CN114125884A - Uplink capacity optimization method, device, network node and storage medium - Google Patents
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Abstract
The application discloses an uplink capacity optimization method, an uplink capacity optimization device, a network node and a storage medium. The method comprises the following steps: optimizing a first objective function by adopting a first uplink capacity optimization model to obtain an optimal solution about the first objective function; the function value of the first objective function represents the uplink capacity of the network; the first objective function takes the transmission power of each first terminal in at least one first terminal accessing the network as a variable; configuring a corresponding first power value for the transmission power of each of the at least one first terminal; the first uplink capacity optimization model represents an uplink capacity optimization model obtained based on a cuckoo algorithm; and the first power value represents the transmitting power of the corresponding terminal when the first objective function takes the optimal solution.
Description
Technical Field
The present application relates to the field of communications, and in particular, to a method and an apparatus for optimizing uplink capacity, a network node, and a storage medium.
Background
The sixth Generation mobile communication technology (6G, 6th Generation mobile networks) will build a network that is ubiquitous (covering land, sea, air, sky) and unconnected (connecting people, machines, things). That is, the network structure of the 6G communication system will change greatly, and especially when the number of antenna arrays approaches or exceeds the number of users, the network structure of the cellular network will fail, and thus the cellular network becomes a candidate networking technology of 6G.
When the related technology optimizes the uplink capacity of the network, the local optimization of the uplink capacity of the network is realized in the coverage range of the base station, and the related technology cannot be applied to the network structure of the cellular network.
Disclosure of Invention
In order to solve the related technical problems, embodiments of the present application provide an uplink capacity optimization method, apparatus, network node, and storage medium.
The embodiment of the application provides an uplink capacity optimization method, which is applied to a first network node and comprises the following steps:
optimizing a first objective function by adopting a first uplink capacity optimization model to obtain an optimal solution about the first objective function; the function value of the first objective function represents the uplink capacity of the network; the first objective function takes the transmission power of each first terminal in at least one first terminal accessing the network as a variable;
configuring a corresponding first power value for the transmission power of each of the at least one first terminal; wherein the content of the first and second substances,
the first uplink capacity optimization model represents an uplink capacity optimization model obtained based on a cuckoo algorithm; and the first power value represents the transmitting power of the corresponding terminal when the first objective function takes the optimal solution.
Wherein, in an embodiment, the first objective function further comprises a first penalty function and a second penalty function; wherein the content of the first and second substances,
the function value of the first penalty function represents the sum of the transmission power of all second terminals in the at least one first terminal; the function value of the second penalty function represents the number of all second terminals in the at least one first terminal; the second terminal is a terminal of which the corresponding transmitting power is greater than or equal to the rated maximum transmitting power in the at least one first terminal.
In an embodiment, before the optimizing the first objective function by using the first uplink capacity optimization model to obtain the optimal solution about the first objective function, the method further includes:
and determining a first weight value corresponding to the first penalty function, and determining a second weight value corresponding to the second penalty function.
In an embodiment, the signal quality parameter of each of the at least one first terminal is greater than or equal to a first set threshold.
In one embodiment, the signal quality parameter comprises one of:
a reference signal received power;
a reference signal reception quality;
signal to noise ratio.
In an embodiment, when the first objective function is optimized by using the first uplink capacity optimization model to obtain an optimal solution for the first objective function, the method includes:
and sorting all solutions based on the fitness of each solution in all solutions by adopting a pareto optimal solution algorithm, and determining the optimal solution according to a sorting result.
In an embodiment, when the pareto optimal solution algorithm is used to rank all solutions based on the fitness of each solution in all solutions and the optimal solution is determined according to the ranking result, the method includes:
and determining the fitness of the corresponding solution based on the sequence number of the layer where the current solution is located and the Euclidean distance of the signal-to-noise ratio between any two first terminals.
The embodiment of the present application further provides an uplink capacity optimization apparatus, including:
the calculating unit is used for optimizing a first objective function by adopting a first uplink capacity optimization model to obtain an optimal solution about the first objective function; the function value of the first objective function represents the uplink capacity of the network; the first objective function takes the transmission power of each first terminal in at least one first terminal accessing the network as a variable;
a configuration unit, configured to configure a corresponding first power value for the transmission power of each of the at least one first terminal; wherein the content of the first and second substances,
the first uplink capacity optimization model represents an uplink capacity optimization model obtained based on a cuckoo algorithm; and the first power value represents the transmitting power of the corresponding terminal when the first objective function takes the optimal solution.
