CN117858166A - Data aggregation load balancing method and system based on optimal multi-user detection - Google Patents
Data aggregation load balancing method and system based on optimal multi-user detection Download PDFInfo
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Abstract
本发明公开一种基于最优多用户检测的数据聚合负载均衡方法和系统，方法包括：在卫星星座中部署GT接收机；基于卫星星座构建信道模型，并采用MAC层中的随机访问机制；所述GT接收机接收来自多个中继节点的无线信号；基于信道模型计算GT接收机的接收信号的信噪比，过滤低于信噪比阈值的无线信号；采用最优多用户检测算法从过滤后的接收信号中重构出每个用户的信息；采用平行干扰消除技术消除重构出的每个用户的信息中来自其他用户的多址接入干扰；用K近邻分类器将消除多址接入干扰的多用户无线信号聚类后组成信号矩阵；对信号矩阵内的信号数据进行负载均衡。本发明增强信号的同时减少了由于多址接入造成的可能的信道阻塞，采用软负载技术使负载均衡。
The present invention discloses a data aggregation load balancing method and system based on optimal multi-user detection, the method comprising: deploying a GT receiver in a satellite constellation; constructing a channel model based on the satellite constellation and adopting a random access mechanism in a MAC layer; the GT receiver receives wireless signals from multiple relay nodes; calculating the signal-to-noise ratio of the received signal of the GT receiver based on the channel model, filtering wireless signals below the signal-to-noise ratio threshold; reconstructing the information of each user from the filtered received signal using an optimal multi-user detection algorithm; eliminating the multi-access interference from other users in the reconstructed information of each user using a parallel interference elimination technique; clustering the multi-user wireless signals that eliminate the multi-access interference using a K nearest neighbor classifier to form a signal matrix; and load balancing the signal data in the signal matrix. The present invention reduces possible channel blocking caused by multi-access while enhancing the signal, and uses a soft load technique to balance the load.
Description
Technical Field
The invention belongs to the field of wireless communication, and particularly relates to a data aggregation load balancing method and system based on optimal multi-user detection.
Background
The world economy is developing into economy integration and knowledge economy, and networking, virtualization, digitization and knowledge are becoming important features of modern economy development, which makes the business environment facing enterprises become complex and changeable. Sensor networks are typically used to monitor specific physical environments, such as temperature fields or spectrum activity. The corresponding data traffic characteristics are determined by the corresponding physical environment or transmissions. Different kinds of sparsity can appear in the sensor network, thereby proving that compressed sensing can be applied. This patent describes the application of compressed sensing in multiple access of a communication system and analyzes the similarity of compressed sensing and CDMA multi-user detection. Many multi-user detection algorithms can find their own control in compressed sensing. Thereby improving the condition of insufficient precision of compressed sensing in the multiple access of the communication system.
UAV (unmanned aerial vehicle): unmanned aerial vehicles, abbreviated as "unmanned aerial vehicles", abbreviated as "UAVs", are unmanned aerial vehicles that are operated by means of radio remote control devices and self-contained programmed control devices, or are operated autonomously, either entirely or intermittently, by an onboard computer.
Istio: istio is an open source project commonly developed by Google, IBM and Lyft, and aims to provide a unified micro-service connection, security assurance, management and monitoring mode.
Multiple access is where multiple users transmit their respective data to the same receiver, and the receiver needs to identify and reconstruct the data from different users. A schematic diagram of multiple access is shown in fig. 2.
The orthogonal multiple access includes time division multiple access, orthogonal frequency division multiple access, and carrier sense multiple access. In time division multiple access, each transmitter is allocated one slot, and data can only be transmitted in the allocated slot. Thus, signals from different transmitters are separated in time. The orthogonal frequency division multiple access is similar to the time division multiple access, and the only difference between the two is that the orthogonal frequency division multiple access allocates different frequency bands to different transmitters and distinguishes transmitting ends in the frequency domain. Carrier sense multiple access also distinguishes between different transmitting ends in the time domain. However, the transmitter has no fixed transmit time slots. Firstly, a transmitting end needs to monitor surrounding spectrum environments, and if no other communication links exist, the transmitting end transmits own data; otherwise, it will fall back for a random period of time, after which it listens again to the environment until it sends out its own data packet.
