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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Publication number
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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mrow
msub
msup
mfrac
user
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李宏伟
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0004—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by the type of physiological signal transmitted
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming

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

A kind of patient monitor based on Internet of Things
Technical field
The invention belongs to medical monitoring arts field, more particularly to a kind of patient monitor based on Internet of Things.
Background technology
At present, introduced with the patient monitor equipment of various big hospital, the function of patient monitor is increasingly paid attention to by people, medical treatment Patient monitor can be to measure and control physiological parameters of patients, and can be compared with known set value, if there is exceeded, can send out Go out the device or system of alarm.Patient monitor it must 24 hours on-line monitor patients physiological parameter, detect variation tendency, it is indicated that Situation In danger, for doctor's emergency processing and the foundation treated, is minimized complication and reaches alleviation and eliminate the state of an illness Purpose.The purposes of patient monitor includes the situation of monitoring and processing medication and perioperatively in addition to measuring and guarding physiological parameter. However, present patient monitor, has limitation using scope, if patient household is not at the scene, it is impossible to grasp patient's in time The state of an illness.
In summary, the problem of present technology is present be：, can only can be with hospital medical personnel using range-restricted Know, and the state of an illness of patient can not be obtained in time far away from household elsewhere.
The content of the invention
The problem of existing for prior art, the invention provides the patient monitor based on Internet of Things.
The present invention is achieved in that a kind of patient monitor based on Internet of Things, and the patient monitor based on Internet of Things is set Have：
Monitoring station；
The monitoring station includes monitor station and memory；
The memory carries out positional information of the clustering processing including user to user and described with current position coordinates：
li=(xi,yi)；
Wherein xi, yiUser i transverse and longitudinal coordinate value is represented respectively, for user i, builds a content requests frequency vector：
ni=(ni,1,ni,2,...,ni,c)；
Wherein ni,cUser's i request contents c number of times is represented, one content requests vector of each user's correspondence, the vector is anti- The content requests preference of user is reflected；
Positional information and content requests preference information based on user are clustered to user, inclined with Similar content request Get well and the close user in position assign to a multicast group, the similarity between two users is calculated using cosine similarity criterion, Calculated with equation below：
Wherein β is the weight coefficient between a 0-1；
Using K-Means clustering methods, user D all in cell is clustered, ui={ li,niRepresent user i's Clustering information, the purpose of cluster is that original user is divided into C class D={ D1,…,DC, it is to ask following formula minimum in mathematical modeling Value：
Wherein γkFor the center of customer group；
The video request information counted in the positional information and current slot based on user, is carried out to user Clustering processing is comprised the following steps that：
Step one, C user is taken at random from D, is used as the center of C customer group；
Step 2, according to the calculation formula of similarity, calculates remaining user to the similarity of C user group center, will User is divided into similarity highest customer group；
Step 3, according to cluster result, updates the central gamma of C customer groupk={ lk,nk, use equation below：
Wherein miIt is the weight coefficient between a 0-1, repeat step two and step 3, until cluster centre no longer occurs Change；
It is described according to user clustering result, according to the positional information of each customer group, calculate each customer group centre bit The horizontal azimuth and vertical elevation put are specifically included：
Using active antenna wave beam forming model, base station has a particular beam to each customer group, i.e., to each user Group sets the wave beam of a specific electrical tilt angle and vertical half-power bandwidth, and base station coordinates are origin O (0,0, HBS), user Group k barycenter is γk, position coordinates is (xk,yk,zk), vertical elevation and horizontal azimuth are
Based on the customer group positional information after cluster, the horizontal direction angle of user group center and vertical elevation pass through following Formula is obtained：
Obviously, the span of vertical elevation and horizontal azimuth is θ1∈(0,π),
The antenna for base station wave beam realizes that the accurate alignment to customer group is specifically included：
Step one, electrical tilt angle, the electronic water straight angle and the half-power bandwidth of wave beam will be adjusted, makes the radiation direction of wave beam The center of customer group is directed at, makes all users in half power bandwidth wide scope cover subscriber group, base station to user has a down dip Angle and horizontal angle will be adjusted to：
Wherein,And θkUser clustering result, the user calculated using the center of customer group are based on for base station The horizontal azimuth and vertical elevation of group center；
Step 2, determines beam angle, the overlay area of customer group is circle of the round dot in user group center, then should In the customer group with a distance from the user farthest from center and center, i.e., the radius of border circular areas is：
Wherein (xk,yk) be customer group k central gammakCoordinate, then the vertical half power bandwidth of k-th of wave beam is a width of：
The antenna model of the use active antenna array, and determine that base station is specifically wrapped to the channel gain model of user Include：
Step one, according to the positional information of each user and affiliated customer group wave beam, each customer location is calculated Real standard azimuth and vertical elevation, calculate horizontal azimuths of the user i relative to base stationWith vertical elevation θ 'i, If user i belongs to multicast group k, user i real standard azimuth and vertical elevation are equal to：