An embodiment of the present application further provides a network node, including: a first processor and a first communication interface; wherein the content of the first and second substances,
the first processor to:
optimizing a first objective function by adopting a first uplink capacity optimization model to obtain an optimal solution about the first objective function; the function value of the first objective function represents the uplink capacity of the network; the first objective function takes the transmission power of each first terminal in at least one first terminal accessing the network as a variable;
configuring a corresponding first power value for the transmission power of each of the at least one first terminal; wherein the content of the first and second substances,
the first uplink capacity optimization model represents an uplink capacity optimization model obtained based on a cuckoo algorithm; and the first power value represents the transmitting power of the corresponding terminal when the first objective function takes the optimal solution.
An embodiment of the present application further provides a network node, including: a first processor and a first memory for storing a computer program capable of running on the processor,
wherein the first processor is configured to execute any of the above steps of the uplink capacity optimization method when running the computer program.
An embodiment of the present application further provides a storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of any one of the above uplink capacity optimization methods.
According to the uplink capacity optimization method, the uplink capacity optimization device, the network node and the storage medium, the network node optimizes a first objective function by adopting a first uplink capacity optimization model to obtain an optimal solution about the first objective function; configuring a corresponding first power value for the transmission power of each first terminal in the at least one first terminal; wherein the function value of the first objective function characterizes the upstream capacity of the network; the first objective function takes the transmission power of each first terminal in at least one first terminal accessing the network as a variable; the first uplink capacity optimization model represents an uplink capacity optimization model obtained based on a cuckoo algorithm; and the first power value represents the transmitting power of the corresponding terminal when the first objective function takes the optimal solution. The overall optimization of the network uplink capacity is carried out by taking a user as a center through a cuckoo algorithm to obtain an optimization result about the transmitting power of each terminal, so that the global optimization of the network uplink capacity is realized, and the method is suitable for a large-scale network structure of a cellular network.
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Fig. 1 is a schematic flow chart of an uplink capacity optimization method according to an embodiment of the present application;
FIG. 2 is a schematic diagram illustrating an example of the overall structure of a cellular network according to an embodiment of the present application;
fig. 3 is a schematic flow chart of an uplink capacity optimization method according to another embodiment of the present application;
fig. 4 is a schematic structural diagram of an uplink capacity optimization apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a first network node according to an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples.
The uplink capacity of the network mainly depends on the distribution situation of the terminals in the network and the transmission power of the terminals, and when the uplink capacity of the network is optimized, the related art mainly realizes local optimization of the uplink capacity of the network within the coverage range of the base station. In a 6G communication system, the network structure is changed greatly, and a cellular network becomes one of the candidate networking technologies of 6G, so that the scheme for locally optimizing the uplink capacity of the network is not suitable for the network structure of the cellular network any more.
Based on this, in various embodiments of the present application, global optimization of network uplink capacity is implemented, where a network node optimizes a first objective function by using a first uplink capacity optimization model to obtain an optimal solution for the first objective function; configuring a corresponding first power value for the transmitting power of each first terminal in the at least one first terminal to realize global optimization of network uplink capacity; wherein the function value of the first objective function characterizes the upstream capacity of the network; the first objective function takes the transmission power of each first terminal in at least one first terminal accessing the network as a variable; the first uplink capacity optimization model represents an uplink capacity optimization model obtained based on a cuckoo algorithm; and the first power value represents the transmitting power of the corresponding terminal when the first objective function takes the optimal solution.
An embodiment of the present application provides an uplink capacity optimization method, which is applied to a network node, and as shown in fig. 1, the method includes:
step 101: and optimizing a first objective function by adopting a first uplink capacity optimization model to obtain an optimal solution about the first objective function.
Wherein the function value of the first objective function characterizes the upstream capacity of the network; the first objective function takes the transmission power of each of at least one first terminal accessing the network as a variable.
Step 102: configuring a corresponding first power value for the transmission power of each of the at least one first terminal.
The first uplink capacity optimization model represents an uplink capacity optimization model obtained based on a cuckoo algorithm; and the first power value represents the transmitting power of the corresponding terminal when the first objective function takes the optimal solution. And controlling the transmitting power of each terminal based on the transmitting power of the corresponding terminal when the first objective function is used for obtaining the optimal solution, thereby realizing the optimization of the uplink capacity of the network.
