CN117749255A - Terminal grouping method and system for large-scale MIMO satellite communication - Google Patents
Terminal grouping method and system for large-scale MIMO satellite communication Download PDFInfo
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Abstract
The invention relates to the technical field of low-orbit satellite and terminal communication, in particular to a terminal grouping method and a system for large-scale MIMO satellite communication, comprising the steps of establishing a total rate model of a low-orbit satellite communication system according to a channel model of the large-scale MIMO; establishing a mathematical model taking the maximum system total rate as an optimization target by taking the grouping number of the ground terminal equipment, the active variable of the ground terminal equipment and the total power as constraint conditions; and designing a terminal grouping algorithm based on deep reinforcement learning to solve a mathematical model, so as to ensure the optimal total rate of the communication system.
Description
Technical Field
The invention relates to the technical field of low-orbit satellite and terminal communication, in particular to a terminal grouping method and system for large-scale MIMO satellite communication.
Background
Large-scale Multiple-Input Multiple-Output (MIMO) technology utilizes a large number of antennas to serve Multiple users on the same time-frequency resource, thereby greatly improving the spectral efficiency of the system. The large-scale MIMO technology is expanded and applied to the low-orbit satellite communication system, so that the space degree of freedom of the large-scale MIMO can be fully utilized, the capacity of the low-orbit satellite communication system is obviously improved, and the utilization rate of spectrum resources is improved. However, the number of terminals that can be served simultaneously by a low-orbit satellite is affected by the number of satellite-side transmit antennas. When the number of terminals exceeds the number of terminals which can support simultaneous communication by the low-orbit satellite, if the terminals are randomly selected to be accessed, the total speed and the performance of the system are not optimal. Therefore, reasonable terminal grouping in a massive MIMO system plays a key role in improving system capacity, total rate and spectrum utilization.
Currently, most grouping algorithms researched in a large-scale MIMO scene are applied to the ground base station and the user, and the grouping algorithms realize main targets of maximizing the total rate of the system in the modes of antenna power distribution, user grouping and the like, but do not consider the processing capacity of a receiving end. In a low-orbit satellite system of massive MIMO, if the limitation condition of the processing capacity of the satellite is not considered, the situation that the received data of the satellite exceeds the processing capacity of the low-orbit satellite can occur, so that the problems of congestion and packet loss of an on-board buffer queue are caused.
Therefore, the invention aims at the problems and designs a terminal grouping method and a system for large-scale MIMO satellite communication, which aim at optimizing the total rate of a low-orbit satellite system on the premise of ensuring that the received data quantity of the low-orbit satellite does not exceed the processing capacity of the low-orbit satellite, and solve the optimal solution of user grouping by using a deep reinforcement learning algorithm.
Disclosure of Invention
In order to solve the problems, the invention provides a terminal grouping method and a system for large-scale MIMO satellite communication.
In a first aspect, the present invention provides a terminal grouping method for massive MIMO satellite communication, including the steps of:
s1, establishing a total rate model of a low-orbit satellite communication system according to a channel model of large-scale MIMO;
s2, establishing a mathematical model taking the maximum system total rate as an optimization target by taking the grouping number of the ground terminal equipment, the active variables of the ground terminal equipment and the total power as constraint conditions;
s3, designing a terminal grouping algorithm based on deep reinforcement learning to solve a mathematical model, and guaranteeing the optimal total speed of the communication system.