Step 2, the antenna model of active antenna array：
3D antenna gains model uses the active antenna array radiation patterns proposed in 3GPP standards, antenna gain model table Show as follows：
Wherein,The antenna gain model of active antenna list array element when for angle of declination being 0,It is that user is real with θ Azimuth and vertical elevation on the position of border, ρ are the coefficient correlation of array antenna, wm,nAnd vm,nRespectively weight and user Offset phase, is expressed as follows respectively：
M=1,2 ... NH；N=1,2 ... NV；
M=1,2 ... NH；N=1,2 ... NV；
Wherein, θetiltThe angle of declination of antenna beam is represented,Represent antenna is horizontally diverted angle, for different users Group, the θ of antennaetiltWithConfiguration it is different；
Step 3, the channel gain model of base station to user, using Multicast Channel gain model, in a multicast group User receives data with identical speed, and the maximum that the transmission rate of base station has exceeded some user in this group bears speed, Then this user can not normally decode the data, and base station is with rate transmissioning data minimum in customer group, therefore customer group k Base station is equal to the worst channel gain of user in the customer group to the equivalent channel gain of user, i.e.,：
WhereinRepresent user i (i ∈ Dk) channel gain in carrier wave n, it is made up of 3 parts：Decline soon Fall, the 3D antenna gains of the path loss of base station to user and user, following expression：
Wherein, F and PL represent rapid fading and path loss respectively,Represent k-th of wave beam to user i's 3D antenna gains；
The proposition customer group cluster algorithm, according to the positional information of customer group, sub-clustering processing is carried out to customer group specific Including：
Knowledge based on graph theory carries out sub-clustering to customer group, defines the interference figure G=(V, E) between wave beam, wherein V represents ripple The set of beam, as the summit of interference figure, E represents the interference coefficient between wave beam, as the side of interference figure, defines indicator function e (vk,vm) (k ≠ m) indicate wave beam k and wave beam m between interference：
Wherein OkAnd OmCustomer group k and customer group m radius, r are represented respectivelythRepresent that two inter-beam interference flickers are disregarded Threshold distance, in addition, defining e (vk,vk)=0, represents that interference is not present in wave beam itself, according to indicator function, builds one two It is worth interference matrix：
Define the degree of disturbance of wave beam：
Work as dG(vkDuring)=0, claim vkFor zero degree node；
Sub-clustering is comprised the following steps that：
Step one, interference matrix A is built with vertex set VG, initialize iteration factor h=1, isolated node setSub-clustering setNode set
Step 2, finds all zero degree node vk, update S=S ∪ vk；Remaining node set is designated as Φ1=V-S；
Step 3, sub-clustering：a)Look for node k=argmax (dG(vk)), make the row k of interference matrix, kth be classified as 0, update node set Bh=Bh∩vk；B) circulation is performed a) until AG=0；C) Φ is updatedh=Φh-Bh, then ΦhFor h-th of cluster；
Step 4, uses node set BhRebuild AG≠ 0, update node set Φh+1=Bh, update iteration factor h=h + 1, perform step (3)；If AG=0 or | Bh|=1, if | Bh|=1, then Φh+1=Bh；
Step 5, isolated node set S is assigned in the cluster of minimum nodes；
After sub-clustering processing by customer group, customer group D={ D1,…,Dk,…,DCΦ is divided into by cluster algorithm ={ Φ1,…,Φh..., ΦhRepresent that total user's transmission rate in h-th of user's group variety, each cluster is：
The total handling capacity of system is the transmission rate sum of all user's group varietys：
WhereinFor user's group variety ΦhUsing the indicator of carrier wave n, accordingly,The condition of satisfaction is：
Condition (2) represents that a carrier wave can only distribute to user's group variety, with the shared load of customer group in cluster Customer group in ripple resource, different clusters cannot be multiplexed；
The carrier assignment algorithm based on maximize handling capacity is comprised the following steps that：
Step one, according to formula：
Calculate overall transmission rate of the user in each cluster in carrier wave n；
Step 2, in order to maximize the handling capacity of system, finds the carrier wave and user's group variety for obtaining maximum rate, divides first User's group variety is given with the carrier wave, according to formula：
Carrier wave n is distributed into user's group variety ΦhMaximum transmission rate is obtained, carrier wave n distributes to cluster ΦhSpectrum utilization Rate highest, so carrier wave n is distributed into user's group variety Φh；
Step 3, carrier wave n is removed from carrier set F, meanwhile, by user's group variety ΦhRemoved from set Φ；
Step 4, repeats step 2 and step 3, until carrier set or customer group gathering synthesize empty set；
The wireless receiving station passes through wire connection server；
The method of the inter-signal interference relationship analysis of the wireless receiving station comprises the following steps：
Step one, multidimensional interference space model is built, interference signal characteristic vector to be analyzed is determinedWith contrast signal Characteristic vector
Step 2, based on interference space model, for interference signal characteristic vectorIt is defined to contrast signal Characteristic Vectors AmountDisplacement vector
Step 3, defines displacement vectorIt is interference signal to the projection of some latitude coordinates axle in interference space Characteristic vectorTo contrast signal characteristic vectorDistance in the CP dimensions, that is, have：
Wherein PRJ () operator representation is directed to the project of a certain CP dimensions, calculatesValue；
Step 4, it is S to the disturbance state of contrast signal to define interference signal, to represent interference signal to contrast signal Interference relationships so that judge whether interference；Determination methods comprise the following steps：
1) for the single mode interference signal and contrast signal that are represented by independent interference vector, when interference signal vector is to reference Signal phasor is when the distance of each dimension is respectively less than the resolution ratio of the dimension in spatial model, represents interference signal to reference to believing Number produce interference, S=1；If conversely, in the presence of interference signal vector on certain dimension or multiple dimensions to contrast signal vector Distance is more than or equal to the resolution ratio of the dimension, then it represents that interference signal is not disturbed contrast signal formation, S=0, i.e. interference signal In the dimension it is separable with contrast signal；
