CN104581918B - Satellite layer-span combined optimization power distribution method based on non-cooperative game - Google Patents
Satellite layer-span combined optimization power distribution method based on non-cooperative game Download PDFInfo
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- CN104581918B CN104581918B CN201410778831.1A CN201410778831A CN104581918B CN 104581918 B CN104581918 B CN 104581918B CN 201410778831 A CN201410778831 A CN 201410778831A CN 104581918 B CN104581918 B CN 104581918B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/30—TPC using constraints in the total amount of available transmission power
- H04W52/34—TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/14—Relay systems
- H04B7/15—Active relay systems
- H04B7/185—Space-based or airborne stations; Stations for satellite systems
- H04B7/1853—Satellite systems for providing telephony service to a mobile station, i.e. mobile satellite service
- H04B7/18539—Arrangements for managing radio, resources, i.e. for establishing or releasing a connection
- H04B7/18543—Arrangements for managing radio, resources, i.e. for establishing or releasing a connection for adaptation of transmission parameters, e.g. power control
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/14—Relay systems
- H04B7/15—Active relay systems
- H04B7/185—Space-based or airborne stations; Stations for satellite systems
- H04B7/18578—Satellite systems for providing broadband data service to individual earth stations
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Abstract
The present invention relates to a kind of satellite layer-span combined optimization power distribution method based on static non-cooperative game.The described method includes：Synchronous wideband satellite physical layer counts the real time information of each user；Consideration satellite is power constrained system, is needed power distribution problems being modeled as multi-user's static state non-cooperative game model according to the QoS of different business；Corresponding coding mode for different business distribution power and is used for different business in application layer according to solution；Repeat at periodic or other desired above step.Cross-layer optimizing power distribution method provided by the invention enables to the channel state feedback information that satellite network control centre provides according to physical layer, and consider the QoS needs of different business, service power assignment problem is modeled as multi-service static state non-cooperative game model, corresponding coding mode is used for different business in application layer according to different cross-layer solutions at the same time, the communication quality for improving user in mobile communication system, lifting system performance can be integrated.
Description
Technical Field
The invention relates to the technical field of space communication, in particular to a satellite cross-layer joint optimization power distribution method based on a non-cooperative game.
Background
With the commercial maturation of terrestrial cellular 4G technology, research related to 5G technology is also being actively conducted. Mobile satellite communication systems introduced to solve the problem of coverage of terrestrial cellular mobile communication systems have also become a research focus accordingly. An integrated mobile communication system in which a satellite mobile communication system and a terrestrial mobile communication system are integrated is considered to be an important component of a next-generation communication network.
For a synchronous satellite communication system, because a satellite is located in a synchronous satellite orbit far away from the ground, and the RTT (Round-Trip Time, round-Trip delay) of a data packet is as long as 500ms, it is difficult for the satellite to obtain CSI (Channel State Information) in Time for resource management, and thus, a mature and complex resource management scheduling scheme on the ground is not suitable for being directly used on the satellite for providing effective QoS (Quality of Service) guarantee of on-satellite forwarding Service. Due to the technical conditions of the existing satellite architecture design and the actual factors such as satellite-ground distance and space interference, the broadband satellite communication system is actually a power-limited system (here, particularly, the satellite architecture), and therefore, a more intensive study on power control and distribution is needed when designing a satellite resource management scheme.
From the aspect of the function implementation of the radio resource management, the radio resource management system includes core functions such as a satellite MAC (Media Access Control) protocol, an Access Control protocol, link layer bandwidth allocation, and physical layer resource allocation. A traditional satellite wireless resource management system is designed in a layered mode, optimization is usually performed only on each layer independently, and the overall joint optimization performance of a network is less considered. In order to further fully utilize scarce power and bandwidth resources on the satellite, a cross-layer resource management design is required to be adopted, joint optimization is carried out under the conditions of specific service type QoS guarantee and system resource constraint, the time delay guarantee of services with strict time delay requirements is realized, the satisfaction degree of other services is maximized, and the overall performance of the broadband satellite mobile communication system is optimized.
Disclosure of Invention
Aiming at the defects, the invention provides a cross-layer power optimization method based on multi-user static non-cooperative game, which maximizes the satisfaction degree of other services under the condition of ensuring the QoS of the services with strict requirements on time delay.
A cross-layer power optimization method based on a multi-user static non-cooperative game specifically comprises the following steps:
s1: under the control of a satellite network control center, a synchronous broadband satellite physical layer counts real-time information of each service and uploads the real-time information to the satellite network control center;
s2: the satellite network control center defines a utility function of the network based on the physical layer statistical information and QoS requirements of different services, and models a service power distribution problem into a multi-service static non-cooperative game model;
s3: distributing the whole useful power to different beams according to the service and system requirements according to the profit vector of the model, and adopting corresponding coding modes aiming at different services at an application layer;
s4: steps S1 to S3 are repeated according to a predetermined cycle.