Further, the step S1 specifically includes the following sub-steps:
s11, constructing a large-scale MIMO low-orbit satellite communication system comprising a low-orbit satellite and a plurality of ground terminal devices; wherein, the low orbit satellite is provided with N antennas and N RF A plurality of radio frequency links, and n=n RF The method comprises the steps of carrying out a first treatment on the surface of the All the ground terminal equipment is provided with a single antenna; all ground terminal devices are divided into L groups, the number of ground terminal devices of the L groups is expressed as |l| and |l| is less than or equal to N, i=1, 2, … RF The ith ground terminal equipment of the first group is denoted as U (l, i); all the ground terminal devices in the same group can transmit data on the same time-frequency resource, and the ground terminal devices of different groups are served in different time slots by using a time division multiple access technology;
s12, constructing a large-scale MIMO system channel model based on a large-scale MIMO low-orbit satellite communication system, wherein the method comprises the following steps of:
setting channel state matrix，，h nm Representing complex channel gains of an nth antenna and an mth ground terminal equipment antenna on a low-orbit satellite;
signals sent by the first group of ground terminal devices are received by the low-orbit satellite after passing through the channel, and the signals are expressed as:
wherein y is l Representing signals received by the low-orbit satellites transmitted via the first group of ground terminal devices of the channel transmission,a set of channel noises representing the signal transmitted by the first group of ground terminal devices through the channel, all the channel noises obey gaussian distribution,representing data transmitted by a first group of ground terminal devicesIn combination with the beamforming matrix:
wherein,representing a beamforming matrix, w, in relation to a channel state matrix, H i Representing the ith column vector of the beamforming matrix W,representing a transmission power matrix of the first group of ground terminal devices;
the signal received by the ith ground terminal equipment of the first group and transmitted through the channel is expressed as:
wherein n is i Channel noise representing the channel through which signals of the ith ground terminal device of the first group pass;
the SINR for the ith ground terminal equipment of the first group is expressed as:
wherein,representing the power of channel noise corresponding to the ith ground terminal equipment;
s13, constructing a total rate of the system when t time slots are constructed according to a large-scale MIMO system channel model, wherein the total rate is expressed as follows:
using active variablesIndicating whether the ith ground terminal equipment of the first group has data transmission in t time slots or not whenThe ith ground terminal equipment of the first group has data to be transmitted in the t time slot whenIndicating that the ith ground terminal equipment of the first group has no data to be transmitted in the t time slot; at the same time adopt decision variablesIndicating whether the ith ground terminal equipment of the first group can perform data transmission in t time slots or not whenThe ith ground terminal equipment representing the first group can transmit data in time slot t whenIndicating that the ith ground terminal equipment of the first group cannot transmit data in the time slot t; wherein,time of dayIs 1;
based on this, the total rate model of the low-orbit satellite communication system is expressed as:
。
further, the mathematical model with the maximum system total rate as the optimization target is expressed as:
wherein R represents the total rate of the system,，h nm representing complex channel gains of an nth antenna and an mth ground terminal equipment antenna on a low-orbit satellite; w (w) i Representing the ith column vector, P, of the beamforming matrix W i Representing the transmission power of the ith ground terminal equipment of the first group;indicating that the ith ground terminal equipment of the ith group is at t l The active variable of the time slot,representing the decision variable of the ith ground terminal equipment of the first group at time slot t,representing the power of the channel noise corresponding to the ith ground terminal equipment.
Further, the relevant constraint conditions of the mathematical model are:
wherein l represents the ground of the first groupThe number of the surface terminal devices, N represents the number of antennas of the low-orbit satellite, and P max Indicating the maximum transmit power of the low-orbit satellite,representing satellite processing capacity, i.e. the speed of satellite processing data, q represents the length of the satellite buffer queue, t 1 Representing the duration of time slot t, R max Indicating the maximum total system rate.
Further, step S3 designs a terminal grouping algorithm based on deep reinforcement learning, including:
s31, setting a state set of t time slots as follows:，for the state of the ith ground terminal equipment in t time slots, SINR ti For the SINR value of the ith ground terminal equipment in the t time slot, d ti E {0,1} is the active variable value of the ith ground terminal equipment in the t time slot;
s32, setting an action set of t time slots as，The action of the ith ground terminal equipment in the t time slot is carried out, and M is the number of the ground terminal equipment;
s33, defining a reward value r as:
wherein,representing the power of the channel noise corresponding to the ith ground terminal equipment, d ti E {0,1} is the active variable value of the ith ground terminal equipment in the t time slot;，h nm representing complex channel gains of an nth antenna and an mth ground terminal equipment antenna on a low-orbit satellite; w (w) i Representing the ith column vector, P, of the beamforming matrix W i Representing the transmission power of the ith ground terminal equipment of the first group;representing the speed of satellite processing data, q represents the length of a satellite buffer queue, R max Representing a maximum system overall rate;
s34, the ith ground terminal equipment adjusts the state of the next time slot t+1 according to the decision of the time slot t and the previous time slot, and the state set of the next time slot t+1 is expressed as，The method comprises the steps of carrying out a first treatment on the surface of the Then according to the action set of the next time slot t+1When the terminal is grouped in the time slot t+1, the state value of the state set of the time slot t+1 is adjusted, which is expressed as:
wherein,representing a state adjustment factor proportional to the time slot t and the number of times that the previous time slot decision can be transmitted,representing the status of the ith ground terminal equipment at time slot t +1,indicating the action of the ith ground terminal equipment in time slot t+1.