2) for the multimode situation of each self-contained some interference characteristic vectors of interference signal and contrast signal, interference now State S (VI, VS) can be calculated as below：
Wherein S [VI, VS]M×NIt is referred to as each element in disturbance state matrix, matrixRepresent VIIn K-th of characteristic vector and VSIn l-th of characteristic vector disturbance state；Each element in only two characteristic vector set When not disturbing, S (VI, VS)=0, interference signal is not just disturbed contrast signal formation；Conversely, S (VI, VS) ＞ 0, now disturb Signal will form interference to contrast signal；
Step 5, on the premise of interference has been formed, chooses and determines interference effect parameter EP, for interference signal Speech, the parameter is usually signal power p or energy e；Further, it is G to the annoyance level of contrast signal to define interference signal, Calculate interference effect degree of the interference signal to contrast signal；Computational methods comprise the following steps：
3) to only including the single mode interference signal and contrast signal of independent characteristic vector, interference signal vector is to contrast signal Annoyance level G (the V of vectorI, VS), it is estimated using interference effect parameter EP：
4) to multimode interference signal and contrast signal comprising some characteristic vectors, now interference signal is to contrast signal Annoyance level G (VI, VS) define the annoyance level of the interference signal that is represented with characteristic vector set to contrast signal；Meter now Calculate as follows：
The server connects monitor by wire；
The normalization Higher Order Cumulants equation group construction method of the monitor time-frequency overlapped signal includes：
The signal model for receiving signal is expressed as：
R (t)=x1(t)+x2(t)+…+xn(t)+v(t)
Wherein, xi(t) it is each component of signal of time-frequency overlapped signal, each component signal is independently uncorrelated, n is time-frequency weight The number of folded component of signal, θkiRepresent the modulation to each component of signal carrier phase, fciFor carrier frequency, AkiFor i-th of letter Amplitude number at the k moment, TsiFor Baud Length, pi(t) it is raised cosine shaping filter function that rolloff-factor is α, andN (t) is that average is 0, and variance is σ2Stationary white Gaussian noise；
The Higher Order Cumulants formula of mixed signal is as follows：
Both sides simultaneously divided by mixed signal second moment k/2 powers：
It is further deformed into：
WhereinWithRepresent each component signal power and the ratio and noise power and the ratio of general power of general power Value, is expressed asAnd λv, because the Higher Order Cumulants of white Gaussian noise are 0, institute's above formula is expressed as：
Thus, normalization Higher Order Cumulants equation group is built：
The server connects monitoring station by wire.
Further, the monitor station passes through wire memory.
Further, the server is attached by network interface；
The method of the fast wake-up association of the wireless network of the server uses unicast association, specifically includes：
Step one, Hub is corresponding value according to SSS, Asso_ctrl domain is set the need for present communications, constructs Wakeup Frame；After Wakeup frames are sent, T-Poll frames are sent to node；
Step 2, node is received after wake-up association, obtains the configuration information of this secondary association and Hub public key PKb, Ran Houxuan Select the private key SK of oneselfaA length of 256 bit, calculates public key PKa=SKa× G, is calculated after public key, and node is calculated based on password again Public key, PKa'=PKa- Q (PW), Q (PW)=(QX, QY), QX=232×PW+MX；Node is according in the Wakeup frames received Nonce_b and the Nonce_a of itself selection are calculated：
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)；
Utilize the information PK of above-mentioned calculatinga, KMAC_2A construct the first association request frame, and sent to Hub；
Step 3, Hub is received after the first association request frame, and the public key PK of present node is restored firsta=PKa'+Q (PW), Q (PW)=(QX, QY), QX=232×PW+MX；MXTo make QXMeet the minimum nonnegative integer of the point on elliptic curve；Calculate DHKey =X (SKb×PKa)=X (SKa×SKb× G), X () function is the X-coordinate value for taking elliptic curve key, Temp_1=here RMB_128 (DHKey), is calculated according to the information that the information and calculating that receive are obtained：
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)
The KMAC_2B that the KMAC_2A and calculating received is obtained, if the same continues the second association request frame of construction and goes forward side by side The step of entering this association request five, this association request is cancelled if different；
Step 4, node receives the second association request frame, contrasts the KMAC_1A calculated in step 2 and receives KMAC_1B, cancels this association request, if the same into the step of this secondary association five if different；
Step 5, node and Hub calculate MK=CMAC (Temp_2, Nonce_a Nonce_b, 128)
Temp_2=LMB (DHKey), is most left 128 of DHKey；Both sides complete to wake up association.
Advantages of the present invention and good effect are：Using the patient monitor, the people of different places can be facilitated to obtain disease in time The real-time state of an illness of people, effectively can be nursed to patient, preferably treat patient, and therapeutic effect is obvious, by wireless Data transfer, sphere of action is wider, advantageously the treatment of patient Yu.
Brief description of the drawings
Fig. 1 is the patient monitor structural representation provided in an embodiment of the present invention based on Internet of Things；
In figure：1st, wireless receiving station；2nd, wire；3rd, server；4th, monitoring station；4-1, monitor station；4-2, memory；5th, supervise Control instrument；6th, mobile end equipment.
Embodiment
In order to further understand the content, features and effects of the present invention, hereby enumerating following examples, and coordinate accompanying drawing Describe in detail as follows.
The structure to the present invention is explained in detail below in conjunction with the accompanying drawings.
As shown in Figure 1：Wireless receiving station 1 passes through the connection server 3 of wire 2；Server 3 connects monitoring station by wire 2 4；Monitoring station 4 includes monitor station 5 and memory 6；Server 3 connects monitor 7 by wire 2.