Orthogonal multiple access readily distinguishes signals from different transmitters. The main challenges of this type of multiple access are how to ensure that the data channels (time-slot or frequency-domain channels) are orthogonal (e.g., how to ensure that multiple transmitters are synchronized in time) and that the channel fading caused by multiple access or remote access requires enhancement of the signal.
Orthogonal multiple access requires signal enhancement and interference cancellation.
Because the mass unknown signal data easily causes the overload of the receiver and the disaster caused by breakdown, the hardware load balancing capacity expansion cost is higher.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a data aggregation load balancing method and system based on optimal multi-user detection.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a data aggregation load balancing method based on optimal multi-user detection comprises the following steps:
deploying a GT receiver in a satellite constellation, a relay node of the satellite constellation comprising an HFH and a UAV;
constructing a channel model based on a satellite constellation, wherein the channel model is specifically an air-to-ground channel leading in line-of-sight transmission, and adopts a random access mechanism in an MAC layer;
the GT receiver receives wireless signals from a plurality of relay nodes; calculating the signal-to-noise ratio of the received signal of the GT receiver based on the channel model, and filtering the wireless signal below the signal-to-noise ratio threshold;
reconstructing information of each user from the filtered received signals by adopting an optimal multi-user detection algorithm;
the parallel interference elimination technology is adopted to eliminate the multiple access interference from other users in the reconstructed information of each user;
clustering the multi-user wireless signals with the elimination of the multiple access interference by using a K neighbor classifier to form a signal matrix;
and carrying out load balancing on the signal data in the signal matrix.
In order to optimize the technical scheme, the specific measures adopted further comprise:
further, the specific process of calculating the signal-to-noise ratio of the received signal of the GT receiver based on the channel model is as follows:
the channel power gain of the communication link between the GT receiver and the relay node is calculated as follows:
in the formula g j (r, β) represents the channel power gain of the communication link between the GT receiver and the jth relay node, r represents the received signal of the GT receiver, β represents the small-scale fading of Gamma distribution, β 0 Represents channel power, H, at a reference distance of 1 meter j Representing the height of the relay node;
channel power gain g using a communication link between a GT receiver and a relay node j (r, β) calculating a signal-to-noise ratio of a received signal of the GT receiver based on:
in the SNR j (beta, r) represents the signal-to-noise ratio, W, of the received signal received by the GT receiver from the jth relay node j Is the transmission power of the jth relay node, sigma 2 Is the noise power of the GT receiver.
Further, the specific process of reconstructing the information of each user from the filtered received signal by adopting the optimal multi-user detection algorithm is as follows:
the filtered received signal is written in vector form as:
in the method, in the process of the invention,representing the filtered received signal, S is a matrix of codewords assigned to each user, s= (S) 1 ，s 2 ，...，s K )，s K A is a diagonal matrix composed of channel gains for each user, a=diag (g 1 ，g 2 ，…，g K )，g K Representing channel gain for the kth user; b is the information symbol to be estimated reconstructed from the filtered received signal, b= { b k } k＝1，2，…，K ，b k Is the reconstructed information symbol sent by the kth user; n is additive white gaussian noise;
the optimal multi-user detection algorithm is specifically combined with optimal multi-user detection, and information symbols sent by users are reconstructed from the filtered received signals and expressed as follows by a formula:
in the method, in the process of the invention,representing the received signal from the filtering +.>Information symbol transmitted by the user reconstructed in (a), is shown in (a)>Representing the variance of the additive gaussian white noise n.
