CN117200857A - Cross-correlation function-based beam hopping resource allocation method under cognitive satellite network - Google Patents
Cross-correlation function-based beam hopping resource allocation method under cognitive satellite network Download PDFInfo
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Abstract
The invention discloses a method for distributing hopping beam resources based on a cross correlation function under a cognitive satellite network, which comprises the steps of firstly dividing the ground by a cellular network, generating cellular patterns for ground user cells, and defining the service sequence of the cluster cells corresponding to the beams in a cluster; then, based on a particle swarm algorithm with the characteristics of self-adaptive field mode and global mode conversion, power distribution among clusters is carried out so as to ensure that service time in each cluster is consistent; then adopting a convex optimization method to solve the optimal time slot number of the beam allocation in the cluster, and generating a time slot primary allocation matrix; modeling the service area group, mapping the cell to a polar coordinate complex plane, and circularly shifting the primary allocation matrix by searching peak values through a correlation function between clusters to obtain a time slot adjustment matrix; and finally, the time slot is adjusted again according to the occupation condition of the cognitive primary network, and the beam revisiting time is considered to obtain a beam hopping time slot plan. The total interference of the beam-hopping slot communication scheme can be reduced.
Description
Technical Field
The invention belongs to the technical field of satellite communication, and particularly relates to a beam hopping resource allocation method based on a cross-correlation function.
Background
At present, most satellite communication systems adopt a mode of monopolizing authorized static planning spectrum, and when using authorized spectrum, the situation of excessive waste or overcrowding exists; on the other hand, the FR2 band (24.25 GHz-52.6 GHz) of the future 5/6G technology has potential conflict with the satellite Ka band (26.5-40 GHz). In order to solve the problem of low utilization rate of spectrum resources, a learner proposes a cognitive radio technology and is continuously developed in the follow-up. In this technology, the communication network is divided into a primary network and a secondary network, and users of the primary network can use statically allocated frequency band resources, and users of the secondary network can use the resources when the primary user is idle. Therefore, the method can meet the requirements of different users when the traffic is large, improves the utilization rate of spectrum resources, and has very bright application prospect. Currently, the multi-beam antenna technology can better solve the problem and can be put into practical use, such as Spaceway3, iridium second generation, O3b and the like.
However, multi-beam satellite systems also suffer from a significant disadvantage. If a large amount of interference signals exist between beams and users, the communication capacity of the satellite-to-ground network is limited; the geographic position distribution of the ground users and the different requirements of the cognitive satellite network service can also cause the mismatch of satellite resource allocation and user requirements, and mutual interference is serious. To overcome the shortages of the multi-beam technology, the beam hopping technology becomes a new research direction for the development of satellite communication. The beam hopping technology utilizes the idea of time division multiplexing, generates corresponding power distribution strategies which are covered according to the needs of users in each beam in real time, adopts a time isolation mode to plan the beam hopping sequence, and can effectively reduce the mutual interference between beams and between ground communication users, thereby improving the spectrum utilization efficiency and reducing the resource waste.
For LEO or VLEO satellites, the satellite service beam radius is small, the inter-cell service demand is unevenly distributed, and a beam hopping technique is required.
Traditional beam hopping pattern algorithms, such as maximum beam first algorithms, focus on cells with large traffic. The satellite beam hopping pattern design needs to consider the avoidance of co-channel interference, and in the conventional beam hopping pattern design scheme, interference information is only calculated from the absolute positions of all cells, and then cells and sequences are lightened simultaneously, so that obviously, the problem is NP-Hard, and when the number of time slots in a frame is large or the total number of cells is large, it is not practical to find the optimal time slot allocation scheme in polynomial time.
Under the cognitive satellite network, the satellite downlink as a secondary network is also influenced by a ground primary network scheme, and the real-time performance and the calculation complexity control requirements on the distribution scheme are high. If the time slot allocation is carried out according to the tradition, the allocation of the beam which remains furthest is preferentially selected, so that the distance between the lighting beams is too close when the last allocation in the frame is carried out; if any cell is not lightened for avoiding interference under the condition of weak co-channel interference, satellite resource waste is caused.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a method for distributing beam hopping resources based on a cross-correlation function under a cognitive satellite network, which comprises the steps of firstly dividing the ground by a cellular network, generating cellular patterns for ground user cells, and defining the service sequence of the cluster cells corresponding to beams in a cluster; then, based on a particle swarm algorithm with the characteristics of self-adaptive field mode and global mode conversion, power distribution among clusters is carried out so as to ensure that service time in each cluster is consistent; then adopting a convex optimization method to solve the optimal time slot number of the beam allocation in the cluster, and generating a time slot primary allocation matrix; modeling the service area group, mapping the cell to a polar coordinate complex plane, and circularly shifting the primary allocation matrix by searching peak values through a correlation function between clusters to obtain a time slot adjustment matrix; and finally, the time slot is adjusted again according to the occupation condition of the cognitive primary network, and the beam revisiting time is considered to obtain a beam hopping time slot plan. The total interference of the beam-hopping slot communication scheme can be reduced.
