CN107277167A - A kind of patient monitor based on Internet of Things - Google Patents

A kind of patient monitor based on Internet of Things Download PDF

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CN107277167A
CN107277167A CN201710619932.8A CN201710619932A CN107277167A CN 107277167 A CN107277167 A CN 107277167A CN 201710619932 A CN201710619932 A CN 201710619932A CN 107277167 A CN107277167 A CN 107277167A
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李宏伟
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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00Network arrangements or protocols for supporting network services or applications
    • H04L67/01Protocols
    • H04L67/12Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0004Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by the type of physiological signal transmitted
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04BTRANSMISSION
    • H04B7/00Radio transmission systems, i.e. using radiation field
    • H04B7/02Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613Diversity 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
    • H04B7/0615Diversity 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
    • H04B7/0617Diversity 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 for beam forming

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Abstract

The invention belongs to medical monitoring arts field, a kind of patient monitor based on Internet of Things is disclosed, the patient monitor based on Internet of Things is provided with:Wireless receiving station passes through wire connection server;Server connects monitoring station by wire;Monitoring station includes monitor station and memory;Server connects monitor by wire.The present invention is the patient monitor based on Internet of Things, use the patient monitor, the people of different places can be facilitated to obtain the real-time state of an illness of patient in time, effectively patient can be nursed, patient is preferably treated, therapeutic effect substantially, passes through wireless data transfer, sphere of action is wider, advantageously the treatment of patient Yu.

Description

Monitor based on Internet of things
Technical Field
The invention belongs to the technical field of medical monitoring, and particularly relates to a monitor based on the Internet of things.
Background
At present, with the introduction of monitor equipment in various hospitals, the functions of the monitors are more and more emphasized by people, and medical monitors can measure and control physiological parameters of patients, can compare the physiological parameters with known set values, and can send out alarm devices or systems if the physiological parameters exceed the standard. The monitor continuously monitors physiological parameters of patients for 24 hours, detects the change trend, points out the imminent condition, provides the basis for emergency treatment and treatment of doctors, and reduces the complications to the minimum to achieve the purposes of relieving and eliminating the illness state. The monitor is used for measuring and monitoring physiological parameters, and monitoring and processing the condition of the medicine and before and after the operation. However, the current monitor has a limited application range, and if the family of the patient is not on the spot, the patient's condition cannot be known in time.
In summary, the problems of the prior art are as follows: the use range is limited, and the patient condition can only be known by medical staff in a hospital, but the condition of the patient cannot be obtained by family members far away from other places in time.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a monitor based on the Internet of things.
The invention is realized in such a way that the monitor based on the Internet of things is provided with the following components:
a monitoring station;
the monitoring station comprises a monitoring station and a memory;
the memory clustering the users comprises that the position information of the users is described by the current position coordinates:
li=(xi,yi);
wherein xi,yiRespectively representing the horizontal and vertical coordinate values of the user i, and constructing a content request frequency vector for the user i:
ni=(ni,1,ni,2,...,ni,c);
wherein n isi,cRepresenting the number of times user i requests content c, each user corresponding to a content request vector reflecting the user's content request preferences;
clustering users based on the position information and the content request preference information of the users, dividing the users with similar content request preference and similar positions into a multicast group, calculating the similarity between the two users by using a cosine similarity criterion, and calculating by using the following formula:
wherein β is a weight coefficient between 0 and 1;
using a K-Means clustering method to cluster all users D in the cell, ui={li,niDenotes the clustering information of user i, the purpose of clustering is to classify the original users into class C D ═ D }1,…,DCMathematically, the minimum is calculated as:
wherein gamma iskIs the center of the user group;
the specific steps of clustering the users based on the position information of the users and the video request information counted in the current time period are as follows:
step one, randomly taking C users from D as the centers of C user groups;
calculating the similarity from the rest users to the centers of the C user groups according to a calculation formula of the similarity, and dividing the users into the user groups with the highest similarity;
step three, updating the center gamma of the C user groups according to the clustering resultk={lk,nkUsing the following formula:
wherein m isiThe weight coefficient is between 0 and 1, and the second step and the third step are repeated until the clustering center is not changed any more;
the calculating the horizontal azimuth angle and the vertical elevation angle of the central position of each user group according to the user clustering result and the position information of each user group specifically comprises:
the base station has a specific beam for each user group by adopting an active antenna beam forming model, namely, a specific electronic downward inclination angle and a beam with a vertical half-power bandwidth are set for each user group, and the coordinates of the base station are an origin O (0,0, H)BS) The centroid of the user group k is gammakPosition coordinates of (x)k,yk,zk) Vertical elevation angle and horizontal azimuth angle of
Based on the clustered user group position information, the horizontal direction angle and the vertical elevation angle of the user group center are calculated by the following formulas:
obviously, the vertical elevation angle and the horizontal azimuth angle have the value range of theta1∈(0,π),
The precise alignment of the base station antenna beam to the user group specifically includes:
step one, adjusting an electronic downtilt angle, an electronic horizontal angle and a half-power bandwidth of a beam, enabling a radiation direction of the beam to be aligned to a central position of a user group, enabling a half-power bandwidth range to cover all users in the user group, and adjusting the downtilt angle and the horizontal angle from a base station to the users as follows:
wherein,and thetakCalculating the horizontal azimuth angle and the vertical elevation angle of the center of the user group by using the center position of the user group for the base station based on the user clustering result;
step two, determining the beam width, wherein the coverage area of the user group is a circle with a circular point at the center of the user group, and the radius of the circular area is the distance between the user farthest from the center position in the user group and the center, that is:
wherein (x)k,yk) Is the center gamma of the user group kkThen the vertical half-power bandwidth of the kth beam is:
the determining the channel gain model from the base station to the user by using the antenna model of the active antenna array specifically includes:
step one, according to the position information of each user and the user group wave beam to which the user belongs, the actual horizontal azimuth angle and the vertical elevation angle of each user position are calculated, and the horizontal azimuth angle of a user i relative to a base station is calculatedAnd vertical elevation angle theta'iIf user i belongs to multicast group k, the actual horizontal azimuth and vertical elevation of user i is equal to:
step two, an antenna model of the active antenna array:
the 3D antenna gain model adopts an active antenna array radiation model proposed in the 3GPP standard, and the antenna gain model is expressed as follows:
wherein,an antenna gain model of an active antenna element with a downtilt angle of 0,theta is the azimuth and vertical elevation at the actual location of the user, p is the correlation coefficient of the array antenna, wm,nAnd vm,nThe weight factor and the user offset phase are respectively expressed as follows:
m=1,2,...NH;n=1,2,...NV
m=1,2,...NH;n=1,2,...NV
wherein, thetaetiltRepresenting the downtilt angle of the antenna beam,indicating the horizontal steering angle of the antenna, theta of the antenna for different user groupsetiltAnddifferent in configuration;
step three, the channel gain model from the base station to the user adopts the multicast channel gain model, the user in a multicast group receives data at the same rate, the transmission rate of the base station exceeds the maximum bearing rate of a certain user in the group, then the user can not decode the data normally, the base station transmits the data at the minimum rate in the user group, therefore, the equivalent channel gain from the base station to the user in the user group k is equal to the worst channel gain of the user in the user group, namely:
whereinRepresenting user i (i ∈ D)k) The channel gain on carrier n, consists of 3 parts: fast fading, base station to user path loss, and user 3D antenna gain, as follows:
where F and PL denote fast fading and path loss, respectively,represents the 3D antenna gain of the kth beam to user i;
the method for clustering the user group according to the position information of the user group comprises the following specific steps:
clustering a user group based on knowledge of graph theory, defining an interference graph G (V, E) among beams, wherein V represents a set of beams, the vertex of the interference graph is used as V, E represents an interference coefficient among beams, and an indicating function E (V, E) is defined as an edge of the interference graphk,vm) (k ≠ m) indicates the interference between beam k and beam m:
wherein O iskAnd OmRespectively representing the radius, r, of user group k and user group mthA threshold distance representing a negligible interference between two beams, and e (v) is definedk,vk) And (0) indicating that no interference exists in the beam, and constructing a binary interference matrix according to an indication function:
interference degree of the defined beam:
when d isG(vk) When it is 0, it is called vkIs a zero degree node;
the clustering method comprises the following specific steps:
step one, constructing an interference matrix A by using a vertex set VGInitialization iteration factor h 1, set of isolated nodesClustering collectionsNode set
