CN114666883B - NOMA downlink power distribution method based on artificial fish swarm algorithm - Google Patents

NOMA downlink power distribution method based on artificial fish swarm algorithm Download PDF

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CN114666883B
CN114666883B CN202210189602.0A CN202210189602A CN114666883B CN 114666883 B CN114666883 B CN 114666883B CN 202210189602 A CN202210189602 A CN 202210189602A CN 114666883 B CN114666883 B CN 114666883B
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陆音
彭叙杰
万成
朱斌
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Nanjing University of Posts and Telecommunications
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Abstract

The invention discloses a NOMA downlink power distribution method based on an artificial fish swarm algorithm. Establishing an energy efficiency function in a sub-channel of the NOMA system, and constructing constraint conditions according to average throughput and user fairness; normalizing the power distribution coefficient of the user in the subchannel to a multidimensional position vector of the shoal in the artificial shoal algorithm, maximizing the energy efficiency in the subchannel of the NOMA system to be an objective function, and solving the optimal power distribution coefficient by using the optimized artificial shoal algorithm; and calculating average throughput and user fairness based on the solved optimal power distribution coefficient, and if the optimal power distribution coefficient does not meet the constraint condition, adjusting the parameter factors of the artificial fish swarm algorithm, and continuing to perform optimizing search so as to obtain an optimal power distribution model in the sub-channel. The method has stronger capability of jumping out the local optimal solution, and obtains good balance between energy efficiency and system fairness by comparing EPA, FPA, FTPA algorithm in NOMA.

Description

NOMA downlink power distribution method based on artificial fish swarm algorithm
Technical Field
The invention relates to a non-orthogonal multiple access technology, belongs to the technical field of communication, and particularly relates to a NOMA downlink power distribution method based on an artificial fish swarm algorithm (Artificial Fish Swarm Algorithm, AFSA).
Background
The development of wireless communication networks changes the way people communicate and acquire information daily, and with the popularization of the fifth generation mobile communication system (the 5th Generation,5G), the transmission delay, the wireless coverage performance and the user experience of the mobile communication system are all significantly improved. But the cost of the operators is nearly half of the energy consumption from the base station, and optimizing the power consumption of the base station is beneficial to reducing the use cost of users. For sustainable development, green communication is a core goal in the development of next generation mobile communication.
In the technical standard of 5G, the energy efficiency is formulated as a system performance reference standard for the first time, NOMA is used as a key technology of 5G, the whole throughput of the system and the service quality of edge users are effectively improved through power domain multiplexing, the requirement of exponentially growing dense access of terminal users is met, and therefore the improvement of the energy efficiency of NOMA becomes an important direction in the development and research of 5G.
The current direction of NOMA research is mainly focused on user grouping and power allocation, but the prior art power allocation schemes, such as equal power allocation (Equal Power Allocation, EPA), fixed power allocation (Fixed Power Allocation, FPA), fractional transmission power allocation (Fractional Transmit Power Allocation, FTPA) algorithms, sacrifice some edge user fairness. With the continuous development of computer technology, a large number of group intelligent bionic algorithms are applied in the field of communication, and the algorithms are bionic random search algorithms provided by simulating genetic evolution mechanisms and group cooperative behaviors of the biological kingdom. The bionic algorithm is widely focused by people at high-efficiency optimizing speed without considering the characteristics of excessive initial information of the problem and the like. In order to reduce energy consumption and operation cost, the application of intelligent bionic algorithm to NOMA system and the improvement of energy efficiency (Energy Efficiency, EE) of the system are a new research direction.
Disclosure of Invention
The invention aims to: the invention provides a NOMA downlink power distribution method based on an artificial fish swarm algorithm, which aims at maximizing the energy efficiency of a NOMA downlink. Aiming at the problems of overhigh computational complexity or poor system performance of the traditional algorithm, the method optimizes convergence accuracy and iteration speed by optimizing the search factor in the fish shoal foraging behavior, and obtains good balance between energy efficiency and system fairness.
