WO2021184424A1 - 基于机器学习和ofdm的室内可见光通信定位方法及系统 - Google Patents

基于机器学习和ofdm的室内可见光通信定位方法及系统 Download PDF

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WO2021184424A1
WO2021184424A1 PCT/CN2020/082658 CN2020082658W WO2021184424A1 WO 2021184424 A1 WO2021184424 A1 WO 2021184424A1 CN 2020082658 W CN2020082658 W CN 2020082658W WO 2021184424 A1 WO2021184424 A1 WO 2021184424A1
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data
machine learning
sub
receiver
identity information
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French (fr)
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游善红
倪珅晟
胡剑凌
刘武
王峰
韩淑莹
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Suzhou University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B10/00—Transmission systems employing electromagnetic waves other than radio-waves, e.g. infrared, visible or ultraviolet light, or employing corpuscular radiation, e.g. quantum communication
    • H04B10/11—Arrangements specific to free-space transmission, i.e. transmission through air or vacuum
    • H04B10/114—Indoor or close-range type systems
    • H04B10/116—Visible light communication
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/0001—Arrangements for dividing the transmission path
    • H04L5/0003—Two-dimensional division
    • H04L5/0005—Time-frequency
    • H04L5/0007—Time-frequency the frequencies being orthogonal, e.g. OFDM(A) or DMT
    • H04L5/0008—Wavelet-division
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/0091—Signalling for the administration of the divided path, e.g. signalling of configuration information
    • H04L5/0094—Indication of how sub-channels of the path are allocated

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  • the present invention relates to the technical field of optical communication, in particular to an indoor visible light communication positioning method and system based on machine learning and OFDM.
  • LED-based indoor visible light communication has obvious advantages compared with radio frequency wireless communication in terms of electromagnetic radiation, use environment, and safety. It can realize the dual functions of lighting and communication at the same time. Due to the many advantages of Visible Light Communication (VLC), the VLC system has received extensive attention and research. In order to further develop and improve the functions of the VLC system, related technologies that use visible light communication to achieve indoor positioning have subsequently appeared, which are similar to the above-mentioned traditional positioning technologies. In contrast, the positioning technology based on visible light communication has the advantages of high positioning accuracy, good confidentiality, and low cost. For these reasons, the use of visible light communication to achieve indoor positioning is considered an efficient solution.
  • VLC Visible Light Communication
  • the signal generated by the signal generator passes through the LED drive circuit and is loaded on the LED lighting equipment, and the signal transmission is realized by controlling the brightness of the LED.
  • the commonly used signal modulation techniques include On-Off Keying (OOK), Pulse Positioning Modulation (PPM), Carrier-less Amplitude and Phase (CAP) and Quadrature Orthogonal Frequency Division Multiplexing (OFDM).
  • OOK On-Off Keying
  • PPM Pulse Positioning Modulation
  • CAP Carrier-less Amplitude and Phase
  • OFDM Quadrature Orthogonal Frequency Division Multiplexing
  • both CAP and OFDM modulation technologies can achieve high-speed transmission with high spectrum efficiency under limited bandwidth conditions, and OFDM is widely used because it can effectively combat frequency selective fading.
  • the methods for realizing indoor visible light positioning based on white light LEDs are mainly divided into four categories: geometric measurement method, scene analysis method, approximate perception method and image sensor imaging method.
  • the geometric measurement method and the approximate perception method are relatively simple to implement, but the positioning accuracy is low.
  • the scene analysis method is also called the fingerprint positioning method, which requires the establishment of a fingerprint database. The richer the samples in the fingerprint database, the higher the positioning accuracy.
  • the process of finding matching data in this method is time-consuming and the algorithm has poor adaptability. Once the indoor scene changes, its positioning accuracy will be greatly affected.
  • the image sensor imaging law uses an optical camera for positioning, which has relatively high accuracy, but its algorithm is more complex, takes a long time to calculate, and uses a camera to achieve communication speed and reliability is low.
  • ML machine learning
  • ANN Artificial Neural Network
  • SVM Support Vector Machine
  • SVM can implement classification and regression tasks. SVM can perform non-linear classification and non-linear regression by introducing Kernel Method. Both types of machine learning methods need to be trained with sufficient data samples before they can be used.
  • the data is usually divided into training set and test set.
  • the training set is used to train the machine learning model
  • the test set is used to test the performance of the machine learning model.
  • the structure and parameters of the model can be adjusted appropriately, and better performance can be obtained after retraining.
  • a well-trained machine learning model can fully approximate complex nonlinear relationships, and the machine learning model has a self-learning function, which can make the system more adaptable. Therefore, if machine learning algorithms can be applied to visible light positioning, the positioning accuracy can be further improved.
  • the VLC system can be regarded as a multiple input single output (Multiple Input Single Output, MISO) communication system, so what the receiver captures is a superimposed signal. If the transmitted signal is modulated by OFDM, the receiver cannot directly demodulate the signal.
  • MISO Multiple Input Single Output
  • STBC Space Time Block Code
  • the technical problem to be solved by the present invention is to overcome the problem that the prior art cannot meet the positioning requirements in medium and large indoor positioning scenarios, thereby providing a machine learning and OFDM-based method that can meet the positioning requirements in medium and large indoor positioning scenarios.
  • Indoor visible light communication positioning method and system is to overcome the problem that the prior art cannot meet the positioning requirements in medium and large indoor positioning scenarios, thereby providing a machine learning and OFDM-based method that can meet the positioning requirements in medium and large indoor positioning scenarios.
  • an indoor visible light communication positioning method based on machine learning and OFDM of the present invention includes: dividing a medium and large indoor plane into a plurality of connected sub-areas, and distributing a plurality of LED devices on each sub-areas , And a plurality of LED devices are respectively fixed on the vertices of the sub-regions, and adjacent sub-regions share LED devices, wherein the LED devices are provided with identity information; the binary data stream to be sent is subjected to quadrature amplitude modulation, Then place the communication data and identity information data on their assigned subcarriers, and perform STBC encoding on the communication data; perform DC bias optical orthogonal frequency division multiplexing modulation on the data after the above processing; The respective data on the multiple LED devices in the area are sent in the form of optical signals, and the optical signals are sent to the receiver after being transmitted in free space, and received by the receiver.
