WO2023077724A1 - 基于波束索引地图的智能反射面通信波束选择方法 - Google Patents

基于波束索引地图的智能反射面通信波束选择方法 Download PDF

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WO2023077724A1
WO2023077724A1 PCT/CN2022/086405 CN2022086405W WO2023077724A1 WO 2023077724 A1 WO2023077724 A1 WO 2023077724A1 CN 2022086405 W CN2022086405 W CN 2022086405W WO 2023077724 A1 WO2023077724 A1 WO 2023077724A1
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user
communication
index map
base station
optimal
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PCT/CN2022/086405
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French (fr)
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曾勇
丁丁杨
吴迪
金石
张瑞
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东南大学
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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
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0636—Feedback format
    • H04B7/0639—Using selective indices, e.g. of a codebook, e.g. pre-distortion matrix index [PMI] or for beam selection
    • 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
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0686—Hybrid systems, i.e. switching and simultaneous transmission
    • H04B7/0695—Hybrid systems, i.e. switching and simultaneous transmission using beam selection
    • 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
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H04B7/0868—Hybrid systems, i.e. switching and combining
    • H04B7/088—Hybrid systems, i.e. switching and combining using beam selection
    • 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

Definitions

  • the invention relates to the field of wireless communication standardization process, in particular to an intelligent reflection surface communication beam selection method based on a beam index map.
  • the sixth generation (6G) mobile communication network facing 2030 needs to support ultra-high spectral efficiency and energy efficiency, ultra-wide wireless coverage and giant site connection, as well as ultra-high reliability and low-latency communication.
  • Existing 5G technology is difficult to fully meet these requirements .
  • Shortening the communication distance by adding more active nodes such as base stations, access points, and relays can improve network coverage and capacity, but it also leads to higher energy and hardware overhead, as well as more complex and severe problems. Interference issues; by using more antennas to obtain massive MIMO gain, the complexity of signal processing also increases; by using higher frequency bands such as millimeter wave or even terahertz frequency bands for communication, more active nodes and Antenna to make up for the defect of its large propagation loss.
  • a smart reflector is a plane composed of a large number of passive reflective elements, each capable of independently applying a controllable amplitude and phase change to the incoming signal.
  • the traditional beam selection methods mainly include the following methods:
  • the method based on beam training that is, the base station and the smart reflector end respectively traverse all the beams in their own codebook before communication, and finally select the beam pair with the largest receiving signal-to-noise ratio at the user end for communication.
  • This method has high training overhead and short effective communication time, resulting in low communication efficiency.
  • some scholars have proposed a method based on layered codebooks to reduce training overhead, the design of the codebook is complex and does not avoid the training overhead from the root.
  • the method based on pilot frequency training that is, the base station sends a pilot sequence known to the user end before communication, and then the user end compares the received signal with the known signal to estimate the channel, and based on Estimated channel selection beam.
  • the smart reflector unit is passive and has no ability to send and receive pilot signals, and the smart reflector is composed of a large number of reflector units, there are many channel coefficients to be estimated, and channel estimation requires a large amount of pilot overhead, thus crowding out communication The time reduces the communication rate.
  • Some researchers have proposed to group smart reflector units for channel estimation, use different time scales to estimate the channels from the base station to the smart reflector and from the smart reflector to the user, and use the sparseness of the millimeter-wave channel to perform channel estimation with the help of compressed sensing. Estimation and other methods to reduce the training overhead, but the number of pilots required by these methods increases with the number of antennas at the base station and the number of reflection units at the smart reflector.
  • the technical problem to be solved by the present invention is that, aiming at the defects proposed in the background technology, the mapping relationship between the position of the user end and the optimal beam pair at the base station end and the intelligent reflector end can be established by using the beam index map, and a beam index map based beam index map is proposed. Intelligent reflective surface communication beam selection method.
  • the present invention proposes a beam index selection method for intelligent reflective surface communication based on a beam index map.
  • the location information of the user terminal is used as an input, and the optimal beam pair corresponding to the base station end and the intelligent reflective surface end is used as an output.
  • the steps are as follows:
  • step (1) is based on geographical environment information and signal propagation environment information, through offline ray tracing simulation calculation, on-site measurement and online measurement
  • the method obtains the optimal beam pair corresponding to the user's location information at the base station end and the smart reflector end, and constructs a beam index map.