The technical scheme is as follows: in order to achieve the above purpose, the invention adopts the following technical scheme:
a NOMA downlink power allocation method based on artificial fish swarm algorithm, comprising:
constructing an objective function by maximizing energy efficiency in a sub-channel of a NOMA system, and constructing constraint conditions by average throughput and user fairness to obtain a power distribution model in the sub-channel, wherein the power distribution model comprises power distribution coefficients of all users in the sub-channel;
normalizing the power distribution coefficient of the user in the subchannel to a multidimensional position vector of the shoal in the artificial shoal algorithm, and solving the objective function by using the optimized artificial shoal algorithm to obtain an optimal power distribution coefficient;
calculating average throughput and user fairness based on the solved optimal power distribution coefficient;
and judging whether the calculated average throughput and user fairness meet constraint conditions, if not, adjusting parameter factors of an artificial fish swarm algorithm, and continuing to perform optimizing search until the average throughput and the user fairness meet the constraint conditions, thereby obtaining an optimal power distribution model in the sub-channel.
Further, the objective function is:
η：
where η is the energy efficiency in the subband of the NOMA system, M is the number of users superimposed on the subband, R m For the data rate of the mth user in the sub-band, P total Indicating the total transmitting power of the base station, P c Is a fixed loop power loss, B is the size of the frequency band occupied by the sub-band, h m Representing channel gain, alpha, between mth user and base station in sub-channel m Representing the power allocation coefficient of the mth user in the subband, assuming the channel gains of the M users |h 1 | 2 ≥|h 2 | 2 ≥...≥|h M | 2 ≥0，P i ＝α i P total Representing the power level and sigma allocated to the ith user in the ith users before user m 2 Representing the noise power.
Further, the constraint condition is:
s.t.C1:
C2:
wherein R is min And F min Is a preset constraint value, R avg For the geometric average throughput of the system, M is the number of users superimposed on the subband, R m F (R) is user fairness for the data rate of the mth user in the subband.
Further, normalizing the power distribution coefficient of the user in the sub-channel to a multidimensional location vector of the fish farm in the artificial fish farm algorithm includes:
the position of the nth fish in the artificial fish shoal is expressed as an M-dimensional vector:
wherein,,the position of the nth fish in the artificial fish swarm is given, and N is the population scale of the artificial fish;
the power distribution coefficient of all users in the sub-band is used as vector alpha= [ alpha ] 1 ，α 2 ，...，α M ] T Representing the position vector of the shoal in the artificial shoal algorithmOne-to-one correspondence with the power distribution coefficient vector α:
[x 1 ,x 2 ,...,x M ]＝[α 1 ，α 2 ，...，α M ]
0＜α 1 ≤α 2 ≤...≤α M ＜1
wherein alpha is m Representing the power allocation coefficient for the mth user in the sub-band, M being the number of users on the sub-band.
Further, the solving the objective function by using the optimized artificial fish swarm algorithm to obtain an optimal power distribution coefficient comprises:
step 201, initializing artificial fish shoals, and calculating an objective function value of each artificial fish to obtain an optimal fitness value of the artificial fish;
step 202, respectively executing a clustering behavior and a rear-end collision behavior on the artificial fish, comparing the magnitude of objective function values obtained by the two behaviors, selecting a behavior with a larger function value for execution, and recording updated artificial fish states and function values;
step 203, comparing the newly obtained function value with the optimal fitness value, and if the newly obtained function value is larger than the optimal fitness value, updating the optimal fitness value and the artificial fish state;
step 204, judging whether the iteration number reaches the maximum iteration number, if so, stopping iteration, outputting the optimal position vector of the fish school, otherwise, turning to step 202 to carry out the next iteration;
in the clustering behavior and the rear-end collision behavior, the moving step length of the artificial fish is dynamically updated according to the optimal fitness value.
Further, the clustering behavior includes:
current artificial fish X i Searching for the number of partners n in its field of view f And the central position, if Y c /n f ＞εY i X is then i Moving one step towards the centre of the buddy, otherwise performing foraging behaviour, wherein Y c A function value representing the central position, epsilon being a crowding factor, Y i Is artificial fish X i Is a function of (a).
Further, the rear-end collision behavior includes:
artificial fish X i Searching the individual X with the highest corresponding function value in the artificial fish in the visual field range j If Y j /n f ＞εY i X is then i Orientation X j Moving in the direction by one step, otherwise executing foraging behavior, wherein Y j Is X j The corresponding function value.