In a second aspect, based on the method of the first aspect, the present invention further provides a terminal grouping system for massive MIMO satellite communication, including:
the total rate model building module is used for building a total rate model of the low-orbit satellite communication system according to the large-scale MIMO system channel model; it comprises the following steps:
a communication system establishing unit for establishing a large-scale MIMO low-orbit satellite communication system including one low-orbit satellite and a plurality of ground terminal devices;
the channel model building unit is used for building a channel model of the large-scale MIMO according to the large-scale MIMO low-orbit satellite communication system;
the optimization target construction module is used for constructing a mathematical model taking the maximum system total rate as an optimization target by taking the grouping number of the ground terminal equipment, the active variable and the total power of the ground terminal equipment as constraint conditions;
and the solving module is used for solving the mathematical model based on a terminal grouping algorithm of the deep reinforcement learning.
The invention has the beneficial effects that:
according to the invention, the channel state of the terminal is analyzed, the maximum total system speed is targeted on the premise of ensuring that the received data quantity of the low-orbit satellite does not exceed the processing capacity of the low-orbit satellite, and the optimal solution of the terminal grouping is solved by using the deep reinforcement learning algorithm, so that the near-optimal performance and the bearable computational complexity can be realized compared with the traditional grouping algorithm.
Drawings
FIG. 1 is a model of a low-orbit satellite and ground terminal communication network used in an embodiment of the present invention;
FIG. 2 is a flow chart of a grouping algorithm based on deep reinforcement learning in an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a terminal grouping method for large-scale MIMO satellite communication, which is shown in figure 2 and comprises the following steps:
s1, establishing a total rate model of a low-orbit satellite communication system according to a channel model of large-scale MIMO;
s2, establishing a mathematical model taking the maximum system total rate as an optimization target by taking the grouping number of the ground terminal equipment, the active variables of the ground terminal equipment and the total power as constraint conditions;
s3, designing a terminal grouping algorithm based on deep reinforcement learning to solve a mathematical model, and guaranteeing the optimal total speed of the communication system.
In one embodiment, a massive MIMO low-orbit satellite communication system is constructed as shown in fig. 1, comprising one low-orbit satellite and a plurality of ground terminal devices. A plurality of antennas and a plurality of Radio Frequency (RF) links are deployed on a low-orbit satellite, and signals sent by the low-orbit satellite firstly need to be transmitted through the RF links and then are transmitted by the antennas; the number of antennas at the low orbit satellite side is recorded as N, and the number of radio frequency links is recorded as N RF General N>N RF For convenience, the present invention assumes that n=n RF . Meanwhile, assume that M ground terminal devices exist in the system and N is>>M, configuring a single antenna for each ground terminal device; therefore, a plurality of ground terminal devices can be served at the same time frequency in a space multiplexing mode, so that the frequency spectrum utilization rate and the channel capacity are greatly improved.
To achieve the goal of maximizing the system channel capacity without exceeding satellite processing capacity, the present embodiment divides all ground terminal devices into L groups, with the number of ground terminal devices in the L groups denoted as |l| and |l| being less than or equal to N, l=1, 2, … RF =n, representing the ith ground terminal device of the first group as U (l, i); all ground terminal devices within the same group may communicate with the low-orbit satellite in the same time slot, with different groups of ground terminal devices being served in different time slots using time division multiple access techniques.
Specifically, based on a massive MIMO low-orbit satellite communication system, constructing a massive MIMO system channel model comprises the following steps:
setting channel state matrix，，h nm Representing complex channel gains of an nth antenna and an mth ground terminal equipment antenna on a low-orbit satellite;
signals sent by the first group of ground terminal devices are received by the low-orbit satellite after passing through the channel, and the signals are expressed as:
wherein y is l Representing signals received by the low-orbit satellites transmitted via the first group of ground terminal devices of the channel transmission,a set of channel noises representing the signal transmitted by the first group of ground terminal devices through the channel, all the channel noises obey gaussian distribution,representing data transmitted by a first group of ground terminal devicesIn combination with the beamforming matrix:
wherein,representing a beamforming matrix, w, in relation to a channel state matrix, H i Representing the ith column vector of the beamforming matrix W,representing a transmission power matrix of the first group of ground terminal devices;
the signal received by the ith ground terminal equipment of the first group and transmitted through the channel is expressed as:
wherein the first item is a desired signal for an ith ground terminal equipment of the first group; the second term is multi-terminal interference; third item n i Channel noise representing the channel through which signals of the ith ground terminal device of the first group pass; which is an additive white gaussian noise.