Step two, finding all zero-degree nodes vkUpdate S-S ∪ vk(ii) a The set of remaining nodes is recorded as Φ1=V-S;
Step three, clustering: a)find node k ═ argmax (d)G(vk) Let the k-th row and k-th column of the interference matrix be 0, update the node set Bh=Bh∩vk(ii) a b) Cyclically executing a) until AG0; c) updating phih=Φh-BhThen phi ishIs the h cluster;
step four, using the node to assemble BhReconstruction of AGNot equal to 0, updating the node set phih+1=BhUpdating the iteration factor h to h +1, and executing the step (3); if A isG0 or | Bh1 if | BhIf 1, thenh+1=Bh
Step five, distributing the isolated node set S to a cluster with least nodes;
after the clustering processing of the user group, the user group D ═ D1,…,Dk,…,DCIs divided into phi through a clustering algorithm{Φ1,…,Φh,…},ΦhAnd representing the h-th user cluster, wherein the total user transmission rate in each cluster is as follows:
the total throughput of the system is the sum of the transmission rates of all user clusters:
whereinClustering phi for usershThe carrier n is used as an indicator, and correspondingly,the conditions are satisfied as follows:
the condition (2) indicates that one carrier can only be allocated to one user group cluster, the user groups in the same cluster share one carrier resource, and the user groups in different clusters can not be multiplexed;
the carrier allocation algorithm based on the maximized throughput specifically comprises the following steps:
step one, according to a formula:
calculating the total transmission rate of the users in each cluster on the carrier n;
step two, in order to maximize the throughput of the system, find out the carrier and user cluster which obtain the maximum rate, distribute the carrier to the user cluster at first, according to the formula:
allocating carrier n to user cluster ΦhThe maximum transmission rate is obtained, and carrier n is allocated to cluster phihSo that carrier n is allocated to user cluster phih
Step three, removing the carrier n from the carrier set F, and simultaneously, clustering the users to form a cluster phihRemoving from the set Φ;
step four, the step two and the step three are repeatedly executed until the carrier set or the user cluster set is combined into an empty set;
the wireless receiving station is connected with the server through a wire;
the method for analyzing the interference relationship between signals of the wireless receiving station comprises the following steps:
step one, constructing a multi-dimensional interference space model, and determining an interference signal feature vector to be analyzedAnd reference signal feature vector
Step two, aiming at the interference signal characteristic vector based on the interference space modelDefining its characteristic vector to the reference signalMeasurement ofOf a displacement vector
Step three, defining displacement vectorThe projection of a certain dimension coordinate axis in the interference space is an interference signal feature vectorTo reference signal feature vectorThe distance in the CP dimension is:
wherein the PRJ (-) operator represents a projection operation, computation, for a certain CP dimensionA value of (d);
step four, defining the interference state of the interference signal to the reference signal as S, and representing the interference relation of the interference signal to the reference signal, thereby judging whether the interference exists; the judging method comprises the following steps:
1) for a single-mode interference signal and a reference signal represented by an individual interference vector, when the distance of the interference signal vector to the reference signal vector in each dimension in the space model is smaller than the resolution of the dimension, representing that the interference signal interferes with the reference signal, and S is 1; on the contrary, if the distance from the interference signal vector to the reference signal vector in a certain dimension or a plurality of dimensions is greater than or equal to the resolution of the dimension, the interference signal does not interfere with the reference signal, and S is 0, that is, the interference signal and the reference signal are separable in the dimension;
2) interference state S (V) in a multimode case where interference signal and reference signal each include a plurality of interference feature vectorsI,VS) It can be calculated as follows:
wherein S [ V ]I,VS]M×NCalled the interference state matrix, each element of the matrixRepresents VIThe k-th sum of feature vectors V inSThe interference state of the ith feature vector; s (V) when only each element in the two feature vector sets does not interfereI,VS) When the reference signal is equal to 0, the interference signal does not interfere with the reference signal; otherwise, S (V)I,VS) If the signal is more than 0, the interference signal will interfere the reference signal;
selecting and determining an interference action parameter EP on the premise of forming interference, wherein for an interference signal, the parameter is usually signal power p or energy e; further, defining the interference degree of the interference signal to the reference signal as G, and calculating the interference influence degree of the interference signal to the reference signal; the calculation method comprises the following steps:
3) for single-mode interfering signals and references comprising only individual eigenvectorsDegree of interference G (V) of signal, interference signal vector to reference signal vectorI,VS) Evaluation was carried out using the interference parameters EP:
4) for multi-mode interference signal and reference signal containing several characteristic vectors, the interference degree G (V) of interference signal to reference signalI,VS) Defining the interference degree of the interference signal expressed by the characteristic vector set to the reference signal; the calculation at this time is as follows:
the server is connected with the monitor through a lead;
the method for establishing the normalized high-order cumulant equation set of the monitor time-frequency overlapping signals comprises the following steps:
the signal model of the received signal is represented as:
r(t)=x1(t)+x2(t)+…+xn(t)+v(t)
wherein x isi(t) is each signal component of the time-frequency overlapping signal, each component signal is independent and uncorrelated, n is the number of the time-frequency overlapping signal components, thetakiRepresenting the modulation of the phase of the carrier of the respective signal component, fciIs a carrier frequency, AkiAmplitude of the i-th signal at time k, TsiIs the length of a symbol, pi(t) is a raised cosine shaping filter function with a roll-off factor of α, andn (t) is a mean of 0 and a variance of σ2Smooth white gaussian noise;
the formula for the high order cumulant of the mixed signal is as follows:
both sides are simultaneously divided by the second moment k/2 power of the mixed signal:
the further modification is that:
whereinAndrepresenting the ratio of the power of each component signal to the total power and the ratio of the noise power to the total power, respectivelyAnd λvSince the high-order cumulative amount of gaussian white noise is 0, the above formula is expressed as:
thus, a normalized higher order cumulant equation set is constructed:
the server is connected with the monitoring station through a wire.
Further, the monitoring station passes through a wire storage.
Further, the server is connected through a network interface;
the method for the quick wake-up association of the wireless network of the server adopts unicast association, and specifically comprises the following steps:
firstly, Hub sets SSS and Asso _ ctrl fields as corresponding values according to the current communication requirement, and constructs a Wakeup frame; after the Wakeup frame is sent, a T-Poll frame is sent to the node;
step two, after receiving the awakening frame, the node obtains the associated configuration information and the public key PK of HubbThen selects its own private key SKa256 bits long, computing the public key PKa=SKa× G, after computing the public key, the node computes the password-based public key, PKa'=PKa-Q(PW),Q(PW)=(QX,QY),QX=232×PW+MX(ii) a The node calculates according to the Nonce _ b in the received Wakeup frame and the Nonce _ a selected by the node:
KMAC_1A=CMAC(Temp_1,Add_a||Add_b||Nonce_a||Nonce_b||SSS,64)
KMAC_2A=CMAC(Temp_1,Add_b Add_a Nonce_b Nonce_a SSS,64);
information PK calculated using the aboveaThe KMAC _2A constructs a first association request frame and transmits the first association request frame to the Hub;
step three, after receiving the first association request frame, the Hub firstly restores the public key PK of the current nodea=PKa'+Q(PW),Q(PW)=(QX,QY),QX=232×PW+MX;MXTo make QXSatisfying the minimum of points on an elliptic curveA non-negative integer; calculating DHKey X (SK)b×PKa)=X(SKa×SKb× G), where the X () function takes the X coordinate value of the elliptic curve key, Temp _1 ═ RMB _128(DHKey), and based on the received information and the calculated information:
KMAC_1B=CMAC(Temp_1,Add_a Add_b Nonce_a Nonce_b SSS,64)
KMAC_2B=CMAC(Temp_1,Add_b Add_a Nonce_b Nonce_a SSS,64)
if the received KMAC _2A and the calculated KMAC _2B are the same, continuing to construct a second association request frame and entering the step five of the association request of the time, and if the received KMAC _2A and the calculated KMAC _2B are different, canceling the association request of the time;
step four, the node receives a second association request frame, compares the KMAC _1A calculated in the step two with the received KMAC _1B, cancels the association request if the KMAC _1A is different from the received KMAC _1B, and enters the step five of association if the KMAC _1A is the same as the KMAC _ 1B;
step five, the node and Hub calculate MK ═ CMAC (Temp _2, Nonce _ a Nonce _ b,128)
Temp _2 ═ lmb (DHKey), the leftmost 128 bits of DHKey; both parties complete the wake-up association.
The invention has the advantages and positive effects that: by using the monitor, people in different places can conveniently acquire the real-time illness state of the patient in time, the patient can be effectively nursed, the patient can be treated better, the treatment effect is obvious, the action range is wider through wireless data transmission, and the treatment of the patient is more facilitated.
Drawings
Fig. 1 is a schematic structural diagram of a monitor based on the internet of things according to an embodiment of the present invention;
in the figure: 1. a wireless receiving station; 2. a wire; 3. a server; 4. a monitoring station; 4-1, a monitoring desk; 4-2, a memory; 5. a monitor; 6. and (4) moving the terminal equipment.
Detailed Description
In order to further understand the contents, features and effects of the present invention, the following embodiments are illustrated and described in detail with reference to the accompanying drawings.
The structure of the present invention will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1: the wireless receiving station 1 is connected with a server 3 through a lead 2; the server 3 is connected with the monitoring station 4 through a wire 2; the monitoring station 4 comprises a monitoring station 5 and a memory 6; the server 3 is connected with the monitor 7 through a lead 2.
