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CN-116095770-B - Cross-region cooperation self-adaptive switching judgment method in ultra-dense heterogeneous wireless network

CN116095770BCN 116095770 BCN116095770 BCN 116095770BCN-116095770-B

Abstract

The invention discloses a method for judging the cross-region cooperation self-adaptive switching in an ultra-dense heterogeneous wireless network, which belongs to the field of mobile communication and specifically comprises the following steps of firstly, predicting the positions of two moments under a switching terminal according to a Kalman filter position prediction model after the historical track information of a vehicle is improved. Second, an alternative network set for handover and hopping is generated in advance according to the predicted location. Then, by defining a jump factor and adopting a multi-attribute decision algorithm for correcting the interval number of Jaccard similarity, a self-adaptive switching decision scheme of cross-region cooperation is provided to generate an optimal switching strategy for the terminal. Finally, experimental simulation shows that the algorithm can reduce the switching times of the vehicle-mounted terminal, reduce the switching failure rate and improve the transmission efficiency of the network.

Inventors

  • WU LIPING
  • ZHONG SHILIN
  • MA BIN
  • CHEN XIN

Assignees

  • 重庆邮电大学

Dates

Publication Date
20260512
Application Date
20221208

Claims (5)

  1. 1. A method for judging the cross-region cooperation self-adaptive switching in ultra-dense heterogeneous wireless network is characterized by comprising the following steps: 101. The switching triggering step is to periodically collect the signal intensity and the network bandwidth of the vehicle-mounted terminal in the current network and calculate the switching triggering factor If terminal i switches trigger in network j Triggering switching, otherwise, not triggering; 102. The mobility prediction step is that motion data of a vehicle are collected through positioning equipment on the vehicle and recorded into a historical track database, and after the vehicle-mounted terminal triggers switching, the position of the terminal at the next two moments is predicted through an improved Kalman filter model by utilizing the historical track information of the vehicle; 103. Firstly, generating a candidate network set CNS_1 for switching and a cooperative network set CNS_2 for jumping in advance according to a predicted position, defining a jumping factor by adopting a network topology and a terminal motion state, calculating the score of each network in the CNS_1 and the CNS_2 by a multi-attribute decision algorithm of the number of intervals for correcting Jaccard similarity, and finally generating an optimal switching strategy for the terminal according to the jumping factor and a network scoring result; Step 102 is to collect motion data of a vehicle through positioning equipment on the vehicle and record the motion data to a historical track database, and when the vehicle-mounted terminal triggers switching, predict the position of the terminal at the next two moments through an IKF model by utilizing the historical track information of the vehicle, and specifically comprises the following steps: 301. Theoretical prediction assuming that the state of the vehicle at time t is Wherein Is the dimension data at the time t, Longitude data, if the best estimated state at time t-1 is Estimating the predicted state at the time t according to the theoretical model as follows: (4) Wherein, the Representing a state transition matrix describing how the state at the previous moment transitions to the next state, Representing the prediction noise, the error is represented as a covariance matrix: (5) By using And (3) representing noise of the prediction model, and deriving a transmission process of an adjacent moment error covariance matrix by using the formula (4) to the formula (5) is as follows: (6) 302. GPS measurement, namely, the state observed by the GPS positioning equipment at the moment t is recorded as The observation matrix is recorded as The observation noise is expressed as The transformation process of the predicted state to the observed state of the vehicle at time t can be expressed as: (7) 303. The state updating comprises the steps of respectively obtaining a predicted state and an observed state at the moment t in a formula (4) and a formula (7), and correcting the predicted value through the observed value to obtain the corrected optimal estimated state, wherein the corrected optimal estimated state is as follows: (8) Wherein the method comprises the steps of As a residual of the actual observations and the expected observations, The calculation process of the Kalman gain at the time t is as follows: (9) the effect of the Kalman gain is to balance the predicted state covariance And observed state covariance To determine the ratio of the prediction model to the observation model in the prediction process, and to update the noise covariance matrix of the optimal estimation state for the next prediction after obtaining the Kalman gain Wherein Is a unit matrix; (10) 304. The prediction model is improved by introducing an attenuation memory filtering method to improve a Kalman filter, so as to obtain an improved Kalman filter position prediction model, which is characterized in that a noise covariance matrix in a formula (6) is given Multiplied by an attenuation factor having a value greater than 1 ; The specific steps of the interval number multi-attribute decision algorithm for correcting the Jaccard similarity in the step 103 are as follows: (1) Constructing a section number decision matrix, namely assuming N networks in a network set to be evaluated, acquiring the section numbers of M attributes of the N networks before decision, determining the section numbers of all the network attributes through the maximum and minimum values in multiple data sampling, and respectively acquiring the maximum and minimum values obtained by multiple sampling of the kth attribute of the network j as follows And Then the number of intervals for the kth attribute of network j may be expressed as The interval number decision matrix to be decided can thus be expressed as: (14) (2) Normalized attribute interval number: pair interval number matrix Normalizing to obtain normalized matrix, and marking as Wherein Formulas (15) and (16) are normalized procedures for benefit-type and cost-type network parameters, respectively: (15) (16) (3) Determining interval type ideal scheme for better measuring the difference between networks, and assuming the interval type ideal scheme of each network attribute is Wherein Can be expressed as: (17) (4) Calculating corrected Jaccard similarity, wherein the Jaccard similarity is used for describing similarity and difference between sets, and the larger the Jaccard similarity is, the higher the similarity of the sets is, and the number of intervals can be regarded as a set of numbers, so that the normalized attribute value is related to an ideal solution The Jaccard similarity of (c) can be expressed as: (18) Since the similarity between two sections cannot be compared with the similarity between another section when the middle points of the sections are the same, the right end point of the section can be added into the calculation process of Jaccard to correct the sections, and the corrected Jaccard similarity can be expressed as: (19) Thus, the modified Jaccard similarity for each network attribute value in the normalized decision matrix corresponding to an ideal solution can be represented by the matrix as ; (5) Determining the optimal weight of each network attribute, namely determining the weight of each network attribute in the judging process according to the minimum sum of deviation, wherein the corresponding optimization model is as follows: (20) (6) Calculating comprehensive similarity, namely obtaining the weight of each network attribute, and obtaining the comprehensive similarity of the networks j in the network set to be evaluated through weighted summation ; (21)。
  2. 2. The method for determining handover in a heterogeneous ultra-dense wireless network according to claim 1, wherein step 101 periodically collects signal strength and network bandwidth of a vehicle terminal in a current network, and calculates a handover trigger factor The method specifically comprises the following steps: 201. the received signal strength at the t-th moment, the received signal strength of the terminal i accessing the network j is expressed as: (1) Wherein, the Representing the wireless signal transmit power of network j, The path loss factor is represented by a value, Representing the distance of the terminal i from the network j at t moments, For a mean value of 0, the variance is Is a gaussian random variable of (c); 202. network bandwidth: the network bandwidth obtained by the terminal i accessing the network j at the t-th moment can be expressed as: (2) Wherein, the Indicating the number of in-vehicle terminals in the access network j at time t, Representing the bandwidth of each resource block, Indicates the number of resource blocks allocated to the network j by the in-vehicle terminal i, Indicating the maximum number of resource blocks that can be provided by the network j, therefore, the switching trigger factor of the vehicle-mounted terminal i in the original access network j at the t-th moment Can be expressed as: (3); Wherein when the RSS is lower than the set threshold And hysteresis margin (HYSTERESIS MARGIN, HM) or bandwidth below the minimum bandwidth requirement required by terminal i to operate the service In the time-course of which the first and second contact surfaces, A handover needs to be triggered, otherwise, And does not trigger.
  3. 3. The method for determining handover adaptation in a heterogeneous ultra-dense wireless network according to claim 1, wherein the step 103 generates a candidate network set cns_1 for handover and a cooperative network set cns_2 for hopping, specifically comprising: The method comprises the steps that if a vehicle-mounted terminal i triggers switching at a t-th moment, the position of the vehicle-mounted terminal at the t-th moment can be predicted by utilizing a historical motion track of the vehicle-mounted terminal at the t-th moment according to an IKF model, when a switching request of a user arrives, a background discovers networks in all connection ranges according to the predicted position, the network obtained at the position is taken as a candidate network set after triggering switching and is recorded as CNS_1, similarly, the position at the t-th moment and the t-th moment can be predicted according to the historical motion track of the t-th moment and the t-th moment, the network obtained at the position is taken as a cooperation network set for jumping switching after switching triggering and is recorded as CNS_2, and CNS_1 and CNS_2 are collectively called as an alternative network set.
  4. 4. The method for judging handover adaptive in a heterogeneous ultra-dense wireless network according to claim 3, wherein the handover judging parameters specifically comprise that when the vehicle-mounted terminal triggers handover, a new network is needed to be decided out for the terminal in an alternative network set CNS_1 and CNS_2 for access, the data transmission rate, the network delay and the packet loss rate are key indexes for measuring the access network performance of the vehicle-mounted terminal in the moving process, the 3 parameters are used for evaluating the network performance, and the network topology and the terminal moving state are influenced, so that the access of a new target network may cause frequent handover of the terminal, a jump factor is defined, and the network which is easy to cause frequent handover in the alternative network set is marked as the network which needs to be skipped.
  5. 5. The method for determining handover adaptation in a heterogeneous ultra-dense wireless network according to claim 1, wherein the generating the optimal handover policy specifically comprises: after the vehicle-mounted terminal triggers the switching, the candidate network set CNS_1 and the cooperative network set CNS_2 of the terminal can be generated in advance by adopting an IKF position prediction model, and the comprehensive similarity scores of all networks in the two network sets can be calculated through an MJS-INMADM algorithm and recorded as And Jump factor of each network in combination with alternative network set The optimal switching strategy can be generated for the terminal, and the generation process is as follows: Selecting a network with highest comprehensive similarity from CNS_1 Wherein If the network is Is a jump factor of (2) The optimal strategy is to switch to the network directly Otherwise, obtaining the network with highest comprehensive similarity and jump factor value of 1 from CNS_2 Wherein The optimal strategy is to switch to the network directly 。