  • step (2) specifically includes:
  • (202) according to the position information of the user, find the K measurement point positions closest to the user position in the beam index map, and use the optimal beam pairs corresponding to the K measurement point positions in the beam index map to construct a candidate beam pair set;
  • (203) directly select the beam pair with the largest number of occurrences in the candidate beam pair set as the beam pair for the user's communication, when there are multiple beam pairs with the largest number of occurrences and equal beam pairs in the candidate beam pair set, calculate the beam pairs corresponding to different beam pairs The sum of the reciprocal distances from the position of the measurement point to the user’s position, and the beam pair with the largest sum of the corresponding distance reciprocals is selected as the beam pair for the user’s communication;
  • the base station end and the intelligent reflector end perform beam scanning on the set of candidate beam pairs, and select the optimal beam pair according to the signal-to-noise ratio received by the user;
  • the base station end and the intelligent reflector end use the optimal beam pair obtained in step (203) or (204) to communicate with the user within the next duration of t;
  • the present invention proposes a method for selecting beams for intelligent reflective surface communication based on a beam index map.
  • the beam index map constitutes a database for storing the optimal beam index of the base station end and the intelligent reflective surface end based on location information;
  • step (1) When the use time of the map exceeds the time limit T, or when the actual geographical environment or signal propagation environment corresponding to the map changes to a certain extent, perform step (1) to update the map.
  • the intelligent reflective surface communication beam selection method based on the beam index map proposed by the present invention for the scene where the base station and the user terminal are seriously blocked, that is, the base station mainly communicates with the user terminal through the intelligent reflective surface, so as to relatively
  • the optimal beam at the base station end is updated at a lower frequency
  • the optimal beam at the smart reflector end is updated at a relatively high frequency.
  • the beam index map-based intelligent reflective surface communication beam selection method proposed by the present invention for each measurement point position in the beam index map, there are different optimal beam pairs at different times, in the database Multiple optimal beam pairs are stored for each measurement point.
  • the K value in the candidate beam pair set for each user position corresponds to the number of available beam pairs, and the subsequent beam
  • the computation and training overhead during the selection process is proportional.
  • step (203) is used to obtain the beam pair for user communication; when the signal propagation environment changes rapidly , then adopt step (204) to select the optimal beam pair as the beam pair for user communication.
  • the present invention adopts the above technical scheme and has the following technical effects:
  • This method can be fully based on the position provided by the beam index map and the corresponding optimal beam information, which reflects the actual geographical environment and signal propagation environment to a certain extent, ensuring the accuracy and environmental adaptability of the selected beam .
  • This method can make full use of the existing base station beam scanning method.
  • the beam selection method based on light training in step (204) actually reduces the original beam scanning range by means of the beam index map, thereby reducing the beam scanning s expenses.
  • This method can select different beam index maps according to different communication scenarios and requirements, determine different beam candidate set sizes, and different beam selection methods. It has strong flexibility and a wide range of applications.
  • FIG. 1 is a schematic diagram of constructing a beam index map provided by an embodiment of the present invention
  • Fig. 2 is a schematic diagram of an example of intelligent reflective surface communication beam selection based on a beam index map provided by an embodiment of the present invention
  • Fig. 3 is a schematic diagram of a communication beam selection process of an intelligent reflective surface based on a beam index map provided by an embodiment of the present invention
  • the present invention uses the beam index map to establish the mapping relationship between the user terminal position and the optimal beam pair at the base station end and the intelligent reflector end.
  • the beam index map can be based on the stored position and the corresponding beam index.
  • the information provides the optimal beam pair between the base station and the smart reflector, which greatly reduces the training overhead on the one hand, and on the other hand, because the information stored in the beam index map can reflect the actual communication environment information to a certain extent, it ensures that the provided beam accuracy and environmental adaptability.
  • the present invention proposes an intelligent reflective surface communication beam selection method based on a beam index map, and obtains the optimal beam selection method based on the actual geographical environment and signal propagation environment through offline ray tracing simulation calculations, on-site measurements, or online real-time measurements.
  • Beam information build an optimal beam database indexed by position information.
  • a new type of communication beam selection method for intelligent reflectors is proposed, that is, using Beidou, GPS, 5G, LiDAR and other positioning systems to obtain user location information, and obtain candidate beams at this location through the beam index map
  • the beam selection is directly performed or the beam scan is performed on the candidate beam pair set, so as to realize the beam selection of free channel training or light channel training, and reduce the cost of beam selection for intelligent reflector communication.
  • Overhead and complexity improve the effective communication rate.
  • the intelligent reflective surface communication beam selection method proposed by the present invention includes the following main steps:
  • the base station end and the intelligent reflector end perform beam scanning on the set of candidate beam pairs, and select the optimal beam pair according to the signal-to-noise ratio received by the user;
  • the base station and the smart reflector end use the beam pair corresponding to the optimal beam pair index obtained in step d) or e) to communicate with the user within the next time period of t;
  • the base station or the user terminal judges whether the communication is over, if the communication is over, the process is complete; if the communication continues, the process b)-g) is repeated until the communication ends;
  • the beam selection method takes the location information of the user terminal as input, and the optimal beam pair corresponding to the base station terminal and the intelligent reflector terminal as the output.