The memory clustering the users comprises that the position information of the users is described by the current position coordinates:
li=(xi,yi);
wherein xi,yiRespectively representing the horizontal and vertical coordinate values of the user i, and constructing a content request frequency vector for the user i:
ni=(ni,1,ni,2,...,ni,c);
wherein n isi,cRepresenting the number of times user i requests content c, each user corresponding to a content request vector reflecting the user's content request preferences;
clustering users based on the position information and the content request preference information of the users, dividing the users with similar content request preference and similar positions into a multicast group, calculating the similarity between the two users by using a cosine similarity criterion, and calculating by using the following formula:
wherein β is a weight coefficient between 0 and 1;
using a K-Means clustering method to cluster all users D in the cell, ui={li,niDenotes the clustering information of user i, the purpose of clustering is to classify the original users into class C D ═ D }1,…,DCMathematically, the minimum is calculated as:
wherein gamma iskIs the center of the user group;
the specific steps of clustering the users based on the position information of the users and the video request information counted in the current time period are as follows:
step one, randomly taking C users from D as the centers of C user groups;
calculating the similarity from the rest users to the centers of the C user groups according to a calculation formula of the similarity, and dividing the users into the user groups with the highest similarity;
step three, updating the center gamma of the C user groups according to the clustering resultk={lk,nkUsing the following formula:
wherein m isiThe weight coefficient is between 0 and 1, and the second step and the third step are repeated until the clustering center is not changed any more;
the calculating the horizontal azimuth angle and the vertical elevation angle of the central position of each user group according to the user clustering result and the position information of each user group specifically comprises:
the base station has a specific beam for each user group by adopting an active antenna beam forming model, namely, a specific electronic downward inclination angle and a beam with a vertical half-power bandwidth are set for each user group, and the coordinates of the base station are an origin O (0,0, H)BS) The centroid of the user group k is gammakPosition coordinates of (x)k,yk,zk) Vertical elevation angle and horizontal azimuth angle of
Based on the clustered user group position information, the horizontal direction angle and the vertical elevation angle of the user group center are calculated by the following formulas:
obviously, the vertical elevation angle and the horizontal azimuth angle have the value range of theta1∈(0,π),
The precise alignment of the base station antenna beam to the user group specifically includes:
step one, adjusting an electronic downtilt angle, an electronic horizontal angle and a half-power bandwidth of a beam, enabling a radiation direction of the beam to be aligned to a central position of a user group, enabling a half-power bandwidth range to cover all users in the user group, and adjusting the downtilt angle and the horizontal angle from a base station to the users as follows:
wherein,and thetakCalculating the horizontal azimuth angle and the vertical elevation angle of the center of the user group by using the center position of the user group for the base station based on the user clustering result;
step two, determining the beam width, wherein the coverage area of the user group is a circle with a circular point at the center of the user group, and the radius of the circular area is the distance between the user farthest from the center position in the user group and the center, that is:
wherein (x)k,yk) Is the center gamma of the user group kkThen the vertical half-power bandwidth of the kth beam is:
the determining the channel gain model from the base station to the user by using the antenna model of the active antenna array specifically includes:
step one, according to the position information of each user and the user group wave beam to which the user belongs, the actual horizontal azimuth angle and the vertical elevation angle of each user position are calculated, and the horizontal azimuth angle of a user i relative to a base station is calculatedAnd vertical elevation angle theta'iIf user i belongs to multicast group k, the actual horizontal azimuth and vertical elevation of user i is equal to:
step two, an antenna model of the active antenna array:
the 3D antenna gain model adopts an active antenna array radiation model proposed in the 3GPP standard, and the antenna gain model is expressed as follows:
wherein,an antenna gain model of an active antenna element with a downtilt angle of 0,theta is the azimuth and vertical elevation at the actual location of the user, p is the correlation coefficient of the array antenna, wm,nAnd vm,nThe weight factor and the user offset phase are respectively expressed as follows:
m=1,2,...NH;n=1,2,...NV
m=1,2,...NH;n=1,2,...NV
wherein, thetaetiltRepresenting the downtilt angle of the antenna beam,indicating the horizontal steering angle of the antenna, theta of the antenna for different user groupsetiltAnddifferent in configuration;
step three, the channel gain model from the base station to the user adopts the multicast channel gain model, the user in a multicast group receives data at the same rate, the transmission rate of the base station exceeds the maximum bearing rate of a certain user in the group, then the user can not decode the data normally, the base station transmits the data at the minimum rate in the user group, therefore, the equivalent channel gain from the base station to the user in the user group k is equal to the worst channel gain of the user in the user group, namely:
whereinRepresenting user i (i ∈ D)k) The channel gain on carrier n, consists of 3 parts: fast fading, base station to user path loss, and user 3D antenna gain, as follows:
where F and PL denote fast fading and path loss, respectively,represents the 3D antenna gain of the kth beam to user i;
the method for clustering the user group according to the position information of the user group comprises the following specific steps:
clustering a user group based on knowledge of graph theory, and defining an inter-beam interference graph G ═ V, E, wherein V represents a set of beams as a vertex of the interference graph, and E represents inter-beam interferenceCoefficients, which are edges of the interference graph, defining an indicator function e (v)k,vm) (k ≠ m) indicates the interference between beam k and beam m:
wherein O iskAnd OmRespectively representing the radius, r, of user group k and user group mthA threshold distance representing a negligible interference between two beams, and e (v) is definedk,vk) And (0) indicating that no interference exists in the beam, and constructing a binary interference matrix according to an indication function:
interference degree of the defined beam:
when d isG(vk) When it is 0, it is called vkIs a zero degree node;
the clustering method comprises the following specific steps:
step one, constructing an interference matrix A by using a vertex set VGInitialization iteration factor h 1, set of isolated nodesClustering collectionsNode set
Step two, finding all zero-degree nodes vkUpdateS=S∪vk(ii) a The set of remaining nodes is recorded as Φ1=V-S;
Step three, clustering: a)find node k ═ argmax (d)G(vk) Let the k-th row and k-th column of the interference matrix be 0, update the node set Bh=Bh∩vk(ii) a b) Cyclically executing a) until AG0; c) updating phih=Φh-BhThen phi ishIs the h cluster;
step four, using the node to assemble BhReconstruction of AGNot equal to 0, updating the node set phih+1=BhUpdating the iteration factor h to h +1, and executing the step (3); if A isG0 or | Bh1 if | BhIf 1, thenh+1=Bh
Step five, distributing the isolated node set S to a cluster with least nodes;
after the clustering processing of the user group, the user group D ═ D1,…,Dk,…,DCThe data is divided into phi and phi through a clustering algorithm1,…,Φh,…},ΦhAnd representing the h-th user cluster, wherein the total user transmission rate in each cluster is as follows:
the total throughput of the system is the sum of the transmission rates of all user clusters:
whereinClustering phi for usershThe carrier n is used as an indicator, and correspondingly,the conditions are satisfied as follows:
the condition (2) indicates that one carrier can only be allocated to one user group cluster, the user groups in the same cluster share one carrier resource, and the user groups in different clusters can not be multiplexed;
the carrier allocation algorithm based on the maximized throughput specifically comprises the following steps:
step one, according to a formula:
calculating the total transmission rate of the users in each cluster on the carrier n;
step two, in order to maximize the throughput of the system, find out the carrier and user cluster which obtain the maximum rate, distribute the carrier to the user cluster at first, according to the formula:
allocating carrier n to user cluster ΦhThe maximum transmission rate is obtained, and carrier n is allocated to cluster phihSo that carrier n is allocated to user cluster phih