The memory carries out positional information of the clustering processing including user to user and described with current position coordinates：
li=(xi,yi)；
Wherein xi, yiUser i transverse and longitudinal coordinate value is represented respectively, for user i, builds a content requests frequency vector：
ni=(ni,1,ni,2,...,ni,c)；
Wherein ni,cUser's i request contents c number of times is represented, one content requests vector of each user's correspondence, the vector is anti- The content requests preference of user is reflected；
Positional information and content requests preference information based on user are clustered to user, inclined with Similar content request Get well and the close user in position assign to a multicast group, the similarity between two users is calculated using cosine similarity criterion, Calculated with equation below：
Wherein β is the weight coefficient between a 0-1；
Using K-Means clustering methods, user D all in cell is clustered, ui={ li,niRepresent user i's Clustering information, the purpose of cluster is that original user is divided into C class D={ D1,…,DC, it is to ask following formula minimum in mathematical modeling Value：
Wherein γkFor the center of customer group；
The video request information counted in the positional information and current slot based on user, is carried out to user Clustering processing is comprised the following steps that：
Step one, C user is taken at random from D, is used as the center of C customer group；
Step 2, according to the calculation formula of similarity, calculates remaining user to the similarity of C user group center, will User is divided into similarity highest customer group；
Step 3, according to cluster result, updates the central gamma of C customer groupk={ lk,nk, use equation below：
Wherein miIt is the weight coefficient between a 0-1, repeat step two and step 3, until cluster centre no longer occurs Change；
It is described according to user clustering result, according to the positional information of each customer group, calculate each customer group centre bit The horizontal azimuth and vertical elevation put are specifically included：
Using active antenna wave beam forming model, base station has a particular beam to each customer group, i.e., to each user Group sets the wave beam of a specific electrical tilt angle and vertical half-power bandwidth, and base station coordinates are origin O (0,0, HBS), user Group k barycenter is γk, position coordinates is (xk,yk,zk), vertical elevation and horizontal azimuth are
Based on the customer group positional information after cluster, the horizontal direction angle of user group center and vertical elevation pass through following Formula is obtained：
Obviously, the span of vertical elevation and horizontal azimuth is θ1∈(0,π),
The antenna for base station wave beam realizes that the accurate alignment to customer group is specifically included：
Step one, electrical tilt angle, the electronic water straight angle and the half-power bandwidth of wave beam will be adjusted, makes the radiation direction of wave beam The center of customer group is directed at, makes all users in half power bandwidth wide scope cover subscriber group, base station to user has a down dip Angle and horizontal angle will be adjusted to：
Wherein,And θkUser clustering result, the user calculated using the center of customer group are based on for base station The horizontal azimuth and vertical elevation of group center；
Step 2, determines beam angle, the overlay area of customer group is circle of the round dot in user group center, then should In the customer group with a distance from the user farthest from center and center, i.e., the radius of border circular areas is：
Wherein (xk,yk) be customer group k central gammakCoordinate, then the vertical half power bandwidth of k-th of wave beam is a width of：
The antenna model of the use active antenna array, and determine that base station is specifically wrapped to the channel gain model of user Include：
Step one, according to the positional information of each user and affiliated customer group wave beam, each customer location is calculated Real standard azimuth and vertical elevation, calculate horizontal azimuths of the user i relative to base stationWith vertical elevation θ 'i, If user i belongs to multicast group k, user i real standard azimuth and vertical elevation are equal to：
Step 2, the antenna model of active antenna array：
3D antenna gains model uses the active antenna array radiation patterns proposed in 3GPP standards, antenna gain model table Show as follows：
Wherein,The antenna gain model of active antenna list array element when for angle of declination being 0,It is that user is real with θ Azimuth and vertical elevation on the position of border, ρ are the coefficient correlation of array antenna, wm,nAnd vm,nRespectively weight and user Offset phase, is expressed as follows respectively：
M=1,2 ... NH；N=1,2 ... NV；
M=1,2 ... NH；N=1,2 ... NV；
Wherein, θetiltThe angle of declination of antenna beam is represented,Represent antenna is horizontally diverted angle, for different users Group, the θ of antennaetiltWithConfiguration it is different；
Step 3, the channel gain model of base station to user, using Multicast Channel gain model, in a multicast group User receives data with identical speed, and the maximum that the transmission rate of base station has exceeded some user in this group bears speed, Then this user can not normally decode the data, and base station is with rate transmissioning data minimum in customer group, therefore customer group k Base station is equal to the worst channel gain of user in the customer group to the equivalent channel gain of user, i.e.,：
WhereinRepresent user i (i ∈ Dk) channel gain in carrier wave n, it is made up of 3 parts：Decline soon Fall, the 3D antenna gains of the path loss of base station to user and user, following expression：
Wherein, F and PL represent rapid fading and path loss respectively,Represent k-th of wave beam to user i's 3D antenna gains；
The proposition customer group cluster algorithm, according to the positional information of customer group, sub-clustering processing is carried out to customer group specific Including：
Knowledge based on graph theory carries out sub-clustering to customer group, defines the interference figure G=(V, E) between wave beam, wherein V represents ripple The set of beam, as the summit of interference figure, E represents the interference coefficient between wave beam, as the side of interference figure, defines indicator function e (vk,vm) (k ≠ m) indicate wave beam k and wave beam m between interference：
Wherein OkAnd OmCustomer group k and customer group m radius, r are represented respectivelythRepresent that two inter-beam interference flickers are disregarded Threshold distance, in addition, defining e (vk,vk)=0, represents that interference is not present in wave beam itself, according to indicator function, builds one two It is worth interference matrix：
Define the degree of disturbance of wave beam：
Work as dG(vkDuring)=0, claim vkFor zero degree node；