Further, the specific process of using parallel interference cancellation technology to cancel the multiple access interference from other users in the reconstructed information of each user is:
step 0, initializing
Step l, l<0<l max Canceling the interference and estimating bits using:
in the method, in the process of the invention,information symbol s transmitted by kth user representing the first iteration estimate k Indicating the code word to which the kth user is assigned, the superscript T indicating the transpose, r indicating the received signal of the GT receiver, g n Channel gain, indicative of nth user, +.>Representing the information symbol sent by the nth user of the previous iteration estimation; s is(s) n Indicating the code word to which the nth user is assigned, l max Representing the maximum number of iterations.
Further, the specific process of clustering the multi-user wireless signals with the multiple access interference eliminated by the K-nearest neighbor classifier to form a signal matrix is as follows:
the K neighbor classifier is used for identifying a user from which multi-user wireless signals eliminating multiple access interference come, a signal matrix is formed after marking is carried out according to different sources, a sub-signal matrix is formed according to different services and application classifications in the signal matrix, and if the data between the sub-signal matrices have relevance, marking is carried out, so that matrix marking is uniformly generated.
Further, the load balancing of the signal data in the signal matrix is specifically:
performing load balancing distribution on data between different sub-signals in the same signal source or data between the same sub-signals in the same signal source in a signal matrix according to matrix marks by using Istio;
each receiver cluster maps a sidecar service, and calls the RPC to issue each verification request in the sidecar service to the database at the server side, and different signal source data are stored to databases with different structures through the RPC while load balancing strategy scheduling is completed.
The invention also provides a data aggregation load balancing system based on optimal multi-user detection, which comprises:
a filtering module, configured to calculate a signal-to-noise ratio of a received signal of the GT receiver based on the channel model, and filter a wireless signal that is lower than a signal-to-noise ratio threshold;
the optimal multi-user detection module is used for reconstructing information of each user from the filtered received signals by adopting an optimal multi-user detection algorithm;
the parallel interference elimination module adopts a parallel interference elimination technology to eliminate multiple access interference from other users in the reconstructed information of each user;
the signal clustering module is used for clustering the multi-user wireless signals eliminating the multiple access interference by using the K neighbor classifier to form a signal matrix;
and the load balancing module is used for carrying out load balancing on the signal data in the signal matrix.
The beneficial effects of the invention are as follows:
the invention enhances the signal and simultaneously reduces possible channel blockage caused by multiple access through the optimal multi-user detection and parallel interference elimination technology.
The invention adopts the soft load technology to carry out load balancing processing on the wireless signals after the receiver is deployed, thereby solving the problem of receiver breakdown.
Drawings
FIG. 1 is a flow chart of a data aggregation load balancing method based on optimal multi-user detection;
fig. 2 is a diagram of multiple access;
fig. 3 is a schematic diagram of a signal matrix.
Detailed Description
The invention will now be described in further detail with reference to the accompanying drawings.
In an embodiment, the present invention proposes a data aggregation load balancing method based on optimal multi-user detection, and the overall flow of the method is shown in fig. 1, including:
deploying a GT receiver in a satellite constellation, a relay node of the satellite constellation comprising an HFH and a UAV;
constructing a channel model based on a satellite constellation, wherein the channel model is specifically an air-to-ground channel leading in line-of-sight transmission, and adopts a random access mechanism in an MAC layer;
the GT receiver receives wireless signals from a plurality of relay nodes; the signal to noise ratio of the received signal of the GT receiver is calculated based on the channel model, and the specific process is as follows:
the channel power gain of the communication link between the GT receiver and the relay node is calculated as follows:
in the formula g i (r, β) represents the channel power gain of the communication link between the GT receiver and the jth relay node, r represents the received signal of the GT receiver, β represents the small-scale fading of Gamma distribution, β 0 Represents channel power, H, at a reference distance of 1 meter j Representing the height of the relay node, is the distance from GT to the horizontal projection point of the HFH or UAV. The channels used in the channel model are orthogonal. In this way, there is no interference between the channels, and thus the interference problem between the channels is not considered in the model.