Step three, removing the carrier n from the carrier set F, and simultaneously, clustering the users to form a cluster phihRemoving from the set Φ;
step four, the step two and the step three are repeatedly executed until the carrier set or the user cluster set is combined into an empty set;
the method for analyzing the interference relationship between signals of the wireless receiving station comprises the following steps:
step one, constructing a multi-dimensional interference space model, and determining an interference signal feature vector to be analyzedAnd reference signal feature vector
Step two, aiming at the interference signal characteristic vector based on the interference space modelDefining its feature vector to the reference signalOf a displacement vector
Step three, defining displacement vectorThe projection of a certain dimension coordinate axis in the interference space is an interference signal feature vectorTo reference signal characteristicsVectorThe distance in the CP dimension is:
wherein the PRJ (-) operator represents a projection operation, computation, for a certain CP dimensionA value of (d);
step four, defining the interference state of the interference signal to the reference signal as S, and representing the interference relation of the interference signal to the reference signal, thereby judging whether the interference exists; the judging method comprises the following steps:
1) for a single-mode interference signal and a reference signal represented by an individual interference vector, when the distance of the interference signal vector to the reference signal vector in each dimension in the space model is smaller than the resolution of the dimension, representing that the interference signal interferes with the reference signal, and S is 1; on the contrary, if the distance from the interference signal vector to the reference signal vector in a certain dimension or a plurality of dimensions is greater than or equal to the resolution of the dimension, the interference signal does not interfere with the reference signal, and S is 0, that is, the interference signal and the reference signal are separable in the dimension;
2) interference state S (V) in a multimode case where interference signal and reference signal each include a plurality of interference feature vectorsI,VS) It can be calculated as follows:
wherein S [ V ]I,VS]M×NCalled the interference state matrix, each element of the matrixRepresents VIThe k-th sum of feature vectors V inSThe interference state of the ith feature vector; s (V) when only each element in the two feature vector sets does not interfereI,VS) When the reference signal is equal to 0, the interference signal does not interfere with the reference signal; otherwise, S (V)I,VS) If the signal is more than 0, the interference signal will interfere the reference signal;
selecting and determining an interference action parameter EP on the premise of forming interference, wherein for an interference signal, the parameter is usually signal power p or energy e; further, defining the interference degree of the interference signal to the reference signal as G, and calculating the interference influence degree of the interference signal to the reference signal; the calculation method comprises the following steps:
5) for single-mode interference signals and reference signals comprising only individual feature vectors, the degree of interference G (V) of the interference signal vector with respect to the reference signal vectorI,VS) Evaluation was carried out using the interference parameters EP:
6) for multi-mode interference signal and reference signal containing several characteristic vectors, the interference degree G (V) of interference signal to reference signalI,VS) Defining the interference degree of the interference signal expressed by the characteristic vector set to the reference signal; the calculation at this time is as follows:
the method for establishing the normalized high-order cumulant equation set of the monitor time-frequency overlapping signals comprises the following steps:
the signal model of the received signal is represented as:
r(t)=x1(t)+x2(t)+…+xn(t)+v(t)
wherein x isi(t) is each signal component of the time-frequency overlapping signal, each component signal is independent and uncorrelated, n is the number of the time-frequency overlapping signal components, thetakiRepresenting the modulation of the phase of the carrier of the respective signal component, fciIs a carrier frequency, AkiAmplitude of the i-th signal at time k, TsiIs the length of a symbol, pi(t) is a raised cosine shaping filter function with a roll-off factor of α, andn (t) is a mean of 0 and a variance of σ2Smooth white gaussian noise;
the formula for the high order cumulant of the mixed signal is as follows:
both sides are simultaneously divided by the second moment k/2 power of the mixed signal:
the further modification is that:
whereinAndrepresenting the ratio of the power of each component signal to the total power and the ratio of the noise power to the total power, respectivelyAnd λv. Since the high-order accumulation amount of gaussian white noise is 0, the above equation is expressed as:
thus, a normalized higher order cumulant equation set is constructed:
the method for the quick wake-up association of the wireless network of the server adopts unicast association, and specifically comprises the following steps:
firstly, Hub sets SSS and Asso _ ctrl fields as corresponding values according to the current communication requirement, and constructs a Wakeup frame; after the Wakeup frame is sent, a T-Poll frame is sent to the node;
step two, after receiving the awakening frame, the node obtains the associated configuration information and the public key PK of HubbThen selects its own private key SKa256 bits long, computing the public key PKa=SKa× G, after computing the public key, the node computes the password-based public key, PKa'=PKa-Q(PW),Q(PW)=(QX,QY),QX=232×PW+MX(ii) a The node calculates according to the Nonce _ b in the received Wakeup frame and the Nonce _ a selected by the node:
KMAC_1A=CMAC(Temp_1,Add_a||Add_b||Nonce_a||Nonce_b||SSS,64)
KMAC_2A=CMAC(Temp_1,Add_b Add_a Nonce_b Nonce_a SSS,64);
information PK calculated using the aboveaThe KMAC _2A constructs a first association request frame and transmits the first association request frame to the Hub;
step three, after receiving the first association request frame, the Hub firstly restores the public key PK of the current nodea=PKa'+Q(PW),Q(PW)=(QX,QY),QX=232×PW+MX;MXTo make QXA minimum non-negative integer satisfying a point on the elliptic curve; calculating DHKey X (SK)b×PKa)=X(SKa×SKb× G), where the X () function takes the X coordinate value of the elliptic curve key, Temp _1 ═ RMB _128(DHKey), and based on the received information and the calculated information:
KMAC_1B=CMAC(Temp_1,Add_a Add_b Nonce_a Nonce_b SSS,64)
KMAC_2B=CMAC(Temp_1,Add_b Add_a Nonce_b Nonce_a SSS,64)
if the received KMAC _2A and the calculated KMAC _2B are the same, continuing to construct a second association request frame and entering the step five of the association request of the time, and if the received KMAC _2A and the calculated KMAC _2B are different, canceling the association request of the time;
step four, the node receives a second association request frame, compares the KMAC _1A calculated in the step two with the received KMAC _1B, cancels the association request if the KMAC _1A is different from the received KMAC _1B, and enters the step five of association if the KMAC _1A is the same as the KMAC _ 1B;
step five, the node and Hub calculate MK ═ CMAC (Temp _2, Nonce _ a Nonce _ b,128)
Temp _2 ═ lmb (DHKey), the leftmost 128 bits of DHKey; both parties complete the wake-up association.
After the monitor is started, the monitor monitors physical indexes of a patient in real time, if the indexes are displayed abnormally, the monitor transmits patient illness state data to the server, the server feeds the data back to the monitoring station in real time, a monitor processes the data through the monitoring station, and the data are stored in the memory; on the other hand, the information is fed back to the wireless receiving station, and the family members far away from the wireless receiving station acquire the illness state of the patient through the mobile terminal equipment.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and all simple modifications, equivalent changes and modifications made to the above embodiment according to the technical spirit of the present invention are within the scope of the technical solution of the present invention.

Claims (3)

1. The utility model provides a monitor based on thing networking which characterized in that, monitor based on thing networking is provided with:
a monitoring station;
the monitoring station comprises a monitoring station and a memory;
the memory clustering the users comprises that the position information of the users is described by the current position coordinates:
li=(xi,yi);
wherein xi,yiRespectively represent the horizontal and vertical coordinates of user iValue, for user i, construct a content request frequency vector:
ni=(ni,1,ni,2,...,ni,c);
wherein n isi,cRepresenting the number of times user i requests content c, each user corresponding to a content request vector reflecting the user's content request preferences;
