CN110753365A - Heterogeneous cellular network interference coordination method - Google Patents
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- H04W28/0268—Traffic management, e.g. flow control or congestion control using specific QoS parameters for wireless networks, e.g. QoS class identifier [QCI] or guaranteed bit rate [GBR]
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Abstract
The invention discloses a heterogeneous cellular network interference coordination method, which comprises the following steps: under a simulated real downlink network link scene, determining an optimization problem by respectively taking a user QoS requirement, a macro base station maximum transmitting power, a micro base station maximum transmitting power, a base station access limit and a channel multiplexing limit as constraint conditions with the aim of minimizing the total transmitting power consumption of a base station; solving an optimization problem; and decoding the solved result to obtain the corresponding user access, channel allocation and power control strategy. The method establishes a user access strategy, channel allocation and power control cooperative optimization scheme by adopting a network scene with higher reduction degree and reliability and taking a user access strategy, channel allocation and power control strategy into consideration based on the scene, and provides a heuristic algorithm with lower complexity to solve the problem, so that the total power consumption of the system can be minimized under the conditions of effectively ensuring the differentiated QoS (quality of service) requirements of users and the maximum transmission power constraint of a base station.
Description
Technical Field
The invention belongs to the field of mobile communication, and particularly relates to a resource scheduling optimization technology in a heterogeneous cellular network.
Background
With the increasing demand and diversification of applications of mobile users, the volume of mobile data is approaching the upper limit of the capacity of the existing cellular mobile network at an exponential increasing speed, so that the cellular network is overloaded and the service quality is reduced, and therefore, the capacity of the cellular mobile network needs to be further increased to meet the increasing demand of users. One solution is to adopt heterogeneous layered coverage and increase base station deployment, and then derive an intensive heterogeneous network, and heterogeneous networking can effectively improve the communication capacity of a cell.
Although the capacity of the mobile communication network is improved to a certain extent by intensive heterogeneous networking, serious intra-layer interference is brought by intensive deployment of base stations, and a spectrum sharing mechanism adopted by the heterogeneous network can also cause serious cross-layer interference, which causes low spectrum efficiency of the heterogeneous network. In addition, since the traffic load often presents the characteristic of local hot spot, the multi-dimensional resource allocation in the heterogeneous network is also a great challenge. If all base stations work at the maximum transmitting power, energy waste and energy efficiency reduction are caused, too large interference among the base stations is caused, channel quality is reduced, and user experience is influenced. By optimizing the association between the user and the base station and the channel and the power control, the interference between the base stations and the power consumption are reduced on the premise of ensuring the Quality of Service (QoS) of the user, which is a main problem in the research and discussion in the field.
The research on heterogeneous cellular networks is currently extensive, including related technologies of cellular network communication, application scenarios, problems that may exist in practical applications (such as access policy, resource management, power control), and the like. The document 'Liehui. intensive heterogeneous network service offloading technology research [ D ]. Beijing post and telecommunications university, 2017' proposes an effective-capacity-based delay service offloading scheme, and maximizes the energy efficiency of the system under the condition of meeting the limitation of user service quality; the literature, "royal dew, research on user offloading strategies for heterogeneous cellular networks [ D ]. university of china science and technology, 2017" constructs a joint utility optimization problem by comprehensively considering parameters such as user affiliation, almost blank subframe ratio, and the like, and solves the problem by using a Gauss-Seidel method.
The existing research shows that the introduction of an effective resource scheduling strategy in the dense heterogeneous cellular network can improve the spectrum utilization rate of the system, reduce the power consumption of the base station, and solve some practical problems faced by the dense heterogeneous network to a certain extent through a reasonable access strategy, channel allocation and effective interference coordination; however, in an actual communication scenario, different QoS requirements of different users need to be effectively guaranteed, and the maximum transmission power and other performances of different base stations are different. None of the above prior art addresses well.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a heterogeneous cellular network interference coordination method, which specifically comprises the following steps:
step S1: under a simulated real downlink network link scene, determining an optimization problem by respectively taking a user QoS requirement, a macro base station maximum transmitting power, a micro base station maximum transmitting power, a base station access limit and a channel multiplexing limit as constraint conditions with the aim of minimizing the total transmitting power consumption of a base station;
step S2: solving step S1 to determine an optimization problem;
step S3: and decoding the result obtained in the step S2 to obtain the corresponding user access, channel allocation and power control strategy.