The technical scheme adopted by the invention for solving the technical problems comprises the following steps:
step 1: generating a ground user cell honeycomb pattern;
dividing the beam and the ground serving cell:
according to the cell shape criterion: (1) the whole service area is covered without gaps; (2) when the total area of the service areas is the same, the number of the cells is the smallest;
determining that the shape of the cell is regular hexagon under a plane map; according to the group composition requirement: (1) the whole service area is covered without gaps; (2) the adjacent co-channel cells are equidistant;
determining that the number of beams of the beam cluster is equal to the number of cells in the ground cluster, which is N Cell ＝i 2 +i*j+j 2 Wherein i and j respectively represent the number of cells that need to be crossed along the vertical direction of the hexagonal edge to reach the same dyed cell; defining a cluster cell beam sequence by using a counter-clockwise direction from inside to outside;
splitting part of cells further according to the vertexes to obtain hot spot areas;
step 2: inter-cluster power allocation based on a particle swarm algorithm;
step 2-1: establishing a communication model of the cognitive satellite-ground network based on LEO;
LEO is adopted as a medium tool in a satellite communication network, an underway mode is adopted as a spectrum access mode of a cognitive network, namely, a user in a satellite downlink is adopted as a secondary user, and a user in a ground network is adopted as a primary user;
step 2-2: quantifying the relation between satellite supply power and user demand, actual supply quantity and channel actual condition;
quantifying the requirement of the user into an information transmission speed, and regarding one beam as one user; establishing power P, signal-to-noise ratio SNR and actual offered traffic T ci Quantitative relationship among the three:
T ci ＝Blog 2 (1+SNR i )
the objective function and constraints are as follows:
b is the working bandwidth;
SNR is the signal-to-noise ratio, which is the ratio of signal to noise power;
R ci the unit is Bit/s for the user demand;
T ci the service quantity is actually provided, namely, the information transmission speed is actually provided for the corresponding user according to the power distribution;
p is the total allocable power of the satellite;
P i to represent beam i transmit power;
n represents the number of beam clusters;
aiming at the objective function and the constraint condition, a particle swarm algorithm is adopted to find an approximate global optimal solution;
step 3: the method comprises the steps of (1) performing intra-cluster time slot primary allocation based on a convex optimization method;
step 3-1: after the resource allocation result of the system for allocating service capacity of each cluster is obtained through a power resource allocation algorithm among clusters, the time slot allocation in the clusters is calculated to obtain the number of the time slots of the jumping beam of each beam, and finally the jumping beam is usedThe pattern design obtains the quantity and the sequence of the BHS of each beam in the beam hopping period BHP, namely the beam hopping time plan BHTP, and each time slot BHS is long by T s The dynamic changes corresponding to the map appear as a jumped beam pattern;
step 3-2: establishing an n-level difference target function, and adopting a time slot allocation matrix T= [ T ] 1 ,t 2 ,…,t N ] T Describing each beam time slot allocation case, wherein t i ＝[t i1 ,t i2 ,…,t iW ]Representing the time slot allocation of beam i, t ij A boolean value, which when true indicates that a slot j is assigned to beam i; beam i is assigned to the number of slots asRecording system can work maximum wave beam number N at the same time max Transmitting saturated power P in corresponding time slot sat The method comprises the steps of carrying out a first treatment on the surface of the Consider that only one beam is operating for the same slot within a cluster, the number of beam clusters c=n max To ensure that time resource utilization is maximized, the optimization problem is written as:
wherein R is i Indicating the amount of traffic demand that is to be requested,representing the service providing amount, W representing the total number of service time slots in one frame;
step 3-3: the beam i capacity is given by shannon's formula:B tot representing the beam operating bandwidth; g mark T 、G R 、G i Respectively representing the gain of a transmitting antenna, the gain of a receiving antenna, the gain of a beam i channel and N 0 Represents noise power, L free And L rain And respectively representing free space loss and rain fade, and then the signal to noise ratio of the beam is as follows:
adopting a convex optimization method and combining Karush-Kuhn-Tucker conditions to establish a Lagrange function:the optimal time slot allocation number of the beam i is obtained by derivative solution:
step 4: adjusting a beam hopping time slot allocation scheme based on cross correlation;
modeling the cell position in a cluster according to polar coordinates by adopting a time slot sequence allocation scheme based on cross-correlation operation:
firstly, determining a central cell in a cluster, obtaining a beam coordinate vector of each cell in the cluster according to the central coordinates of the cell, and for the j-th beam, the geographic coordinates are as follows:
wherein P is j Representing the power of the beam cluster, K is the path attenuation index, A j Representing the distance, e, of cell j from the center of the cell within the beam cluster jw Representing the azimuth relation;
therefore, after the time slot allocation quantity of each beam is determined, converting a time slot allocation matrix of NumCluster W into a relative position matrix CellLocationCirc, wherein each row of the time slot allocation matrix represents different beams, each row represents the beams which are lightened simultaneously, and each row represents the beams which are arranged in a anticlockwise sequence from inside to outside;
when the traffic is balanced, if each beam cluster performs time slot allocation according to the sequence of simultaneous lighting of beams at the same relative position, the sum of the same-frequency interference is minimum;
when the traffic is unbalanced, the number of time slots allocated by different beams in each cluster is different, and the number of the beams in each color meeting all the corresponding lighting sequences is firstly separated, namely, the minimum value of the time slots allocated by the beams in each corresponding relative position in all the clusters is solved, so that a time slot allocation matrix with partial corresponding lighting sequences is obtained;
for the rest time slots, carrying out cross-correlation operation on every two of the time slots, and if the peak value is not at 0, correspondingly circularly shifting the two beam time slots to the corresponding positions so as to obtain the optimal lighting sequence matching state;
step 5: time slot adjustment based on the occupation condition of the cognitive primary network;
when the conflict of the primary service time slot and the secondary service time slot is detected, checking and correcting a satellite beam-jumping time slot plan; the beams of adjacent cells are used for alternately conflicting with the beams of the time slots, and the adjacent cells are selected according to the principle of minimizing the same-frequency interference in the time slots.
Traversing slot allocation scheme, inserting control slots to access beam revisit time T rv Preventing user service interruption;
step 6: evaluating and comparing time slot schemes;
in terms of user satisfaction, under the condition that the total power of the satellite is limited, an inter-cluster power optimal allocation scheme is generated through a particle swarm algorithm, and the inter-cluster power allocation scheme is used as the lighting power of a working beam in each time slot, so that the user demand is maximally met in the overall time slot;
in the aspect of communication quality, the measurement of the co-channel interference is taken as a standard, and the measurement is compared with a time slot allocation scheme; the magnitude of co-channel interference depends on the magnitude of the distance between the centers of two beams operating simultaneously and on the same frequency, so that the center distances of the beams operating in the same time slot are considered, and the sum of the interference in each time slot is taken as the interference quantity of the whole time slot allocation scheme.
Preferably, n is 2.
The beneficial effects of the invention are as follows:
in the wave beam hopping time slot allocation scheme of the traditional LEO satellite-ground communication system, the invention adopts time slot allocation sequence adjustment based on a cross-correlation function, after the adjustment, the wave beam scheme which is lightened simultaneously in each time slot can be optimized under the condition of weighing global interference, but only the wave beam number selection with the least interference at the farthest distance is pursued in each time slot, thereby avoiding an important problem in the background of a plurality of actual communication demands, namely that the more the time slots are allocated, the fewer the wave beams with the more distant distance are allocated, namely the more difficult the allocation is, and thus the total interference of the wave beam hopping time slot communication scheme can be reduced.
Drawings
FIG. 1 is a diagram of a cognitive satellite-to-earth network down-hop beam satellite coverage scenario;
FIG. 2 is a diagram of a beam hopping pattern pre-allocation embodiment and corresponding polar correlation pattern, with cell number of 7 as an example;
FIG. 3 is a schematic diagram of a beam hopping slot plan;
FIG. 4 is a flow chart of a hopping beam resource allocation scheme based on a cross-correlation function under a cognitive satellite-to-ground network;
fig. 5 is a diagram of an inter-cluster power allocation scheme;
fig. 6 is a chart showing the comparison of the interference amounts of each time slot in different schemes.
Detailed Description
The invention will be further described with reference to the drawings and examples.
Aiming at the same frequency interference problem existing in the prior beam hopping technology, the invention provides a beam hopping resource allocation method based on the distribution of the cross correlation function of the polar coordinate position of the cell, and the full scheduling of system resources is realized under smaller calculated amount by presetting the time slot lighting sequence of the beam hopping pattern and the cyclic displacement processing after the cross correlation calculation in advance, so that the blind search of the time slot lighting sequence is avoided, thereby improving the anti-interference performance of the system.
