CN112583566A - Network resource allocation method based on air-space-ground integrated system - Google Patents
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Abstract
一种基于空天地一体化系统的网络资源分配方法，所述方法根据电力用户业务优先级、用户速率最低要求、功率及QoS需求建立多小区协作资源分配模型，再用动态迭代算法给用户分配临时子信道，然后采用考虑KKT条件的迭代注水算法对小区用户的功率进行分配，得到多小区协作下用户的最优解，使得各优先级的电力用户信息选择最佳的载波频段及资源单元进行传输。本发明在现有的空地一体化系统架构的基础上，找到了覆盖更广，容量更大，成本更低，更易实现的框架设计，并实现了功率的最佳分配，使得各优先级的电力用户信息在满足速率、时延、功率等条件下，选择最佳的载波频段及资源单元进行传输，为电力生产的应急通信需求提供了有力的信息传输支撑。
A network resource allocation method based on an air-space-ground integrated system, the method establishes a multi-cell cooperative resource allocation model according to power user service priorities, minimum user rate requirements, power and QoS requirements, and then uses a dynamic iterative algorithm to allocate temporary resources to users. Then use the iterative water filling algorithm considering the KKT condition to allocate the power of cell users, and obtain the optimal solution of users under multi-cell cooperation, so that the power user information of each priority selects the best carrier frequency band and resource unit for transmission. . On the basis of the existing air-ground integrated system architecture, the invention finds a frame design with wider coverage, larger capacity, lower cost and easier realization, and realizes the optimal distribution of power, so that the power of each priority is Under the conditions of satisfying rate, delay, power, etc., the user information selects the best carrier frequency band and resource unit for transmission, which provides a strong information transmission support for the emergency communication needs of power production.
Description
Technical Field
The invention relates to a power communication resource allocation method, and belongs to the technical field of communication.
Background
In recent years, with the rise of smart devices and wave tides, the concept of the mobile internet is widely concerned by people, and new technologies such as unmanned driving, smart cities and smart grids bring great challenges to network architectures while bringing convenience to life of people. To meet these demands, new network architectures and technologies are developed, and the Internet of Things (IoT) is one of them. In the IoT, there are a large number of intelligent devices and sensors widely deployed, and the internet of things not only can serve densely populated areas, but also can provide support and management for the intelligent devices in desert, ocean, and other areas. When smart devices are densely distributed in a wide area or in Remote areas not served by a terrestrial access network, the Internet of Things in this particular case is called Remote Internet of Things (IoRT).
The satellite has the advantages of high service reliability, wide coverage range, large communication capacity and the like which are incomparable with other communication modes, the broadband internet service provided by the satellite can meet the requirement of internet connection in the internet of things, the service quality of users is greatly improved, and meanwhile, the broadband internet service is also an important factor for providing continuous communication service in natural disaster or artificial disaster scenes. At present, nearly hundreds of spacecraft operate in orbit in China, so that various services such as communication, surveying and mapping, navigation, manned space flight, deep space exploration and the like are provided, and meanwhile, ground network systems matched for use are increasingly mature, and comprise satellite operation and control stations, monitoring stations, measurement and control stations and the like. Ground networks have failed to meet the ever-increasing demand, and in wide oceans, remote mountainous areas, vast deserts, and wide wraps of space, ground base stations have not been fully deployed and cannot access the ground networks, while satellite communications can deal well with this problem. In the above-mentioned areas and disaster areas, the only communication mode that can be deployed rapidly is satellite communication. In view of this, in the information age, satellite communication has become an indispensable communication method.
The power information communication requirement brings new challenges for wireless communication technology by its "small data, multi-access, multi-service" feature, which is different from the traditional mobile communication. Therefore, how to find a framework design which is wider in coverage, larger in capacity, lower in cost and easier to implement on the basis of the existing air-ground integrated system architecture and find an optimal power distribution mode by comprehensively considering base station load balance in an air-ground integrated system based on multi-cell joint cooperation becomes a problem faced by related technicians.
Disclosure of Invention
The invention aims to provide a network resource allocation method based on an air-space-ground integrated system aiming at the defects of the prior art.
The problems of the invention are solved by the following technical scheme:
a network resource allocation method based on an air-space-ground integrated system is characterized by firstly establishing a multi-cell cooperative resource allocation model according to service priorities of power users, minimum requirements of user rates, power and QoS requirements, then allocating temporary sub-channels to the users by using a dynamic iterative algorithm, and then allocating the power of the cell users by using an iterative water injection algorithm considering KKT conditions to obtain the optimal solution of the users under multi-cell cooperation, so that the power user information of each priority is transmitted by selecting the optimal carrier frequency band and resource units under the conditions of rate, time delay and power.