Sub-clustering is comprised the following steps that：
Step one, interference matrix A is built with vertex set VG, initialize iteration factor h=1, isolated node setSub-clustering setNode set
Step 2, finds all zero degree node vk, update S=S ∪ vk；Remaining node set is designated as Φ1=V-S；
Step 3, sub-clustering：a)Look for node k=argmax (dG(vk)), make the row k of interference matrix, kth be classified as 0, update node set Bh=Bh∩vk；B) circulation is performed a) until AG=0；C) Φ is updatedh=Φh-Bh, then ΦhFor h-th of cluster；
Step 4, uses node set BhRebuild AG≠ 0, update node set Φh+1=Bh, update iteration factor h=h + 1, perform step (3)；If AG=0 or | Bh|=1, if | Bh|=1, then Φh+1=Bh；
Step 5, isolated node set S is assigned in the cluster of minimum nodes；
After sub-clustering processing by customer group, customer group D={ D1,…,Dk,…,DCΦ is divided into by cluster algorithm ={ Φ1,…,Φh..., ΦhRepresent that total user's transmission rate in h-th of user's group variety, each cluster is：
The total handling capacity of system is the transmission rate sum of all user's group varietys：
WhereinFor user's group variety ΦhUsing the indicator of carrier wave n, accordingly,The condition of satisfaction is：
Condition (2) represents that a carrier wave can only distribute to user's group variety, with the shared load of customer group in cluster Customer group in ripple resource, different clusters cannot be multiplexed；
The carrier assignment algorithm based on maximize handling capacity is comprised the following steps that：
Step one, according to formula：
Calculate overall transmission rate of the user in each cluster in carrier wave n；
Step 2, in order to maximize the handling capacity of system, finds the carrier wave and user's group variety for obtaining maximum rate, divides first User's group variety is given with the carrier wave, according to formula：
Carrier wave n is distributed into user's group variety ΦhMaximum transmission rate is obtained, carrier wave n distributes to cluster ΦhSpectrum utilization Rate highest, so carrier wave n is distributed into user's group variety Φh；
Step 3, carrier wave n is removed from carrier set F, meanwhile, by user's group variety ΦhRemoved from set Φ；
Step 4, repeats step 2 and step 3, until carrier set or customer group gathering synthesize empty set；
The method of the inter-signal interference relationship analysis of the wireless receiving station comprises the following steps：
Step one, multidimensional interference space model is built, interference signal characteristic vector to be analyzed is determinedWith contrast signal Characteristic vector
Step 2, based on interference space model, for interference signal characteristic vectorIt is defined to contrast signal Characteristic Vectors AmountDisplacement vector
Step 3, defines displacement vectorIt is interference signal to the projection of some latitude coordinates axle in interference space Characteristic vectorTo contrast signal characteristic vectorDistance in the CP dimensions, that is, have：
Wherein PRJ () operator representation is directed to the project of a certain CP dimensions, calculatesValue；
Step 4, it is S to the disturbance state of contrast signal to define interference signal, to represent interference signal to contrast signal Interference relationships so that judge whether interference；Determination methods comprise the following steps：
1) for the single mode interference signal and contrast signal that are represented by independent interference vector, when interference signal vector is to reference Signal phasor is when the distance of each dimension is respectively less than the resolution ratio of the dimension in spatial model, represents interference signal to reference to believing Number produce interference, S=1；If conversely, in the presence of interference signal vector on certain dimension or multiple dimensions to contrast signal vector Distance is more than or equal to the resolution ratio of the dimension, then it represents that interference signal is not disturbed contrast signal formation, S=0, i.e. interference signal In the dimension it is separable with contrast signal；
2) for the multimode situation of each self-contained some interference characteristic vectors of interference signal and contrast signal, interference now State S (VI, VS) can be calculated as below：
Wherein S [VI, VS]M×NIt is referred to as each element in disturbance state matrix, matrixRepresent VIIn K characteristic vector and VSIn l-th of characteristic vector disturbance state；Each element is not in only two characteristic vector set During interference, S (VI, VS)=0, interference signal is not just disturbed contrast signal formation；Conversely, S (VI, VS) ＞ 0, now disturb letter Number interference will be formed to contrast signal；
Step 5, on the premise of interference has been formed, chooses and determines interference effect parameter EP, for interference signal Speech, the parameter is usually signal power p or energy e；Further, it is G to the annoyance level of contrast signal to define interference signal, Calculate interference effect degree of the interference signal to contrast signal；Computational methods comprise the following steps：
5) to only including the single mode interference signal and contrast signal of independent characteristic vector, interference signal vector is to contrast signal Annoyance level G (the V of vectorI, VS), it is estimated using interference effect parameter EP：
6) to multimode interference signal and contrast signal comprising some characteristic vectors, now interference signal is to contrast signal Annoyance level G (VI, VS) define the annoyance level of the interference signal that is represented with characteristic vector set to contrast signal；Meter now Calculate as follows：
The normalization Higher Order Cumulants equation group construction method of the monitor time-frequency overlapped signal includes：
The signal model for receiving signal is expressed as：
R (t)=x1(t)+x2(t)+…+xn(t)+v(t)
Wherein, xi(t) it is each component of signal of time-frequency overlapped signal, each component signal is independently uncorrelated, n is time-frequency weight The number of folded component of signal, θkiRepresent the modulation to each component of signal carrier phase, fciFor carrier frequency, AkiFor i-th of letter Amplitude number at the k moment, TsiFor Baud Length, pi(t) it is raised cosine shaping filter function that rolloff-factor is α, andN (t) is that average is 0, and variance is σ2Stationary white Gaussian noise；
The Higher Order Cumulants formula of mixed signal is as follows：
Both sides simultaneously divided by mixed signal second moment k/2 powers：
It is further deformed into：
WhereinWithRepresent each component signal power and the ratio and noise power and the ratio of general power of general power Value, is expressed asAnd λv.Because the Higher Order Cumulants of white Gaussian noise are 0, institute's above formula is expressed as：
Thus, normalization Higher Order Cumulants equation group is built：