  • the positioning technology can be applied to the acquisition of the location information of the client.
  • the beam index map is based on information such as geographical environment information and signal propagation environment, and pays attention to the actual transmission environment of the signal.
  • information such as geographical environment information and signal propagation environment, and pays attention to the actual transmission environment of the signal.
  • offline ray tracing simulation Through offline ray tracing simulation, on-site measurement, online measurement and other methods, the latest information of the base station end and the smart reflector end corresponding to the user location information is obtained.
  • Various types of beam index maps of the same service area can be constructed according to the antenna configuration of the base station, the configuration of the reflective unit of the smart reflector, weather conditions, and mission requirements.
  • Configuration, weather conditions, mission requirements, etc. invoke different kinds of beam index maps to improve the applicability and accuracy of beam index maps.
  • the beam index map constitutes a database for storing the optimal beam index of the base station end and the intelligent reflector end based on position information.
  • step a) is performed to update the map.
  • the base station and the user end are seriously blocked, that is, the base station mainly communicates with the user end through the smart reflector, because the channel between the base station and the smart reflector changes more slowly than the channel between the smart reflector and the user , so that the optimal beam at the base station end can be updated at a lower frequency, and the optimal beam at the smart reflector end can be updated at a higher frequency.
  • each measurement point position in the beam index map there may be different optimal beam pairs at different times, and multiple optimal beam pairs can be stored, thereby improving the adaptability to environmental changes to a certain extent.
  • the size K of the candidate beam pair set at each user position determines the number of available beam pairs on the one hand, and on the other hand, it is also related to the calculation and training overhead in the subsequent beam selection process. For some situations where the environment changes slowly or the processing performance of the equipment is limited, a smaller K value can be selected, and for some situations where the environment changes quickly or the equipment processing performance is superior, a larger K value can be selected.
  • step d) the method of directly selecting the optimal beam pair at the base station end and the smart reflector end based on the number of occurrences of different beam pairs does not require additional training overhead, and is suitable for scenarios where the signal propagation environment is relatively stable.
  • the method of beam scanning to select the optimal beam pair at the base station side and the smart reflector side requires light training overhead and is suitable for scenarios where the signal propagation environment changes rapidly.
  • step d) or e) based on the user position information, the optimal beam pair at the base station intelligent reflector end obtained online in real time through the beam index map can be fed back to the map in real time online to build an update system, or it can be uploaded to the user end at the base station first. Cache, and when the time is right or a certain amount of data is reached, it will be fed back to the map construction update system offline.
  • the method is not only applicable to the simple scene of a single smart reflector for a single user, but also applicable to the complex scene of multiple smart reflectors for multiple users.
  • Different types of beam index maps can be selected according to different scenes, and then the base station and the smart reflector can be further selected.
  • Fig. 1 shows the data source and construction method of the beam index map mentioned in the present invention according to an exemplary embodiment.
  • Fig. 2 is a diagram showing an actual scene and an example effect of intelligent reflective surface communication beam selection based on a beam index map according to an exemplary embodiment. According to different user locations, the base station and the smart reflector end select different optimal beams to communicate with users based on the beam index map.
  • Q represents the set of measurement point locations
  • q i represents the position of each measurement point
  • F i represents the optimal beam index at the base station corresponding to position q i
  • V i represents the intelligent beam index corresponding to position q i
  • Optimal beam index at reflector end In the information acquisition phase, q represents the obtained user location information.
  • W represents the set of candidate beam pairs
  • K represents the number of elements in the set of candidate beam pairs.
  • t represents the coherent time slot of the channel
  • T represents the beam index map usage period.
  • the corresponding optimal beam pairs at the base station end and the smart reflector end are obtained by means of measurement, ray tracing simulation, or online measurement, and their indexes (F i , V i ) in the codebook are stored to form a database.
  • the base station Before communication, the base station obtains the location information q of the user terminal through the positioning of the user terminal, or the user terminal obtains the location information through a positioning system, such as GPS, Beidou, cellular network, laser radar, etc., and sends the location information q to the base station end.
  • a positioning system such as GPS, Beidou, cellular network, laser radar, etc.
  • the base station selects the corresponding beam index map through the analysis of equipment configuration, weather factors, mission requirements and other factors.
  • the base station Based on the user location information q obtained in (2), the base station finds the K measurement points closest to q in the beam index map and their corresponding optimal beam pair indexes between the base station and the smart reflector to form candidate beam pairs Set W.
  • Beam selection stage Calculate the number of occurrences of different beam pairs in the candidate beam pair set W, and select the beam pair with the largest number of occurrences as the beam pair for communicating with the user.