  • the size of the sub-region is adjusted according to the power of the LED device, the viewing angle of the receiver, the illumination requirement, and the size of the indoor space and other physical conditions.
  • the original data content sent by each LED device in the sub-area must be the same.
  • the communication data and the identity information data are allocated to different frequency bands, where the data used for communication has a larger proportion in the data packet, and it is placed On a continuous segment of carrier.
  • the frequency bands occupied by the identity information subcarriers allocated by the plurality of LED devices are different from each other.
  • the present invention also provides an indoor visible light communication positioning method based on machine learning and OFDM, which includes the following steps: the receiving end processes the received data; separates the communication data and the identity information data, and for the identity information data, according to the restoration of the identity Information data determines the sub-area where the receiver is located; for communication data, extract the pilot data and calculate the channel estimation matrix, after extracting the eigenvalues, use the machine learning model to locate, obtain the relative coordinates of the receiver, and use the channel estimation matrix for STBC decoding , Get the communication data; combine the obtained relative coordinates with the identity information data obtained by the receiver to calculate the final coordinates of the receiver.
  • the method for the receiving end to process the received data is: through DC bias optical orthogonal frequency division multiplexing demodulation, the time domain signal is converted into the frequency domain signal, and then used
  • the frequency domain channel equalization technology performs channel estimation, equalizes the data of the identity information, decodes the communication data, and outputs a binary data stream after the identity information data and the communication data are restored and undergoes quadrature amplitude demodulation.
  • the method for positioning using a machine learning model is: in the offline training process, collecting training samples; extracting pilot data, calculating the channel estimation matrix, and extracting the eigenvalues.
  • machine learning model training is used to obtain machine learning model positioning.
  • the machine learning model training after the machine learning model training is completed, it is stored in the local memory of the receiver or in the cloud.
  • the input parameters of the network are uploaded to the cloud, and the cloud server is used to calculate the coordinates.
  • the cloud server calculates the coordinates and then downloads them to the lower computer.
  • the present invention also provides an indoor visible light communication positioning system based on machine learning and OFDM, including a dividing module for dividing a medium and large indoor plane into multiple connected sub-areas, and multiple LED devices are allocated on each sub-areas , And multiple LED devices are respectively fixed on the vertices of the sub-regions, and adjacent sub-regions share LED devices, wherein the LED devices are provided with identity information;
  • the processing module is used to process the binary data stream that needs to be sent Quadrature amplitude modulation, and then place the communication data and identity information data on their assigned sub-carriers, and perform STBC encoding on the communication data;
  • the modulation module performs DC offset optical orthogonal frequency division on the data after the above processing Multiplexing modulation; sending module, used to send the respective data on multiple LED devices in the same sub-area in the form of optical signals, and the optical signals are transmitted to the receiver after being transmitted in free space, and then received by the receiver.
  • the indoor visible light communication positioning method and system based on machine learning and OFDM of the present invention can enable the indoor visible light communication positioning method based on machine learning algorithm and OFDM modulation technology to support positioning area division, so that this type of system can be suitable for medium and large indoors Scenes.
  • the proposed method does not need to increase additional hardware cost and algorithm complexity, and the reliability of the system is high.
  • Figure 1 is a flowchart of the first embodiment of the present invention
  • Figure 2 is a schematic diagram of the LED distribution structure
  • Fig. 3 is a schematic diagram of a spatial model of a single sub-region of the present invention.
  • Figure 4 is a schematic diagram of data sub-carrier allocation in the method of the present invention.
  • Fig. 5 is a flowchart of the second embodiment of the present invention.
  • this embodiment provides an indoor visible light communication positioning method based on machine learning and OFDM, which includes: Step S1: Divide a medium and large indoor plane into a plurality of connected sub-areas, and allocate on each sub-areas Multiple LED devices, and multiple LED devices are respectively fixed on the vertices of the sub-regions, and adjacent sub-regions share LED devices, wherein the LED devices are provided with identity information; Step S2: Binary data to be sent The stream undergoes quadrature amplitude modulation, and then the communication data and identity information data are respectively placed on their assigned sub-carriers, and the communication data is encoded by STBC; Step S3: DC bias optical quadrature is performed on the data after the above processing Frequency division multiplexing modulation; Step S4: Send the respective data on the multiple LED devices in the same sub-area in the form of optical signals, and the optical signals are transmitted to the receiver after being transmitted in free space, and received by the receiver .
  • the medium and large indoor plane is divided into multiple connected sub-areas, and multiple sub-areas are allocated to each sub-areas.
  • LED devices, and multiple LED devices are respectively fixed on the vertices of the sub-regions, and adjacent sub-regions share LED devices, wherein the LED devices are provided with identity information, which is beneficial for the receiver to determine its specifics based on the positioning information.
  • the position is conducive to the completion of positioning; in the step S2, since the positioning function is based on data communication, at the transmitting end, the binary data stream to be sent is subjected to quadrature amplitude modulation, and after quadrature amplitude modulation
  • the signal is a complex signal, and then the communication data and identity information data are respectively placed on their assigned subcarriers, and the communication data is STBC encoded, that is, the complex signal is STBC encoded, and the identity information data is not STBC encoded , And after quadrature amplitude modulation, the identity information data is placed on the allocated sub-carriers, so as to help ensure that the receiver can receive the identity information sent by the multiple LED devices; in step S3,
  • the processed data is subjected to DC bias optical orthogonal frequency division multiplexing modulation.
  • the electrical signal can be converted into an optical signal by hardware, which is conducive to the LED sending data; in the step S4, it will be in the same sub-region
  • the respective data on the multiple LED devices in the LED devices are sent in the form of optical signals, and the optical signals are sent to the receiver after being transmitted in free space, and received by the receiver, which is beneficial to the receiver according to its relative coordinates and all data.
  • the final coordinates are calculated from the position of the sub-area.
  • This application can enable the indoor visible light communication positioning method based on machine learning algorithms and OFDM modulation technology to support positioning area division, so that it can be applied to medium and large indoor scenes without adding additional hardware costs and The algorithm complexity, so the reliability is high.