The network resource allocation method based on the air-space-ground integrated system comprises the following steps:
a. prioritization of power traffic
Dividing all the electric power user terminals into three sets according to the service priority: video image monitoring terminal set AVISDistribution automation terminal set ADAElectricity consumption information collecting terminal set AEICAnd arranging the terminals in each set in descending order according to the value of the following formula:
in the formula Qi,j,kIndicating the priority of the terminal on the resource block, ri,j,kWhich represents the instantaneous transmission rate of the terminal,
denotes the average rate, τ, of all terminals in the setiIndicates the waiting time of the head of queue data packet in the buffer queue of the terminal i, f (tau)i) The priority of the video stream data packet with long waiting time in RB allocation is improved, so that the video stream data packet with long waiting time is preferentially transmitted in RB allocation, and the definition is as follows:
in the formula of UvideoRepresenting a set of all video streams UE, τmaxRepresents the maximum latency tolerable for a video stream packet, wheni＞τmaxThen, the data packet waiting at the head of the queue is discarded, the original second data packet is changed into the head of the queue data packet, and the tau is recalculated according to the data packetiAnd f (τ)i)；
b. Establishing a resource scheduling model:
where B is the total bandwidth of the system, N is the number of subchannels into which the system is divided,
is the channel response of base station m and user k on subchannel n,
is the power allocated by base station m for subchannel n,
is a set of power allocations that are made to,
is the corresponding noise power;
c. and solving the optimal solution of the users under the multi-cell cooperation by the resource scheduling model to realize the allocation of network resources.
The specific method for solving the optimal solution of the users under the multi-cell cooperation by the resource scheduling model in the network resource allocation method based on the air-space-ground integrated system is as follows:
(ii) dynamic iterative subchannel allocation
Designing a potential function corresponding to cross-layer resource allocation:
the optimization model of the resource scheduling model is converted into:
the dynamic subchannel allocation strategy is as follows:
wherein
Sub-letter representing base station mTemporarily allocating channel n to user k, assuming that the power is averagely allocated in the first iteration, and allocating a temporary sub-channel to the user by using a dynamic iteration algorithm;
② iterative water injection power distribution based on KKT
Converting the optimization model of the resource scheduling model into:
the optimization problem in the equation is expressed as a partial lagrange function containing a binary form of the power constraint, as shown in the following equation:
wherein λ ism,μm,vmAnd solving the water injection solution corresponding to the target power by using a Lagrange multiplier and a power distribution matrix P to be solved to obtain a power distribution mode:
thirdly, repeating the step I to obtain the next temporary sub-channel of the user according to the formula
Updating the sub-channel distribution mode;
fourthly, repeating the step II to obtain the power distribution mode of the user in the next temporary sub-channel according to the formula
Updating the power;
iterating n times until
And converging to obtain the optimal solution of the user under the multi-cell cooperation.
Advantageous effects
On the basis of the existing air-ground integrated system architecture, the invention finds a framework design which is wider in coverage, larger in capacity, lower in cost and easier to realize, and realizes the optimal distribution of power, so that the power user information of each priority level can select the optimal carrier frequency band and resource units to transmit under the conditions of meeting the speed, time delay, power and the like, and powerful information transmission support is provided for the emergency communication requirement of power production.
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The present invention will be described in further detail with reference to the accompanying drawings.
FIG. 1 is a system architecture diagram of an air-ground integrated system.
Fig. 2 is a communication flow framework diagram of the air-space-ground integrated system.
Fig. 3 is a flow chart of a multi-cell joint resource allocation algorithm based on the air-space-ground integrated system.
The symbols in the text are respectively expressed as: a. theVISRepresenting a set of video image monitoring terminals, ADARepresenting a distribution automation terminal set, AEICRepresenting a set of electricity consumption information collection terminals, Qi,j,kIndicating the priority of the terminal on the resource block, ri,j,kWhich represents the instantaneous transmission rate of the terminal,
denotes the average rate, τ, of all terminals in the setiIndicates the waiting time of the head of queue data packet in the buffer queue of the terminal i, f (tau)i) For increasing priority of long-latency video stream data packets in RB allocation, UvideoRepresenting a set of all video streams UE, τmaxRepresenting the maximum latency that a video stream packet can tolerate, B being the total bandwidth of the system, N being the number of subchannels into which the system is divided,
is the channel response of base station m and user k on subchannel n,
is the power allocated by base station m for subchannel n,
is a set of power allocations that are made to,
is the power of the corresponding noise or noise,
a subchannel n representing a base station m is temporarily allocated to a user k, λm,μm,vmIs the lagrange multiplier and P is the power distribution matrix to be solved.