Further, the real link scenario simulated in step S1 includes: the system comprises M mobile users, N base stations (L macro base stations and N-L micro base stations), wherein the N base stations are uniformly deployed to form a cell, and the M mobile users are randomly distributed in the cell; all base stations are deployed at the same frequency, the frequency band is divided into I orthogonal channels, each mobile user can be connected with only one base station and obtains service from one or more channels, signals transmitted by other base stations at the same base station can generate interference to the user, and interference does not exist among different channels.
Further, the step S1 determines that the optimization problem is a mixed integer nonlinear optimization problem.
Further, the step S2 is specifically performed by using a hybrid heuristic search algorithm based on a genetic algorithm and a particle swarm optimization.
Further, the concrete steps of solving are as follows:
s21: using the same format to encode gene sequences in the genetic algorithm and position information in the particle swarm algorithm, and initializing the genetic algorithm;
s22: iteratively solving a group of feasible solutions by utilizing a finite genetic algorithm;
s23: initializing a particle swarm algorithm, and inputting a result obtained by the genetic algorithm into the particle swarm optimization algorithm for solving;
s24: and inputting the solution obtained by the particle swarm optimization algorithm into the genetic algorithm again until the result is converged or the upper limit of the iteration times is reached.
Further, the chromosome sequence in the genetic algorithm and the position information in the particle swarm optimization algorithm used in step S21 are a 0-1 integer matrix corresponding to the user access policy and the channel allocation policy, and a real matrix related to the power control of the base station, and the fitness expression of the individual and the particle is the total power consumption of the optimization target base station of the problem.
Further, in a large loop, after the genetic algorithm is iterated each time and the cross operation and the mutation operation are sequentially carried out on the parent population to generate the child population, the optimal strategy and the optimal fitness are recorded, then the selection operation is executed to enter the next iteration, after the limited iterations, the genetic algorithm is forcibly stopped, and the current population is output.
Further, when the particle swarm optimization algorithm is started each time, all the particle speeds are reset to 0, the genetic algorithm is input to obtain the gene information of the current population as the position information, after one iteration, the particle speeds are calculated, and the iteration is executed.
The invention has the beneficial effects that: the heterogeneous cellular network interference coordination method establishes a user access strategy, channel allocation and power control collaborative optimization scheme by adopting a network scene with higher reduction degree and reliability and taking a user access strategy, channel allocation and power control strategy into collaborative consideration based on the scene, and provides a heuristic algorithm with lower complexity to solve the problem. The method of the invention aims at the downlink transmission scene in the heterogeneous cellular network, and can realize the minimization of the total power consumption of the system under the condition of effectively ensuring the differentiated QoS requirements of users and the maximum transmission power constraint of the base station.
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Fig. 1 is a schematic view of a system scenario according to an embodiment of the present invention.
FIG. 2 is a flow chart of a method according to an embodiment of the present invention.
Detailed Description
In order to facilitate the understanding of the technical contents of the present invention by those skilled in the art, the present invention will be further explained with reference to the accompanying drawings.
Fig. 1 shows a network scenario constructed according to an embodiment of the present invention, in which a downlink and a base station are deployed at the same frequency. Wherein MBS represents macro base station, SBS represents micro base station, MU represents mobile user, AN×MAnd BM×IRespectively representing the association condition of the user and the base station and the condition that the user occupies the channel; the dashed lines represent interfering signals and the solid lines the useful signals.
In this embodiment, simulating a real downlink communication scene specifically includes: a heterogeneous cellular network downlink system consisting of N base stations and M users comprises L macro base stations and N-L micro base stations, and M mobile users are randomly distributed in an area.