The method of the fast wake-up association of the wireless network of the server uses unicast association, specifically includes：
Step one, Hub is corresponding value according to SSS, Asso_ctrl domain is set the need for present communications, constructs Wakeup Frame；After Wakeup frames are sent, T-Poll frames are sent to node；
Step 2, node is received after wake-up association, obtains the configuration information of this secondary association and Hub public key PKb, Ran Houxuan Select the private key SK of oneselfaA length of 256 bit, calculates public key PKa=SKa× G, is calculated after public key, and node is calculated based on password again Public key, PKa'=PKa- Q (PW), Q (PW)=(QX, QY), QX=232×PW+MX；Node is according in the Wakeup frames received Nonce_b and the Nonce_a of itself selection are calculated：
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)；
Utilize the information PK of above-mentioned calculatinga, KMAC_2A construct the first association request frame, and sent to Hub；
Step 3, Hub is received after the first association request frame, and the public key PK of present node is restored firsta=PKa'+Q (PW), Q (PW)=(QX, QY), QX=232×PW+MX；MXTo make QXMeet the minimum nonnegative integer of the point on elliptic curve；Calculate DHKey =X (SKb×PKa)=X (SKa×SKb× G), X () function is the X-coordinate value for taking elliptic curve key, Temp_1=here RMB_128 (DHKey), is calculated according to the information that the information and calculating that receive are obtained：
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)
The KMAC_2B that the KMAC_2A and calculating received is obtained, if the same continues the second association request frame of construction and goes forward side by side The step of entering this association request five, this association request is cancelled if different；
Step 4, node receives the second association request frame, contrasts the KMAC_1A calculated in step 2 and receives KMAC_1B, cancels this association request, if the same into the step of this secondary association five if different；
Step 5, node and Hub calculate MK=CMAC (Temp_2, Nonce_a Nonce_b, 128)
Temp_2=LMB (DHKey), is most left 128 of DHKey；Both sides complete to wake up association.
After the patient monitor starts, the body index of patient can be monitored in real time, if index shows abnormal, patient monitor can be by Patient's state of an illness data transfer is to server, and on the one hand server meeting is real-time to feed back to monitoring station by data, and care provider passes through prison Television stations is handled, but data reserve is into memory；On the other hand wireless receiving station is fed back to, the family members far away from its ground are led to Cross and move the state of an illness that end equipment gets patient by wireless receiving station.
It is described above to be only the preferred embodiments of the present invention, any formal limitation not is made to the present invention, Every technical spirit according to the present invention is belonged to any simple modification made for any of the above embodiments, equivalent variations and modification In the range of technical solution of the present invention.

Claims (3)

1. a kind of patient monitor based on Internet of Things, it is characterised in that the patient monitor based on Internet of Things is provided with：
Monitoring station；
The monitoring station includes monitor station and memory；
The memory carries out positional information of the clustering processing including user to user and described with current position coordinates：
li=(xi,yi)；
Wherein xi, yiUser i transverse and longitudinal coordinate value is represented respectively, for user i, builds a content requests frequency vector：
ni=(ni,1,ni,2,...,ni,c)；
Wherein ni,cUser's i request contents c number of times is represented, one content requests vector of each user's correspondence, the vector is reflected The content requests preference of user；
Positional information and content requests preference information based on user are clustered to user, with Similar content ask preference and The close user in position assigns to a multicast group, and the similarity between two users is calculated using cosine similarity criterion, with such as Lower formula is calculated：
<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 the weight coefficient between a 0-1；
Using K-Means clustering methods, user D all in cell is clustered, ui={ li,niRepresent user i cluster Information, the purpose of cluster is that original user is divided into C class D={ D1,…,DC, it is that following formula is minimized in mathematical modeling：
<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 γkFor the center of customer group；
The video request information counted in the positional information and current slot based on user, is clustered to user Processing is comprised the following steps that：
Step one, C user is taken at random from D, is used as the center of C customer group；
Step 2, according to the calculation formula of similarity, calculates remaining user to the similarity of C user group center, by user It is divided into similarity highest customer group；
Step 3, according to cluster result, updates the central gamma of C customer groupk={ lk,nk, use equation below：
<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 miIt is the weight coefficient between a 0-1, repeat step two and step 3, until cluster centre no longer changes；
It is described according to user clustering result, according to the positional information of each customer group, calculate each customer group center Horizontal azimuth and vertical elevation are specifically included：
Using active antenna wave beam forming model, base station has a particular beam to each customer group, i.e., each customer group is set The wave beam of a specific electrical tilt angle and vertical half-power bandwidth is put, base station coordinates are origin O (0,0, HBS), customer group k Barycenter be γk, position coordinates is (xk,yk,zk), vertical elevation and horizontal azimuth are
Based on the customer group positional information after cluster, the horizontal direction angle of user group center and vertical elevation pass through following formula Obtain：
<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 span of vertical elevation and horizontal azimuth is θ1∈(0,π),
The antenna for base station wave beam realizes that the accurate alignment to customer group is specifically included：
Step one, electrical tilt angle, the electronic water straight angle and the half-power bandwidth of wave beam will be adjusted, the radiation direction of wave beam is directed at The center of customer group, makes all users in half power bandwidth wide scope cover subscriber group, the angle of declination of base station to user and Horizontal angle will be adjusted to：
Wherein,And θkUser clustering result, the user group center calculated using the center of customer group are based on for base station Horizontal azimuth and vertical elevation；