Channel power gain g using a communication link between a GT receiver and a relay node j (r, β) calculating a signal-to-noise ratio of a received signal of the GT receiver based on:
in the SNR j (beta, r) represents GT receiver receptionSignal-to-noise ratio, W, of received signal to the jth relay node j Is the transmission power of the jth relay node, sigma 2 Is the noise power of the GT receiver.
Wireless signals below a signal-to-noise threshold are filtered. Such as IEEE 802.11 distributed coordination function protocol. When the HFH and the UAV achieve communication coverage, the signal-to-noise ratio (Signal to Noise Ratio, SNR) of the ground terminal receiver must be greater than a threshold.
Reconstructing information of each user from the filtered received signals by adopting an optimal multi-user detection algorithm; the specific process is as follows:
the filtered received signal is written in vector form as:
in the method, in the process of the invention,representing the filtered received signal, S is a matrix of codewords assigned to each user, s= (S) 1 ，s 2 ，...，s K )，s K A is a diagonal matrix composed of channel gains for each user, a=diag (g 1 ，g 2 ，…，g K )，g K Representing channel gain for the kth user; b is the information symbol to be estimated reconstructed from the filtered received signal, b= { b k } k＝1，2，…，K ，b k Is the reconstructed information symbol sent by the kth user; n is additive white gaussian noise;
the optimal multi-user detection algorithm is specifically combined with optimal multi-user detection, and information symbols sent by users are reconstructed from the filtered received signals and expressed as follows by a formula:
in the method, in the process of the invention,representing the received signal from the filtering +.>Information symbol transmitted by the user reconstructed in (a), is shown in (a)>Representing the variance of the additive gaussian white noise n.
The parallel interference cancellation (Parallel Interference Cancelation, PIC) technology is adopted to cancel the multiple access interference from other users in the reconstructed information of each user; the specific process is as follows:
step 0, initializing
Step l, l<0<l max Canceling the interference and estimating bits using:
in the method, in the process of the invention,information symbol s transmitted by kth user representing the first iteration estimate k Indicating the code word to which the kth user is assigned, the superscript T indicating the transpose, r indicating the received signal of the GT receiver, g n Channel gain, indicative of nth user, +.>Representing the information symbol sent by the nth user of the previous iteration estimation; s is(s) n Indicating the code word to which the nth user is assigned, l max Representing the maximum number of iterations.
Clustering the multi-user wireless signals with the elimination of the multiple access interference by using a K neighbor classifier to form a signal matrix; the specific process is as follows:
the K neighbor classifier is used for identifying a user from which multi-user wireless signals eliminating multiple access interference come, a signal matrix is formed after marking is carried out according to different sources, a sub-signal matrix is formed according to different services and application classifications in the signal matrix, and if the data between the sub-signal matrices have relevance, marking is carried out, so that matrix marking is uniformly generated.
The K neighbor classifier is a relatively simple and example-based classification learning method, does not need to build a classification model through a complex training process, and can be used for classifying attributes and classifying continuous attributes. It has found application in the fields of fraud detection, customer response prediction and collaborative filtering (collaborative filtering), etc.
The basic idea of the K-nearest neighbor classifier is to give a sample x of an undetermined class, search through sample space to find K samples x nearest to the undetermined class sample i (i=1, 2, …, k), which class the sample to be classified belongs to is determined by the class to which the sample most of the k neighbors belong. It can be seen that the main problem of k nearest neighbor classification is to determine the appropriate sample set, distance function, combining function and k value. For various types of attributes, the distance function may refer to a metric formula of sample similarity in the cluster analysis, and the combined function may use a simple unweighted voting (voting) or weighted voting method. In a simple unweighted voting, each neighbor x i The effect on the x-classification is considered the same. By x for k neighbors i The belonging category counts, and x is classified into the category with the most count.
Wherein: n represents a counting function, if x i ∈C j Then eta (x) i ∈C j ) =1, otherwise η (x i ∈C j )＝0。
When the category counts are the same, a category is randomly selected for x. The weighted vote weights each count.