Further, the foraging act includes:
artificial fish X i Randomly selecting another visible state X within its field of view j When X is j The function value of the position is larger than the current X i Function value of X i To X direction j Moving one step, otherwise, randomly selecting a new state again;
if artificial fish X i The number of states selected in the field of view exceeds the number of retries, and the artificial fish randomly selects one direction and moves one step in the water area so that X i A new state is reached.
Further, the method for dynamically updating the moving step length of the artificial fish comprises the following steps:
introducing adaptation degree change times Changes to determine the size of a moving step, wherein the initial value of the Changes is 0, and when one fish updates the optimal adaptation degree, adding 1 to the value of the Changes, otherwise, not performing any operation;
a minimum value tau limit is made on the moving Step, the default size is adopted for the moving Step when the operation starts, and the iteration time T is set in advance 1 Every time the algorithm iteration number reaches a threshold, the next 5T is reached 1 The fish shoal in the iteration times is dynamically updated Step by adopting the following steps:
wherein, lambda is a preset Step factor and lambda epsilon (0, 1), once Step is smaller than minimum value after Step dynamic update, step default size is set as minimum value tau, and the iteration number is full of 5T when algorithm is running 1 After that, changes are reset to 0.
Further, in performing foraging, the Visual field is changed according to the following formula:
wherein T represents the iteration number of algorithm operation, T 2 To advance a given constant, β represents the decay factor, and β e (0, 1).
The beneficial effects are that:
the invention aims at maximizing the energy efficiency of a NOMA downlink and provides a NOMA downlink power distribution method based on an artificial fish swarm algorithm aiming at the problem of user power distribution in a sub-channel. Firstly, constructing a relation between a shoal position vector and a power distribution factor; secondly, aiming at the problems of overhigh computational complexity or poor system performance of the traditional algorithm, the invention adjusts the searching factor in the fish farm foraging behavior, not only adjusts the fish farm visual field range factor with a fixed value into dynamic change, but also dynamically updates the moving step length of the artificial fish according to the optimal adaptation value. Compared with AFSA and PSO algorithms, the method has better convergence speed and precision, and generally reaches an optimal value after iteration within 30 times; compared with the FTPA algorithm, the method has the maximum improvement of 10% in energy efficiency; compared with EPA and FPA algorithms, the method can give consideration to average throughput of the system and individual fairness of users along with the increase of the number of users in the sub-channels, and ensures the rate of edge users.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a flow chart of an artificial fish swarm optimization algorithm;
FIG. 3 is a comparison of iteration of AFSA, PSO and IAFSA algorithms of the invention;
FIG. 4 is a graph comparing the energy efficiency of the FTPA algorithm, the OFDMA scheme, and the IAFSA algorithm of the present invention to maximize the NOMA system;
FIG. 5 is a graph comparing user fairness of NOMA system obtained by EPA and FPA algorithms and IAFSA algorithm of the invention;
FIG. 6 is a graph comparing geometric average throughput of NOMA systems obtained by using EPA and FPA algorithms and IAFSA algorithms of the present invention.
Detailed Description
The invention is further described below in connection with specific embodiments. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
As shown in fig. 1, a NOMA downlink power allocation method based on an artificial fish swarm algorithm includes:
s1, constructing an objective function by maximizing energy efficiency in a sub-channel of a NOMA system, and constructing constraint conditions by average throughput and user fairness to obtain a power distribution model in the sub-channel, wherein the power distribution model comprises power distribution coefficients of all users in the sub-channel;
firstly, constructing a NOMA system model, which specifically comprises the following steps:
assuming that the transmit power of the base station is P total The total bandwidth of the system is W, the system is divided into L sub-bands in total, and M users are overlapped on each sub-band to transmit signals at most, so that the mth user U in the current sub-channel m The received signal may be expressed as:
y m,l ＝h m,l x l +n m,l (1)
wherein n is m,l Representing compliance with N (0, sigma) 2 ) Normally distributed additive white gaussian noise, x l Is the user signal superimposed on the first sub-band, the superimposed signal x l The calculation formula of (2) is as follows:
wherein x is m,l Is user U on the first sub-band m Signals of P m,l Is allocated to user U by base station m Is a power of (2);
h m,l representing user U m Channel gain with base station, h m,l It can also be expressed as:
wherein eta m For the base station to user m Rayleigh Li Cuila coefficient, D m For the distance of user m from the base station, ρ is the path loss index.
