤：
    步骤一：选择被服务设备，确定其相干时间持续的符号数τ c；
    步骤二：根据被服务设备的数量K，确定τ c中被参考信号RS用于信道估计的符号数τ p，利用RS得到各被服务设备上行信道估计值
    Figure PCTCN2022126961-appb-100001
    步骤三：根据被服务设备的数量K，确定功率分配法则，并根据该法则确定被服务设备的发射功率；
    步骤四：利用双重深度Q学习DDQL算法，以最大化系统容量为目标，对波束成形矩阵以及接入点AP的分簇方案进行联合优化。
  2. 根据权利要求1所述的去蜂窝大规模MIMO网络低功耗大连接方法，其特征在于，步骤三中的功率分配法则包括Ξ 1以及Ξ 2两种，其中，Ξ 1为p k←ζN k,noise(I k,MMSE) -1，Ξ 2为p k←ζN k,noise(I k,MR) -1,k＝{1,...,K}，p k表示被服务设备k的发射功率，I k,MMSE以及I k,MR分别表示经过最小均方根误差MMSE处理以及最大比MR处理后设备k处的干扰情况，N k,noise表示被服务设备k与噪声相关的参量，ζ为可调参量。
  3. 根据权利要求2所述的去蜂窝大规模MIMO网络低功耗大连接方法，其特征在于，若K≤L，则采用功率分配法则Ξ 1；若K＞L，则采用功率分配法则Ξ 2；L表示系统中AP的数量。
  4. 根据权利要求1所述的去蜂窝大规模MIMO网络低功耗大连接方法，其特征在于，步骤四中的AP分簇与波束成形联合优化问题表示如下：
    Figure PCTCN2022126961-appb-100002
    s.t.0≤w nk≤1
    ||w k|| 2≤1
    其中，C j表示AP分簇方案集合C中序号为j的分簇方案，C＝{C 1,...,C S}，j＝1,2,…,S，S表示AP分簇方案数量；W为波束成形矩阵，其第k列元素构成的向量w k＝[w 1k,...,w Nk]，w nk表示被服务设备k与C j中第n个AP簇之间的波束成形参量；
    Figure PCTCN2022126961-appb-100003
    表示当采用分簇方案C j时，被服务设备k与AP通信时的信干噪比SINR。
  5. 根据权利要求4所述的去蜂窝大规模MIMO网络低功耗大连接方法，其特征在于，AP分簇与波束成形联合优化问题的最优解是能够最大化上行链路传输速率的AP分簇方案
    Figure PCTCN2022126961-appb-100004
    以及与之对应的波束成形矩阵W *。
  6. 根据权利要求4所述的去蜂窝大规模MIMO网络低功耗大连接方法，其特征在于，
    Figure PCTCN2022126961-appb-100005
    其中，g mnk表示被服务设备k与C j中第n个AP簇内第m n个AP之间的信道增益，g mnl表示被服务设备l与C j中第n个AP簇内第m n个AP之间的信道增益，g mnv表示被服务设备v与C j中第n个AP簇内第m n个AP之间的信道增益，g mnu表示被服务设备u与C j中第n个AP簇内第m n个AP之间的信道增益，D n表示C j中第n个AP簇内包含的AP数量。
  7. 根据权利要求4所述的去蜂窝大规模MIMO网络低功耗大连接方法，其特征在于：步骤四中双重深度Q学习DDQL算法包括了对深度确定性策略梯度DDPG和双重深度Q学习DDQL两个子网络的训练；
    状态空间s为被服务设备与AP通信时的SINR所构成的矩阵，s＝[s 1,...,s K]，其中，
    Figure PCTCN2022126961-appb-100006
    动作空间a＝(ω,C j)，其中，ω表示DDPG的连续动作；
    DDQL算法中DDPG子网络训练的迭代步骤如下：
    步骤4-1-1.利用目标Q网络计算Q网络的时序差分TD目标；
    步骤4-1-2.利用TD目标损失函数最小均方误差的梯度更新评论参数；
    步骤4-1-3.利用梯度蒙特卡罗估计更新演员actor参数；
    步骤4-1-4.每经过P次迭代，更新一次目标评论critic和目标策略policy网络；
    DDQL算法中DDQL子网络训练的迭代步骤如下：
    步骤4-2-1.利用目标Q网络选择动作，即
    Figure PCTCN2022126961-appb-100007
    其中，a *为最优动作，
    Figure PCTCN2022126961-appb-100008
    为目标Q网络的参数；
    步骤4-2-2.对DDQL的Q网络参数
    Figure PCTCN2022126961-appb-100009
    进行更新；
    步骤4-2-3.每经过P次迭代，更新一次目标Q网络Q′ c。
  8. 根据权利要求1所述的去蜂窝大规模MIMO网络低功耗大连接方法，其特征在于：根据步骤四中联合优化得到分簇方案对AP进行分簇，并将每个AP簇作为一个虚拟AP，组成一个分布式天线系统DAS。
  9. 根据权利要求1所述的去蜂窝大规模MIMO网络低功耗大连接方法，其特征在于：被参考信号用于传输数据的符号数为τ c-τ p。
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