clustering users based on the position information and the content request preference information of the users, dividing the users with similar content request preference and similar positions into a multicast group, calculating the similarity between the two users by using a cosine similarity criterion, and calculating by using the following formula:
<mrow> <mi>s</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>&amp;beta;</mi> <mfrac> <mrow> <msub> <mi>l</mi> <mi>i</mi> </msub> <mo>&amp;CenterDot;</mo> <msub> <mi>l</mi> <mi>j</mi> </msub> </mrow> <mrow> <mo>|</mo> <mo>|</mo> <msub> <mi>l</mi> <mi>i</mi> </msub> <mo>|</mo> <mo>|</mo> <mo>&amp;CenterDot;</mo> <mo>|</mo> <mo>|</mo> <msub> <mi>l</mi> <mi>j</mi> </msub> <mo>|</mo> <mo>|</mo> </mrow> </mfrac> <mo>+</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mi>&amp;beta;</mi> <mo>)</mo> </mrow> <mfrac> <mrow> <msub> <mi>n</mi> <mi>i</mi> </msub> <mo>&amp;CenterDot;</mo> <msub> <mi>n</mi> <mi>j</mi> </msub> </mrow> <mrow> <mo>|</mo> <mo>|</mo> <msub> <mi>n</mi> <mi>i</mi> </msub> <mo>|</mo> <mo>|</mo> <mo>&amp;CenterDot;</mo> <mo>|</mo> <mo>|</mo> <msub> <mi>n</mi> <mi>j</mi> </msub> <mo>|</mo> <mo>|</mo> </mrow> </mfrac> <mo>;</mo> </mrow>
wherein β is a weight coefficient between 0 and 1;
using a K-Means clustering method to cluster all users D in the cell, ui={li,niDenotes the clustering information of user i, the purpose of clustering is to classify the original users into class C D ═ D }1,…,DCMathematically, the minimum is calculated as:
<mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>C</mi> </munderover> <munder> <mo>&amp;Sigma;</mo> <mrow> <msub> <mi>u</mi> <mi>i</mi> </msub> <mo>&amp;Element;</mo> <msub> <mi>D</mi> <mi>k</mi> </msub> </mrow> </munder> <mo>|</mo> <mo>|</mo> <msub> <mi>u</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>&amp;gamma;</mi> <mi>k</mi> </msub> <mo>|</mo> <mo>|</mo> <mo>;</mo> </mrow>
wherein gamma iskIs the center of the user group;
the specific steps of clustering the users based on the position information of the users and the video request information counted in the current time period are as follows:
step one, randomly taking C users from D as the centers of C user groups;
calculating the similarity from the rest users to the centers of the C user groups according to a calculation formula of the similarity, and dividing the users into the user groups with the highest similarity;
step three, updating the center gamma of the C user groups according to the clustering resultk={lk,nkUsing asThe following formula:
<mrow> <msub> <mi>l</mi> <mi>k</mi> </msub> <mo>=</mo> <mrow> <mo>(</mo> <mfrac> <mrow> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>&amp;Element;</mo> <msub> <mi>D</mi> <mi>k</mi> </msub> </mrow> </munder> <msub> <mi>m</mi> <mi>i</mi> </msub> <msub> <mi>x</mi> <mi>i</mi> </msub> </mrow> <mrow> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>&amp;Element;</mo> <msub> <mi>D</mi> <mi>k</mi> </msub> </mrow> </munder> <msub> <mi>m</mi> <mi>i</mi> </msub> </mrow> </mfrac> <mo>,</mo> <mfrac> <mrow> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>&amp;Element;</mo> <msub> <mi>D</mi> <mi>k</mi> </msub> </mrow> </munder> <msub> <mi>m</mi> <mi>i</mi> </msub> <msub> <mi>y</mi> <mi>i</mi> </msub> </mrow> <mrow> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>&amp;Element;</mo> <msub> <mi>D</mi> <mi>k</mi> </msub> </mrow> </munder> <msub> <mi>m</mi> <mi>i</mi> </msub> </mrow> </mfrac> <mo>)</mo> </mrow> <mo>;</mo> </mrow>
<mrow> <msub> <mi>n</mi> <mi>k</mi> </msub> <mo>=</mo> <mrow> <mo>(</mo> <mfrac> <mrow> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>&amp;Element;</mo> <msub> <mi>D</mi> <mi>k</mi> </msub> </mrow> </munder> <msub> <mi>m</mi> <mi>i</mi> </msub> <msub> <mi>n</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> </mrow> <mrow> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>&amp;Element;</mo> <msub> <mi>D</mi> <mi>k</mi> </msub> </mrow> </munder> <msub> <mi>m</mi> <mi>i</mi> </msub> </mrow> </mfrac> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mfrac> <mrow> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>&amp;Element;</mo> <msub> <mi>D</mi> <mi>k</mi> </msub> </mrow> </munder> <msub> <mi>m</mi> <mi>i</mi> </msub> <msub> <mi>n</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>c</mi> </mrow> </msub> </mrow> <mrow> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>&amp;Element;</mo> <msub> <mi>D</mi> <mi>k</mi> </msub> </mrow> </munder> <msub> <mi>m</mi> <mi>i</mi> </msub> </mrow> </mfrac> <mo>)</mo> </mrow> <mo>;</mo> </mrow>
wherein m isiThe weight coefficient is between 0 and 1, and the second step and the third step are repeated until the clustering center is not changed any more;
the calculating the horizontal azimuth angle and the vertical elevation angle of the central position of each user group according to the user clustering result and the position information of each user group specifically comprises:
the base station has a specific beam for each user group by adopting an active antenna beam forming model, namely, a specific electronic downward inclination angle and a beam with a vertical half-power bandwidth are set for each user group, and the coordinates of the base station are an origin O (0,0, H)BS) The centroid of the user group k is gammakPosition coordinates of (x)k,yk,zk) Vertical elevation angle and horizontal azimuth angle of
Based on the clustered user group position information, the horizontal direction angle and the vertical elevation angle of the user group center are calculated by the following formulas:
<mrow> <msub> <mi>&amp;theta;</mi> <mi>k</mi> </msub> <mo>=</mo> <mi>a</mi> <mi>c</mi> <mi> </mi> <mi>t</mi> <mi>a</mi> <mi>n</mi> <mrow> <mo>(</mo> <mfrac> <mrow> <msub> <mi>H</mi> <mrow> <mi>B</mi> <mi>S</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>z</mi> <mi>k</mi> </msub> </mrow> <msqrt> <mrow> <mo>(</mo> <msubsup> <mi>x</mi> <mi>k</mi> <mn>2</mn> </msubsup> <mo>+</mo> <msubsup> <mi>y</mi> <mi>k</mi> <mn>2</mn> </msubsup> <mo>)</mo> </mrow> </msqrt> </mfrac> <mo>)</mo> </mrow> <mo>+</mo> <mi>&amp;pi;</mi> <mo>/</mo> <mn>2</mn> <mo>;</mo> </mrow>
obviously, the vertical elevation angle and the horizontal azimuth angle have the value range of theta1∈(0,π),
The precise alignment of the base station antenna beam to the user group specifically includes:
step one, adjusting an electronic downtilt angle, an electronic horizontal angle and a half-power bandwidth of a beam, enabling a radiation direction of the beam to be aligned to a central position of a user group, enabling a half-power bandwidth range to cover all users in the user group, and adjusting the downtilt angle and the horizontal angle from a base station to the users as follows:
wherein,and thetakCalculating the horizontal azimuth angle and the vertical elevation angle of the center of the user group by using the center position of the user group for the base station based on the user clustering result;
step two, determining the beam width, wherein the coverage area of the user group is a circle with a circular point at the center of the user group, and the radius of the circular area is the distance between the user farthest from the center position in the user group and the center, that is:
<mrow> <msub> <mi>r</mi> <mi>k</mi> </msub> <mo>=</mo> <munder> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> <mrow> <mi>i</mi> <mo>&amp;Element;</mo> <msub> <mi>D</mi> <mi>k</mi> </msub> </mrow> </munder> <msqrt> <mrow> <msup> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>x</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>y</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>y</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> <mo>;</mo> </mrow>
wherein (x)k,yk) Is the center gamma of the user group kkThen the vertical half-power bandwidth of the kth beam is:
<mrow> <msubsup> <mi>&amp;theta;</mi> <mrow> <mn>3</mn> <mi>d</mi> <mi>B</mi> </mrow> <mi>k</mi> </msubsup> <mo>=</mo> <mi>arctan</mi> <mfrac> <mrow> <msqrt> <mrow> <mo>(</mo> <msubsup> <mi>x</mi> <mi>k</mi> <mn>2</mn> </msubsup> <mo>+</mo> <msubsup> <mi>y</mi> <mi>k</mi> <mn>2</mn> </msubsup> <mo>)</mo> </mrow> </msqrt> <mo>+</mo> <msub> <mi>r</mi> <mi>k</mi> </msub> </mrow> <msub> <mi>H</mi> <mrow> <mi>B</mi> <mi>S</mi> </mrow> </msub> </mfrac> <mo>-</mo> <mi>arctan</mi> <mfrac> <mrow> <msqrt> <mrow> <mo>(</mo> <msubsup> <mi>x</mi> <mi>k</mi> <mn>2</mn> </msubsup> <mo>+</mo> <msubsup> <mi>y</mi> <mi>k</mi> <mn>2</mn> </msubsup> <mo>)</mo> </mrow> </msqrt> <mo>-</mo> <msub> <mi>r</mi> <mi>k</mi> </msub> </mrow> <msub> <mi>H</mi> <mrow> <mi>B</mi> <mi>S</mi> </mrow> </msub> </mfrac> <mo>;</mo> </mrow>
the determining the channel gain model from the base station to the user by using the antenna model of the active antenna array specifically includes:
step one, according to the position information of each user and the user group wave beam to which the user belongs, the actual horizontal azimuth angle and the vertical elevation angle of each user position are calculated, and the horizontal azimuth angle of a user i relative to a base station is calculatedAnd vertical elevation angle theta'iIf user i belongs to multicast group k, the actual horizontal azimuth and vertical elevation of user i is equal to:
step two, an antenna model of the active antenna array:
the 3D antenna gain model adopts an active antenna array radiation model proposed in the 3GPP standard, and the antenna gain model is expressed as follows:
wherein,an antenna gain model of an active antenna element with a downtilt angle of 0,theta is the azimuth and vertical elevation at the actual location of the user, p is the correlation coefficient of the array antenna, wm,nAnd vm,nThe weight factor and the user offset phase are respectively expressed as follows:
wherein, thetaetiltRepresenting the downtilt angle of the antenna beam,indicating the horizontal steering angle of the antenna, theta of the antenna for different user groupsetiltAnddifferent in configuration;
step three, the channel gain model from the base station to the user adopts the multicast channel gain model, the user in a multicast group receives data at the same rate, the transmission rate of the base station exceeds the maximum bearing rate of a certain user in the group, then the user can not decode the data normally, the base station transmits the data at the minimum rate in the user group, therefore, the equivalent channel gain from the base station to the user in the user group k is equal to the worst channel gain of the user in the user group, namely:
whereinRepresenting user i (i ∈ D)k) The channel gain on carrier n, consists of 3 parts: fast fading, base station to user path loss, and user 3D antenna gain, as follows:
where F and PL denote fast fading and path loss, respectively,represents the 3D antenna gain of the kth beam to user i;
the method for clustering the user group according to the position information of the user group comprises the following specific steps:
clustering a user group based on knowledge of graph theory, defining an interference graph G (V, E) among beams, wherein V represents a set of beams, the vertex of the interference graph is used as V, E represents an interference coefficient among beams, and an indicating function E (V, E) is defined as an edge of the interference graphk,vm) (k ≠ m) indicates the interference between beam k and beam m:
<mrow> <mi>e</mi> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>k</mi> </msub> <mo>,</mo> <msub> <mi>v</mi> <mi>m</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mn>1</mn> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mo>|</mo> <msub> <mi>O</mi> <mi>k</mi> </msub> <mo>-</mo> <msub> <mi>O</mi> <mi>m</mi> </msub> <mo>|</mo> <mo>&lt;</mo> <msub> <mi>r</mi> <mi>k</mi> </msub> <mo>+</mo> <msub> <mi>r</mi> <mi>m</mi> </msub> <mo>+</mo> <msub> <mi>r</mi> <mrow> <mi>t</mi> <mi>h</mi> </mrow> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>0</mn> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mo>|</mo> <msub> <mi>O</mi> <mi>k</mi> </msub> <mo>-</mo> <msub> <mi>O</mi> <mi>m</mi> </msub> <mo>|</mo> <mo>&amp;GreaterEqual;</mo> <msub> <mi>r</mi> <mi>k</mi> </msub> <mo>+</mo> <msub> <mi>r</mi> <mi>m</mi> </msub> <mo>+</mo> <msub> <mi>r</mi> <mrow> <mi>t</mi> <mi>h</mi> </mrow> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>;</mo> </mrow>
wherein O iskAnd OmRespectively representing the radius, r, of user group k and user group mthA threshold distance representing a negligible interference between two beams, and e (v) is definedk,vk) And (0) indicating that no interference exists in the beam, and constructing a binary interference matrix according to an indication function:
interference degree of the defined beam:
<mrow> <msub> <mi>d</mi> <mi>G</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>m</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mi>m</mi> <mo>&amp;NotEqual;</mo> <mi>k</mi> </mrow> <mi>C</mi> </munderover> <mi>e</mi> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>k</mi> </msub> <mo>,</mo> <msub> <mi>v</mi> <mi>m</mi> </msub> <mo>)</mo> </mrow> <mo>;</mo> </mrow>
when d isG(vk) When it is 0, it is called vkIs a zero degree node;
the clustering method comprises the following specific steps:
step one, constructing an interference matrix A by using a vertex set VGInitialization iteration factor h 1, set of isolated nodesClustering collectionsNode set
Step two, finding all zero-degree nodes vkUpdate S-S ∪ vk(ii) a The set of remaining nodes is recorded as Φ1=V-S;
Step three, clustering: a)find node k ═ argmax (d)G(vk) Let the k-th row and k-th column of the interference matrix be 0, update the node set Bh=Bh∩vk(ii) a b) Cyclically executing a) until AG0; c) updating phih=Φh-BhThen phi ishIs the h cluster;
step four, using the node to assemble BhReconstruction of AGNot equal to 0, updating the node set phih+1=BhUpdating the iteration factor h to h +1, and executing the step (3); if A isG0 or | Bh1 if | BhIf 1, thenh+1=Bh
Step five, distributing the isolated node set S to a cluster with least nodes;
after the clustering processing of the user group, the user group D ═ D1,…,Dk,…,DCThe data is divided into phi and phi through a clustering algorithm1,…,Φh,…},ΦhAnd representing the h-th user cluster, wherein the total user transmission rate in each cluster is as follows:
<mrow> <msubsup> <mi>R</mi> <mi>n</mi> <msub> <mi>&amp;Phi;</mi> <mi>h</mi> </msub> </msubsup> <mo>=</mo> <munder> <mo>&amp;Sigma;</mo> <mrow> <msub> <mi>D</mi> <mi>k</mi> </msub> <mo>&amp;Element;</mo> <msub> <mi>&amp;Phi;</mi> <mi>h</mi> </msub> </mrow> </munder> <msubsup> <mi>R</mi> <mi>n</mi> <mi>k</mi> </msubsup> <mo>;</mo> </mrow>
the total throughput of the system is the sum of the transmission rates of all user clusters:
<mrow> <mi>U</mi> <mo>=</mo> <munder> <mo>&amp;Sigma;</mo> <mrow> <msub> <mi>&amp;Phi;</mi> <mi>h</mi> </msub> <mo>&amp;Element;</mo> <mi>&amp;Phi;</mi> </mrow> </munder> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>n</mi> <mo>&amp;Element;</mo> <mi>F</mi> </mrow> </munder> <msub> <mi>&amp;alpha;</mi> <mrow> <mi>n</mi> <mo>,</mo> <msub> <mi>&amp;Phi;</mi> <mi>h</mi> </msub> </mrow> </msub> <msubsup> <mi>R</mi> <mi>n</mi> <msub> <mi>&amp;Phi;</mi> <mi>h</mi> </msub> </msubsup> <mo>;</mo> </mrow>
whereinClustering phi for usershThe carrier n is used as an indicator, and correspondingly,the conditions are satisfied as follows:
<mrow> <msub> <mi>&amp;alpha;</mi> <mrow> <mi>n</mi> <mo>,</mo> <msub> <mi>&amp;Phi;</mi> <mi>h</mi> </msub> </mrow> </msub> <mo>=</mo> <mo>{</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>}</mo> <mo>,</mo> <msub> <mi>&amp;Phi;</mi> <mi>h</mi> </msub> <mo>&amp;Element;</mo> <mi>&amp;Phi;</mi> <mo>,</mo> <mi>n</mi> <mo>&amp;Element;</mo> <mi>F</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow>
<mrow> <munder> <mo>&amp;Sigma;</mo> <mrow> <msub> <mi>&amp;Phi;</mi> <mi>h</mi> </msub> <mo>&amp;Element;</mo> <mi>&amp;Phi;</mi> </mrow> </munder> <msub> <mi>&amp;alpha;</mi> <mrow> <mi>n</mi> <mo>,</mo> <msub> <mi>&amp;Phi;</mi> <mi>h</mi> </msub> </mrow> </msub> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mi>n</mi> <mo>&amp;Element;</mo> <mi>F</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow>
the condition (2) indicates that one carrier can only be allocated to one user group cluster, the user groups in the same cluster share one carrier resource, and the user groups in different clusters can not be multiplexed;
the carrier allocation algorithm based on the maximized throughput specifically comprises the following steps:
step one, according to a formula:
<mrow> <msubsup> <mi>R</mi> <mi>n</mi> <msub> <mi>&amp;Phi;</mi> <mi>h</mi> </msub> </msubsup> <mo>=</mo> <munder> <mo>&amp;Sigma;</mo> <mrow> <msub> <mi>D</mi> <mi>k</mi> </msub> <mo>&amp;Element;</mo> <msub> <mi>&amp;Phi;</mi> <mi>h</mi> </msub> </mrow> </munder> <msubsup> <mi>R</mi> <mi>n</mi> <mi>k</mi> </msubsup> <mo>,</mo> <mi>n</mi> <mo>&amp;Element;</mo> <mi>F</mi> <mo>,</mo> <msub> <mi>&amp;Phi;</mi> <mi>h</mi> </msub> <mo>&amp;Element;</mo> <mi>&amp;Phi;</mi> <mo>;</mo> </mrow>
calculating the total transmission rate of the users in each cluster on the carrier n;
step two, in order to maximize the throughput of the system, find out the carrier and user cluster which obtain the maximum rate, distribute the carrier to the user cluster at first, according to the formula:
<mrow> <mo>(</mo> <mi>n</mi> <mo>,</mo> <msub> <mi>&amp;Phi;</mi> <mi>h</mi> </msub> <mo>)</mo> <mo>=</mo> <mi>arg</mi> <munder> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> <mrow> <mi>n</mi> <mo>&amp;Element;</mo> <mi>F</mi> <mo>,</mo> <msub> <mi>&amp;Phi;</mi> <mi>h</mi> </msub> <mo>&amp;Element;</mo> <mi>&amp;Phi;</mi> </mrow> </munder> <msubsup> <mi>R</mi> <mi>n</mi> <msub> <mi>&amp;Phi;</mi> <mi>h</mi> </msub> </msubsup> <mo>;</mo> </mrow>4
allocating carrier n to user cluster ΦhThe maximum transmission rate is obtained, and carrier n is allocated to cluster phihSo that carrier n is allocated to user cluster phih
Step three, removing the carrier n from the carrier set F, and simultaneously, clustering the users to form a cluster phihRemoving from the set Φ;
step four, the step two and the step three are repeatedly executed until the carrier set or the user cluster set is combined into an empty set;
the wireless receiving station is connected with the server through a wire;
the method for analyzing the interference relationship between signals of the wireless receiving station comprises the following steps:
step one, constructing a multi-dimensional interference space model, and determining an interference signal feature vector to be analyzedAnd reference signal feature vector
Step two, aiming at the interference signal characteristic vector based on the interference space modelDefining its feature vector to the reference signalOf a displacement vector