Assuming that there are 2 total users in the current sub-band, user U 1,l Is a central user, user U 2,l Is an edge user far from the base station, which has poor channel condition, and makes H m ＝|h m,l | 2 I.e. H 1 ≥H 2 . The NOMA system adopts a serial interference cancellation technology to demodulate the user signal at the receiving end, and when the power difference between multiple users is larger, the demodulation effect of the receiving end is best, so the system tends to distribute higher power for the far-end users. According to shannon's formula, the central user U 1,l And edge user U 2,l Can be expressed as:
b in the above way represents the size of the frequency band occupied by L sub-bands after sharing the total bandwidth of the system, and is popularized to the scene of multiple users in the current channel, then the user U m The user rate expression is as follows:
let the channel gains of M users |h 1 | 2 ≥|h 2 | 2 ≥...≥|h M | 2 More than or equal to 0, P i ＝α i P total The power allocated to the ith user in the previous i users of the user m is shown.
Total throughput of system R total I.e. all user rates in a subbandSum of system geometric average throughput R avg The definition is as follows:
the conventional power utilization efficiency criterion defines the energy efficiency of the system as the ratio of the input power to the output power of the system, but the criterion cannot reflect the specific energy consumption condition in the system, and based on the TBPUE link energy efficiency criterion, the energy consumption of the system is defined as the data quantity which can be transmitted per 1W of power consumed in unit time, and the expression is as follows:
considering the loop power loss of the system, the energy efficiency in the sub-band of the NOMA system is defined as:
wherein P is total Indicating the total transmitting power of the base station, P c Is a fixed loop power loss, using M-dimensional vector α= [ α ] 1 ，α 2 ，...，α M ]Indicating the power distribution coefficient, then the sub-band in-band user U in (6) m The allocated power is of the size P m ＝α m ·P total Equation (6) can continue to be expressed as:
to avoid sacrificing excessive edge user data rates while maximizing NOMA system energy efficiency, the Jain index is introducedTo evaluate the overall fairness of the system, the expression is as follows:
wherein,,1 means that the system is completely fair in the allocation of theta, i.e. the resources allocated by M components are equal; />The closer the value is to 0, the less fair the system. The Jain index is used for evaluating whether a user obtains fair sharing of system resources, but cannot analyze individual fairness, and substitutes the individual fairness into a NOMA system model, and the final expression is as follows:
the NOMA system model is set as a cellular cell under a single base station, the radius is 500m, all users in a sub-band are uniformly distributed in the cell, the users are ordered according to the distance from the users to the base station, 1/3 users with the farthest distance in the total number of the users are defined as edge users, and the average throughput of the edge users is conveniently calculated by rounding up less than 1/3 users.
Based on the NOMA system model, constructing an objective function with maximized energy efficiency in a sub-band of the NOMA system, and constructing constraint conditions with average throughput and user fairness, wherein the obtained mathematical model is as follows:
η：
s.t.C1:
C2:
wherein R is min And F min Is a preset constraint value, R avg For the system geometry average throughput, F (R) is user fairness.
Step S2, normalizing the power distribution coefficient of the user in the sub-channel to a multidimensional position vector of the shoal in the artificial shoal algorithm, and solving the objective function by utilizing the optimized artificial shoal algorithm to obtain an optimal power distribution coefficient;
the artificial fish swarm algorithm is the same as the common ant swarm algorithm and the particle swarm algorithm, is a group intelligent bionic algorithm, and in a water area, the place with the largest survival number of the fishes is the place with the largest nutrient substances in the water area, and the AFSA algorithm simulates four behaviors of the fish swarm according to the characteristics: the method comprises the following steps of foraging behavior, clustering behavior, rear-end collision behavior and random behavior, so that global optimization is realized. The position vector of the fish shoal in the iterative process is a candidate solution in a solution space, and assuming that the population scale of the artificial fish is N and the candidate solution is an M-dimensional vector, the position of the nth fish can be expressed as an M-dimensional vector:
while the power allocation coefficients of users in the sub-band can be represented by the vector α= [ α ] 1 ，α 2 ，...，α M ] T Representing the position vector X of the fish shoal in the AFSA algorithm i One-to-one correspondence with the power distribution coefficient vector α:
[x 1 ,x 2 ,...,x M ]＝[α 1 ，α 2 ，...，α M ] (14)
0＜α 1 ≤α 2 ≤...≤α M ＜1 (16)
thus, when the AFSA algorithm iteratively converges to the optimal solution, the optimal power distribution system of the user is obtainedNumber of digits
The parameters of the artificial fish swarm algorithm mainly comprise a fish swarm scale N, a Visual field range Visual, a moving Step length Step, a crowding factor epsilon and retry times delta, and the five parameters are required to be initialized before the algorithm is started to be executed.