  • Beam selection stage W calculates the sum of the reciprocal distances from the position of the measurement point corresponding to each beam pair to q, and select the beam pair corresponding to the maximum value as the A pair of beams for user communication.
  • Beam selection stage The base station and the smart reflector end traverse different beam pairs in the beam candidate set W, and select the optimal beam pair as the beam pair for communicating with the user according to the signal-to-noise ratio received by the user end.
  • Beam reselection stage When the time t is over, the base station and the user judge whether to continue the communication, and if it is not necessary to continue the communication, the whole communication process ends. If it is necessary to continue the communication, repeat (2)-(7) process until the communication ends.
  • step (1) is performed to update the map.
  • the above method obtains the optimal beams corresponding to the base station end and the smart reflector end corresponding to different measurement point positions through offline field measurement, ray tracing simulation, and online real-time measurement, and constructs a beam index map (step (1)). Based on the beam index map, the optimal beam index of the base station end and the smart reflector end can be provided for all spatial positions within the service range through the beam selection method without channel training or light channel training (steps (2)-(7)). The method also includes an update of the beam index map to improve its accuracy and sustainability (step (9)).
  • the present invention solves the problems of high difficulty in estimating the communication channel of intelligent reflectors, high training cost and cumbersome training process of the traditional beam scanning method by constructing and using an environment-aware beam index map, combined with increasingly accurate and diversified positioning methods , Simplify the real-time beam selection process, so as to realize the environment-aware communication without channel training or light channel training, and greatly improve the effective communication rate.

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Abstract