Step 2, determines beam angle, the overlay area of customer group is circle of the round dot in user group center, then the circle In the customer group with a distance from the user farthest from center and center, i.e., the radius in region 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 (xk,yk) be customer group k central gammakCoordinate, then the vertical half power bandwidth of k-th of wave beam is a width of：
<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 antenna model of the use active antenna array, and determine that base station is specifically included to the channel gain model of user：
Step one, according to the positional information of each user and affiliated customer group wave beam, the reality of each customer location is calculated International standard azimuth and vertical elevation, calculate horizontal azimuths of the user i relative to base stationWith vertical elevation θ 'iIf, with Family i belongs to multicast group k, then user i real standard azimuth and vertical elevation are equal to：
Step 2, the antenna model of active antenna array：
3D antenna gains model uses the active antenna array radiation patterns proposed in 3GPP standards, and antenna gain model is represented such as Under：
Wherein,The antenna gain model of active antenna list array element when for angle of declination being 0,It is user's actual bit with θ The azimuth put and vertical elevation, ρ are the coefficient correlation of array antenna, wm,nAnd vm,nRespectively weight and user are offset Phase, is expressed as follows respectively：
Wherein, θetiltThe angle of declination of antenna beam is represented,Represent antenna is horizontally diverted angle, for different customer groups, The θ of antennaetiltWithConfiguration it is different；
Step 3, the channel gain model of base station to user, using Multicast Channel gain model, the user in a multicast group Data are received with identical speed, the maximum that the transmission rate of base station has exceeded some user in this group bears speed, then this Individual user can not normally decode the data, and base station is with base station in rate transmissioning data minimum in customer group, therefore customer group k Equivalent channel gain to user is equal to the worst channel gain of user in the customer group, i.e.,：
WhereinRepresent user i (i ∈ Dk) channel gain in carrier wave n, it is made up of 3 parts：Rapid fading, base Stand to the path loss and the 3D antenna gains of user of user, following expression：
Wherein, F and PL represent rapid fading and path loss respectively,Represent k-th of wave beam by 3D days of user i Line gain；
The proposition customer group cluster algorithm, according to the positional information of customer group, sub-clustering processing is carried out to customer group and is specifically included：
Knowledge based on graph theory carries out sub-clustering to customer group, defines the interference figure G=(V, E) between wave beam, wherein V represents wave beam Set, as the summit of interference figure, E represents the interference coefficient between wave beam, as the side of interference figure, defines indicator function e (vk, vm) (k ≠ m) indicate wave beam k and wave beam m between interference：
<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 OkAnd OmCustomer group k and customer group m radius, r are represented respectivelythRepresent the door that two inter-beam interference flickers are disregarded Range line is from addition, define e (vk,vk)=0, represents that interference is not present in wave beam itself, according to indicator function, builds a two-value and does Disturb matrix：
Define the degree of disturbance of wave 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>
Work as dG(vkDuring)=0, claim vkFor zero degree node；
Sub-clustering is comprised the following steps that：
Step one, interference matrix A is built with vertex set VG, initialize iteration factor h=1, isolated node setSub-clustering SetNode set
Step 2, finds all zero degree node vk, update S=S ∪ vk；Remaining node set is designated as Φ1=V-S；
Step 3, sub-clustering：a)Look for node k=argmax (dG(vk)), make the row k of interference matrix, kth be classified as 0, more New node set Bh=Bh∩vk；B) circulation is performed a) until AG=0；C) Φ is updatedh=Φh-Bh, then ΦhFor h-th of cluster；
Step 4, uses node set BhRebuild AG≠ 0, update node set Φh+1=Bh, iteration factor h=h+1 is updated, Perform step (3)；If AG=0 or | Bh|=1, if | Bh|=1, then Φh+1=Bh；
Step 5, isolated node set S is assigned in the cluster of minimum nodes；
After sub-clustering processing by customer group, customer group D={ D1,…,Dk,…,DCBy cluster algorithm be divided into Φ= {Φ1,…,Φh..., ΦhRepresent that total user's transmission rate in h-th of user's group variety, each cluster is：
<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 handling capacity of system is the transmission rate sum of all user's group varietys：
<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>
WhereinFor user's group variety ΦhUsing the indicator of carrier wave n, accordingly,The condition of satisfaction is：
<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>
Condition (2) represents that a carrier wave can only distribute to user's group variety, with the shared carrier wave money of customer group in cluster Customer group in source, different clusters cannot be multiplexed；
The carrier assignment algorithm based on maximize handling capacity is comprised the following steps that：
Step one, according to 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>
Calculate overall transmission rate of the user in each cluster in carrier wave n；
Step 2, in order to maximize the handling capacity of system, finds the carrier wave and user's group variety for obtaining maximum rate, and distribution first should Carrier wave gives user's group variety, according to 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
Carrier wave n is distributed into user's group variety ΦhMaximum transmission rate is obtained, carrier wave n distributes to cluster ΦhThe availability of frequency spectrum most Height, so carrier wave n is distributed into user's group variety Φh；
Step 3, carrier wave n is removed from carrier set F, meanwhile, by user's group variety ΦhRemoved from set Φ；
Step 4, repeats step 2 and step 3, until carrier set or customer group gathering synthesize empty set；
The wireless receiving station passes through wire connection server；
The method of the inter-signal interference relationship analysis of the wireless receiving station comprises the following steps：
Step one, multidimensional interference space model is built, interference signal characteristic vector to be analyzed is determinedWith contrast signal Characteristic Vectors Amount
Step 2, based on interference space model, for interference signal characteristic vectorIt is defined to contrast signal characteristic vector Displacement vector
Step 3, defines displacement vectorIt is interference signal feature to the projection of some latitude coordinates axle in interference space VectorTo contrast signal characteristic vectorDistance in the CP dimensions, that is, have：