Wherein: the weight is generally defined as w i ＝1/d(x，x i ) 2 ，d(x，x i ) Representing sample x and neighbor x i Is a distance of (3).
The K-nearest neighbor classifier predicts based on local data and is relatively sensitive to noise. The choice of k value is data dependent. An excessively large k value may reduce the influence of noise, but makes the number of neighbor samples of an undetermined class sample point large, possibly leading to a classification error. While too small a value of k may lead to voting failure or be affected by noise. A better k value can be obtained by various heuristic techniques.
Finding the nearest neighbor sample of a sample may calculate the distance between all pairs of samples. To efficiently find nearest neighbors, a clustering algorithm may be used to classify the training sample set, and if the centers of the two are relatively far apart, the samples in the corresponding cluster are generally unlikely to be nearest neighbors. The neighbors of a sample can be found by calculating the distance between samples of adjacent clusters.
And carrying out load balancing on the signal data in the signal matrix. The method comprises the following steps:
carrying out load balancing distribution on data between different sub-signals in the same signal source or data between the same sub-signals in the same signal source in a signal matrix according to matrix marks by using Istio to a receiver cluster (the receiver cluster is equal to the load balancing cluster);
each receiver cluster maps a sidecar service, and calls the RPC to issue each verification request in the sidecar service to the database at the server side, and different signal source data are stored to databases with different structures through the RPC while load balancing strategy scheduling is completed.
In another embodiment, the present invention proposes a data aggregation load balancing system based on optimal multi-user detection corresponding to the method of the first embodiment, including:
a filtering module, configured to calculate a signal-to-noise ratio of a received signal of the GT receiver based on the channel model, and filter a wireless signal that is lower than a signal-to-noise ratio threshold;
the optimal multi-user detection module is used for reconstructing information of each user from the filtered received signals by adopting an optimal multi-user detection algorithm;
the parallel interference elimination module adopts a parallel interference elimination technology to eliminate multiple access interference from other users in the reconstructed information of each user;
the signal clustering module is used for clustering the multi-user wireless signals eliminating the multiple access interference by using the K neighbor classifier to form a signal matrix;
and the load balancing module is used for carrying out load balancing on the signal data in the signal matrix.
The functions of the modules in the system and the implementation method of the functions are completely consistent with the first embodiment.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above examples, and all technical solutions belonging to the concept of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to the invention without departing from the principles thereof are intended to be within the scope of the invention as set forth in the following claims.
Claims (7)
1. The data aggregation load balancing method based on the optimal multi-user detection is characterized by comprising the following steps of:
deploying a GT receiver in a satellite constellation, a relay node of the satellite constellation comprising an HFH and a UAV;
constructing a channel model based on a satellite constellation, wherein the channel model is specifically an air-to-ground channel leading in line-of-sight transmission, and adopts a random access mechanism in an MAC layer;
the GT receiver receives wireless signals from a plurality of relay nodes; calculating the signal-to-noise ratio of the received signal of the GT receiver based on the channel model, and filtering the wireless signal below the signal-to-noise ratio threshold;
reconstructing information of each user from the filtered received signals by adopting an optimal multi-user detection algorithm;
the parallel interference elimination technology is adopted to eliminate the multiple access interference from other users in the reconstructed information of each user; clustering the multi-user wireless signals with the elimination of the multiple access interference by using a K neighbor classifier to form a signal matrix;
and carrying out load balancing on the signal data in the signal matrix.
2. The data aggregation load balancing method based on optimal multi-user detection as claimed in claim 1, wherein the specific process of calculating the signal-to-noise ratio of the received signal of the GT receiver based on the channel model is as follows:
the channel power gain of the communication link between the GT receiver and the relay node is calculated as follows:
in the formula g j (r, β) represents the channel power gain of the communication link between the GT receiver and the jth relay node, r represents the received signal of the GT receiver, β represents the small-scale fading of Gamma distribution, β 0 Represents channel power, H, at a reference distance of 1 meter j Representing the height of the relay node;
channel power gain g using a communication link between a GT receiver and a relay node j (r, β) calculating a signal-to-noise ratio of a received signal of the GT receiver based on:
in the SNR j (beta, r) represents the signal-to-noise ratio, W, of the received signal received by the GT receiver from the jth relay node j Is the transmission power of the jth relay node, sigma 2 Is the noise power of the GT receiver.