本发明公开了一种基于波束索引地图的智能反射面通信波束选择方法，包括：获取每个用户位置对应的基站端和智能反射面端最优波束对信息，构建并及时更新基于用户端位置的最优波束索引地图；当基站端通过智能反射面与用户进行通信时，用户端通过多种定位方式获取实时地理位置，借助所建立的波束索引地图获取候选波束对集合，并根据不同波束对出现次数直接进行波束选择或对候选波束对集合进行实时波束扫描，从而实现免信道训练或轻量信道训练的智能反射面通信波束选择。本发明解决了智能反射面通信信道估计难度高、传统波束扫描方法训练开销大、训练过程繁琐等问题，简化实时波束选择流程，极大提升有效通信速率。

Description

基于波束索引地图的智能反射面通信波束选择方法 技术领域
本发明涉及无线通信标准化进程领域，特别是涉及一种基于波束索引地图的智能反射面通信波束选择方法。
背景技术
面向2030年的第六代(6G)移动通信网络需要支撑超高频谱效率和能量效率、超大范围无线覆盖和巨址连接、以及超高可靠和低延迟通信，现有5G技术难以完全满足这些要求。通过增加更多的基站、接入点、中继等有源节点来缩短通信距离，可以提升网络覆盖范围和容量，但同时也导致了更高的能量和硬件开销，以及更为复杂及严峻的干扰问题；通过使用更多的天线来获得大规模MIMO增益，信号处理的复杂度也随之增加；通过使用更高的频段比如毫米波甚至太赫兹频段进行通信，需要更多的有源节点和天线来弥补其传播损耗大的缺陷。
针对上述问题，最近学术界提出了一种低成本、低复杂度、低能耗的新型通信技术——智能反射面通信技术。智能反射面是由大量无源反射单元组成的平面，每个单元能够独立地对入射信号施加一个可控的幅度和相位变化。通过在无线网络中密集地部署智能反射面并根据需求灵活地调控反射信号，可以从根本上解决信道衰落和干扰的问题，极大地提升无线通信的容量和可靠性。智能反射面技术有着以下优势：(1)部署成本低；(2)可以在全双工模式下工作；(3)与现有通信系统兼容，易于整合。
为了充分发挥智能反射面灵活调控信号传播环境的能力，需要正确选择基站端和智能反射面端的波束，传统的波束选择方式主要有以下几种方式：
(1)基于波束训练的方法，即基站端和智能反射面端在通信前分别遍历自身码本中所有波束，最后选择用户端接收信噪比最大的波束对进行通信。该方式训练开销大，有效通信时长短，导致通信效率低。虽然有学者提出了基于分层码本的方法来减少训练开销，但其码本设计复杂，且没有从根源上避免训练开销。
(2)基于导频训练的方法，即基站端在通信前先发送一段用户端已知的导频序列，然后用户端根据接收到的信号与已知信号进行对比，对信道进行估计，并基于估计到的信道选择波束。但是，由于智能反射面单元是无源的，没有收发导频信号的能力，而且智能反射面由大量反射单元构成，需要估计的信道系数多，信道估计需要大量的导频开销，从而挤占了通信的时间，降低了通信的速率。有学者提出将智能反射面单元分组进行信道估计，用不同的时间尺度分别估计基站端到智能反射面端和智能反射面端到用户端的信道，利用毫米波信道稀疏性借助压缩感知的方法进行信道估计等方式来减少训练开销，但这些方法需要的导频数量随着基站端天线 数和智能反射面端反射单元数增加。
(3)基于统计模型的方法，该方法主要基于一些信道参数(信道增益，阴影衰落，有无视距链路)的概率分布或者周围环境的信息(建筑物位置和高度)等对信道进行建模，但是这种方法只利用了粗糙的收发机位置信息和信号传播环境信息，忽略了实际通信过程中的环境因素，得到的估计信道与实际信道之间有较大的偏差。
基于对上述三种传统的波束选择的方法分析，可以看出在未来的智能反射面通信系统中，急需一种能够兼具环境感知与低开销的波束选择方法。
发明内容
本发明所要解决的技术问题是，针对背景技术提出的缺陷，利用波束索引地图可以建立用户端位置与基站端和智能反射面端最优波束对的映射关系，提出了一种基于波束索引地图的智能反射面通信波束选择方法。
本发明为解决上述技术问题采用以下技术方案：
本发明提出一种基于波束索引地图的智能反射面通信波束选择方法，以用户端的位置信息作为输入，以对应该位置的基站端和智能反射面端的最优波束对作为输出，包括步骤如下：
(1)、获得基于实际地理环境和信号传播环境的最优波束信息，构建以位置信息为索引的最优波束数据库，即波束索引地图；
(2)、基于构建的波束索引地图，利用多种定位系统获取用户实时地理位置信息，并通过波束索引地图获得该位置下的候选波束对集合，根据候选波束对集合中不同波束对出现次数直接进行波束选择，或对候选波束对集合进行波束扫描选择最优波束对；基站端和智能反射面端在设定的时长内，使用所选择的波束对与用户进行通信。
进一步的，本发明所提出的一种基于波束索引地图的智能反射面通信波束选择方法，步骤(1)是基于地理环境信息及信号传播环境信息，通过离线射线追踪仿真计算、实地测量以及在线测量的方法获取对应于用户位置信息的基站端和智能反射面端的最优波束对，构建波束索引地图。
进一步的，本发明所提出的一种基于波束索引地图的智能反射面通信波束选择方法，步骤(2)具体包括：
(201)、当基站端与用户端需要进行通信时，通过定位系统获取用户的实时地理位置信息；