The energy efficiency function in equation (9) is used as the optimization target of the AFSA algorithm, and the preparation stage of the algorithm comprises the initialization of parameters and the position vectorIs initialized of>Is generated by means of random numbers. In the operation phase of the AFSA algorithm, four main behaviors in fish survival activities are mainly relied on: foraging behavior, clustering behavior, rear-end collision behavior, and random behavior.
In the operation process of the AFSA algorithm, two core behaviors, namely a clustering behavior and a rear-end collision behavior, are mainly adopted. The artificial fish selecting behavior mode is mainly influenced by the concentration of food in a water area, in the group-gathering and rear-end collision behaviors, if the fish finds out the crowd degree around the group-gathering object or the rear-end collision object is too large, the artificial fish can perform foraging behavior at the moment, meanwhile, if the artificial fish with higher adaptation value than the self adaptation value is not found in the foraging behavior, the artificial fish randomly moves in the water area according to the moving Step length, and finally, the adaptation values obtained in the group-gathering behavior and the rear-end collision behavior are compared and updated, the artificial fish meeting the standard is filtered out and used as the object of the next circulation, and the optimal vector is output.
Specifically, the objective function is solved by using an optimized artificial fish swarm algorithm to obtain an optimal power distribution coefficient, which comprises the following steps:
step 201, initializing artificial fish shoals, and calculating an objective function value of each artificial fish to obtain an optimal fitness value of the artificial fish;
step 202, respectively executing a clustering behavior and a rear-end collision behavior on the artificial fish, comparing the magnitude of objective function values obtained by the two behaviors, selecting a behavior with a larger function value for execution, and recording updated artificial fish states and function values;
wherein the clustering behavior comprises:
current artificial fish X i Searching for a range (d) ij Number of partners n < Visual) f Center position X c Then use Y c /n f Calculating the state of the center position, wherein Y c Representing the central position X c Is used for the adaptation value of the (c). If Y c /n f ＞εY i Indicating that the buddy state of the current fish centre is optimal and less crowded, X i Moving one step towards the centre of the buddy, otherwise performing the foraging action. X in clustering behavior next The expression is as follows:
where Rand represents a system generated random number, rand is between 0 and 1, and Step represents a movement Step.
Wherein the foraging behavior comprises:
suppose the individual state of the nth artificial fish is X i Randomly selecting another visible state X within its Visual field j X is then j The computational expression is as follows:
let use Y i And Y j Respectively represent X i And X j Adaptation value of (1), then when X j The food concentration at the location is greater than the current food concentration, i.e. Y j ＞Y i If so, the method proceeds to the direction, otherwise, a new state is selected randomly again. X in foraging behavior next The expression is as follows:
in the aboveRepresenting the distance between individuals of artificial fish, if artificial fish X i The number of states selected in the field of view exceeds the number of retries delta, at which time the artificial fish will randomly select a direction and move one step in the body of water such that X i To a new state, X next The random behavior is represented as follows:
among them, the rear-end collision behavior includes:
artificial fish X i Will search for individuals X with the highest fitness in their field of view j If Y j /n f ＞εY i Indicating X j Is not very crowded, at which point X i Moving the fish to the optimal adjacent fish for one step, otherwise, executing foraging behavior and X in rear-end collision behavior next The expression is as in formula (19).
Step 203, comparing the newly obtained function value with the optimal fitness value, and if the newly obtained function value is larger than the optimal fitness value, updating the optimal fitness value and the artificial fish state;
step 204, judging whether the iteration number reaches the maximum iteration number, if so, stopping iteration, outputting the optimal position vector of the fish school, otherwise, turning to step 202 to carry out the next iteration.