The SINR for the ith ground terminal equipment of the first group is expressed as:
wherein,representing the power of channel noise corresponding to the ith ground terminal equipment;
then the total achievable rate for the system at time t slots is:
using active variablesIndicating whether the ith ground terminal equipment of the first group has data transmission in t time slots or not whenThe ith ground terminal equipment of the first group has data to be transmitted in the t time slot whenIndicating that the ith ground terminal equipment of the first group has no data to be transmitted in the t time slot; at the same time adopt decision variablesIndicating whether the ith ground terminal equipment of the first group can perform data transmission in t time slots or not whenThe ith ground terminal equipment representing the first group can transmit data in time slot t whenIndicating that the ith ground terminal equipment of the first group cannot transmit data in the time slot t; wherein,time of dayIs 1;
based on this, the total rate model of the low-orbit satellite communication system is expressed as:
。
specifically, a mathematical model with the maximum system total rate as an optimization target is constructed, expressed as:
wherein R represents the total rate of the system,，h nm representing complex channel gains of an nth antenna and an mth ground terminal equipment antenna on a low-orbit satellite; w (w) i Representing the ith column vector, P, of the beamforming matrix W i Representing the transmission power of the ith ground terminal equipment of the first group;indicating that the ith ground terminal equipment of the ith group is at t l The active variable of the time slot,represents the first groupDecision variables of i ground terminal devices at t time slots,representing the power of the channel noise corresponding to the ith ground terminal equipment.
The relevant constraints of the mathematical model are:
wherein l represents the number of ground terminal devices of the first group, N represents the number of antennas of the low-orbit satellite, and P max Indicating the maximum transmit power of the low-orbit satellite,representing satellite processing capacity, i.e. the speed of satellite processing data, q represents the length of the satellite buffer queue, t 1 Representing the duration of time slot t, R max Indicating the maximum total system rate. Constraint condition C1 indicates that the total transmitting power of each group of ground terminal equipment needs to be less than or equal to the maximum transmitting power, constraint condition C2 indicates that the number of the ground terminal equipment of each group is less than or equal to the number of the low-orbit satellite antennas, constraint condition C3 indicates that the ground terminal equipment i can be selected when data needs to be transmitted, and constraint condition C4 indicates that the size of a low-orbit satellite buffer queue can ensure that the data cannot overflow in the time slot.
Specifically, in order to quickly find an optimal solution of terminal grouping, the invention adopts a deep reinforcement learning algorithm to solve the problem of terminal grouping, and comprises the following steps:
s31, setting t time slotsThe state set of (2) is，For the state of the ith ground terminal equipment in t time slots, SINR ti For the SINR value of the ith ground terminal equipment in the t time slot, d ti E {0,1} is the active variable value of the ith ground terminal equipment in the t time slot; when (when)Indicating that the ith ground terminal equipment has data to be transmitted in t time slots whenIndicating that the ith ground terminal equipment has no data to be transmitted in the t time slot;
s32, setting an action set of t time slots as，For the action of the ith ground terminal equipment in the t time slot whenIndicating that the ith ground terminal equipment can transmit data in time slot t whenIndicating that the ith ground terminal equipment cannot transmit data in a time slot t; m is the number of ground terminal equipment;
s33, defining a reward value r as:
s34, the ith ground terminal equipment adjusts the state of the next time slot t+1 according to the decision of the time slot t and the previous time slot, and the state set of the next time slot is expressed as，The method comprises the steps of carrying out a first treatment on the surface of the For example, avoid partial terminal starvation, and then based on the action set of the next time slot t+1When the terminal groups in the time slot t+1, the state value of the terminal needs to be adjusted, and the adjusted state value can be expressed as:
wherein the method comprises the steps ofThe state adjustment factor is expressed, which is proportional to the time slot t and the number of times that the previous time slot can be transmitted, and a reasonable value is set according to the difference value of the state values of all the ground terminal devices. If all the ground terminal devices are allocated once in the time slot t and the previous time slot, the state value is not required to be adjusted, and the state value at the time of the time slot t+1 is maintained。
The terminal grouping algorithm based on deep reinforcement learning comprises the following specific steps:
(1) Initializing terminal statesTerminal assignment actionEstimating Q network。
(2) Selecting actions according to epsilon-greedy policyOtherwise, select action with probability of (1- ε)A prize r is obtained.