(202)、根据用户的位置信息，在波束索引地图中找到距离用户位置最近的K个测量点位置，利用波束索引地图中对应这K个测量点位置的最优波束对构建候选波束对集合；
(203)、直接选择候选波束对集合中出现次数最多的波束对作为该用户通信的波束对，当候选波束对集合中有多个出现次数最多且相等的波束对时，计算对应于不同波束对的测量点位 置到该用户位置的距离倒数之和，选择相应距离倒数之和最大的波束对作为该用户通信的波束对；
(204)、基站端和智能反射面端对候选波束对集合进行波束扫描，根据用户接收到的信噪比大小选择最优波束对；
(205)、基站端和智能反射面端在接下来时长为t的时间内，使用步骤(203)或(204)中得到的最优波束对与用户进行通信；
(206)、当t时刻结束，则基站端或用户端判断通信是否结束，若通信结束，则该过程完毕；若通信继续，则重复过程(201)-(205)直至通信结束。
进一步的，本发明所提出的一种基于波束索引地图的智能反射面通信波束选择方法，该波束索引地图构成一个数据库，用于存储基于位置信息的基站端和智能反射面端的最佳波束索引；当地图使用时长超过时限T，或地图所对应的实际地理环境或信号传播环境发生一定变化时，执行步骤(1)操作对地图进行更新。
进一步的，本发明所提出的一种基于波束索引地图的智能反射面通信波束选择方法，对于基站端与用户端被严重阻挡的场景，即基站端主要通过智能反射面与用户端通信，以相对较低的频率更新基站端的最优波束，而以相对较高的频率更新智能反射面端的最优波束。
进一步的，本发明所提出的一种基于波束索引地图的智能反射面通信波束选择方法，对于波束索引地图中的每个测量点位置，在不同的时间有不同的最优波束对，在数据库中每个测量点存储多个最优波束对。
进一步的，本发明所提出的一种基于波束索引地图的智能反射面通信波束选择方法，每个用户位置的候选波束对集合中的K值对应可供选择的波束对的个数，与后续波束选择过程中计算和训练的开销成正比。
进一步的，本发明所提出的一种基于波束索引地图的智能反射面通信波束选择方法，当信号传播环境较为稳定时，采用步骤(203)得到用户通信的波束对；当信号传播环境变化较快时，则采用步骤(204)选择最优波束对作为用户通信的波束对。
本发明采用以上技术方案与现有技术相比，具有以下技术效果：
(1)本方法能够充分基于波束索引地图提供的位置和相应的最优波束信息，这些信息一定程度上反映了实际的地理环境和信号传播环境，保证了所选择波束的准确性和环境适应性。
(2)本方法能够充分利用现有基站波束扫描的方法，步骤(204)中基于轻量训练的波束选择方法实际上是借助于波束索引地图缩减了原来的波束扫描范围，从而减少了波束扫描的开销。
(3)本方法能够根据不同的通信场景和需求选择不同的波束索引地图，确定不同的波束候 选集合大小，以及不同的波束选择方法，具有很强的灵活性，适用范围很广。
附图说明
图1是本发明实施例提供的波束索引地图构建示意图；
图2是本发明实施例提供的基于波束索引地图的智能反射面通信波束选择示例示意图；
图3是本发明实施例提供的基于波束索引地图的智能反射面通信波束选择流程示意图；
具体实施方式
这里将详细地对示例性实施例进行说明，其示例表示在附图中。
本技术领域技术人员可以理解的是，除非另外定义，这里使用的所有术语(包括技术术语和科学术语)具有与本发明所属领域中的普通技术人员的一般理解相同的意义。还应该理解的是，诸如通用字典中定义的那些术语应该被理解为具有与现有技术的上下文中的意义一致的意义，并且除非像这里一样定义，不会用理想化或过于正式的含义来解释。
本发明利用波束索引地图可以建立用户端位置与基站端和智能反射面端最优波束对的映射关系，对于服务区域内的每个用户位置，波束索引地图可以根据储存的位置和相应的波束索引信息提供基站端和智能反射面端的最优波束对，一方面极大地减少了训练的开销，另一方面由于波束索引地图存储的信息能够一定程度上反映实际的通信环境信息，保证了所提供波束的准确性和环境适应性。
据此，本发明提出了一种基于波束索引地图的智能反射面通信波束选择方法，通过离线的射线追踪仿真计算、实地测量或在线实时测量等方式获得基于实际地理环境和信号传播环境的最优波束信息，构建以位置信息为索引的最优波束数据库。基于构建的波束索引地图，提出一种新型的智能反射面通信波束选择方法，即利用北斗、GPS、5G、激光雷达等定位系统获取用户位置信息，并通过波束索引地图获得该位置下的候选波束对集合，根据候选波束对集合中不同波束对出现次数直接进行波束选择或对候选波束对集合进行波束扫描，从而实现免信道训练或轻量信道训练的波束选择，降低智能反射面通信波束选择的开销和复杂度，提升有效通信速率。
参考图3所示的流程示意图，本发明所提出的智能反射面通信波束选择方法，包括如下主要步骤：
a)、基于地理环境信息及信号传播环境等信息，通过离线射线追踪仿真计算、实地测量以及在线测量的方法获取对应于用户位置信息的基站端和智能反射面端的最优波束对，构建波束索引地图；
b)、当基站端与用户端需要进行通信时，通过GPS、北斗、蜂窝网络、激光雷达等定位系统获取用户的实时地理位置；
c)、根据用户的位置信息，在波束索引地图中找到距离用户位置最近的K个测量点位置， 利用波束索引地图中对应这K个测量点位置的最优波束对构建候选波束对集合；