Assuming that a fish group position vector is initialized by AFSA_init in MATLAB, a clustering behavior function is represented by AFSA_SWARM, a rear-end collision behavior function is represented by AFSA_FOLLOW, a power allocation algorithm based on NOMA downlink energy efficiency priority is designed as FOLLOWs:
table 1 artificial fish swarm algorithm design
The artificial fish swarm algorithm has some limitations, such as insufficient solving precision, easy sinking into a local optimal solution, and relatively large blindness in later searching of the algorithm. Aiming at parameters of an artificial fish swarm algorithm, two improvement measures are provided:
(1) Visual field is adjusted to a dynamic factor. In the initial stage of the execution of the AFSA algorithm, a Visual field factor with a large value can expand the optimizing range, but along with the increase of the iteration times of the fish shoal, the Visual field range Visual is properly reduced to accelerate the convergence rate of the algorithm, and the expression of the changed Visual is defined as follows:
wherein T represents the iteration number of algorithm operation, T 2 In order to give a constant in advance, beta represents an attenuation factor, beta epsilon (0, 1) is used for avoiding influence of reduction of Visual on the grouping behavior and the rear-end behavior of the fish shoal and reducing the running speed of an algorithm, so that only Visual in the foraging behavior is modified.
(2) And dynamically updating the moving Step length Step of the AFSA algorithm. In order to ensure that the shoal of fish can effectively finish iterative optimization, a minimum value tau limit is made on the moving Step length Step, the Step length is prevented from being reduced to 0 algorithm to be stopped, and adaptation change times Changes are introduced to determine the moving Step length. The initial value of Changes is 0, and when one fish updates the optimal fitness, the value of Changes is added with 1, otherwise, no operation is performed.
The default size is adopted by the moving Step when the AFSA algorithm starts to run, and the iteration time T is set in advance 1 Every time the algorithm iteration number reaches a threshold, the next 5T is reached 1 The fish shoal in the iteration times is dynamically updated Step by adopting the following steps:
wherein, lambda is a preset Step factor and lambda epsilon (0, 1), once Step is smaller than minimum value after Step dynamic update, step default size is set as minimum value tau, and the iteration number is full of 5T when algorithm is running 1 After that, changes are reset to 0.
Step S3, calculating average throughput and user fairness based on the solved optimal power distribution coefficient;
substituting the optimal shoal best position vector obtained in the step S2 into the formulas (7) and (12) respectively, and calculating the average throughput of the system and the fairness of the user.
And S4, judging whether the calculated average throughput and the user fairness meet constraint conditions, and if the calculated average throughput and the user fairness do not meet constraint conditions, adjusting parameter factors of an artificial fish swarm algorithm, and continuing to perform optimizing search until the average throughput and the user fairness meet constraint conditions, so that an optimal power distribution model in a sub-channel is obtained.
The method of the present invention is compared with a power allocation algorithm of a conventional NOMA system.
The power allocation algorithm of the NOMA system mainly comprises an EPA algorithm, an FPA algorithm and an FTPA algorithm.
(1) EPA algorithm
The EPA algorithm distributes the total power in the sub-channels to the base station and distributes the total power to each user superimposed on the sub-channels, but the algorithm does not consider the user channel gain in the actual application process.
(2) FPA algorithm
The FPA algorithm is a classical static allocation algorithm. The main algorithm idea is that in the process of transmitting signals by NOMA system, a transmitting end firstly obtains large-scale path fading loss and small-scale Rayleigh fading factors in the channel, normalizes the channel gains of users in a set, and arranges the channel gains according to ascending order, and the power distributed by the system to the current sub-channel I is assumed to be P t The number of users in the set is M, and the sequence number of the M-th user after channel gain sequencing isThe power allocated by the user can be expressed as:
wherein a is fpa Is a preset power distribution factor (0 < a < 1), when a fpa When the frequency is reduced, users with poor channel gain in the sub-band are allocated more power, so that the fairness of the users is improved, but the maximum throughput of the system is reduced. At this time, the difficulty of demodulating the user signal by the receiving end is reduced because the power difference between the users becomes large.
(3) FTPA algorithm
The FTPA algorithm fully considers the channel gain of each user in the channel and dynamically distributes the power of the transmitting end. Assuming that the total power of channels within the system is limited to P t And multiplexing the same time-frequency domain resource by M users, wherein the distribution power of each user is as follows:
wherein h is m Channel gain, sigma, for the mth user in the subband m 2 Representing noise power, alpha ftpa (0≤α ftpa Less than or equal to 1) represents an attenuation factor for adjusting the difference in power allocation between users when alpha ftpa When the total power of the channel is 0, the total power of the channel is equally distributed to each sub-user, and the total power of the channel is equal to alpha ftpa The system will allocate more power to users with poor channel gain while alpha ftpa The impact on system throughput and user fairness is large.