  • the medium and large indoor plane is divided into a plurality of connected rectangular sub-areas, each sub-area is allocated four LED devices, and the four LED devices are fixed in the rectangular sub-areas. On the four vertices, two adjacent sub-areas share two LED devices.
  • each LED device is assigned a unique identification (ID) information, and the data packet sent by each LED device contains its ID information (where the ID information includes the coordinates, number, status and other information of the LED device).
  • ID information includes the coordinates, number, status and other information of the LED device.
  • the size of each sub-area can be appropriately adjusted according to the size of the indoor space, the power of the LED device, the viewing angle of the receiver, and the illumination requirements.
  • each LED device needs to be assigned a unique number, and each group of LED numbers corresponds to a sub-area number.
  • the receiver can know which sub-area it is in according to the positioning information, which is very important for the receiver to complete the final positioning.
  • the communication data sent by the four LED devices in the same sub-area are the same, and they are sent simultaneously after STBC coding and OFDM modulation.
  • this application proposes to use most of the data sub-carriers (Sub-Carrier, SC) for the transmission of communication data, and a small part of the data sub-carriers.
  • SC data sub-carrier
  • the data on these sub-carriers for transmitting ID information does not need to be STBC encoded, but it is necessary to ensure that the frequency bands occupied by the ID information sub-carriers allocated by each LED device are different.
  • the original data content sent by each LED device in the sub-region must be the same.
  • the communication data and ID information data need to be allocated to different frequency bands.
  • the data used for communication has a larger proportion in the data packet, so more subcarriers need to be allocated, so it is placed on a continuous segment of carrier and STBC encoding is performed.
  • each LED device in the same sub-area is allocated a carrier to send its own ID information. Only a small amount of sub-carriers used to transmit ID information are required.
  • each LED device is assigned an ID information carrier
  • the four ID information are placed on the four carriers in order. And this carrier is only used by one LED device in the sub-area, and the other three LED devices do not send any data on this carrier.
  • the total frequency band length occupied by the LED devices in each sub-area for sending ID information is the same, but the sub-carriers allocated to each LED device need to be allocated appropriately to prevent the occurrence of the same sub-carriers allocated to different LED devices in the same sub-area of.
  • the data on the four carriers used to transmit ID information does not need to be STBC encoded, just put it behind the communication data carrier. After all data is loaded on the carrier according to this principle, OFDM modulation is performed.
  • the indoor visible light communication positioning function is based on data communication. Therefore, at the sending end, the binary data stream to be sent is first subjected to quadrature amplitude modulation (Quadrature Amplitude Modulation, QAM).
  • QAM Quadrature Amplitude Modulation
  • the application takes 4-QAM as an example, and other quadrature amplitude modulation formats can also be selected; the signal after quadrature amplitude modulation is a complex signal, and then STBC encoding is performed on the complex signal. It is worth noting that the data in the ID information part does not need to be encoded by STBC. After quadrature amplitude modulation, it can be placed on the assigned sub-carrier; then the combined data is subjected to DC offset optical orthogonal frequency division.
  • this embodiment provides an indoor visible light communication positioning method based on machine learning and OFDM, which includes the following steps: Step S1: The receiving end processes the received data; Step S2: Separate communication data and identity Information data, for identity information data, determine the sub-area where the receiver is located according to the recovered identity information data; for communication data, extract pilot data and calculate the channel estimation matrix, after extracting the eigenvalues, use the machine learning model to locate and get the receiver’s Relative coordinates, while using the channel estimation matrix to perform STBC decoding to obtain communication data; combine the obtained relative coordinates with the identity information data obtained by the receiver to calculate the final coordinates of the receiver.
  • the receiving end processes the received data, thereby facilitating obtaining the final coordinates; in the step S2 Separate communication data and identity information data.
  • identity information data determine the sub-area where the receiver is located according to the recovered identity information data; for communication data, extract pilot data and calculate channel estimation matrix, after extracting eigenvalues, use machine learning The model is located, the received relative coordinates are obtained, and the channel estimation matrix is calculated at the same time, and then STBC is used to decode the communication data to obtain the final coordinates of the receiver, which is beneficial to the calculation of the final coordinates.
  • identity information data determine the sub-area where the receiver is located according to the recovered identity information data; for communication data, extract pilot data and calculate channel estimation matrix, after extracting eigenvalues, use machine learning The model is located, the received relative coordinates are obtained, and the channel estimation matrix is calculated at the same time, and then STBC is used to decode the communication data to obtain the final coordinates of the receiver, which is beneficial to the calculation of the final coordinates.
  • This application can be
  • the method for the receiving end to process the received data is: converting time-domain signals into frequency-domain signals through DC offset optical orthogonal frequency division multiplexing demodulation, and then using frequency-domain channel equalization technology to perform channel estimation,
  • the data of the identity information is equalized, the communication data is decoded, and after the identity information data and the communication data are restored, the binary data stream is output after quadrature amplitude demodulation.
  • the receiver uses the pilot information (this part of the pilot information requires STBC encoding) to calculate four channel estimation matrices, and then uses the channel estimation matrix and the decoder to obtain communication data, and the ID information is also passed
  • the pilot information is restored after channel equalization, but since the ID information is not encoded by STBC, this part of the pilot information does not need to be encoded. Therefore, the receiver can obtain the positioning information sent by each LED device while receiving the communication data.
  • the receiver can know which sub-area it is in according to the positioning information, which is very important for the receiver to complete the final positioning.
  • the online positioning process firstly, data processing is also performed to obtain the channel estimation matrix, and the input parameters of the model are further obtained. At this time, input the obtained characteristic parameters into the trained model to obtain the relative three-dimensional coordinates of the receiver.
  • the relative coordinates are the coordinates of the receiver in the current sub-region as the coordinate system, not the indoor space.
  • the coordinates of the coordinate system, the ID information is first extracted from the data packet, and the ID information data can be recovered after another channel estimation.
  • the receiver can determine which sub-area it is currently in based on the four ID information; finally, The receiver further calculates the final coordinates according to the relative coordinates and the position of the sub-area.