In order to achieve a compromise between the frequency band utilization efficiency and the weakening of co-channel interference, all beams are clustered, full frequency multiplexing is performed among the clusters, power among the clusters is optimized, and beam hopping time slot allocation is performed in the clusters. Only one cell beam is illuminated within the same time cluster.
The steps of the resource allocation scheme of the present invention are shown in fig. 4, and specifically include the following steps:
step one: ground user cell pattern generation.
As shown in fig. 1, the beam and the terrestrial serving cell may be divided in advance with reference to the cluster composition of the cellular network system. According to the cell shape criterion: (1) the whole service area is covered without gaps; (2) when the total area of the service areas is the same, the number of the cells is the smallest; we determine that the cell shape is regular hexagon under the planar map. According to the group composition requirement: (1) the whole service area is covered without gaps; (2) the adjacent co-channel cells are equidistant; the invention determines that the number of beams of the beam cluster is equal to the number of cells in the ground area cluster and is N Cell ＝i 2 +i*j+j 2 Where i and j represent the number of cells that need to be crossed along the hexagonal edge in the vertical direction to reach the same dyed cell, respectively. Since the cells pre-generate a color order, i.e. a beam firing order, the adjacent numbered cells should be in adjacent locations in the geospatial, the present invention uses a counter-clockwise direction from inside to outside to define the intra-cluster cell beam order. For example, consider that taking i=1, j=2, i.e. the number of beams in a cluster is 7, a dyeing pre-allocation scheme can be obtained with two adjacent clusters as an example.
If the hot spot area is needed, part of the cells can be further split according to the vertex, and the cell splitting scheme belongs to the known technology. Cell splitting does not affect the subsequent steps, and only needs to pay attention to the fact that the non-split cells in different clusters correspond to split cell slices.
Step two: inter-cluster power allocation based on particle swarm algorithm.
Firstly, establishing a communication model of the cognitive satellite-ground network based on LEO. The communication model adopts LEO as a medium tool in a satellite communication network, adopts an underway mode as a spectrum access mode of a cognitive network, namely, a user in a satellite downlink is used as a secondary user, and a user in a ground network is used as a primary user.
And secondly, quantifying the relation between satellite supply power and user requirements, actual supply quantity and channel actual conditions. The demands of users are quantified as information transmission speeds (Bit/s), and one beam is regarded as one user. Establishing powerP, in case of small interference (calculated power SINR is replaced by SINR), SINR SNR and offered service quantity T ci Quantitative relationship among the three:
T ci ＝Blog 2 (1+SNR i )
the objective function and constraints are as follows:
aiming at the objective function and the constraint condition, the invention adopts a particle swarm algorithm with the characteristics of self-adaptive field mode and global mode conversion, has the advantages of strong universality, easy realization and high convergence rate, and has leap property to enable the optimization problem to find the approximate global optimal solution more easily.
Step three: and (5) performing intra-cluster time slot initial allocation based on a convex optimization method.
After the power resource allocation algorithm between clusters obtains the system resource allocation results such as the allocation service capacity of each cluster, the intra-cluster time slot allocation calculation is still needed to obtain the number of the beam hopping time slots of each beam, and finally, the number and the sequence of the BHS of each beam in the beam hopping period BHP (long W) are obtained according to the beam hopping pattern design, namely, the beam hopping time plan BHTP is obtained, as shown in figure 3, wherein each time slot BHS is long T s The dynamic changes corresponding to the map appear as a jumped beam pattern.
An n-level differential target function is established, where n may be 2. Using a time slot allocation matrix t= [ T ] 1 ,t 2 ,…,t N ] T To describe each beam slot allocation case, where t i ＝[t i1 ,t i2 ,…,t iW ]Representing the time slot allocation of beam i, t ij A boolean value, which when true indicates that a slot j is assigned to beam i; beam i is assigned to the number of slots asRecording system can work maximum wave beam number N at the same time max Transmitting saturated power P in corresponding time slot sat . Consider that only one beam is operating for the same slot within a cluster, the number of beam clusters c=n max . To ensure maximum time resource utilization, the optimization problem can be written as:
as can be obtained from shannon's formula, the beam i capacity is:g mark T 、G R 、G i Respectively representing the gain of a transmitting antenna, the gain of a receiving antenna, the gain of a beam i channel, and P i Representing the beam i transmit power, N 0 Represents noise power, L free And L rain Representing free space loss and rain fade, the beam signal to noise ratio is:
adopting a convex optimization method and combining KKT (Karush-Kuhn-Tucker) conditions to establish a Lagrangian function:the optimal time slot allocation number of the beam i is obtained by derivative solution:
step four: and adjusting a cross-correlation-based beam hopping slot allocation scheme.