The optimized artificial fish swarm algorithm is defined as IAFSA (Improved Artificial Fish Swarm Algorithm), other algorithms are used as comparison objects, and the system simulation parameters are set as shown in the following table 2:
table 2 simulation parameters of the system
Running IAFSA, comparing the algorithm with AFSA and PSO algorithm in MATLAB simulation, and as shown in figure 3, the iteration times of the IAFSA algorithm, the AFSA algorithm and the PSO algorithm reaching optimal values are respectively as follows: 25. 65 and 80, it can be seen that the IAFSA algorithm of the present invention has better convergence speed and accuracy.
Comparing IAFSA algorithm, FTPA algorithm and OFDMA scheme to maximize energy efficiency of NOMA system, wherein the OFDMA scheme uses throughput formula R m Calculating energy efficiency, and dividing frequency band B and power P by m users total . As shown in fig. 4, it can be seen that the IAFSA algorithm can maximize the energy efficiency of the NOMA system compared to the other two algorithms, and the IAFSA algorithm has a maximum 10% improvement in energy efficiency compared to the FTPA algorithm.
FIG. 5 is a graph comparing user fairness for NOMA systems using EPA and FPA algorithms and IAFSA algorithms of the present invention. As shown in fig. 5, the IAFSA algorithm can better consider individual fairness of system users as the number of users in the sub-channel increases, compared with the EPA and FPA algorithm.
FIG. 6 is a graph comparing geometric average throughput of NOMA systems obtained by using EPA and FPA algorithms and IAFSA algorithms of the present invention. As can be seen from the figure, the IAFSA algorithm can better compromise the average throughput of the system as the number of users in the sub-channels increases compared with the EPA and FPA algorithms.
The present invention has been disclosed in the preferred embodiments, but the invention is not limited thereto, and the technical solutions obtained by adopting equivalent substitution or equivalent transformation fall within the protection scope of the present invention.

Claims (9)

1. A NOMA downlink power allocation method based on an artificial fish swarm algorithm, comprising:
constructing an objective function by maximizing energy efficiency in a sub-channel of a NOMA system, and constructing constraint conditions by average throughput and user fairness to obtain a power distribution model in the sub-channel, wherein the power distribution model comprises power distribution coefficients of all users in the sub-channel;
normalizing the power distribution coefficient of the user in the subchannel to a multidimensional position vector of the shoal in the artificial shoal algorithm, and solving the objective function by using the optimized artificial shoal algorithm to obtain an optimal power distribution coefficient;
calculating average throughput and user fairness based on the solved optimal power distribution coefficient;
judging whether the calculated average throughput and user fairness meet constraint conditions, if not, adjusting parameter factors of an artificial fish swarm algorithm, and continuing to perform optimizing search until the average throughput and the user fairness meet the constraint conditions, so as to obtain an optimal power distribution model in the sub-channel;
the method for solving the objective function by using the optimized artificial fish swarm algorithm to obtain an optimal power distribution coefficient comprises the following steps:
step 201, initializing artificial fish shoals, and calculating an objective function value of each artificial fish to obtain an optimal fitness value of the artificial fish;
step 202, respectively executing a clustering behavior and a rear-end collision behavior on the artificial fish, comparing the magnitude of objective function values obtained by the two behaviors, selecting a behavior with a larger function value for execution, and recording updated artificial fish states and function values;
step 203, comparing the newly obtained function value with the optimal fitness value, and if the newly obtained function value is larger than the optimal fitness value, updating the optimal fitness value and the artificial fish state;
step 204, judging whether the iteration number reaches the maximum iteration number, if so, stopping iteration, outputting the optimal position vector of the fish school, otherwise, turning to step 202 to carry out the next iteration;
in the clustering behavior and the rear-end collision behavior, the moving step length of the artificial fish is dynamically updated according to the optimal fitness value.