  • the method of positioning using the machine learning model is: in the offline training process, collecting training samples; extracting pilot data, calculating the channel estimation matrix, extracting the eigenvalues, using the eigenvalues as the input parameters of the machine learning model, using Machine learning model training obtains machine learning model positioning.
  • a suitable machine learning model such as ANN and SVM, etc.
  • sufficient training samples need to be collected first to ensure a good training effect.
  • the channel estimation matrix can be obtained during the data processing at the receiving end
  • the channel estimation matrix can be obtained during the data processing at the receiving end
  • the feature values will be used as the input parameters of the machine learning model; after collecting a large number of samples, you can start training.
  • the structure and various parameters of the model can be adjusted appropriately to achieve better performance, and the trained machine learning
  • the model can be stored in the local storage of the receiver; it can also be stored in the cloud.
  • this embodiment provides an indoor visible light communication positioning system based on machine learning and OFDM.
  • the principle of solving the problem is similar to the indoor visible light communication positioning method based on machine learning and OFDM described in the first embodiment. I won't repeat it here.
  • the dividing module is used to divide the medium and large indoor plane into multiple connected sub-areas, and multiple LED devices are allocated to each sub-region, and the multiple LED devices are respectively fixed on the vertices of the sub-regions and are adjacent to each other. Share LED equipment in the sub-areas of, where the LED equipment is provided with identity information;
  • the processing module is used to perform quadrature amplitude modulation on the binary data stream that needs to be sent, and then place the communication data and the identity information data on their assigned subcarriers respectively, and perform STBC encoding on the communication data;
  • the modulation module performs DC bias optical orthogonal frequency division multiplexing modulation on the data after the above processing
  • the sending module is used to send the respective data on multiple LED devices in the same sub-area in the form of optical signals, and the optical signals are sent to the receiver after being transmitted in free space, and received by the receiver.
  • this embodiment provides a second indoor visible light communication positioning system based on machine learning and OFDM.
  • the principle of solving the problem is similar to the indoor visible light communication positioning method based on machine learning and OFDM described in the second embodiment, repeating I won't repeat it here.
  • the data processing module is used for the receiving end to process the received data
  • the coordinate confirmation module is used to separate communication data and identity information data.