Wherein PRJ () operator representation is directed to the project of a certain CP dimensions, calculatesValue；
Step 4, it is S to the disturbance state of contrast signal to define interference signal, to represent that interference signal is done to contrast signal Relation is disturbed, so as to judge whether interference；Determination methods comprise the following steps：
1) for the single mode interference signal and contrast signal that are represented by independent interference vector, when interference signal vector is to contrast signal Vector represents that interference signal is produced to contrast signal when the distance of each dimension is respectively less than the resolution ratio of the dimension in spatial model Raw interference, S=1；If conversely, in the presence of distance of the interference signal vector on certain dimension or multiple dimensions to contrast signal vector More than or equal to the resolution ratio of the dimension, then it represents that interference signal is not disturbed contrast signal formation, S=0, i.e. interference signal and ginseng In the dimension it is separable according to signal；
<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) for the multimode situation of each self-contained some interference characteristic vectors of interference signal and contrast signal, disturbance state now S(VI, VS) can be calculated as below：
Wherein S [VI, VS]M×NIt is referred to as each element in disturbance state matrix, matrixRepresent VIIn k-th Characteristic vector and VSIn l-th of characteristic vector disturbance state；Each element is not done in only two characteristic vector set When disturbing, S (VI, VS)=0, interference signal is not just disturbed contrast signal formation；Conversely, S (VI, VS) ＞ 0, now interference signal Interference will be formed to contrast signal；
Step 5, on the premise of interference has been formed, chooses and determines interference effect parameter EP, for interference signal, The parameter is usually signal power p or energy e；Further, it is G, meter to the annoyance level of contrast signal to define interference signal Calculate interference effect degree of the interference signal to contrast signal；Computational methods comprise the following steps：
1) to only including the single mode interference signal and contrast signal of independent characteristic vector, interference signal vector is to contrast signal vector Annoyance level G (VI, VS), it is estimated using interference effect parameter 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) to multimode interference signal and contrast signal comprising some characteristic vectors, now interference of the interference signal to contrast signal Degree G (VI, VS) define the annoyance level of the interference signal that is represented with characteristic vector set to contrast signal；Calculating now is such as Under：
The server connects monitor by wire；
The normalization Higher Order Cumulants equation group construction method of the monitor time-frequency overlapped signal includes：
The signal model for receiving signal is expressed 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, xi(t) it is each component of signal of time-frequency overlapped signal, each component signal is independently uncorrelated, n is the overlapping letter of time-frequency The number of number component, θkiRepresent the modulation to each component of signal carrier phase, fciFor carrier frequency, AkiExist for i-th of signal The amplitude at k moment, TsiFor Baud Length, pi(t) it is raised cosine shaping filter function that rolloff-factor is α, andN (t) is that average is 0, and variance is σ2Stationary white Gaussian noise；
The Higher Order Cumulants formula of 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 simultaneously divided by mixed signal second moment k/2 powers：
<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>
It is further deformed into：
<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>
WhereinWithEach component signal power and the ratio and noise power and the ratio of general power of general power are represented, point It is not expressed asAnd λv；Because the Higher Order Cumulants of white Gaussian noise are 0, institute's above formula 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, normalization Higher Order Cumulants equation group is built：
<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 connects monitoring station by wire.
2. the patient monitor as claimed in claim 1 based on Internet of Things, it is characterised in that the monitor station is stored by wire Device.
3. the patient monitor as claimed in claim 1 based on Internet of Things, it is characterised in that the server is entered by network interface Row connection；
The method of the fast wake-up association of the wireless network of the server uses unicast association, specifically includes：
Step one, Hub is corresponding value according to SSS, Asso_ctrl domain is set the need for present communications, constructs Wakeup frames； Send after Wakeup frames, T-Poll frames are sent to node；
Step 2, node is received after wake-up association, obtains the configuration information of this secondary association and Hub public key PKb, then select certainly Oneself private key SKaA length of 256 bit, calculates public key PKa=SKa× G, is calculated after public key, and node calculates the public affairs based on password again Key, PKa'=PKa- Q (PW), Q (PW)=(QX, QY), QX=232×PW+MX；Node is according in the Wakeup frames received Nonce_b and the Nonce_a of itself selection are calculated：
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)；
Utilize the information PK of above-mentioned calculatinga, KMAC_2A construct the first association request frame, and sent to Hub；
Step 3, Hub is received after the first association request frame, and the public key PK of present node is restored firsta=PKa'+Q (PW), Q (PW) =(QX, QY), QX=232×PW+MX；MXTo make QXMeet the minimum nonnegative integer of the point on elliptic curve；Calculate DHKey=X (SKb×PKa)=X (SKa×SKb× G), X () function is the X-coordinate value for taking elliptic curve key, Temp_1=RMB_ here 128 (DHKey), are calculated according to the information that the information and calculating that receive are obtained：
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)
The KMAC_2B that the KMAC_2A and calculating received is obtained, if the same continues to construct the second association request frame and enters this The step of secondary association is asked five, this association request is cancelled if different；
Step 4, node receives the second association request frame, contrasts the KMAC_1A calculated in step 2 and the KMAC_1B received, Cancel this association request if different, if the same into the step of this secondary association five；
Step 5, node and Hub calculate MK=CMAC (Temp_2, Nonce_a Nonce_b, 128)
Temp_2=LMB (DHKey), is most left 128 of DHKey；Both sides complete to 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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