d)、直接选择候选波束对集合中出现次数最多的波束对作为该用户通信的波束对，当候选波束对集合中有多个出现次数最多且相等的波束对时，计算对应于不同波束对的测量点位置到该用户位置的距离倒数之和，选择相应距离倒数之和最大的波束对作为该用户通信的波束对；
e)、基站端和智能反射面端对候选波束对集合进行波束扫描，根据用户接收到的信噪比大小选择最优波束对；
f)、基站端和智能反射面端在接下来时长为t的时间内，使用步骤d)或e)中得到的最优波束对索引所对应的波束对与用户进行通信；
g)、当t时刻结束，则基站端或用户端判断通信是否结束，若通信结束，则该过程完毕；若通信继续，则重复过程b)-g)直至通信结束；
h)、当地图使用时长超过时限T，或地图所对应的实际地理环境或信号传播环境有了较大变化时，执行a)操作对地图进行更新。
其中：
该波束选择方法以用户端的位置信息作为输入，以对应该位置的基站端和智能反射面端的最优波束对作为输出，现有的GPS、北斗、蜂窝网络、激光雷达等定位技术以及其它不断发展的定位技术都可应用于用户端位置信息的获取。
该波束索引地图基于地理环境信息及信号传播环境等信息，关注信号实际传输的环境，通过离线射线追踪仿真、实地测量、在线测量等方法获取对应于用户位置信息的基站端和智能反射面端的最优波束对，构建波束索引地图。
根据基站端天线配置、智能反射面反射单元的配置、天气状况、任务需求等可以构建同一服务区的多种类型的波束索引地图，在实际通信场景中，根据不同的基站端和智能反射面端的配置、天气状况、任务需求等调用不同种类的波束索引地图，以提高波束索引地图的适用性和准确性。
该波束索引地图构成一个数据库，用于存储基于位置信息的基站端和智能反射面端的最佳波束索引。当监测到波束索引地图服务区域出现较大环境变化时，执行a)步骤对地图进行更新。对于基站端与用户端被严重阻挡的场景，即基站端主要通过智能反射面与用户端通信，由于基站与智能反射面之间的信道相较于智能反射面与用户之间的信道变化更慢，从而可以以较低的频率更新基站端的最优波束，而以较高的频率更新智能反射面端的最优波束。
对于波束索引地图中的每个测量点位置，在不同的时间可能有不同的最优波束对，可以存储多个最优波束对，从而一定程度上提升对环境变化的适应性。
每个用户位置的候选波束对集合大小K一方面决定了可供选择的波束对的个数，另一方面 也关系到后续波束选择过程中计算和训练的开销。对于一些环境变化较慢或者设备处理性能有限的情况，可以选择较小的K值，对于一些环境变化较快或者设备处理性能优越的情况，可以选择较大的K值。
步骤d)中直接根据不同波束对出现次数选择基站端和智能反射面端最优波束对的方法不需要额外的训练开销，适用于信号传播环境较为稳定的场景，而步骤e)中通过小范围的波束扫描选择基站端和智能反射面端最优波束对的方法需要轻量的训练开销，适用于信号传播环境变化较快的场景。
步骤d)或e)中基于用户位置信息通过波束索引地图在线实时得到的最优基站端智能反射面端波束对，可以通过线上实时反馈至地图构建更新系统，亦可先在基站端用户端缓存，待时机成熟或达到一定数据量后再线下反馈至地图构建更新系统。
所述方法不仅适用于单个用户单个智能反射面的简单场景，亦适用于多用户多个智能反射面的复杂场景，可以根据不同的场景选择不同类型的波束索引地图，再进一步选择基站端和智能反射面端的最优波束对。
图1是根据一示例性实施例示出的本发明所提及的波束索引地图的数据来源和构建方式。
图2是根据一示例性实施例示出的基于波束索引地图进行智能反射面通信波束选择的实际场景及效果示例图。根据不同的用户位置，基站端和智能反射面端基于波束索引地图选择不同的最优波束与用户进行通信。
在波束索引地图构建阶段中，Q表示测量点位置集合,q i表示每个测量点位置，F i表示对应于位置q i的基站端最优波束索引，V i表示对应于位置q i的智能反射面端最优波束索引。在信息获取阶段中，q表示获得的用户位置信息。在波束选择阶段中，W表示候选波束对集合，K表示候选波束对集合中元素的个数。t表示信道的相干时隙，T表示波束索引地图使用周期。
基于以上定义，所提方法的示例性实施例的具体实现步骤可概括为如下：
(1)波束索引地图构建阶段。首先确定波束索引地图测量点位置集合Q＝[q 1,q 2,…,q N],然后对于Q中的每个位置q i，i＝1,2,…,N,利用线下的实地测量、射线追踪仿真或者在线测量等方式获得对应的基站端和智能反射面端最优波束对并将它们在码本中的索引(F i,V i)储存构成数据库。
(2)信息获取阶段。在通信前，基站端通过对用户端的定位获取用户端的位置信息q，或用户端通过定位系统，例如GPS、北斗、蜂窝网络、激光雷达等，获取位置信息，并将该位置信息q发送给基站端。
(3)波束选择阶段。基站端通过对设备配置、天气因素、任务需求等因素的分析，选择相应的波束索引地图。
(4)波束选择阶段。基站端通过(2)中得到的用户端位置信息q，在波束索引地图中找到距离q最近的K个测量点和它们对应的基站端和智能反射面端最优波束对索引，构成候选波束对集合W。
(5)波束选择阶段。计算候选波束对集合W中不同波束对的出现次数，选择出现次数最多的波束对作为与该用户通信的波束对。当候选波束对集合W中有多个波束对出现的次数相等且最多，则分别计算每个波束对对应的测量点位置到q的距离倒数之和，选择其中最大值对应的波束对作为与该用户通信的波束对。