Step three, defining displacement vectorThe projection of a certain dimension coordinate axis in the interference space is an interference signal feature vectorTo reference signal feature vectorThe distance in the CP dimension is:
wherein the PRJ (-) operator represents a projection operation, computation, for a certain CP dimensionA value of (d);
step four, defining the interference state of the interference signal to the reference signal as S, and representing the interference relation of the interference signal to the reference signal, thereby judging whether the interference exists; the judging method comprises the following steps:
1) for a single-mode interference signal and a reference signal represented by an individual interference vector, when the distance of the interference signal vector to the reference signal vector in each dimension in the space model is smaller than the resolution of the dimension, representing that the interference signal interferes with the reference signal, and S is 1; on the contrary, if the distance from the interference signal vector to the reference signal vector in a certain dimension or a plurality of dimensions is greater than or equal to the resolution of the dimension, the interference signal does not interfere with the reference signal, and S is 0, that is, the interference signal and the reference signal are separable in the dimension;
<mrow> <mi>S</mi> <mrow> <mo>(</mo> <mrow> <mover> <msub> <mi>V</mi> <mi>I</mi> </msub> <mo>&amp;RightArrow;</mo> </mover> <mo>,</mo> <mover> <msub> <mi>V</mi> <mi>S</mi> </msub> <mo>&amp;RightArrow;</mo> </mover> </mrow> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mrow> <mo>&amp;Exists;</mo> <msub> <mi>CP</mi> <mi>i</mi> </msub> <mo>,</mo> <msub> <mi>d</mi> <mrow> <msub> <mi>CP</mi> <mi>i</mi> </msub> <mo>,</mo> <mrow> <mo>(</mo> <mrow> <mi>I</mi> <mo>,</mo> <mi>S</mi> </mrow> <mo>)</mo> </mrow> </mrow> </msub> <mo>&amp;GreaterEqual;</mo> <msub> <mi>&amp;Delta;</mi> <mrow> <msub> <mi>CP</mi> <mi>i</mi> </msub> </mrow> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>1</mn> </mtd> <mtd> <mrow> <mo>&amp;ForAll;</mo> <msub> <mi>CP</mi> <mi>i</mi> </msub> <mo>,</mo> <msub> <mi>d</mi> <mrow> <msub> <mi>CP</mi> <mi>i</mi> </msub> <mo>,</mo> <mrow> <mo>(</mo> <mrow> <mi>I</mi> <mo>,</mo> <mi>S</mi> </mrow> <mo>)</mo> </mrow> </mrow> </msub> <mo>&lt;</mo> <msub> <mi>&amp;Delta;</mi> <mrow> <msub> <mi>CP</mi> <mi>i</mi> </msub> </mrow> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
2) interference state S (V) in a multimode case where interference signal and reference signal each include a plurality of interference feature vectorsI,VS) It can be calculated as follows:
wherein S [ V ]I,VS]M×NCalled the interference state matrix, each element of the matrixRepresents VIThe k-th sum of feature vectors V inSIn (1)Interference state of the ith feature vector; s (V) when only each element in the two feature vector sets does not interfereI,VS) When the reference signal is equal to 0, the interference signal does not interfere with the reference signal; otherwise, S (V)I,VS) If the signal is more than 0, the interference signal will interfere the reference signal;
selecting and determining an interference action parameter EP on the premise of forming interference, wherein for an interference signal, the parameter is usually signal power p or energy e; further, defining the interference degree of the interference signal to the reference signal as G, and calculating the interference influence degree of the interference signal to the reference signal; the calculation method comprises the following steps:
1) for single-mode interference signals and reference signals comprising only individual feature vectors, the degree of interference G (V) of the interference signal vector with respect to the reference signal vectorI,VS) Evaluation was carried out using the interference parameters EP:
<mrow> <mi>G</mi> <mrow> <mo>(</mo> <mrow> <mover> <msub> <mi>V</mi> <mi>I</mi> </msub> <mo>&amp;RightArrow;</mo> </mover> <mo>,</mo> <mover> <msub> <mi>V</mi> <mi>S</mi> </msub> <mo>&amp;RightArrow;</mo> </mover> </mrow> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <msub> <mi>ep</mi> <mi>I</mi> </msub> <mo>&amp;CenterDot;</mo> <mi>S</mi> <mrow> <mo>(</mo> <mrow> <mover> <msub> <mi>V</mi> <mi>I</mi> </msub> <mo>&amp;RightArrow;</mo> </mover> <mo>,</mo> <mover> <msub> <mi>V</mi> <mi>S</mi> </msub> <mo>&amp;RightArrow;</mo> </mover> </mrow> <mo>)</mo> </mrow> </mrow> <mrow> <msub> <mi>ep</mi> <mi>S</mi> </msub> </mrow> </mfrac> <mo>;</mo> </mrow>
2) for multi-mode interference signal and reference signal containing several characteristic vectors, the interference degree G (V) of interference signal to reference signalI,VS) Defining the interference degree of the interference signal expressed by the characteristic vector set to the reference signal; the calculation at this time is as follows:
the server is connected with the monitor through a lead;
the method for establishing the normalized high-order cumulant equation set of the monitor time-frequency overlapping signals comprises the following steps:
the signal model of the received signal is represented as:
r(t)=x1(t)+x2(t)+…+xn(t)+v(t)
<mrow> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>=</mo> <munder> <mo>&amp;Sigma;</mo> <mi>k</mi> </munder> <msub> <mi>A</mi> <mrow> <mi>k</mi> <mi>i</mi> </mrow> </msub> <mi>c</mi> <mi>o</mi> <mi>s</mi> <mrow> <mo>(</mo> <mn>2</mn> <msub> <mi>&amp;pi;f</mi> <mi>c</mi> </msub> <mi>t</mi> <mo>+</mo> <msub> <mi>&amp;theta;</mi> <mrow> <mi>k</mi> <mi>i</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>&amp;CenterDot;</mo> <mi>g</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>-</mo> <msub> <mi>kT</mi> <mrow> <mi>s</mi> <mi>i</mi> </mrow> </msub> <mo>)</mo> </mrow> </mrow>
wherein x isi(t) is each signal component of the time-frequency overlapping signal, each component signal is independent and uncorrelated, n is the number of the time-frequency overlapping signal components, thetakiRepresenting the modulation of the phase of the carrier of the respective signal component, fciIs a carrier frequency, AkiAmplitude of the i-th signal at time k, TsiIs the length of a symbol, pi(t) is a raised cosine shaping filter function with a roll-off factor of α, andn (t) is a mean of 0 and a variance of σ2Smooth white gaussian noise;
the formula for the high order cumulant of the mixed signal is as follows:
<mrow> <msub> <mi>C</mi> <mrow> <mi>k</mi> <mo>,</mo> <mi>r</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>C</mi> <mrow> <mi>k</mi> <mo>,</mo> <msub> <mi>x</mi> <mn>1</mn> </msub> </mrow> </msub> <mo>+</mo> <msub> <mi>C</mi> <mrow> <mi>k</mi> <mo>,</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> </mrow> </msub> <mo>+</mo> <mo>...</mo> <msub> <mi>C</mi> <mrow> <mi>k</mi> <mo>,</mo> <msub> <mi>x</mi> <mi>n</mi> </msub> </mrow> </msub> <mo>+</mo> <msub> <mi>C</mi> <mrow> <mi>k</mi> <mo>,</mo> <mi>v</mi> </mrow> </msub> <mo>;</mo> </mrow>
both sides are simultaneously divided by the second moment k/2 power of the mixed signal:
<mrow> <mfrac> <msub> <mi>C</mi> <mrow> <mi>k</mi> <mo>,</mo> <mi>r</mi> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <mi>r</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> </mfrac> <mo>=</mo> <mfrac> <msub> <mi>C</mi> <mrow> <mi>k</mi> <mo>,</mo> <msub> <mi>x</mi> <mn>1</mn> </msub> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <mi>r</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> </mfrac> <mo>+</mo> <mfrac> <msub> <mi>C</mi> <mrow> <mi>k</mi> <mo>,</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <mi>r</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> </mfrac> <mo>+</mo> <mo>...</mo> <mfrac> <msub> <mi>C</mi> <mrow> <mi>k</mi> <mo>,</mo> <msub> <mi>x</mi> <mi>n</mi> </msub> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <mi>r</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> </mfrac> <mo>+</mo> <mfrac> <msub> <mi>C</mi> <mrow> <mi>k</mi> <mo>,</mo> <mi>v</mi> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <mi>r</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> </mfrac> <mo>;</mo> </mrow>
the further modification is that:
<mrow> <mfrac> <msub> <mi>C</mi> <mrow> <mi>k</mi> <mo>,</mo> <mi>r</mi> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <mi>r</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> </mfrac> <mo>=</mo> <mfrac> <msub> <mi>C</mi> <mrow> <mi>k</mi> <mo>,</mo> <msub> <mi>x</mi> <mn>1</mn> </msub> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <msub> <mi>x</mi> <mn>1</mn> </msub> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> </mfrac> <mo>&amp;CenterDot;</mo> <mfrac> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <msub> <mi>x</mi> <mn>1</mn> </msub> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <mi>r</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> </mfrac> <mo>+</mo> <mfrac> <msub> <mi>C</mi> <mrow> <mi>k</mi> <mo>,</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> </mfrac> <mo>&amp;CenterDot;</mo> <mfrac> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <mi>r</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> </mfrac> <mo>+</mo> <mn>...</mn> <mfrac> <msub> <mi>C</mi> <mrow> <mi>k</mi> <mo>,</mo> <msub> <mi>x</mi> <mi>n</mi> </msub> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <msub> <mi>x</mi> <mi>n</mi> </msub> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> </mfrac> <mo>&amp;CenterDot;</mo> <mfrac> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <msub> <mi>x</mi> <mi>n</mi> </msub> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <mi>r</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> </mfrac> <mo>+</mo> <mfrac> <msub> <mi>C</mi> <mrow> <mi>k</mi> <mo>,</mo> <mi>v</mi> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <mi>v</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> </mfrac> <mo>&amp;CenterDot;</mo> <mfrac> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <mi>v</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <mi>r</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> </mfrac> </mrow>