(3) Updating the next state。
(4) If awards are awardedWill beAnd storing into a memory bank D.
(5) If the memory bank is full, select from the memory bankGenerating a target Q value, minimizing a loss function using an Adam optimizer, and updating weights 。
(6) Every time interval T, according to the estimated Q network weightAnd updating the target Q network weight.
In an embodiment, the present invention further provides a terminal grouping system for massive MIMO satellite communication, including:
the total rate model building module is used for building a total rate model of the low-orbit satellite communication system according to the large-scale MIMO system channel model; it comprises the following steps:
a communication system establishing unit for establishing a large-scale MIMO low-orbit satellite communication system including one low-orbit satellite and a plurality of ground terminal devices;
the channel model building unit is used for building a channel model of the large-scale MIMO according to the large-scale MIMO low-orbit satellite communication system;
the optimization target construction module is used for constructing a mathematical model taking the maximum system total rate as an optimization target by taking the grouping number of the ground terminal equipment, the active variable and the total power of the ground terminal equipment as constraint conditions;
and the solving module is used for solving the mathematical model based on a terminal grouping algorithm of the deep reinforcement learning.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "configured," "connected," "secured," "rotated," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly through intermediaries, or in communication with each other or in interaction with each other, unless explicitly defined otherwise, the meaning of the terms described above in this application will be understood by those of ordinary skill in the art in view of the specific circumstances.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. A terminal grouping method for massive MIMO satellite communications, comprising the steps of:
s1, establishing a total rate model of a low-orbit satellite communication system according to a large-scale MIMO system channel model;
s2, establishing a mathematical model taking the maximum system total rate as an optimization target by taking the grouping number of the ground terminal equipment, the active variables of the ground terminal equipment and the total power as constraint conditions;
s3, designing a terminal grouping algorithm based on deep reinforcement learning to solve a mathematical model, and guaranteeing the optimal total speed of the communication system.
2. The terminal grouping method for massive MIMO satellite communication according to claim 1, wherein step S1 specifically comprises the following sub-steps:
s11, constructing a low rail guardA massive MIMO low-orbit satellite communication system of satellites and a plurality of ground terminal devices; wherein, the low orbit satellite is provided with N antennas and N RF A plurality of radio frequency links, and n=n RF The method comprises the steps of carrying out a first treatment on the surface of the All the ground terminal equipment is provided with a single antenna; all ground terminal devices are divided into L groups, the number of ground terminal devices of the L groups is expressed as |l| and |l| is less than or equal to N, i=1, 2, … RF The ith ground terminal equipment of the first group is denoted as U (l, i); all the ground terminal devices in the same group can transmit data on the same time-frequency resource, and the ground terminal devices of different groups are served in different time slots by using a time division multiple access technology;
s12, constructing a large-scale MIMO system channel model based on a large-scale MIMO low-orbit satellite communication system, wherein the method comprises the following steps of:
setting channel state matrix，/>，h nm Representing complex channel gains of an nth antenna and an mth ground terminal equipment antenna on a low-orbit satellite;
signals sent by the first group of ground terminal devices are received by the low-orbit satellite after passing through the channel, and the signals are expressed as:
wherein y is l Representing signals received by the low-orbit satellites transmitted via the first group of ground terminal devices of the channel transmission,a set of channel noises representing the signal transmitted by the first group of ground terminal devices through the channel, all the channel noises obeying a gaussian distribution, +.>Representing data transmitted by a first group of ground terminal devicesIn combination with the beamforming matrix:
wherein,representing a beamforming matrix, w, in relation to a channel state matrix, H i Represents the ith column vector of the beamforming matrix W, < >>Representing a transmission power matrix of the first group of ground terminal devices;
the signal received by the ith ground terminal equipment of the first group and transmitted through the channel is expressed as:
wherein n is i Channel noise representing the channel through which signals of the ith ground terminal device of the first group pass;
the SINR for the ith ground terminal equipment of the first group is expressed as:
wherein,representing the power of channel noise corresponding to the ith ground terminal equipment;
s13, constructing a total rate of the system when t time slots are constructed according to a large-scale MIMO system channel model, wherein the total rate is expressed as follows:
using active variablesIndicating whether the ith ground terminal equipment of the first group has data transmission in t time slots or not whenThe ith ground terminal equipment of the first group has data to be transmitted in time slot t, when +.>Indicating that the ith ground terminal equipment of the first group has no data to be transmitted in the t time slot; at the same time adopt decision variable +.>Indicating whether the ith ground terminal equipment of the first group can perform data transmission in t time slots, when +.>The ith ground terminal equipment of the first group can transmit data in time slot t, when +.>Indicating that the ith ground terminal equipment of the first group cannot transmit data in the time slot t; wherein (1)>Time->Is 1;
based on this, the total rate model of the low-orbit satellite communication system is expressed as:
。
3. the terminal grouping method for massive MIMO satellite communication according to claim 1, wherein the mathematical model for maximizing the total system rate is expressed as:
wherein R represents the total rate of the system,，h nm representing complex channel gains of an nth antenna and an mth ground terminal equipment antenna on a low-orbit satellite; w (w) i Representing the ith column vector, P, of the beamforming matrix W i Representing the transmission power of the ith ground terminal equipment of the first group; />Indicating that the ith ground terminal equipment of the ith group is at t l Active variable of time slot->Decision variables representing the ith ground terminal equipment of the first group at time slot t, +.>Representing the power of the channel noise corresponding to the ith ground terminal equipment.