Order set
Is a set of base stations and is,
in order to be a set of users,
representing an nth base station, representing a macro base station when n is less than L, otherwise representing a micro base station, wherein all base stations are deployed at the same frequency and have same-layer interference and cross-layer interference;denotes the mth user equipment, let a be associated with only one base station for each usern，mE {0, 1} is the associated variable of the user and the base station, an,m1 indicates that user m accesses base station n, otherwise an,m＝0。
In this embodiment, a resource block is used as a basic unit of the system resource of the base station, corresponding to each sub-channel, so that a plurality of users accessing the same base station can share the system resource of the base station, for example, obtain service by time division multiplexing or frequency division multiplexing
I.e. each base station has I subchannels in common for a set of base station resource blocks. bm,iRepresenting the occupation of the ith channel by user m, bm,i1 means that user m occupies the ith sub-channel and is interfered by signals transmitted by other base stations on this channel. Let PSRepresenting maximum transmission power, P, of the micro base stationMRepresenting the maximum transmit power of the macro base station. In addition, the centralized controller exists in the network, so that the states of the base station and the user can be sensed in real time, and corresponding decisions can be made.
For each scheduling period of the network, the controller is assumed to calculate user access, channel allocation and power control strategies in real time according to the current user position information and the channel state information. The constraints obeyed by the respective policies are as follows:
user access strategy: each mobile user is allowed to establish a connection with only one base station, which serves the user without an upper limit (but cannot exceed the number of allocable sub-channels).
Channel allocation strategy: all base stations are deployed at the same frequency, and the frequency domain is divided into a plurality of orthogonal sub-channels. The base station may provide one or more subchannels to the users and ensure that each user occupies at least one subchannel.
And (3) power control strategy: the base station can distribute power on each sub-channel arbitrarily, and only the constraint that the total power does not exceed the maximum transmitting power of the base station is satisfied.
Based on the network scenario, the method for coordinating the interference of the heterogeneous cellular network specifically comprises the following steps:
s1, under the simulated real downlink network scene, with the aim of minimizing the total emission power consumption of the base station, respectively taking the user QoS requirement, the maximum emission power of the macro base station, the maximum emission power of the micro base station, the base station access limit and the channel multiplexing limit as constraint conditions, determining the optimization problem. The method specifically comprises the following steps: minimizing the total power consumption of the system as an objective function, respectively based on the QoS requirement v of the mobile usermMacro base station maximum transmit power pn1,iMaximum transmitting power p of micro base stationn2,iUser access indicator an,mAnd a channel allocation indicator bm,iThe value of (a) is a constraint condition, and a first optimization problem is obtained.
The optimization problem expression is as follows:
P1：
s.t.
C1:an,m∈{0,1}
C2：
C3:bm,i∈{0,1}
C4：
C5：
C6：
C7：
wherein P1 represents an optimization problemThe objective function of (1); in constraint C1, an,mRepresenting the association variable of the user with the base station, an,m1 indicates that user m accesses base station n, an,mIf the value is 0, the user does not establish connection with the station; constraint C2 ensures that a user has at least and at most one access to a base station; in constraint C3, bm,iRepresenting the occupation of the ith sub-channel by user m, bm,i1 indicates that user m occupies the ith sub-channel and is subject to interference from signals transmitted by other base stations on this channel, bm,i0 means that the user is not associated with channel m and channel i; constraint C4 ensures that each of its sub-channels can only be occupied by one mobile user for a particular base station (users accessing different base stations can multiplex the same channel); in the constraints C5 and C6,
corresponds to a macro base station n1And a micro base station n2On the channel i, the two constraints respectively ensure that the total transmission power of the macro base station and the micro base station does not exceed the respective upper limit P of the transmission powerM/PS(ii) a Constraint C7 ensures throughput C for user mmSatisfy its QoS requirement vm(minimum data rate). Maximum transmission power PM＝50w，PSUser differentiated QoS requirements v 20wmIs randomly distributed in [10 ]5,3×105]bit/s。
The QoS of a mobile user, i.e. the data rate the user gets from the associated base station, is calculated as:
wherein, Cn,m,iFor the data rate provided by base station n for user m on the ith subchannel, the expression is as follows:
where B is the bandwidth of each orthogonal sub-channel, N0Power spectral density, H, of background noisen,mFor the channel gain model:
Hn,m＝PLn,m×HR×HS
HRis Rayleigh fading, H, due to multipath effectsSFor random shadow fading, in particular PLn,mThe expression is as follows:
wherein d isn,mRepresenting the distance between the user and the base station.