Detailed Description
The invention provides a resource allocation method based on a power communication air-space-ground integrated system. In order to measure the performance of the designed method, a resource scheduling model of the air-space-ground integrated system under the intelligent power grid is described as follows: suppose that an Orthogonal Frequency Division Multiple Access (OFDMA) network allocates a different set of subchannels to each user, and there are M (M ≧ 2) base station cooperation Access points in the downlink OFDMA cellular network, the total bandwidth of the system is B, which is divided into N subchannels, and the system uses full-Frequency multiplexing. The method comprises the steps of firstly establishing a multi-cell cooperative resource allocation model according to service priorities of power users, minimum requirements of user rates, power and QoS requirements, then allocating temporary sub-channels to the users by using a dynamic iterative algorithm, and then allocating the power of the cell users by considering an iterative water injection algorithm of (Karush-Kuhn-Tucker, KKT) conditions to obtain the optimal solution of the users under multi-cell cooperation, so that the power users of all priorities select the optimal carrier frequency band and resource units to transmit under the conditions of rate, time delay, power and the like, the requirements of power emergency communication are met, and powerful information transmission support is provided for the emergency communication requirements of power production.
The invention selects three representative services, namely, an Electricity Information Collection (EIC) service, a Distribution Automation (DA) service and a Video/Image monitoring (VIS) service, wherein the Electricity Information Collection service is set to have the lowest priority, the Distribution Automation service has the medium priority, and the Video Image monitoring service has the highest priority. In order to achieve the compromise between the network throughput, the User fairness and the packet loss rate, the priority based on the fairness and the time delay is adopted to guide the allocation of Resource Blocks (RBs), wherein a priority calculation formula of a terminal User Equipment (User Equipment, UE) on the RBs is as follows:
q in the formula (1-1)i,j,kIndicating the priority of the terminal on the resource block, ri,j,kWhich represents the instantaneous transmission rate of the terminal,
denotes the average rate, τ, of all terminals in the setiIndicates the waiting time of the head of queue data packet in the buffer queue of the terminal i, f (tau)i) The priority of the video stream packets with long waiting time in RB allocation is improved, so that the video stream packets with long waiting time are preferentially transmitted in RB allocation, and the priority is defined as shown in the following formula:
u in the formula (1-2)videoRepresenting a set of all video streams UE, τmaxRepresents the maximum latency tolerable for a video stream packet, wheni＞τmaxThen the data packet waiting at the head of the queue is discarded, and the original second numberThe packet becomes the head-of-line packet and τ is recalculated based on the packetiAnd f (τ)i)。
The signal-to-noise ratio (SINR) of user k connected to the nth subchannel of cell m can be expressed as:
in the formula (1-3),
is the channel response of base station m and user k on subchannel n, including small-scale fading, large-scale fading, and path loss.
Is the power allocated by base station m for subchannel n.
Is a set of power allocations that are made to,
is the corresponding noise power.
The individual gain obtained from the base station for subchannel n of corresponding cell m may be expressed as:
in order to obtain higher transmission power and better service quality for each user, interference between users is considered in addition to pursuit of utility maximization in the process of multi-cell resource allocation. The invention adopts a pricing mechanism to design a system objective function, finds a compromise between energy consumption and channel quality, and enables the performance of the whole network to reach the optimal balance. Defining U as the total utility that the system can achieve, i.e. summing all channel utilities of all cells, the expression is:
wherein
Represents the individual gain obtained from the base station for subchannel n of cell m,
is a pricing function, QkIs a pricing factor determined by the priority of the selected user, determined by equation (1-1). The individual gain minus the pricing function is the net utility function on subchannel n for cell m. Since the transmission power of the base station is limited by practical engineering, the objective optimization model of the present invention can be expressed as:
the sum of the transmission power of each sub-channel in the cell is the maximum transmission power of the base station, the formula (1-6a) is used for guaranteeing the minimum rate of the power user service in the process of guaranteeing resource scheduling, so as to guarantee the quality of service QoS of the system, and the formula (1-6c) indicates that the minimum transmission power is non-negative.
In order to solve the problem of the optimization model, the optimization strategy of the invention is based on the potential game theory and adopts the mapping among elements to crossThe layer optimization problem is converted into potential function solving, and the existence and uniqueness of Nash equilibrium are guaranteed. Then, the problem is decomposed into two parts to respectively solve the user scheduling and the power distribution. One part uses a dynamic iteration method to carry out resource allocation on users, and the other part carries out dynamic power allocation based on an iteration water filling algorithm so as to solve the optimal solution of resource scheduling.