After the cell honeycomb pattern is pre-generated in the first step, cells with the same color have obvious rules in geographic positions. The invention provides a time slot sequence allocation scheme based on cross-correlation operation. Modeling cell locations within a cluster according to polar coordinates:
taking a cluster of seven cells as an example, firstly determining a central cell in the cluster, obtaining a beam coordinate vector of each cell in the cluster from the central coordinates of the cells, and for the j-th beam, the geographic coordinates are as follows:
wherein P is j Representing the power of the beam cluster, K is the path attenuation index, A j Representing the distance, e, of cell j from the center of the cell within the beam cluster jw The azimuth relationship is represented, and the polar coordinate correlation pattern shown in fig. 2 is constructed by taking the number of cell beams in a cluster as 7 and the power as a unit value as an example.
Therefore, after the number of the time slot allocations of each beam is determined, the time slot allocation matrix of NumCluster W can be converted into a relative position matrix CellLocationCirc, each row of the time slot allocation matrix represents different beams, each row represents the beams which are lightened simultaneously, and the beams are arranged and represented in a anticlockwise order from inside to outside. Considering that under the condition of balanced traffic of each beam, cross-correlation operation is carried out on each row element in the relative position matrix, namely the relative position vector of each beam cluster, the obtained peak value of the relative vector is easy to know to appear at 0, which indicates that when the traffic is balanced, if each beam cluster carries out time slot allocation according to the sequence of simultaneous lighting of beams at the same relative position, the sum of the same frequency interference is minimum.
When the traffic is unbalanced, the number of time slots allocated by different beams in each cluster is different, and the number of beams of each color which can meet all the corresponding lighting sequences is firstly considered to be separated, namely, the minimum value of the time slots allocated by the beams of each corresponding relative position in all the clusters is calculated, so that a time slot allocation matrix with partial corresponding lighting sequences can be obtained.
For the rest time slots, the cross-correlation operation is considered to be carried out between every two of the rest time slots, if the peak value is not at 0, the corresponding cyclic shift of the two wave beam time slots is to the corresponding position, so that the optimal lighting sequence matching state is obtained.
Step five: time slot adjustment based on cognitive primary network occupancy.
Considering the communication problem of the ground primary user under the cognitive satellite network, the satellite beam hopping can only send messages when the satellite beam hopping is idle, otherwise, certain same-frequency interference can be caused. The ground station needs to include its own slot plan when transmitting to the satellite ground subscriber cell traffic information to avoid simultaneous primary and secondary and subscriber communications. Since the satellite-to-ground link acts as a secondary user, it is necessary to adjust the satellite hop beam pattern.
The invention proposes to check and correct the satellite beam hopping slot plan when a primary and secondary service slot conflict is detected. The beams of adjacent cells are used for alternately conflicting with the beams of the time slots, and the adjacent cells are selected according to the principle of minimizing the same-frequency interference in the time slots.
Finally, the slot allocation scheme should also be traversed, the control slot inserted to access the beam revisit time T rv Preventing interruption of user service.
Step six: evaluation and comparison of the slot schemes.
In terms of user satisfaction, under the condition that the total power of the satellite is limited, an inter-cluster power optimal allocation scheme is generated through a particle swarm algorithm, and the inter-cluster power allocation scheme is used as the lighting power of a working beam in each time slot so as to maximally meet the user requirement in the overall time slot (see step two);
in terms of communication quality, the measurement of the co-channel interference is taken as a main standard, and compared with the time slot allocation scheme which is commonly used at present. The magnitude of co-channel interference mainly depends on the magnitude of the distance between the centers of two beams working at the same time and the same frequency, so that the algorithm mainly considers the center distances of each beam working in the same time slot, and takes the sum of the interference in each time slot as the interference quantity of the whole time slot allocation scheme.
The simulation case of the invention is to obtain a final 7×256 time slot allocation scheme, and is to simulate the actual communication scene of 49 beams in total in 7 clusters in 256 time slots. The beam centers of the 49 beams all have definite position coordinates, and the 49 beams are a tightly distributed cellular network surrounded by 7×7 beams. Therefore, in the same time slot, under the scene that each cluster only lights one wave beam, the total channel interference quantity received by the centers of 7 wave beams lighted in the time slot is calculated, then the interference quantity of the 256 time slots is calculated and displayed respectively, and the comparison among different time slot allocation schemes is carried out, so that the advantages and disadvantages of different time slot allocation schemes can be evaluated briefly.