  • identity information data determine the subarea where the receiver is located according to the recovered identity information data; for communication data, extract pilot data and calculate channel estimation matrix to extract feature values Then, use the machine learning model to locate, obtain the relative coordinates of the receiver, and use the channel estimation matrix for STBC decoding to obtain the communication data; combine the obtained relative coordinates with the identity information data obtained by the receiver to calculate the final coordinates of the receiver .
  • this application can be provided as methods, systems, or computer program products. Therefore, this application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.
  • computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
  • These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing equipment to work in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including the instruction device.
  • the device implements the functions specified in one process or multiple processes in the flowchart and/or one block or multiple blocks in the block diagram.
  • These computer program instructions can also be loaded on a computer or other programmable data processing equipment, so that a series of operation steps are executed on the computer or other programmable equipment to produce computer-implemented processing, so as to execute on the computer or other programmable equipment.
  • the instructions provide steps for implementing the functions specified in one process or multiple processes in the flowchart and/or one block or multiple blocks in the block diagram.

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Abstract

本发明涉及一种基于机器学习和OFDM的室内可见光通信定位方法及系统,包括：将中大型室内平面划分成多个相连的子区域，在每一个子区域上分配多个LED设备，且多个LED设备分别固定在所述子区域的顶点上，且相邻的子区域共用LED设备，其中所述LED设备设有身份信息；将需要发送的二进制数据流进行正交幅度调制，然后将通信数据和身份信息数据分别放置在各自分配的子载波上，并对通信数据进行STBC编码；对经过上述处理后的数据进行直流偏置光正交频分复用调制；将在同一子区域内的多个LED设备上的各自数据以光信号的形式发送，且光信号经过自由空间传输后发送至接收机，通过所述接收机接收。本发明不但简单，而且可靠性高。

Description

基于机器学习和OFDM的室内可见光通信定位方法及系统 技术领域
本发明涉及光通信的技术领域，尤其是指一种基于机器学习和OFDM的室内可见光通信定位方法及系统。
背景技术
随着经济和现代技术的不断发展，人们在室内的活动空间越来越庞大和复杂，对导航和定位服务的需求也日益增大，因此定位技术受到广泛关注。目前最常用的室外定位技术有全球定位系统和北斗导航定位系统，这些定位技术在室外的定位结果较好，但由于卫星定位信号难以穿透大型建筑，所以在室内的定位效果较差。为了实现精度较高的室内定位，一些室内定位技术陆续被提出，如红外线、超声波、射频识别(RFID)、无线局域网(WLAN)、蓝牙(Bluetooth)和超宽带(UWB)等。然而，采用上述的技术手段实现定位时，需要搭建复杂的定位设施环境，不仅成本高，定位精度有限，安全性也得不到有效保障。近年来，随着固态照明技术的迅速发展，新一代照明发光二极管(Light-Emitting Diode,LED)以其亮度高、寿命长、响应时间短、成本低等多方面优势正大规模取代传统的白炽灯和节能灯来提供照明。此外，经过科研人员的研究发现利用LED的高速闪烁特性，可以在室内实现短距离的高速无线通信。基于LED的室内可见光通信作为一种新兴的无线通信方式，在电磁辐射、使用环境、安全性等方面与射频无线通信方式相比有明显的优势，能够同时实现照明与通信的双重功能。由于可见光通信(Visible Light Communication,VLC)的诸多优点，VLC系统得到广泛关注和研究，为了进一步开发和提升VLC系统的功能，随后出现利用可见光通信实现室内定位的相关技术，与上述传统的定位技术相比，基于可见光通信的定位技术具有定位精度高、保密性好、成本低等优点。由于这些原因，利用可见光通 信实现室内定位被认为是一种高效的方案。
在VLC系统中，信号发生器产生的信号经过LED驱动电路并加载到LED照明设备上，通过控制LED的亮暗来实现信号的传输。在VLC中，常用的信号调制技术有通断键控(On-Off Keying,OOK)、脉冲幅度调制(Pulse Positioning Modulation,PPM)、无载波调制(Carrier-less Amplitude and Phase,CAP)和正交频分复用技术(Orthogonal Frequency Division Multiplexing，OFDM)。其中，CAP和OFDM调制技术都能够在有限的带宽条件下实现高频谱效率的高速传输，而OFDM由于其可有效对抗频率选择性衰落而被广泛应用。
目前基于白光LED实现室内可见光定位的方法主要分为四大类：几何测量法，场景分析法、近似感知法和图像传感器成像法。几何测量法和近似感知法实施相对简单，但是定位精度较低。场景分析法又称为指纹定位法，需要建立指纹库，指纹库中的样本越丰富则定位的精度越高。但该方法的查找匹配数据过程比较耗时，而且算法的适应性较差，一旦室内场景发生变换，其定位精度会受到很大影响。图像传感器成像法则利用光学照相机进行定位，精度相对较高，但其算法复杂度较高，运算时间久，且利用相机实现通信的速度和可靠性较低。当在大型室内环境情况下，需求的定位区域很大时，仅凭一组LED无法完成整个定位区域的定位任务。所以在大型定位场景下需要部署更多的LED发光设备，并利用LED的布局将整个定位区域划分成多个子定位区域，当接收机到达某个子区域内时，利用属于该子区域内的LED设备发送的数据进行通信和定位。