Further, the real-time information of each service counted by the physical layer of the synchronous broadband satellite in the step S1 includes: coding modulation mode, transmission power, signal-to-noise ratio, available bandwidth, error rate and the like;
further, the real-time information in the step S1 is used for designing a joint optimization scheme of an upper layer and a physical layer under the control of the satellite network control center, so as to maximize the satisfaction of multiple services;
further, the satellite network control center of the step S1 analyzes the real-time information to obtain satellite channel state information;
further, step S2 further includes that the satellite network control center estimates a previous multi-service power allocation scheme according to the satellite channel state information, and determines QoS satisfaction degrees of different services;
further, the satellite network control center in step S2 establishes a multi-service static non-cooperative game model according to feedback satellite channel state information and QoS requirements of different services under the constraint of on-satellite transmission power, and specifically includes:
s21: suppose that N satellite services compete for the useful power capacity of the satellite link in the system, and each service i has the lowest guaranteed power P 1 And variable boost power P 2 ；
S22: in the model, each service iteratively updates its own policy. In each iteration, the current user selects a strategy capable of maximizing the utility function of the current user, and the strategies of other users are kept unchanged;
s23: and carrying out finite iteration under the condition of meeting the power constraint condition according to the model until a convergent optimal solution is obtained or the iteration times are used up.
Furthermore, in each iteration, a user selects an optimal strategy according to the channel state of the terminal and satellite channel information fed back by a satellite control center;
furthermore, the channel and the service of the satellite are constant in a time window, so that the signaling resource can be saved;
further, the utility function is:
wherein, the first and the second end of the pipe are connected with each other,the unit of the power distribution request selected for the ith service is bit/s;obtaining a maximum utility function for the ith service based on the power allocation requests of other services;
further, the overall satellite useful power allocation based on the model optimization solution in step S3 follows the following principle:
the useful power allocated to the service of each terminal is not lower than the minimum guaranteed power and not higher than the maximum useful power value allocable by the wave beam;
preferentially distributing enhanced power for a terminal with good channel condition or a service with high QoS requirement according to the optimal solution of the model and the service QoS requirement;
aiming at the time delay insensitive service, the coding mode is adaptively adjusted at the application layer to reduce the error rate.
Further, the overall useful power allocated to the service of each terminal;
further, the satellite channel state and traffic are considered constant for a predetermined period in step S4; the channel state information is not counted again in a predetermined period, so as to save signaling overhead.
The invention provides a cross-layer optimization satellite power distribution method based on a static non-cooperative game model. Under the control of the satellite network control center, the satellite counts the real-time information of each terminal service and uploads the information to the network control center. And the network control center defines an effect function based on the channel information and the service QoS requirement, models the power distribution problem into a static non-cooperative game model and carries out finite iteration solution. According to the gain vector of the model, the satellite network control center distributes useful power to each beam and different types of services, and meanwhile, the coding mode is adaptively adjusted in an application layer aiming at the delay insensitive service, so that the error rate is reduced. Therefore, the improved cross-layer optimization method can optimize the power distribution in the satellite based on the service QoS requirement and the channel state, improve the communication quality and integrally improve the communication performance.
Drawings
FIG. 1 is a flow chart of a cross-layer optimized power allocation method based on a static non-cooperative game model of the present invention;
fig. 2 is a schematic diagram of satellite and user distribution according to an embodiment of the invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Fig. 1 is a flowchart of a method according to an embodiment of the present invention, which specifically includes the following steps:
step S1: under the control of a satellite network control center, the synchronous broadband satellite physical layer counts the real-time information of each service and uploads the real-time information to the satellite network control center;
step S2: the satellite network control center defines a utility function of the network based on the physical layer statistical information and QoS requirements of different services, and models a service power distribution problem into a multi-service static non-cooperative game model;
and step S3: distributing the whole useful power into different beams according to the service and system requirements according to the income vector of the model, and adopting corresponding coding modes aiming at different services at an application layer;
and step S4: the above steps are repeated according to a predetermined cycle.
Through the steps, the satellite defines an effect function based on channel information and service QoS requirements under the control of a network control center, a static non-cooperative game model is established, iteration is carried out for a limited number of times to obtain an optimal solution, the useful power is distributed to different types of services under each beam according to the lowest guaranteed power and the enhanced power, the coding mode is adaptively adjusted in an application layer according to the delay insensitive service, the error rate is comprehensively reduced, the communication quality of a satellite system is improved, and the communication performance is integrally improved.
Preferably, the real-time information of each service of the satellite physical layer statistics comprises: coding modulation mode, transmission power, signal-to-noise ratio, available bandwidth, bit error rate and the like. The real-time information is used for the design of a joint optimization scheme of an upper layer and a physical layer under the control of a network control center, and the satisfaction degree of multiple services is maximized.