The specific simulation parameters are as follows:
satellite-to-ground communication distance: 1500 (Unit: km)
Total power on board: p= 1396.7 (Unit: W)
The working bandwidth is as follows: b=500 (unit: MHz)
Noise power: var_np0= 1.9953 ×10 -12 (Unit: W)
User demand for seven beam clusters: [5.547 5.957 5.965 5.158 5.970 5.957 5.485] (Unit: mbps)
Lower user demand limit (at least the demand that needs to be met) for seven beam clusters: [5.033 5.677 3.361 4.934 4.873 3.269 3.507] (Unit: mbps)
Examples:
step one: ground user cell pattern generation.
All areas are covered according to the division of the hexagonal cells by the orbit radius, the satellite 3dB power angle and the ground user service condition;
step A1, aiming at a cell corresponding to satellite lower point coordinates, using the center of the cell as a first cluster center, and finding a neighboring cluster center as a neighboring cluster center according to the cluster number NumCluster;
a2, making a satellite beam hopping pre-allocation scheme according to the number NumBeam of each cluster; the number of beams within a cluster should be the same as the number of cluster cells. The cell number follows the principle of counter-clockwise increment from inside to outside.
If the system is a multi-satellite system, an auction mechanism is introduced by taking the cluster as a unit, and different satellites are selected to bear the service. If the number of cells does not meet the rules of the number of cells in the cluster but meets the capability of seamless connection and satellite transmitting antennas on a map, cell beam pre-allocation can also be performed.
Step two: inter-cluster power allocation based on particle swarm algorithm.
And B1, establishing a communication model of the LEO-based cognitive satellite-to-ground network, establishing a probability density model based on fading parameters meeting Nakagami-m distribution, and calculating satellite-to-ground channel fading coefficients under actual communication scene basic parameters.
And step B2, quantifying the relation between satellite supply power and user requirements, actual supply quantity and actual channel conditions. The demands of users are quantified as information transmission speeds (Bit/s), and one beam is regarded as one user. Establishing power P, signal-to-noise ratio SNR and offered traffic T ci Quantitative relationship among the three:
T ci ＝Blog 2 (1+SNR i )
step B3, according to the following objective function and constraint conditions:
the invention adopts a particle swarm algorithm with the characteristics of self-adaptive field mode and global mode conversion to approach the global optimal solution of power distribution among clusters. As shown in fig. 4, taking 7 clusters, and 7 beams in each cluster as an example, the communication bandwidth and the total power of the satellite are known, the communication demand of each user (beam) is reasonably set, and the power allocation scheme which approximates to the global optimal solution can be obtained quickly through the algorithm.
Step three: and (5) performing intra-cluster time slot initial allocation based on a convex optimization method.
Step C1, solving the optimal time slot allocation number NumSlot of each wave beam by the convex optimization problem iBeam The number of slots is rounded down and then the longest beam slot is replenished until the total number of slots in a cluster is the total number of traffic slots in a frame W.
And C2, arranging the wave beam time slots in the clusters in a counterclockwise sequence from inside to outside to obtain an initial allocation matrix SlotAllocation of NumCluster W. If the consistency of the beam lighting is pursued, the primary distribution matrix can be obtained by directly arranging the beams in sequence; if the same-frequency interference is minimized, the largest common beam serial number part among all clusters can be separated first, a matching matrix which is switched simultaneously and has consistent corresponding beam serial numbers is generated, and the rest beam parts of all clusters are arranged in sequence to form an initial distribution matrix.
Step four: and adjusting a cross-correlation-based beam hopping slot allocation scheme.
And D1, converting the time slot allocation matrix into a relative position matrix of polar coordinates.
And D2, circularly traversing each cluster in the primary distribution matrix, and calculating a cross-correlation function of each cluster.
And D3, searching each cross correlation function peak value, and finding the optimal cyclic displacement when the corresponding peak value is found to form a cyclic displacement matrix SlotClustercyclshift.
And D4, executing time slot displacement operation according to the cyclic displacement matrix, wherein the first cluster is taken as a reference by default, the preliminary allocated time slots of other clusters are cyclically displaced, and the cyclic displacement is the peak value coordinate of the cross-correlation function. If the cyclic shift matrix has conflict, the correlation coefficient between the time slot shift amount of the cluster and the adjacent beam cluster of the conflict scheme can be calculated, and the smaller one is taken as the final cyclic shift amount.
Step D5, obtaining a time slot allocation matrix considering the relevant position; if the matching matrix of the partial time slot is separated in the step < C2>, the matching matrix and the matching matrix are spliced and restored into a complete matrix.