近些年来，随着计算机性能的飞速提升和智能设备广泛普及，机器学习(Machine Learning,ML)被广泛应用于各类学科，一些借助于机器学习的产品已成功进入我们的生活，如人脸识别，自动驾驶和智能医疗设备等。人工神经网络(Artificial Neural Network,ANN)是机器学习中一种有监督学习方法，因其具有很强的鲁棒性和容错性，获取复杂问题的优化解的速度十分快，所以应用十分广泛。支持向量机(Support Vector Machine,SVM)也是一种常用的有监督的机器学习算法。SVM可以实现分类和回归两类任务。 SVM可以通过引入核方法(Kernel Method)进行非线性分类和非线性回归。这两类机器学习方法都需要使用充足的数据样本训练后才能使用。在训练时，通常将数据分为训练集和测试集两部分，训练集用于训练机器学习模型，测试集则用于测试机器学习模型的性能。根据测试结果可以适当调整模型的结构和参数，经过重新训练后可以获得更好的性能。训练好的机器学习模型可以充分逼近复杂的非线性关系，而且机器学习模型具有自学习功能，这可以使得系统拥有更强的适应性。所以，如果能将机器学习算法应用到可见光定位中，则可以进一步提高定位精度。
在目前利用机器学习算法实现室内可见光通信定位的方案中大多采用单载波调制格式，也有部分方案使用OFDM调制技术。使用OFDM技术结合时分复进行室内定位，利用时分复用会增加系统的复杂度和降低通信速率，且系统定位区域面积受限。考虑单接收机情况下，VLC系统可以视为一个多输入单输出(Multiple Input Single Output，MISO)的通信系统，所以接收机捕获的是一个叠加信号。如果发送的信号经过OFDM调制，则接收机无法直接解调出信号。使用空时块码(Space Time Block Code,STBC)编码技术可以解决这个问题，但STBC要求应用该编码的所有发送端发送的原始数据是相同的，这时接收机可以很好的恢复出通信数据，但此时每个LED设备就无法发送各自的定位信息，使得系统只能在一块区域内实现定位，这使得系统无法支持定位区域划分，无法满足中大型室内定位场景下的定位需求。
发明内容
为此，本发明所要解决的技术问题在于克服现有技术中无法满足中大型室内定位场景下定位需求的问题，从而提供一种可以满足中大型室内定位场景下的定位需求的基于机器学习和OFDM的室内可见光通信定位方法及系统。
为解决上述技术问题，本发明的一种基于机器学习和OFDM的室内可见光通信定位方法，包括：将中大型室内平面划分成多个相连的子区域，在每一个子区域上分配多个LED设备，且多个LED设备分别固定在所述子区域的 顶点上，且相邻的子区域共用LED设备，其中所述LED设备设有身份信息；将需要发送的二进制数据流进行正交幅度调制，然后将通信数据和身份信息数据分别放置在各自分配的子载波上，并对通信数据进行STBC编码；对经过上述处理后的数据进行直流偏置光正交频分复用调制；将在同一子区域内的多个LED设备上的各自数据以光信号的形式发送，且光信号经过自由空间传输后发送至接收机，通过所述接收机接收。
在本发明的一个实施例中，所述子区域的大小根据所述LED设备的功率，所述接收机视角大小、照度要求以及室内空间大小等物理条件做出调整。
在本发明的一个实施例中，对所述复数信号进行STBC编码时，所述子区域内每个LED设备发送的原始数据内容必须相同。
在本发明的一个实施例中，对所述复数信号进行STBC编码之前，将通信数据和身份信息数据分配到不同频段上，其中用于通信的数据在数据包中的比例较大，将其放置在连续的一段载波上。
在本发明的一个实施例中，所述多个LED设备分配的身份信息子载波各自占用的频段各不相同。
本发明还提供了一种基于机器学习和OFDM的室内可见光通信定位方法，包括如下步骤：接收端对接收到的数据进行处理；分离出通信数据和身份信息数据，对于身份信息数据，根据恢复身份信息数据确定接收机所在的子区域；对于通信数据，提取导频数据并计算信道估计矩阵，提取特征值后，利用机器学习模型定位，得到接收机的相对坐标，同时利用信道估计矩阵进行STBC解码，得到通信数据；将得到的相对坐标和接收机获取的身份信息数据结合，计算出接收机的最终坐标。
在本发明的一个实施例中，所述接收端对接收到的数据进行处理的方法为：通过直流偏置光正交频分复用解调，将时域信号转换成频域信号，然后使用频域信道均衡技术进行信道估计，对身份信息的数据进行均衡，对通信数据进行解码，待所述身份信息数据和通信数据恢复后经过正交幅度解调后，输出二进制数据流。
在本发明的一个实施例中，所述利用机器学习模型定位的方法为：在离线训练过程中，收集训练样本；抽取导频数据，计算信道估计矩阵，提取特征值后，将所述特征值作为机器学习模型的输入参数，利用机器学习模型训练得到机器学习模型定位。
在本发明的一个实施例中，所述利用机器学习模型训练完成后，存储在接收机的本地存储器中或云端，定位时则将网络的输入参数上传至所述云端，使用云服务器计算出坐标，所述云服务器计算出坐标后再下传至下位机。
本发明还提供了一种基于机器学习和OFDM的室内可见光通信定位系统，包括划分模块，用于将中大型室内平面划分成多个相连的子区域，在每一个子区域上分配多个LED设备，且多个LED设备分别固定在所述子区域的顶点上，且相邻的子区域共用LED设备，其中所述LED设备设有身份信息；处理模块，用于将需要发送的二进制数据流进行正交幅度调制，然后将通信数据和身份信息数据分别放置在各自分配的子载波上，并对通信数据进行STBC编码；调制模块，对经过上述处理后的数据进行直流偏置光正交频分复用调制；发送模块，用于将同一子区域内的多个LED设备上各自数据以光信号的形式发送，且光信号经过自由空间传输后发送至接收机，通过所述接收机接收。
本发明的上述技术方案相比现有技术具有以下优点：
本发明所述的基于机器学习和OFDM的室内可见光通信定位方法及系统，可以使得基于机器学习算法和OFDM调制技术的室内可见光通信定位方法支持定位区域划分，使得该类系统可以适用于中大型室内场景。提出的方法不需要增加额外硬件成本和算法复杂度且系统的可靠性较高。
附图说明
为了使本发明的内容更容易被清楚的理解，下面根据本发明的具体实施例并结合附图，对本发明作进一步详细的说明，其中
图1是本发明第一实施例的流程图；
图2是LED分布结构示意图；
图3是本发明单个子区域空间模型示意图；
图4是本发明方法中数据子载波分配示意图；
图5是本发明第二实施例的流程图。
具体实施方式
实施例一
如图1所示，本实施例提供一种基于机器学习和OFDM的室内可见光通信定位方法，包括：步骤S1：将中大型室内平面划分成多个相连的子区域，在每一个子区域上分配多个LED设备，且多个LED设备分别固定在所述子区域的顶点上，且相邻的子区域共用LED设备，其中所述LED设备设有身份信息；步骤S2：将需要发送的二进制数据流进行正交幅度调制，然后将通信数据和身份信息数据分别放置在各自分配的子载波上，并对通信数据进行STBC编码；步骤S3：对经过上述处理后的数据进行直流偏置光正交频分复用调制；步骤S4：将在同一子区域内的多个LED设备上的各自数据以光信号的形式发送，且光信号经过自由空间传输后发送至接收机，通过所述接收机接收。