3. The data aggregation load balancing method based on optimal multi-user detection as claimed in claim 1, wherein the specific process of reconstructing information of each user from the filtered received signal by adopting the optimal multi-user detection algorithm is as follows:
the filtered received signal is written in vector form as:
in the method, in the process of the invention,representing the filtered received signal, S is a matrix of codewords assigned to each user, s= (S) 1 ，s 2 ，…，s K )，s K A is a diagonal matrix composed of channel gains for each user, a=diag (g 1 ，g 2 ，…，g K )，g K Representing channel gain for the kth user; b is the information symbol to be estimated reconstructed from the filtered received signal, b= { b k } k＝1,2,…,K ，b k Is the reconstructed information symbol sent by the kth user; n is additive white gaussian noise;
the optimal multi-user detection algorithm is specifically combined with optimal multi-user detection, and information symbols sent by users are reconstructed from the filtered received signals and expressed as follows by a formula:
in the method, in the process of the invention,representing the received signal from the filtering +.>User transmission reconstructed in the middleInformation symbol of->Representing the variance of the additive gaussian white noise n.
4. The method for data aggregation load balancing based on optimal multi-user detection as claimed in claim 1, wherein the specific process of using parallel interference cancellation technique to cancel the multiple access interference from other users in the reconstructed information of each user is:
step 0, initializing
Step l, l<0<l max Canceling the interference and estimating bits using:
in the method, in the process of the invention,information symbol s transmitted by kth user representing the first iteration estimate k Indicating the code word to which the kth user is assigned, the superscript T indicating the transpose, r indicating the received signal of the GT receiver, g n Channel gain, indicative of nth user, +.>Representing the information symbol sent by the nth user of the previous iteration estimation; s is(s) n Indicating the code word to which the nth user is assigned, l max Representing the maximum number of iterations.
5. The data aggregation load balancing method based on optimal multi-user detection as claimed in claim 1, wherein the specific process of clustering multi-user wireless signals with eliminated multi-access interference by using a K-nearest neighbor classifier to form a signal matrix is as follows:
the K neighbor classifier is used for identifying a user from which multi-user wireless signals eliminating multiple access interference come, a signal matrix is formed after marking is carried out according to different sources, a sub-signal matrix is formed according to different services and application classifications in the signal matrix, and if the data between the sub-signal matrices have relevance, marking is carried out, so that matrix marking is uniformly generated.
6. The method for load balancing data aggregation based on optimal multi-user detection as claimed in claim 5, wherein said load balancing the signal data in the signal matrix is specifically:
performing load balancing distribution on data between different sub-signals in the same signal source or data between the same sub-signals in the same signal source in a signal matrix according to matrix marks by using Istio;
each receiver cluster maps a sidecar service, and calls the RPC to issue each verification request in the sidecar service to the database at the server side, and different signal source data are stored to databases with different structures through the RPC while load balancing strategy scheduling is completed.
7. A data aggregation load balancing system based on optimal multi-user detection, comprising:
a filtering module, configured to calculate a signal-to-noise ratio of a received signal of the GT receiver based on the channel model, and filter a wireless signal that is lower than a signal-to-noise ratio threshold;
the optimal multi-user detection module is used for reconstructing information of each user from the filtered received signals by adopting an optimal multi-user detection algorithm;
the parallel interference elimination module adopts a parallel interference elimination technology to eliminate multiple access interference from other users in the reconstructed information of each user;
the signal clustering module is used for clustering the multi-user wireless signals eliminating the multiple access interference by using the K neighbor classifier to form a signal matrix;
and the load balancing module is used for carrying out load balancing on the signal data in the signal matrix.
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