The total utility function of users on n subchannels of m cells is represented as follows:
designing potential functions corresponding to cross-layer resource allocation as follows:
solving the first derivative of the utility function and the potential function of the user, the first derivative between the utility function and the potential function of the individual user can be obtained to satisfy the following relation:
the potential function can reach nash equilibrium after a finite number of iterations. And potential function
Strictly convex, then it can be known that
Strictly concave, potential function in equilibrium
Get the minimum value, and then find
Finds this nash-equalization state by dynamically iterating the subchannel allocation and an iterative water-filling algorithm based on the KKT condition.
1) Dynamic iterative subchannel allocation
The invention firstly obtains that a certain sub-channel is temporarily allocated to a user under the condition of the current channel state through a dynamic resource allocation strategy based on an iterative algorithm. The optimization model of the resource scheduling model is converted into:
the dynamic subchannel allocation strategy is as follows:
wherein
A subchannel n representing base station m is temporarily allocated to user k, assuming that the power is equally allocated in the first iteration, and then the power allocation is updated using a water-filling algorithm.
2) Iterative water injection power allocation based on KKT
After the sub-channel allocation is completed, the resource allocation problem is converted into a power allocation problem, and according to a convex optimization theory, the optimal solution of power allocation needs to meet the KKT condition. To obtain KKT conditions, the formula (1-10) is converted to:
the optimization problem in the equation is expressed as a partial lagrange function containing a binary form of the power constraint, as shown in the following equation:
wherein λ ism,μm,vmIs the lagrange multiplier and P is the power distribution matrix to be solved. The water injection solution corresponding to the target power is as follows:
and (3) updating k (m, n) according to the formula (1-11), and updating the power solving result according to the formula (1-14) until convergence, so as to obtain the optimal solution of resource allocation.
The resource allocation method based on the air-space-ground integrated system comprises the following steps:
1. and (4) dividing the priority of the power service. Firstly, all the power user terminals are ranked according to service priorityIs three sets AVIS，ADA，AEICAnd the terminals in each set are arranged according to
The values of (A) are arranged in descending order, Q iskAs a pricing factor.
2. And establishing a resource scheduling model. 1) The transmit power of each user is non-negative. 2) The sum of the transmission power of each subchannel of the cell is the maximum transmission power of the base station. 3) And ensuring the minimum rate of the power user service in the resource scheduling process. And (3) establishing a multi-cell cooperative resource allocation model by taking the three problems as constraint conditions of a resource scheduling model and the utility function of the user as a target function.
3. And (4) allocating the sub-channels. And allocating temporary sub-channels to the users by using a dynamic iterative algorithm to obtain a sub-channel allocation mode. The power is assumed to be evenly distributed in the first dynamic iteration, and then the frequency distribution is updated by adopting a water filling algorithm.
4. Allocation of user power. And on the premise of good sub-channel allocation, allocating user power, and allocating the power of cell users by considering an iterative water injection algorithm under the KKT condition to obtain a power allocation mode.
5. And repeating the step 3 to obtain the next temporary sub-channel of the user, and updating the sub-channel distribution mode according to the formula (1-11).
6. And repeating the step 4 to obtain the power distribution mode of the user in the next temporary sub-channel, and updating the power according to the formula (1-14).
7. Iterate n times until
And converging to obtain the optimal solution of the users under the multi-cell cooperation, and selecting the resource allocation mode of the optimal solution by the power users to transmit data.
The method comprises the steps of firstly establishing a multi-cell cooperative resource allocation model according to service priorities of power users, minimum requirements of user rates, power and QoS requirements, then allocating temporary sub-channels to the users by using a dynamic iterative algorithm, and then allocating the power of the cell users by considering an iterative water injection algorithm of a KKT condition to obtain the optimal solution of the users under multi-cell cooperation, so that the power users of all priorities select the optimal carrier frequency band and resource units to transmit under the conditions of rate, time delay, power and the like, the requirements of power emergency communication are met, and powerful information transmission support is provided for the emergency communication requirements of power production. The specific implementation process is as follows:
1. and the power user sends a scheduling request to a resource scheduler of the base station.
2. The user receives the scheduling permission issued by the base station, puts the data which the user wants to send into the cache, and then provides the cache state report to the base station.
3. And an uplink scheduler of the base station determines to allocate time-frequency resources to the UE according to the buffer status report reported by the user, the scheduling request and the channel quality condition.
4. And establishing a multi-cell joint resource scheduling model according to the service priority, the minimum rate and the transmission power of the power user, and iterating for N times by using a dynamic iteration algorithm and an iteration water injection algorithm based on a KKT condition until the transmission sub-channel and the transmission power value of the user are converged to obtain the most available resource allocation strategy.
5. And the uplink scheduler transmits the user data to the ground monitoring center through a relay station and a satellite of the aerial platform according to the optimal resource allocation strategy.
6. And the monitoring center makes a corresponding command to the terminal equipment.
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