4. A terminal grouping method for massive MIMO satellite communication according to claim 3, wherein the constraint associated with the mathematical model is:
wherein l represents the number of ground terminal devices of the first group, N represents the number of antennas of the low-orbit satellite, and P max Indicating the maximum transmit power of the low-orbit satellite,representing satellite processing capacity, i.e. the speed of satellite processing data, q represents the length of the satellite buffer queue, t 1 Representing the duration of time slot t, R max Indicating the maximum total system rate.
5. The terminal grouping method for massive MIMO satellite communication according to claim 1, wherein step S3 designs a deep reinforcement learning-based terminal grouping algorithm, comprising:
s31, setting a state set of t time slots as follows:，/>for the state of the ith ground terminal equipment in t time slots, SINR ti For the SINR value of the ith ground terminal equipment in the t time slot, d ti E {0,1} is the active variable value of the ith ground terminal equipment in the t time slot;
s32, setting an action set of t time slots as，/>Set up for the ith ground terminalThe action of the standby in the t time slot, M is the number of the ground terminal equipment;
s33, defining a reward value r as:
wherein,representing the power of the channel noise corresponding to the ith ground terminal equipment, d ti E {0,1} is the active variable value of the ith ground terminal equipment in the t time slot; />，h nm Representing complex channel gains of an nth antenna and an mth ground terminal equipment antenna on a low-orbit satellite; w (w) i Representing the ith column vector, P, of the beamforming matrix W i Representing the transmission power of the ith ground terminal equipment of the first group; />Representing the speed of satellite processing data, q represents the length of a satellite buffer queue, R max Representing a maximum system overall rate;
s34, the ith ground terminal equipment adjusts the state of the next time slot t+1 according to the decision of the time slot t and the previous time slot, and the state set of the next time slot t+1 is expressed as，The method comprises the steps of carrying out a first treatment on the surface of the Then according to the action set of the next time slot t+1When the terminal is grouped in the time slot t+1, the state value of the state set of the time slot t+1 is adjusted, which is expressed as:
wherein,representing a state adjustment factor proportional to the time slot t and the number of times that the previous time slot decision can be transmitted,representing the status of the ith ground terminal equipment in time slot t+1,/for the time slot t+1>Indicating the action of the ith ground terminal equipment in time slot t+1.
6. A terminal grouping system for massive MIMO satellite communications, comprising:
the total rate model building module is used for building a total rate model of the low-orbit satellite communication system according to the large-scale MIMO system channel model; it comprises the following steps:
a communication system establishing unit for establishing a large-scale MIMO low-orbit satellite communication system including one low-orbit satellite and a plurality of ground terminal devices;
the channel model building unit is used for building a channel model of the large-scale MIMO according to the large-scale MIMO low-orbit satellite communication system;
the optimization target construction module is used for constructing a mathematical model taking the maximum system total rate as an optimization target by taking the grouping number of the ground terminal equipment, the active variable and the total power of the ground terminal equipment as constraint conditions;
and the solving module is used for solving the mathematical model based on a terminal grouping algorithm of the deep reinforcement learning.