The establishment of the static non-cooperative game model is that on the basis of the known channel state information and the QoS requirement of each service, the modeling optimization processing is carried out on the useful power distribution mode distributed to each service in the network control center, and the optimal overall efficiency is realized.
The static non-cooperative game model establishing process comprises the following steps:
s21: suppose that N satellite services compete for the useful power capacity of the satellite link in the system, and each service i has the lowest guaranteed power P 1 And variable boost power P 2 ；
S22: in the model, each service iteratively updates its own policy. In each iteration, the current user selects a strategy capable of maximizing the utility function of the current user, and the strategies of other users are kept unchanged;
s23: carrying out finite iteration under the condition of meeting the power constraint condition according to the model until a convergence optimal solution is obtained or the iteration times are used up;
here we define that one service i in each beam selects one power allocation request x req,i (bit/s)。
As the variable policy competition capacity C, a variable policy set S is defined for each service i i . The variable strategy space of the game is S = S 1 ×S 2 ×…×S N So one variable policy combination is an N-dimensional vector:
the goal of each service is to maximize its utilityDecides that each service passes through the adjustment x req,i The resulting request expectation.The solution of (c) is defined as follows:
in order to establish a connection between the traffic model under consideration and the utility function, a QoS priority weighting parameter Ω is introduced i >, 0, defined as:
wherein q is j Is the weight, h, assigned to each service i (j) Is the percentage of i users j type traffic, so the utility function can be further expressed as:
preferably, in each iteration, the user selects an optimal strategy according to the channel state of the terminal and the satellite channel information fed back by the satellite control center;
preferably, the process of allocating power to each beam and service according to the static non-cooperative game model includes:
s31: distributing useful power not lower than the lowest guaranteed power to the service of each terminal;
s32: preferentially distributing and enhancing useful power for a terminal with good channel condition or a service with high QoS requirement according to the optimal solution of the model and the QoS requirement of the service;
s33: aiming at the time delay insensitive service, the coding mode is adaptively adjusted at the application layer to reduce the error rate.
Preferably, the satellite channel state and traffic are considered constant during the predetermined period in step S4; in step S4, the channel state information is not counted again in the predetermined period, so as to save signaling overhead.
An embodiment of the present invention is given below.
The present embodiment considers the satellite beam edge traffic power allocation case, as shown in fig. 2.
And in a preset period, establishing a static non-cooperative game model according to the statistical real-time information and the QoS requirement of each service, and iterating for a plurality of times to obtain an optimal solution. And allocating useful power to different types of traffic for each beam according to the optimization solution.
The channel conditions are poor for both beam-edge users a, B shown in the figure. Considering that the service types of the user A and the user B are different, the user A is a mobile terminal, and the service type is a voice service; user B serves streaming media such as video. The two traffic types accordingly have different QoS weights when modeled. After the optimization solution is solved, the enhancement power is distributed to the user A on the basis of distributing the minimum guaranteed power so as to ensure the voice service quality; for user B, the coding mode needs to be adjusted at the application layer to reduce the bit error rate and improve the communication performance.
The above embodiments are only for illustrating the invention and are not to be construed as limiting the invention, and those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention, therefore, all equivalent technical solutions also belong to the scope of the invention, and the scope of the invention is defined by the claims.
Claims (10)
1. A satellite cross-layer joint optimization power distribution method based on a static non-cooperative game is characterized by specifically comprising the following steps:
s1: under the control of a satellite network control center, a synchronous broadband satellite physical layer counts real-time information of each service and uploads the real-time information to the satellite network control center;
s2: the satellite network control center defines a utility function of the network based on the physical layer statistical information and QoS requirements of different services, and models a service power distribution problem into a multi-service static non-cooperative game model, wherein the static non-cooperative game model establishing process comprises the following steps:
n satellite services in the system are supposed to compete for the useful power capacity of a satellite link, and each service i has the lowest guaranteed power and variable enhanced power;
in the model, each service iteratively updates its own strategy, and in each iteration, the current user selects a strategy capable of maximizing its own utility function, while the strategies of other users remain unchanged;
carrying out finite iteration under the condition of meeting the power constraint condition according to the model until a convergence optimal solution is obtained or the iteration times are used up;
s3: distributing the whole useful power to different beams according to the service and system requirements according to the income vector of the model, and adopting corresponding coding modes aiming at different services at an application layer;
s4: steps S1 to S3 are repeated according to a predetermined cycle.
2. The method of claim 1, wherein the step S1 of synchronizing the real-time information of each service of the broadband satellite physical layer statistics comprises: code modulation mode, transmission power, signal-to-noise ratio, available bandwidth and bit error rate.