本实施例所述基于机器学习和OFDM的室内可见光通信定位方法，针对发送端，所述步骤S1中，将中大型室内平面划分成多个相连的子区域，在每一个子区域上分配多个LED设备，且多个LED设备分别固定在所述子区域的顶点上，且相邻的子区域共用LED设备，其中所述LED设备设有身份信息，从而有利于接收机根据定位信息判断其具体位置，有利于完成定位；所述步骤S2中，由于定位功能是建立在数据通信基础之上的，因此在发送端，对需要发送的二进制数据流进行正交幅度调制，经过正交幅度调制后的信号是复数信号，然后将通信数据和身份信息数据分别放置在各自分配的子载波上，并对通信数据进行STBC编码，即对复数信号进行STBC编码，其中所述身份信息数据不进行STBC编码，而正交幅度调制后将所述身份信息数据放置在分配到的子载波上，从而有利于保证接收机能收到所述多个LED设备发 送的身份信息；所述步骤S3中，对经过上述处理后的数据进行直流偏置光正交频分复用调制，信号经过调制后才可通过硬件将电信号转换成光信号，有利于LED发送数据；所述步骤S4中，将在同一子区域内的多个LED设备上的各自数据以光信号的形式发送，且光信号经过自由空间传输后发送至接收机，通过所述接收机接收，从而有利于所述接收机根据其相对坐标和所处的子区域位置计算出最终坐标，本申请可以使得基于机器学习算法和OFDM调制技术的室内可见光通信定位方法支持定位区域划分，使得可以适用于中大型室内场景，而且不需要增加额外硬件成本和算法复杂度，因此可靠性较高。
如图2和图3所示，本实施例中，将中大型室内平面划分成多个相连的矩形子区域，每一个子区域分配四个LED设备，四个LED设备分别固定在矩形子区域的四个顶点上，相邻两个子区域共用两个LED设备。此外，每个LED设备都会被分配一个独一无二的身份(ID)信息，且每个LED设备发送的数据包中都包含其ID信息(其中ID信息包含LED设备的坐标、编号、状态等信息)。每个子区域的大小可以根据室内空间的大小、LED设备的功率、接收机视角大小以及照度要求而适当调整。但必须保证所述接收机在任意一个子区域内都可以接收到来自该子区域内四个LED设备发送的信号，且收到来自其他子区域内LED设备发出的干扰信号较小。划分区域后，需要为每一个LED设备分配一个独一无二的编号，且每组LED编号对应一个子区域编号。所述接收机根据定位信息就可以知道其处于哪个子区域内，这对于所述接收机完成最终定位十分重要。
在同一个子区域内的四个LED设备发送的通信数据是相同的，且经过STBC编码和OFDM调制后同时发送。为了保证所示接收机能收到所在子区域内四个LED设备发送的ID信息，本申请提出将大部分的数据子载波(Sub-Carrier,SC)用于传输通信数据，小部分数据子载波用于传输LED设备的ID信息。传输ID信息的这些子载波上的数据不需要进行STBC编码，但需要保证每个LED设备分配的ID信息子载波各自占用的频段是不同的。
如图4所示，当对复数信号进行STBC编码时，所述子区域内每个LED 设备发送的原始数据内容必须相同。为了便于ID信息的收发，需要将所述通信数据和ID信息数据分配到不同频段上。其中用于通信的数据在数据包中的比例较大，所以需要分配较多的子载波，因此将其放置在连续的一段载波上，并进行STBC编码。为了使所述接收机能够接收到每个LED设备发送的ID信息，同一个子区域内的每个LED设备都分配一个载波来发送各自的ID信息。用于传输ID信息的子载波只需少量即可，假设每个LED设备分配一个ID信息载波，则将四个ID信息按顺序分别放置在四个载波上。且该载波仅供子区域中的一个LED设备使用，其余三个LED设备在该载波上不发送任何数据。每个子区域中LED设备发送ID信息占用的总频段长都是一样的，但每个LED设备分配到的子载波需要合理分配，以防止出现同一子区域内不同LED设备分配到的子载波是相同的。用于传输ID信息的四个载波上的数据不需要进行STBC编码，将其放在通信数据载波后面即可。所有数据按这个原则加载到载波上之后，再进行OFDM调制。
本实施例中，所述的室内可见光通信定位功能是建立在数据通信基础之上的，因此在发送端，首先将需要发送的二进制数据流进行正交幅度调制(Quadrature Amplitude Modulation，QAM),本申请以4-QAM为例，也可以选择其他正交幅度调制格式；经过正交幅度调制后的信号是复数信号，然后对复数信号进行STBC编码。值得注意的是，ID信息部分的数据不需要进行STBC编码，在正交幅度调制后将其放置在分配到的子载波上即可；然后将合并好的数据进行直流偏置光正交频分复用调制(Direct Current Biased Optical Orthogonal Frequency Division Multiplexing，DCO-OFDM)；最后，同一子区域内的四个LED设备都将各自的数据以光信号的形式发送，光信号经过自由空间传输后发送至所述接收机。
实施例二
如图5所示，本实施例提供一种基于机器学习和OFDM的室内可见光通信定位方法，包括如下步骤：步骤S1：接收端对接收到的数据进行处理；步骤S2：分离出通信数据和身份信息数据，对于身份信息数据，根据恢复身份信息数据确定接收机所在的子区域；对于通信数据，提取导频数据并计 算信道估计矩阵，提取特征值后，利用机器学习模型定位，得到接收机的相对坐标，同时利用信道估计矩阵进行STBC解码，得到通信数据；将得到的相对坐标和接收机获取的身份信息数据结合，计算出接收机的最终坐标。
本实施例所述的基于机器学习和OFDM的室内可见光通信定位方法，针对接收端，所述步骤S1中，接收端对接收到的数据进行处理，从而有利于获取最终坐标；所述步骤S2中，分离出通信数据和身份信息数据，对于身份信息数据，根据恢复身份信息数据确定接收机所在的子区域；对于通信数据，提取导频数据并计算信道估计矩阵，提取特征值后，利用机器学习模型定位，得到接收到的相对坐标，同时计算信道估计矩阵后，利用STBC解码，得到通信数据，最终得到接收机的最终坐标，从而有利于计算出最终坐标，本申请可以使得基于机器学习算法和OFDM调制技术的室内可见光通信定位方法支持定位区域划分，使得可以适用于中大型室内场景，而且不需要增加额外硬件成本和算法复杂度，因此可靠性较高。
所述接收端对接收到的数据进行处理的方法为：通过直流偏置光正交频分复用解调，将时域信号转换成频域信号，然后使用频域信道均衡技术进行信道估计，对身份信息的数据进行均衡，对通信数据进行解码，待所述身份信息数据和通信数据恢复后经过正交幅度解调后，输出二进制数据流。具体地，在接收端，所述接收机利用导频信息(这部分导频信息需要进行STBC编码)计算得到四个信道估计矩阵，然后利用信道估计矩阵和解码器获得通信数据，ID信息也是通过导频信息经过信道均衡后恢复，但由于ID信息没有经过STBC编码，所以这部分导频信息也不需要进行编码。因此，所述接收机就可以在接收到通信数据的同时获取各LED设备发出的定位信息。所述接收机根据定位信息既可以知道其处于哪个子区域内，这对于所述接收机完成最终定位十分重要。
具体地，在线定位过程中，首先也是进行数据处理来获得信道估计矩阵，并进一步获取模型的输入参数。此时，将得到的特征参数输入到训练好的模型中，即可得到接收机的相对三维坐标，该相对坐标是接收机在以当前子区域为坐标系中的坐标，并非是以室内空间为坐标系的坐标，ID信息首先从 数据包中提取，经过另一次信道估计后ID信息数据即可恢复，所述接收机根据四个ID信息可以判断出其目前处于哪一个子区域中；最后，所述接收机根据其相对坐标和所处的子区域位置进一步计算出最终坐标。