7. The terminal grouping system for massive MIMO satellite communication according to claim 6, wherein the mathematical model targeting the maximum system total rate as an optimization target is expressed as:
wherein R represents the total rate of the system,，h nm representing complex channel gains of an nth antenna and an mth ground terminal equipment antenna on a low-orbit satellite; w (w) i Representing the ith column vector, P, of the beamforming matrix W i Representing the transmission power of the ith ground terminal equipment of the first group; />Indicating that the ith ground terminal equipment of the ith group is at t l Active variable of time slot->Decision variables representing the ith ground terminal equipment of the first group at time slot t, +.>Representing the power of the channel noise corresponding to the ith ground terminal equipment, |l| represents the number of ground terminal equipment in the first group, and N represents the antenna of the low-orbit satelliteNumber of lines, P max Indicating maximum transmit power of low-orbit satellite, +.>Representing satellite processing capacity, i.e. the speed of satellite processing data, q represents the length of the satellite buffer queue, t 1 Representing the duration of time slot t, R max Indicating the maximum total system rate.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117955553A (en) * | 2024-03-26 | 2024-04-30 | 成都本原星通科技有限公司 | Terminal time slot allocation method for low-orbit satellite Internet of things |
| CN117955553B (en) * | 2024-03-26 | 2024-06-04 | 成都本原星通科技有限公司 | Terminal time slot allocation method for low-orbit satellite Internet of things |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110128867A1 (en) * | 2009-11-30 | 2011-06-02 | Qualcomm Incorporated | Forward link data rate control and rate indication for satellite-enabled communications systems |
| CN111641468A (en) * | 2020-06-11 | 2020-09-08 | 海南大学 | Optimization method suitable for energy efficiency of large-scale MIMO system under hardware damage |
| CN113839695A (en) * | 2021-09-16 | 2021-12-24 | 东南大学 | FDD large-scale MIMO and rate optimal statistical precoding method and device |
| CN115865160A (en) * | 2022-11-25 | 2023-03-28 | 西安交通大学 | Beam forming method and system of large-scale MIMO-NOMA system in low-orbit satellite communication scene |
| CN116249180A (en) * | 2023-01-06 | 2023-06-09 | 南京邮电大学 | Satellite Internet of things capacity improving method based on spatial domain and power domain resource joint scheduling |
| US20230413069A1 (en) * | 2022-06-15 | 2023-12-21 | Dongguan University Of Technology | Energy efficiency optimization method for irs-assisted noma thz network |
-
2024
- 2024-02-19 CN CN202410181998.3A patent/CN117749255A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110128867A1 (en) * | 2009-11-30 | 2011-06-02 | Qualcomm Incorporated | Forward link data rate control and rate indication for satellite-enabled communications systems |
| CN111641468A (en) * | 2020-06-11 | 2020-09-08 | 海南大学 | Optimization method suitable for energy efficiency of large-scale MIMO system under hardware damage |
| CN113839695A (en) * | 2021-09-16 | 2021-12-24 | 东南大学 | FDD large-scale MIMO and rate optimal statistical precoding method and device |
| US20230413069A1 (en) * | 2022-06-15 | 2023-12-21 | Dongguan University Of Technology | Energy efficiency optimization method for irs-assisted noma thz network |
| CN115865160A (en) * | 2022-11-25 | 2023-03-28 | 西安交通大学 | Beam forming method and system of large-scale MIMO-NOMA system in low-orbit satellite communication scene |
| CN116249180A (en) * | 2023-01-06 | 2023-06-09 | 南京邮电大学 | Satellite Internet of things capacity improving method based on spatial domain and power domain resource joint scheduling |
Non-Patent Citations (3)
| Title |
|---|
| QI SUN ET AL.: "Sum rate optimization for MIMO non-orthogonal multiple access systems", 《2015 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC)》, 12 March 2015 (2015-03-12) * |
| 王志刚等: "基于相位域乘子法的MIMO雷达抗干扰波形设计快速优化方法", 《现代雷达》, 25 October 2021 (2021-10-25) * |
| 高慧等: "多用户MIMO系统中一种自由度分配算法", 《计算机工程》, 23 March 2015 (2015-03-23) * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117955553A (en) * | 2024-03-26 | 2024-04-30 | 成都本原星通科技有限公司 | Terminal time slot allocation method for low-orbit satellite Internet of things |
| CN117955553B (en) * | 2024-03-26 | 2024-06-04 | 成都本原星通科技有限公司 | Terminal time slot allocation method for low-orbit satellite Internet of things |
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