3. The method of claim 1, wherein the real-time information of step S1 is used for joint optimization scheme design of upper layer and physical layer under control of a network control center to achieve maximum satisfaction of multiple services.
4. The method of claim 1, wherein step S1 the network control center analyzes the real-time information to obtain satellite channel state information.
5. The method of claim 1, wherein in each iteration, the user selects the optimal strategy according to the channel state of the terminal and satellite channel information fed back by the satellite control center.
6. The method of claim 1, wherein the channel and traffic of the satellite are constant over a time window.
7. The method of claim 1, wherein the utility function is:
wherein, the first and the second end of the pipe are connected with each other,selecting a power distribution request for the ith service, wherein the unit is bit/s;for the ith service based on othersThe maximum utility function obtained by the power allocation request of the service.
8. The method of claim 1, wherein the method for allocating the satellite total useful power based on the model optimization solution in step S3 is as follows:
the useful power allocated to the service of each terminal is not lower than the minimum guaranteed power and not higher than the maximum useful power allocable by the wave beam;
preferentially distributing enhanced power for a terminal with good channel condition or a service with high QoS requirement according to the optimal solution of the model and the QoS requirement of the service;
aiming at the time delay insensitive service, the coding mode is adaptively adjusted at the application layer to reduce the error rate.
9. The method of claim 8, wherein the total useful power allocated for the traffic of each terminal is not higher than the maximum useful power value that a beam can be allocated.
10. The method of claim 1, wherein the satellite channel state and traffic are considered constant for a predetermined period in step S4; and not counting the channel state information in the preset period.
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| GB2557628B (en) | 2016-12-13 | 2020-01-01 | Inmarsat Global Ltd | Forward link power control |
| CN106792731B (en) * | 2016-12-23 | 2019-09-03 | 中国电子科技集团公司第五十四研究所 | Satellite communication power distribution method based on non-cooperative game |
| CN109120552B (en) * | 2018-08-15 | 2021-10-19 | 大连大学 | QOS-oriented bandwidth and power multi-target cross-layer optimization method in AOS |
| CN111447668B (en) * | 2020-02-25 | 2023-04-07 | 大连大学 | QoS-based service cross-layer power control method in AOS |
| CN114401037B (en) * | 2022-03-24 | 2022-06-17 | 武汉大学 | Unmanned aerial vehicle communication network flow unloading method and system based on alliance formed game |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1259243A (en) * | 1997-04-04 | 2000-07-05 | 艾利森公司 | Power control for a mobile terminal in a satellite communication system |
| CA2537441A1 (en) * | 2003-09-08 | 2005-03-24 | Qualcomm Incorporated | Apparatus, system, and method for managing reverse link communication |
| CN101902808A (en) * | 2010-05-21 | 2010-12-01 | 南京邮电大学 | Uplink self-adaptive closed loop power control method for satellite communication system |
| CN102655671A (en) * | 2011-03-02 | 2012-09-05 | 北京大学 | Power control method for satellite code division multiple access (CDMA) system |
| CN102905358A (en) * | 2012-10-12 | 2013-01-30 | 南京邮电大学 | Satellite communication system link adaptive-step-size power controlling method |
| CN103281764A (en) * | 2013-06-04 | 2013-09-04 | 北京邮电大学 | Uplink power control method in femtocell double layered network based on convex pricing |
| CN103857027A (en) * | 2014-03-04 | 2014-06-11 | 中国人民解放军理工大学 | Power distribution method in satellite communication system |
-
2014
- 2014-12-15 CN CN201410778831.1A patent/CN104581918B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1259243A (en) * | 1997-04-04 | 2000-07-05 | 艾利森公司 | Power control for a mobile terminal in a satellite communication system |
| CA2537441A1 (en) * | 2003-09-08 | 2005-03-24 | Qualcomm Incorporated | Apparatus, system, and method for managing reverse link communication |
| CN101902808A (en) * | 2010-05-21 | 2010-12-01 | 南京邮电大学 | Uplink self-adaptive closed loop power control method for satellite communication system |
| CN102655671A (en) * | 2011-03-02 | 2012-09-05 | 北京大学 | Power control method for satellite code division multiple access (CDMA) system |
| CN102905358A (en) * | 2012-10-12 | 2013-01-30 | 南京邮电大学 | Satellite communication system link adaptive-step-size power controlling method |
| CN103281764A (en) * | 2013-06-04 | 2013-09-04 | 北京邮电大学 | Uplink power control method in femtocell double layered network based on convex pricing |
| CN103857027A (en) * | 2014-03-04 | 2014-06-11 | 中国人民解放军理工大学 | Power distribution method in satellite communication system |
Non-Patent Citations (1)
| Title |
|---|
| 潜铺型卫星认知通信中上行链路功率控制;陈鹏，邱乐德，王宇;《电子技术应用》;20121230;第38卷(第12期);全文 * |
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