所述利用机器学习模型定位的方法为：在离线训练过程中，收集训练样本；抽取导频数据，计算信道估计矩阵，提取特征值后，将所述特征值作为机器学习模型的输入参数，利用机器学习模型训练得到机器学习模型定位。具体地，为了实现使用机器学习算法进行定位，首先需要选择合适的机器学习模型(如ANN和SVM等)，然后根据物理模型进一步使机器学习模型具体化。在离线训练过程中，需要先收集充足的训练样本以保证良好的训练效果。在记录每一组样本时需要记录接收机的相对三维坐标和当前坐标下对应的信道估计矩阵(在接收端数据处理过程中可以得到信道估计矩阵)，然后利用信道估计矩阵提取出特征值，将所述特征值将作为机器学习模型的输入参数；收集大量的样本后，即可开始训练，在训练过程中可适当调整模型的结构和各项参数以取得较好性能，将训练好的机器学习模型可以存储在接收机的本地存储器中；也可以存储在云端，定位时则将网络的输入参数上传至云端，使用云服务器计算出坐标，服务器计算出坐标后再下传至下位机。
实施例三
基于同一发明构思，本实施例提供一种基于机器学习和OFDM的室内可见光通信定位系统，其解决问题的原理与实施例一中所述基于机器学习和OFDM的室内可见光通信定位方法类似，重复之处不再赘述。
本实施例所述基于机器学习和OFDM的室内可见光通信定位系统，包括：
划分模块，用于将中大型室内平面划分成多个相连的子区域，在每一个子区域上分配多个LED设备，且多个LED设备分别固定在所述子区域的顶点上，且相邻的子区域共用LED设备，其中所述LED设备设有身份信息；
处理模块，用于将需要发送的二进制数据流进行正交幅度调制，然后将通信数据和身份信息数据分别放置在各自分配的子载波上，并对通信数据进行STBC编码；
调制模块，对经过上述处理后的数据进行直流偏置光正交频分复用调制；
发送模块，用于将同一子区域内的多个LED设备上各自数据以光信号的形式发送，且光信号经过自由空间传输后发送至接收机，通过所述接收机接收。
实施例四
基于同一发明构思，本实施例提供第二种基于机器学习和OFDM的室内可见光通信定位系统，其解决问题的原理与实施例二中所述基于机器学习和OFDM的室内可见光通信定位方法类似，重复之处不再赘述。
本实施例所述基于机器学习和OFDM的室内可见光通信定位系统，包括：
数据处理模块，用于接收端对接收到的数据进行处理；
坐标确认模块，用于分离出通信数据和身份信息数据，对于身份信息数据，根据恢复身份信息数据确定接收机所在的子区域；对于通信数据，提取导频数据并计算信道估计矩阵，提取特征值后，利用机器学习模型定位，得到接收机的相对坐标，同时利用信道估计矩阵进行STBC解码，得到通信数据；将得到的相对坐标和接收机获取的身份信息数据结合，计算出接收机的最终坐标。
本领域内的技术人员应明白，本申请的实施例可提供为方法、系统、或计算机程序产品。因此，本申请可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本申请是参照根据本申请实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算 机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
显然，上述实施例仅仅是为清楚地说明所作的举例，并非对实施方式的限定。对于所属领域的普通技术人员来说，在上述说明的基础上还可以做出其它不同形式变化或变动。这里无需也无法对所有的实施方式予以穷举。而由此所引伸出的显而易见的变化或变动仍处于本发明创造的保护范围之中。

Claims (10)

  1. 一种基于机器学习和OFDM的室内可见光通信定位方法，其特征在于，包括如下步骤：
    步骤S1：将中大型室内平面划分成多个相连的子区域，在每一个子区域上分配多个LED设备，且多个LED设备分别固定在所述子区域的顶点上，且相邻的子区域共用LED设备，其中所述LED设备设有身份信息；
    步骤S2：将需要发送的二进制数据流进行正交幅度调制，然后将通信数据和身份信息数据分别放置在各自分配的子载波上，并对通信数据进行STBC编码；
    步骤S3：对经过上述处理后的数据进行直流偏置光正交频分复用调制；
    步骤S4：将在同一子区域内的多个LED设备上的各自数据以光信号的形式发送，且光信号经过自由空间传输后发送至接收机，通过所述接收机接收。
  2. 根据权利要求1所述的基于机器学习和OFDM的室内可见光通信定位方法，其特征在于：所述子区域的大小根据所述LED设备的功率，所述接收机视角大小、照度要求以及室内空间大小等物理条件做出调整。
  3. 根据权利要求1所述的基于机器学习和OFDM的室内可见光通信定位方法，其特征在于：对所述复数信号进行STBC编码时，所述子区域内每个LED设备发送的原始数据内容必须相同。
  4. 根据权利要求1或3所述的基于机器学习和OFDM的室内可见光通信定位方法，其特征在于：对所述复数信号进行STBC编码之前，将通信数据和身份信息数据分配到不同频段上，其中用于通信的数据在数据包中的比例较大，将其放置在连续的一段载波上。
  5. 根据权利要求1所述的基于机器学习和OFDM的室内可见光通信定位 方法，其特征在于：所述多个LED设备分配的身份信息子载波各自占用的频段各不相同。
  6. 一种基于机器学习和OFDM的室内可见光通信定位方法，其特征在于，包括如下步骤：
    步骤S1：接收端对接收到的数据进行处理；
    步骤S2：分离出通信数据和身份信息数据，对于身份信息数据，根据恢复身份信息数据确定接收机所在的子区域；对于通信数据，提取导频数据并计算信道估计矩阵，提取特征值后，利用机器学习模型定位，得到接收机的相对坐标，同时利用信道估计矩阵进行STBC解码，得到通信数据；将得到的相对坐标和接收机获取的身份信息数据结合，计算出接收机的最终坐标。
  7. 根据权利要求6所述的基于机器学习和OFDM的室内可见光通信定位方法，其特征在于：所述接收端对接收到的数据进行处理的方法为：通过直流偏置光正交频分复用解调，将时域信号转换成频域信号，然后使用频域信道均衡技术进行信道估计，对身份信息的数据进行均衡，对通信数据进行解码，待所述身份信息数据和通信数据恢复后经过正交幅度解调后，输出二进制数据流。
  8. 根据权利要求6所述的基于机器学习和OFDM的室内可见光通信定位方法，其特征在于：所述利用机器学习模型定位的方法为：在离线训练过程中，收集训练样本；抽取导频数据，计算信道估计矩阵，提取特征值后，将所述特征值作为机器学习模型的输入参数，利用机器学习模型训练得到机器学习模型定位。
  9. 根据权利要求8所述的基于机器学习和OFDM的室内可见光通信定位方法，其特征在于：所述利用机器学习模型训练完成后，存储在接收机的本地存储器中或云端，定位时则将网络的输入参数上传至所述云端，使用云服务器计算出坐标，所述云服务器计算出坐标后再下传至下位机。
  10. 一种基于机器学习和OFDM的室内可见光通信定位系统，其特征在于，包括：
    划分模块，用于将中大型室内平面划分成多个相连的子区域，在每一个子区域上分配多个LED设备，且多个LED设备分别固定在所述子区域的顶点上，且相邻的子区域共用LED设备，其中所述LED设备设有身份信息；
    处理模块，用于将需要发送的二进制数据流进行正交幅度调制，然后将通信数据和身份信息数据分别放置在各自分配的子载波上，并对通信数据进行STBC编码；
    调制模块，对经过上述处理后的数据进行直流偏置光正交频分复用调制；
    发送模块，用于将同一子区域内的多个LED设备上各自数据以光信号的形式发送，且光信号经过自由空间传输后发送至接收机，通过所述接收机接收。
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