In,m,iFor the interference experienced by user m on the ith subchannel of base station n, the expression is as follows:
s2, solving the mixed integer nonlinear programming problem by combining a genetic algorithm and a particle swarm algorithm,
the problem determined in step S1 is an NP-hard problem that is difficult to solve directly, so a hybrid heuristic search algorithm with low complexity is used to solve the problem. In the large circulation of the algorithm, the genetic algorithm and the particle swarm algorithm are repeatedly operated so as to achieve good global searching performance and accelerate the convergence speed. The specific flow is shown in fig. 2, and the specific steps are as follows:
s21, encoding gene sequences in a genetic algorithm and position information and fitness in a particle swarm algorithm by using the same format, and initializing the algorithm; the concrete implementation is as follows
For genetic algorithms, each individual in the population represents a set of strategies, the individual chromosomes 
Is an integer matrix of 0-1, and the matrix is,
the real number matrix respectively corresponds to the association situation between the user and the base station (the user accesses the number of the base station), the channel allocation strategy (which channels the user occupies to access the base station), and the power allocation strategy (the power allocated by the base station on each sub-channel).
The fitness of an individual represents the performance of a group of strategies, and in the problem, the performance of the strategies is measured by optimizing the total power consumption of a target:
each particle in the particle swarm optimization algorithm represents a group of strategies and has three attributes of position, speed and fitness. The position and the fitness of the particle respectively correspond to the chromosome and the fitness of the genetic algorithm individual and represent the current strategy and performance index; the velocity of the particles is the direction of change of the strategy calculated by the algorithm that is expected to achieve better fitness.
Initializing population quantity and particle swarm particle quantity NP, cycle iteration number T and genetic algorithm stage iteration number T1And maximum iteration number T in particle swarm optimization stage2And setting algorithm parameters as follows: number of progeny populations NP in genetic algorithms1Cross probability PcProbability of mutation PmAcceleration omega and current collective mass center weight c in particle swarm optimization algorithm1History optimum centroid weight c2。
S22, solving a group of feasible solutions by using a finite genetic algorithm;
the operation steps of each genetic algorithm iteration are cross operation, mutation operation and selection operation.
And (3) cross operation: for each generation of the population, two parents were randomly selected, denoted PcExchanging a part of chromosome segments, detecting whether the obtained new strategy meets the constraint condition, and obtaining a filial generation individual if the new strategy meets the constraint condition.
Mutation operation: randomly selecting an individual with a probability PmPerforming mutation operation on it to extract a dyeThe volume segments vary randomly within the feasible domain.
The rules of the mutation operation (i.e., mutation operators) are:
wherein the content of the first and second substances,representing the extracted segment as an integer variable,
representing the extracted fragment as a real-number variable, gamma1、γ2Are all [0,1]Random number over interval (according to
Size of scale, gamma1、γ2Possibly a random number sequence or matrix), round calculator means to take the nearest integer to the value in parentheses, xmax、xmin、ymax、yminRespectively, the maximum value or the minimum value that can be taken by each component of the extracted chromosome segment.
Cycling the above steps to co-produce NP1After each sub-generation individual, calculating the fitness of each individual, and selecting NP individuals as a next generation population by using a roulette algorithm by taking the fitness as a weight value. Particularly, if the individuals with the best fitness in the current individuals are eliminated, the individuals with the lowest fitness in the next generation population are forcibly replaced by the individuals, and the algorithm is ensured to be carried out towards a better direction.
Iteration T1Then, the next stage is entered.
S23, initializing a particle swarm optimization algorithm, and inputting the result obtained in the step S22 into the particle swarm optimization algorithm for solving;
in the particle swarm optimization, the position of each particle represents a scheme, the speed determines the speed of iterative evolution of the scheme, and in order to maintain the consistency of the optimization, the expression of the position is the same as the expression of a chromosome in a genetic algorithm, and the speed is defined as the variation of the position of each particle in two iterations, namely:
pbestr,das the current best individual's location, gbestr,dFor the location where the historical best fitness occurs,
indicating the position of the current particle.
In the above formula, the first and second carbon atoms are,s23 indicates that the velocity is required to be reset to 0 before the particle swarm optimization is started, and the velocity component is added again for optimization after the solution of the genetic algorithm is input and an iteration is performed.
Wherein the content of the first and second substances,representing the inertia of the particle, i.e. the algorithm will try to maintain the direction of change of the last strategy iteration, in order to obtain better results,
indicating that the particle will be drawn toward the current collective centroid,
indicating that the particles will be moving closer to the historical best position.