Step five: time slot adjustment based on cognitive primary network occupancy.
And E1, finding out the conflict beam lightened by the satellite by a ground primary user time slot allocation scheme, and alternately replacing the conflict time slot lightening beam into an adjacent numbered beam.
And E2, traversing the slot allocation matrix finally, and supplementing the control time slot according to the maximum revisit time requirement to prevent the user from being interrupted.
Step six: evaluation and comparison of the slot schemes.
In step F1, in the same time slot, under the scene that each cluster only lights one beam, the total channel interference amount received by each of the 7 beam centers which are lighted in the time slot is calculated.
And F2, respectively calculating and displaying the interference amounts of the 256 time slots, and comparing different time slot allocation schemes to simply evaluate the advantages and disadvantages of the different time slot allocation schemes, as shown in fig. 5.
In fig. 6, the beneficial effect of the present invention on the total interference of the time slot allocation scheme in the analog communication environment can be seen, and the optimized time slot allocation scheme, because the allocation adjustment of each time slot is performed after the global interference amount is weighed, although the interference in some time slots is relatively larger than that in the conventional scheme, the optimized scheme can make the interference in part of time slots to be obviously smaller than that in the conventional scheme as the allocation is performed, so that the total interference amount can be reduced. In addition, the scheme has more obvious effect under the background condition that the communication requirement is concentrated.
Claims (2)
1. A method for allocating beam hopping resources based on a cross-correlation function in a cognitive satellite network is characterized by comprising the following steps:
step 1: generating a ground user cell honeycomb pattern;
dividing the beam and the ground serving cell:
according to the cell shape criterion: (1) the whole service area is covered without gaps; (2) when the total area of the service areas is the same, the number of the cells is the smallest;
determining that the shape of the cell is regular hexagon under a plane map; according to the group composition requirement: (1) the whole service area is covered without gaps; (2) the adjacent co-channel cells are equidistant;
determining beam clustersThe number of beams is equal to the number of cells in the ground area group, N Cell ＝i 2 +i*j+j 2 Wherein i and j respectively represent the number of cells that need to be crossed along the vertical direction of the hexagonal edge to reach the same dyed cell; defining a cluster cell beam sequence by using a counter-clockwise direction from inside to outside;
splitting part of cells further according to the vertexes to obtain hot spot areas;
step 2: inter-cluster power allocation based on a particle swarm algorithm;
step 2-1: establishing a communication model of the cognitive satellite-ground network based on LEO;
LEO is adopted as a medium tool in a satellite communication network, an underway mode is adopted as a spectrum access mode of a cognitive network, namely, a user in a satellite downlink is adopted as a secondary user, and a user in a ground network is adopted as a primary user;
step 2-2: quantifying the relation between satellite supply power and user demand, actual supply quantity and channel actual condition;
quantifying the requirement of the user into an information transmission speed, and regarding one beam as one user; establishing power P, signal-to-noise ratio SNR and actual offered traffic T ci Quantitative relationship among the three:
T ci ＝Blog 2 (1+SNR i )
the objective function and constraints are as follows:
b is the working bandwidth;
SNR is the signal-to-noise ratio, which is the ratio of signal to noise power;
R ci the unit is Bit/s for the user demand;
T ci for actually providing service quantity, i.e. according to workInformation transmission speed actually provided for corresponding users after rate allocation;
p is the total allocable power of the satellite;
P i to represent beam i transmit power;
n represents the number of beam clusters;
aiming at the objective function and the constraint condition, a particle swarm algorithm is adopted to find an approximate global optimal solution;
step 3: the method comprises the steps of (1) performing intra-cluster time slot primary allocation based on a convex optimization method;
step 3-1: after obtaining the resource allocation result of the system for allocating service capacity among clusters through a power resource allocation algorithm, calculating the time slot allocation in the clusters to obtain the number of the time slots of the jumping beams of each beam, and finally obtaining the number and the sequence of the BHS of each beam in the jumping beam period BHP according to the design of the jumping beam pattern, namely, the jumping beam time plan BHTP, wherein the length T of each time slot BHS is longer than the length T of each time slot BHS s The dynamic changes corresponding to the map appear as a jumped beam pattern;
step 3-2: establishing an n-level difference target function, and adopting a time slot allocation matrix T= [ T ] 1 ,t 2 ,…,t N ] T Describing each beam time slot allocation case, wherein t i ＝[t i1 ,t i2 ,…,t iW ]Representing the time slot allocation of beam i, t ij A boolean value, which when true indicates that a slot j is assigned to beam i; beam i is assigned to the number of slots asRecording system can work maximum wave beam number N at the same time max Transmitting saturated power P in corresponding time slot sat The method comprises the steps of carrying out a first treatment on the surface of the Consider that only one beam is operating for the same slot within a cluster, the number of beam clusters c=n max To ensure that time resource utilization is maximized, the optimization problem is written as:
wherein R is i Indicating the amount of traffic demand that is to be requested,representing the service providing amount, W representing the total number of service time slots in one frame;
step 3-3: the beam i capacity is given by shannon's formula:B tot representing the beam operating bandwidth; g mark T 、G R 、G i Respectively representing the gain of a transmitting antenna, the gain of a receiving antenna, the gain of a beam i channel and N 0 Represents noise power, L free And L rain And respectively representing free space loss and rain fade, and then the signal to noise ratio of the beam is as follows:
adopting a convex optimization method and combining Karush-Kuhn-Tucker conditions to establish a Lagrange function:the optimal time slot allocation number of the beam i is obtained by derivative solution:
step 4: adjusting a beam hopping time slot allocation scheme based on cross correlation;
modeling the cell position in a cluster according to polar coordinates by adopting a time slot sequence allocation scheme based on cross-correlation operation:
firstly, determining a central cell in a cluster, obtaining a beam coordinate vector of each cell in the cluster according to the central coordinates of the cell, and for the j-th beam, the geographic coordinates are as follows:
wherein P is j Representing the power of the beam cluster, K is the path attenuation index, A j Representing the distance, e, of cell j from the center of the cell within the beam cluster jw Representing the azimuth relation;
therefore, after the time slot allocation quantity of each beam is determined, converting a time slot allocation matrix of NumCluster W into a relative position matrix CellLocationCirc, wherein each row of the time slot allocation matrix represents different beams, each row represents the beams which are lightened simultaneously, and each row represents the beams which are arranged in a anticlockwise sequence from inside to outside;
when the traffic is balanced, if each beam cluster performs time slot allocation according to the sequence of simultaneous lighting of beams at the same relative position, the sum of the same-frequency interference is minimum;
when the traffic is unbalanced, the number of time slots allocated by different beams in each cluster is different, and the number of the beams in each color meeting all the corresponding lighting sequences is firstly separated, namely, the minimum value of the time slots allocated by the beams in each corresponding relative position in all the clusters is solved, so that a time slot allocation matrix with partial corresponding lighting sequences is obtained;
for the rest time slots, carrying out cross-correlation operation on every two of the time slots, and if the peak value is not at 0, correspondingly circularly shifting the two beam time slots to the corresponding positions so as to obtain the optimal lighting sequence matching state;
step 5: time slot adjustment based on the occupation condition of the cognitive primary network;
when the conflict of the primary service time slot and the secondary service time slot is detected, checking and correcting a satellite beam-jumping time slot plan; using adjacent cell beams to alternately conflict with the beams of the time slots, and selecting adjacent cells according to the principle of minimizing the same-frequency interference in the time slots;
traversing slot allocation scheme, inserting control slots to access beam revisit time T rv Preventing user service interruption;
step 6: evaluating and comparing time slot schemes;
in terms of user satisfaction, under the condition that the total power of the satellite is limited, an inter-cluster power optimal allocation scheme is generated through a particle swarm algorithm, and the inter-cluster power allocation scheme is used as the lighting power of a working beam in each time slot, so that the user demand is maximally met in the overall time slot;
in the aspect of communication quality, the measurement of the co-channel interference is taken as a standard, and the measurement is compared with a time slot allocation scheme; the magnitude of co-channel interference depends on the magnitude of the distance between the centers of two beams operating simultaneously and on the same frequency, so that the center distances of the beams operating in the same time slot are considered, and the sum of the interference in each time slot is taken as the interference quantity of the whole time slot allocation scheme.
2. The method for allocating beam hopping resources based on a cross-correlation function in a cognitive satellite network according to claim 1, wherein n is 2.
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| CN117952026A (en) * | 2024-03-27 | 2024-04-30 | 北京开运联合信息技术集团股份有限公司 | Multi-task multi-user satellite task planning method, system and storage medium |
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| CN117614520B (en) * | 2024-01-23 | 2024-03-29 | 南京控维通信科技有限公司 | Method for optimizing large-scale MIMO (multiple input multiple output) resources by removing cells based on unmanned aerial vehicle-satellite cooperation |
| CN117952026A (en) * | 2024-03-27 | 2024-04-30 | 北京开运联合信息技术集团股份有限公司 | Multi-task multi-user satellite task planning method, system and storage medium |
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