whereinAndrepresenting the ratio of the power of each component signal to the total power and the ratio of the noise power to the total power, respectivelyAnd λv(ii) a Since the high-order accumulation amount of gaussian white noise is 0, the above equation is expressed as:
<mrow> <mfrac> <msub> <mi>C</mi> <mrow> <mi>k</mi> <mo>,</mo> <mi>r</mi> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <mi>r</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> </mfrac> <mo>=</mo> <mfrac> <msub> <mi>C</mi> <mrow> <mi>k</mi> <mo>,</mo> <msub> <mi>x</mi> <mn>1</mn> </msub> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <msub> <mi>x</mi> <mn>1</mn> </msub> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> </mfrac> <mo>&amp;CenterDot;</mo> <msup> <msub> <mi>&amp;lambda;</mi> <msub> <mi>x</mi> <mn>1</mn> </msub> </msub> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> <mo>+</mo> <mfrac> <msub> <mi>C</mi> <mrow> <mi>k</mi> <mo>,</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> </mfrac> <mo>&amp;CenterDot;</mo> <msup> <msub> <mi>&amp;lambda;</mi> <msub> <mi>x</mi> <mn>2</mn> </msub> </msub> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> <mo>+</mo> <mo>...</mo> <mfrac> <msub> <mi>C</mi> <mrow> <mi>k</mi> <mo>,</mo> <msub> <mi>x</mi> <mi>n</mi> </msub> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <msub> <mi>x</mi> <mi>n</mi> </msub> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> </mfrac> <mo>&amp;CenterDot;</mo> <msup> <msub> <mi>&amp;lambda;</mi> <msub> <mi>x</mi> <mi>n</mi> </msub> </msub> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> <mo>;</mo> </mrow>
thus, a normalized higher order cumulant equation set is constructed:
<mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mfrac> <msub> <mi>C</mi> <mrow> <mn>4</mn> <mo>,</mo> <mi>r</mi> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <mi>r</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mfrac> <mo>=</mo> <mfrac> <msub> <mi>C</mi> <mrow> <mn>4</mn> <mo>,</mo> <msub> <mi>x</mi> <mn>1</mn> </msub> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <msub> <mi>x</mi> <mn>1</mn> </msub> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mfrac> <mo>&amp;CenterDot;</mo> <msup> <msub> <mi>&amp;lambda;</mi> <msub> <mi>x</mi> <mn>1</mn> </msub> </msub> <mn>2</mn> </msup> <mo>+</mo> <mfrac> <msub> <mi>C</mi> <mrow> <mn>4</mn> <mo>,</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> </mfrac> <mo>&amp;CenterDot;</mo> <msup> <msub> <mi>&amp;lambda;</mi> <msub> <mi>x</mi> <mn>2</mn> </msub> </msub> <mn>2</mn> </msup> <mo>+</mo> <mn>...</mn> <mfrac> <msub> <mi>C</mi> <mrow> <mn>4</mn> <mo>,</mo> <msub> <mi>x</mi> <mi>N</mi> </msub> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <msub> <mi>x</mi> <mi>N</mi> </msub> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mi>k</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> </mfrac> <mo>&amp;CenterDot;</mo> <msup> <msub> <mi>&amp;lambda;</mi> <msub> <mi>x</mi> <mi>N</mi> </msub> </msub> <mn>2</mn> </msup> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mfrac> <msub> <mi>C</mi> <mrow> <mn>6</mn> <mo>,</mo> <mi>r</mi> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <mi>r</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mn>3</mn> </msup> </mfrac> <mo>=</mo> <mfrac> <msub> <mi>C</mi> <mrow> <mn>6</mn> <mo>,</mo> <msub> <mi>x</mi> <mn>1</mn> </msub> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <msub> <mi>x</mi> <mn>1</mn> </msub> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mn>3</mn> </msup> </mfrac> <mo>&amp;CenterDot;</mo> <msup> <msub> <mi>&amp;lambda;</mi> <msub> <mi>x</mi> <mn>1</mn> </msub> </msub> <mn>3</mn> </msup> <mo>+</mo> <mfrac> <msub> <mi>C</mi> <mrow> <mn>6</mn> <mo>,</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mn>3</mn> </msup> </mfrac> <mo>&amp;CenterDot;</mo> <msup> <msub> <mi>&amp;lambda;</mi> <msub> <mi>x</mi> <mn>2</mn> </msub> </msub> <mn>3</mn> </msup> <mo>+</mo> <mn>...</mn> <mfrac> <msub> <mi>C</mi> <mrow> <mn>6</mn> <mo>,</mo> <msub> <mi>x</mi> <mi>N</mi> </msub> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <msub> <mi>x</mi> <mi>N</mi> </msub> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mn>3</mn> </msup> </mfrac> <mo>&amp;CenterDot;</mo> <msup> <msub> <mi>&amp;lambda;</mi> <msub> <mi>x</mi> <mi>N</mi> </msub> </msub> <mn>3</mn> </msup> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mfrac> <msub> <mi>C</mi> <mrow> <mn>8</mn> <mo>,</mo> <mi>r</mi> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <mi>r</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mn>4</mn> </msup> </mfrac> <mo>=</mo> <mfrac> <msub> <mi>C</mi> <mrow> <mn>8</mn> <mo>,</mo> <msub> <mi>x</mi> <mn>1</mn> </msub> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <msub> <mi>x</mi> <mn>1</mn> </msub> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mn>4</mn> </msup> </mfrac> <mo>&amp;CenterDot;</mo> <msup> <msub> <mi>&amp;lambda;</mi> <msub> <mi>x</mi> <mn>1</mn> </msub> </msub> <mn>4</mn> </msup> <mo>+</mo> <mfrac> <msub> <mi>C</mi> <mrow> <mn>8</mn> <mo>,</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mn>4</mn> </msup> </mfrac> <mo>&amp;CenterDot;</mo> <msup> <msub> <mi>&amp;lambda;</mi> <msub> <mi>x</mi> <mn>2</mn> </msub> </msub> <mn>4</mn> </msup> <mo>+</mo> <mn>...</mn> <mfrac> <msub> <mi>C</mi> <mrow> <mn>8</mn> <mo>,</mo> <msub> <mi>x</mi> <mi>N</mi> </msub> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mrow> <msub> <mi>x</mi> <mi>N</mi> </msub> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mn>4</mn> </msup> </mfrac> <mo>&amp;CenterDot;</mo> <msup> <msub> <mi>&amp;lambda;</mi> <msub> <mi>x</mi> <mi>N</mi> </msub> </msub> <mn>4</mn> </msup> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>...</mn> </mtd> </mtr> </mtable> </mfenced> <mo>;</mo> </mrow>
the server is connected with the monitoring station through a wire.
2. The internet of things-based monitor of claim 1, wherein the monitoring station passes through a wire storage.
3. The internet of things based monitor of claim 1, wherein the server is connected through a network interface;
the method for the quick wake-up association of the wireless network of the server adopts unicast association, and specifically comprises the following steps:
firstly, Hub sets SSS and Asso _ ctrl fields as corresponding values according to the current communication requirement, and constructs a Wakeup frame; after the Wakeup frame is sent, a T-Poll frame is sent to the node;
step two, after receiving the awakening frame, the node obtains the associated configuration information and the public key PK of HubbThen selects its own private key SKa256 bits long, computing the public key PKa=SKa× G, after computing the public key, the node computes the password-based public key, PKa'=PKa-Q(PW),Q(PW)=(QX,QY),QX=232×PW+MX(ii) a The node calculates according to the Nonce _ b in the received Wakeup frame and the Nonce _ a selected by the node:
KMAC_1A=CMAC(Temp_1,Add_a||Add_b||Nonce_a||Nonce_b||SSS,64)
KMAC_2A=CMAC(Temp_1,Add_b Add_a Nonce_b Nonce_a SSS,64);
information PK calculated using the aboveaThe KMAC _2A constructs a first association request frame and transmits the first association request frame to the Hub;
step three, after receiving the first association request frame, the Hub firstly restores the public key PK of the current nodea=PKa'+Q(PW),Q(PW)=(QX,QY),QX=232×PW+MX;MXTo make QXA minimum non-negative integer satisfying a point on the elliptic curve; calculating DHKey X (SK)b×PKa)=X(SKa×SKb× G), where the X () function takes the X coordinate value of the elliptic curve key, Temp _1 ═ RMB _128(DHKey), and based on the received information and the calculated information:
KMAC_1B=CMAC(Temp_1,Add_a Add_b Nonce_a Nonce_b SSS,64)
KMAC_2B=CMAC(Temp_1,Add_b Add_a Nonce_b Nonce_a SSS,64)
if the received KMAC _2A and the calculated KMAC _2B are the same, continuing to construct a second association request frame and entering the step five of the association request of the time, and if the received KMAC _2A and the calculated KMAC _2B are different, canceling the association request of the time;
step four, the node receives a second association request frame, compares the KMAC _1A calculated in the step two with the received KMAC _1B, cancels the association request if the KMAC _1A is different from the received KMAC _1B, and enters the step five of association if the KMAC _1A is the same as the KMAC _ 1B;
step five, the node and Hub calculate MK ═ CMAC (Temp _2, Nonce _ a Nonce _ b,128)
Temp _2 ═ lmb (DHKey), the leftmost 128 bits of DHKey; both parties complete the wake-up association.
CN201710619932.8A 2017-07-26 2017-07-26 A kind of patient monitor based on Internet of Things Pending CN107277167A (en)

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