Description

Cross-region cooperation self-adaptive switching judgment method in ultra-dense heterogeneous wireless network Technical Field The invention belongs to a vertical switching method in an ultra-dense heterogeneous wireless network, and belongs to the field of mobile communication. In particular to a cross-region cooperation self-adaptive switching judgment method. Background With the evolution of 5G technology, deployment of small cell base stations on infrastructures of telegraph poles, street lamps, buses and the like in urban areas becomes possible in the future, dense deployment of small cell networks can improve spectrum efficiency and network access capacity, and conditions are created for explosive growth of data transmission in the Internet of vehicles. However, in the vehicular ad hoc heterogeneous wireless network, due to the high dynamic movement of the vehicle and the miniaturization of the cell structure, the vehicular terminal is also faced with the embarrassment of continuously switching between networks, which tends to increase signaling overhead and risk of link disconnection, thereby affecting user experience. Therefore, how to reduce the switching times as much as possible while guaranteeing the service quality of the vehicle-mounted terminal through a vertical switching algorithm is a hot problem in research in the field aiming at the frequent switching problem caused by the continuous crossing of the 5G microcellular network, the WiFi network and the like by the vehicle-mounted terminal with high dynamic property. Currently, there are several documents that have studied the problem of frequent handover in heterogeneous wireless networks, and all achieve certain results. For example, literature [Palas M.R.,Islam R.,Roy P.,et al.Multi-criteria handover mobility management in 5G cellular network[J].Computer Communications,2021,174(8):81-91] proposes a multi-attribute vertical switching algorithm based on movement trend quantification, and the problem of excessive switching in a common multi-attribute decision algorithm is relieved by predicting a target area of a terminal by considering movement trend quantification parameters of the terminal. Literature [ Yang Mingji, wu, fan Huafeng ] heterogeneous internet of vehicles vertical switching algorithm based on motion trend prediction [ J ] microelectronics and computer, 2018,35 (4): 119-123,129 ] calculates the duration of access of a vehicle-mounted terminal to a base station by predicting the motion trend of a vehicle, divides the terminal into a narrow mobile node and a wide mobile node according to the duration, and then executes corresponding switching strategies respectively, thereby reducing switching delay. Document [Tokuyama K.,Kimura T.,Miyoshi N.Data rate and handoff rate analysis for user mobility in cellular networks[C]//2018IEEE Wireless Communications and Networking Conference(WCNC).Barcelona,Spaina:IEEE Press 2018:1-6.] proposes a time-based jump switching algorithm that reduces the switching rate of the terminal by setting a jump time threshold to control the switching frequency of the mobile user. Document [Al-Naffouri,Tareq Y.,ElSawy,et al.Velocity-aware handover management in two-tier cellular networks[J].IEEE Transactions on Wireless Communications,2017,16(3):1851-1867.] proposes a switching scheme based on speed perception, and establishes a speed perception model according to a random geometric theory, so as to determine a base station to be skipped on a terminal motion track, and reduce the switching failure rate. Literature [Costa A,Pacheco L,D Rosário,et al.Skipping-based handover algorithm for video distribution over ultra-dense VANET[J].Computer Networks,2020,176:1-12.] proposes a multi-attribute-based jump switching algorithm by designing a jump mechanism combining mobility prediction, service quality and experience quality perception, so that switching reliability is improved, and ping-pong switching is relieved. Although the above-mentioned document can alleviate frequent switching to a certain extent, in the vehicular ad hoc heterogeneous wireless network environment, if the movement trend is only predicted, and accurate analysis is not performed on specific position changes of the vehicle-mounted terminal, the frequent switching problem is more serious. Furthermore, the jump handover algorithms mentioned in these documents, although reducing the number of handovers of the terminal to some extent, fail to decide on the target network to which the terminal can access after jumping. In order to solve the problems, a vertical handover algorithm based on position prediction and cross-region cooperation (Location Prediction and Cross Region Cooperation, LPCRC) is provided, the algorithm firstly introduces an improved Kalman filter (Improve KALMAN FILTER, IKF) model to predict the position of a terminal at the next two moments, an alternative network set for handover and jump is generated in advance, and then an adapt