And updating the positions and the speeds of all the particles in each iteration, wherein the iteration expressions of the integer type variable and the real number type variable are as follows:
wherein x represents an integer variable and y represents a continuous variable.
Calculating the fitness of all the particles, recording the position of the particle with the highest current fitness as the current collective centroid, and recording the current position as the historical best centroid if the fitness is better than the historical best fitness, wherein the current fitness is the historical best fitness.
Particle swarm optimization iteration T2Then, the next stage is entered.
S24, inputting the solution obtained in the S23 into the genetic algorithm again until the result converges or the upper limit T of the iteration times is reached;
it should be noted that: the genetic algorithm has better global searching performance, but the convergence speed is slow. The particle swarm optimization algorithm has faster convergence speed, but is easy to converge to local optimum prematurely. And (4) carrying out coarse-grained search by using a genetic algorithm, and inputting the result into a particle swarm algorithm to accelerate the convergence of the algorithm. And when the outer circulation of the algorithm reaches the upper limit of times or the fitness of the best individual of generations is not reduced any more, finishing the algorithm and outputting the gene information and the fitness of the best individual.
And S3, decoding the finally obtained historical optimal collective centroid to obtain corresponding user access, channel allocation and power control strategies, wherein the historical optimal fitness is the minimum total power consumption of the system.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (8)
1. A heterogeneous cellular network interference coordination method specifically comprises the following steps:
step S1: under a simulated real downlink network link scene, determining an optimization problem by respectively taking a user QoS requirement, a macro base station maximum transmitting power, a micro base station maximum transmitting power, a base station access limit and a channel multiplexing limit as constraint conditions with the aim of minimizing the total transmitting power consumption of a base station;
step S2: solving step S1 to determine an optimization problem;
step S3: and decoding the result obtained in the step S2 to obtain the corresponding user access, channel allocation and power control strategy.
2. The method of claim 1, wherein the real link scenario simulated in step S1 includes: the system comprises M mobile users, N base stations (L macro base stations and N-L micro base stations), wherein the N base stations are uniformly deployed to form a cell, and the M mobile users are randomly distributed in the cell; all base stations are deployed at the same frequency, the frequency band is divided into I orthogonal channels, each mobile user can be connected with only one base station and obtains service from one or more channels, signals transmitted by other base stations in the same channel can generate interference on the user, and interference does not exist among different channels.
3. The method for coordinating interference in heterogeneous cellular networks according to claim 1 or 2, wherein the step S1 determines that the optimization problem is a mixed integer nonlinear optimization problem.
4. The method according to claim 3, wherein the step S2 is implemented by using a hybrid heuristic search algorithm based on a genetic algorithm and a particle swarm optimization.
5. The method for coordinating interference in a heterogeneous cellular network according to claim 4, wherein the solving comprises the following steps:
s21: using the same format to encode gene sequences in the genetic algorithm and position information in the particle swarm algorithm, and initializing the genetic algorithm;
s22: iteratively solving a group of feasible solutions by utilizing a finite genetic algorithm;
s23: initializing a particle swarm algorithm, and inputting a result obtained by the genetic algorithm into the particle swarm optimization algorithm for solving;
s24: and inputting the solution obtained by the particle swarm optimization algorithm into the genetic algorithm again until the result is converged or the upper limit of the iteration times is reached.
6. The method for coordinating interference in heterogeneous cellular networks according to claim 5, wherein the chromosome sequences in the genetic algorithm and the location information in the particle swarm optimization algorithm used in step S21 are 0-1 integer matrices corresponding to the user access policy and the channel allocation policy, and real matrices related to power control of the base station, and the fitness expression of the individuals and the particles is the total power consumption of the optimization target base station of the problem.
7. The method according to claim 5, wherein in a major loop, after performing crossover operation and mutation operation on a parent population to generate a child population for each iteration of the genetic algorithm, recording an optimal strategy and optimal fitness, performing selection operation to enter the next iteration, after a limited number of iterations, forcibly stopping the genetic algorithm, and outputting the current population.
8. The method according to claim 5, wherein every time the particle swarm optimization algorithm is started, all particle speeds are reset to 0, the genetic algorithm is input to obtain the genetic information of the current population as position information, and after one iteration, the particle speeds are calculated and the iteration is executed.
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