2. The NOMA downlink power allocation method based on artificial fish swarm algorithm according to claim 1, wherein the objective function is:
where η is the energy efficiency in the subband of the NOMA system, M is the number of users superimposed on the subband, R m For the data rate of the mth user in the sub-band, P total Indicating the total transmitting power of the base station, P c Is a fixed loop power loss, B is the size of the frequency band occupied by the sub-band, h m Representing channel gain, alpha, between mth user and base station in sub-channel m Representing the power allocation coefficient of the mth user in the subband, assuming the channel gains of the M users |h 1 | 2 ≥|h 2 | 2 ≥...≥|h M | 2 ≥0，P i ＝α i P total Representing the power level and sigma allocated to the ith user in the ith users before user m 2 Representing the noise power.
3. The NOMA downlink power allocation method based on artificial fish swarm algorithm according to claim 2, wherein the constraint condition is:
s.t.C1:
C2:
wherein R is min And F min Is a preset constraint value, R avg For the geometric average throughput of the system, M is the number of users superimposed on the subband, R m For the data rate of the mth user in the sub-band,f (R) is user fairness.
4. The NOMA downlink power allocation method based on artificial fish swarm algorithm according to claim 1, wherein normalizing the power allocation coefficients of users in the sub-channels to the multi-dimensional position vector of the fish swarm in the artificial fish swarm algorithm comprises:
the position of the nth fish in the artificial fish shoal is expressed as an M-dimensional vector:
wherein,,the position of the nth fish in the artificial fish swarm is given, and N is the population scale of the artificial fish;
the power distribution coefficient of all users in the sub-band is used as vector alpha= [ alpha ] 1 ，α 2 ，...，α M ] T Representing the position vector of the shoal in the artificial shoal algorithmOne-to-one correspondence with the power distribution coefficient vector α:
[x 1 ,x 2 ,...,x M ]＝[α 1 ，α 2 ，...，α M ]
0＜α 1 ≤α 2 ≤...≤α M ＜1
wherein alpha is m Representing the power allocation coefficient for the mth user in the sub-band, M being the number of users on the sub-band.
5. The NOMA downlink power allocation method based on artificial fish swarm algorithm according to claim 1, wherein the clustering behavior comprises:
current artificial fish X i Searching for the number of partners n in its field of view f And the central position, if Y c /n f ＞εY i X is then i Moving one step towards the centre of the buddy, otherwise performing foraging behaviour, wherein Y c A function value representing the central position, epsilon being a crowding factor, Y i Is artificial fish X i Is a function of (a).
6. The NOMA downlink power allocation method based on artificial fish swarm algorithm according to claim 1, wherein the rear-end collision behavior comprises:
artificial fish X i Searching the individual X with the highest corresponding function value in the artificial fish in the visual field range j If Y j /n f ＞εY i X is then i Orientation X j Moving in the direction by one step, otherwise executing foraging behavior, wherein Y j Is X j The corresponding function value.
7. NOMA downlink power allocation method based on artificial fish swarm algorithm according to claim 5 or 6, wherein said foraging comprises:
artificial fish X i Randomly selecting another visible state X within its field of view j When X is j The function value of the position is larger than the current X i Function value of X i To X direction j Moving one step, otherwise, randomly selecting a new state again;
if artificial fish X i The number of states selected in the field of view exceeds the number of retries, and the artificial fish randomly selects one direction and moves one step in the water area so that X i A new state is reached.
8. The NOMA downlink power allocation method based on artificial fish swarm algorithm according to claim 1, wherein the method for dynamically updating the moving step size of the artificial fish comprises:
introducing adaptation degree change times Changes to determine the size of a moving step, wherein the initial value of the Changes is 0, and when one fish updates the optimal adaptation degree, adding 1 to the value of the Changes, otherwise, not performing any operation;
a minimum value tau limit is made on the moving Step, the default size is adopted for the moving Step when the operation starts, and the iteration time T is set in advance 1 Every time the algorithm iteration number reaches a threshold, the next 5T is reached 1 The fish shoal in the iteration times is dynamically updated Step by adopting the following steps:
wherein, lambda is a preset Step factor and lambda epsilon (0, 1), once Step is smaller than minimum value after Step dynamic update, step default size is set as minimum value tau, and the iteration number is full of 5T when algorithm is running 1 After that, changes are reset to 0.
9. The NOMA downlink power allocation method based on artificial fish swarm algorithm according to claim 5 or 6, wherein when performing foraging, the Visual field is changed according to the following formula:
wherein T represents the iteration number of algorithm operation, T 2 To advance a given constant, β represents the decay factor, and β e (0, 1).
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