(6)波束选择阶段。基站端和智能反射面端根据波束候选集合W遍历其中不同的波束对，根据用户端接收到的信噪比大小选择最优的波束对作为与该用户通信的波束对。
(7)通信阶段。在接下来的t时间段内，基站端和智能反射面端使用(5)或(6)中得到的最优波束对与用户端进行通信。
(8)波束重新选择阶段。当时间t结束后，基站和用户判断是否需要继续通信，如果不需要继续通信，则整个通信过程结束。若需要继续通信，则重复(2)-(7)过程直到通信结束。
(9)地图更新阶段。当地图使用时间到达使用周期T，或监测到地图所服务区域的环境发生较大变化时，执行步骤(1)对地图进行更新。
上述方法通过线下实地测量和射线追踪仿真以及在线实时测量等方式，获得对应于不同测量点位置的基站端和智能反射面端的最优波束，构建波束索引地图(步骤(1))。基于该波束索引地图可通过免信道训练或轻量信道训练的波束选择方法对其服务范围内所有空间位置提供基站端和智能反射面端最优波束索引(步骤(2)-(7))。本方法还包括波束索引地图的更新，以提高其精确度与可持续性(步骤(9))。
综上，本发明通过构建并使用环境感知的波束索引地图，结合日益精准及多样化的定位方法，解决了智能反射面通信信道估计难度高、传统波束扫描方法训练开销大、训练过程繁琐等问题，简化实时波束选择流程，从而实现免信道训练或轻量信道训练的环境感知通信，极大提升有效通信速率。
以上所述仅是本发明的部分实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也应视为本发明的保护范围。

Claims (6)

  1. 一种基于波束索引地图的智能反射面通信波束选择方法，以用户端的位置信息作为输入，以对应该位置的基站端和智能反射面端的最优波束对作为输出，其特征在于，包括步骤如下：
    S1、获得基于实际地理环境和信号传播环境的最优波束信息，构建以位置信息为索引的最优波束数据库，即波束索引地图；
    S2、基于构建的波束索引地图，利用多种定位系统获取用户实时地理位置信息，并通过波束索引地图获得该位置下的候选波束对集合，根据候选波束对集合中不同波束对出现次数直接进行波束选择，或对候选波束对集合进行波束扫描选择最优波束对；基站端和智能反射面端在设定的时长内，使用所选择的波束对与用户进行通信；
    其中，该波束索引地图构成一个数据库，用于存储基于位置信息的基站端和智能反射面端的最佳波束索引；当地图使用时长超过时限T，或地图所对应的实际地理环境或信号传播环境发生一定变化时，执行步骤S1操作对地图进行更新；
    对于基站端与用户端被严重阻挡的场景，即基站端主要通过智能反射面与用户端通信，以相对较低的频率更新基站端的最优波束，而以相对较高的频率更新智能反射面端的最优波束。
  2. 根据权利要求1所述的一种基于波束索引地图的智能反射面通信波束选择方法，其特征在于，步骤S1是基于地理环境信息及信号传播环境信息，通过离线射线追踪仿真计算、实地测量以及在线测量的方法获取对应于用户位置信息的基站端和智能反射面端的最优波束对，构建波束索引地图。
  3. 根据权利要求1所述的一种基于波束索引地图的智能反射面通信波束选择方法，其特征在于，步骤S2具体包括：
    S201、当基站端与用户端需要进行通信时，通过定位系统获取用户的实时地理位置信息；
    S202、根据用户的位置信息，在波束索引地图中找到距离用户位置最近的K个测量点位置，利用波束索引地图中对应这K个测量点位置的最优波束对构建候选波束对集合；
    S203、直接选择候选波束对集合中出现次数最多的波束对作为该用户通信的波束对，当候选波束对集合中有多个出现次数最多且相等的波束对时，计算对应于不同波束对的测量点位置到该用户位置的距离倒数之和，选择相应距离倒数之和最大的波束对作为该用户通信的波束对；
    S204、基站端和智能反射面端对候选波束对集合进行波束扫描，根据用户接收到的信噪比大小选择最优波束对；
    S205、基站端和智能反射面端在接下来时长为t的时间内，使用步骤S203或S204中得到的最优波束对与用户进行通信；
    S206、当t时刻结束，则基站端或用户端判断通信是否结束，若通信结束，则该过程完毕； 若通信继续，则重复步骤S201-S205直至通信结束。
  4. 根据权利要求1所述的一种基于波束索引地图的智能反射面通信波束选择方法，其特征在于：对于波束索引地图中的每个测量点位置，在不同的时间有不同的最优波束对，在数据库中每个测量点存储多个最优波束对。
  5. 根据权利要求1所述的一种基于波束索引地图的智能反射面通信波束选择方法，其特征在于：每个用户位置的候选波束对集合中的K值对应可供选择的波束对的个数，与后续波束选择过程中计算和训练的开销成正比。
  6. 根据权利要求3所述的一种基于波束索引地图的智能反射面通信波束选择方法，其特征在于：当信号传播环境较为稳定时，采用步骤S203得到用户通信的波束对；当信号传播环境变化较快时，则采用步骤S204选择最优波束对作为用户通信的波束对。
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