Here, the uplink capacity of the network is optimized as a whole, and in practical application, the uplink capacity of the network is
Wherein, PkRepresenting the transmitting power of the kth terminal, wherein K is less than or equal to K, and the constraint conditions are as follows: 0<Pk<Pmax，PmaxMaximum transmitting power of terminal, P corresponding to different terminalsmaxCan be the same or different, SINRkAnd characterizing the uplink signal-to-noise ratio of the kth terminal. In the process of calculating the uplink capacity, assuming that the channel fading coefficient calculated by the pilot frequency remains unchanged, the main variable determining the uplink capacity is the transmission power of each terminal in the access network. Here, the uplink snr of the kth terminal is:
wherein, DSkCharacterizing more desirable demodulated signals, BUkCharacterizing error signals, IUI, introduced by imperfect demodulationkk’Characterizing interference signals between terminals, TNkCharacterizing the noise signal introduced after demodulation. For ease of understanding, the following description is provided to determine the uplink snr of the kth terminal:
referring to fig. 2 in conjunction with the overall structure example of the decellularized network of fig. 2, UE1～UE4Characterizing terminals, APs, accessed to and distributed in a network1～AP20The antenna arrays in the network are characterized, and it can be seen that the number of the antenna arrays far exceeds the number of the terminals. For a massive Multiple Input Multiple Output (MIMO) system in a cellular network, considering signal combining gains formed when a plurality of antenna arrays not at the same geographical location receive terminal signals simultaneously, assuming that each antenna array has only one antenna, there are M antenna arrays and K terminals with randomly distributed locations. Defining the channel fading coefficient between the kth terminal and the mth antenna array as
Wherein β represents a large scale fading and h represents a small scale fading that follows a gaussian distribution. In order to effectively estimate the uplink channel fading coefficients, a Minimum Mean square Error method (MMSE) is adopted to estimate all uplink pilot signals. Assuming that the pilot signal length is tau,
wherein
Wherein the content of the first and second substances,
the pilot signal assigned to the kth terminal is characterized with a normalized energy of 1. Thus, the m-th antenna array receives the signal of
Wherein the vector
White noise is characterized as obeying a gaussian distribution. Processing the pilot signal by adopting a matched filtering method, wherein the signal received by the mth antenna array after processing is
Wherein
According to the MMSE algorithm, the channel estimation is obtained as
On the basis of the analysis result
Here, the uplink signal of the kth terminal is defined as:
wherein P iskCharacterizing the transmission power, skCharacterising the uplink symbols, | sk‖21, then the signal received by the mth antenna array is:
wherein n ismIs white noise subject to a gaussian distribution. Processing the received signals by adopting a maximum ratio combining mode to all the signals to obtain
Wherein Z is less than or equal to M, and represents that Z antenna arrays receive the signal of the kth terminal, umkRepresenting the maximum ratio combining coefficient of the kth terminal received by the mth antenna array, and if any terminal is not assumed to be k, u isk＝[u1k … uMk]T，‖uk‖21, then there are
Network-based uplink capacity
In practical application, the uplink capacity is optimized, and a first objective function corresponding to the uplink capacity optimization problem is as follows:
corresponding constraint condition is 0<Pk<PmaxThe function value of the first objective function represents the uplink capacity of the network, and the first objective function takes the transmission power of each of the K terminals accessing the network as a variable.
After the first objective function is determined, the first objective function is optimized by adopting a first uplink capacity optimization model, so that an optimal solution about the first objective function is obtained. Here, the first uplink capacity optimization model is obtained based on a cuckoo algorithm. Among them, the idea of Cuckoo Search is based on nest parasitic behavior of Cuckoo and Levy flight behavior of birds. The cuckoo algorithm has the characteristics of global optimization and high convergence speed. The nest parasitic behavior of cuckoos can be described as: randomly selecting one nest and producing one egg for each cuckoo each time; the nest with the highest quality egg is retained to the next generation; the number of selectable nests is fixed and the probability that a cuckoo egg is found by an original nest master is pa e (0, 1). In this case, the original bird throws away the cuckoo eggs or discards the existing nests, assuming for simplicity that the pa portion of the n nests is replaced by a new nest (novelly). In the cuckoo algorithm, the eggs in each nest represent one solution, the cuckoo eggs represent a new solution, and the goal is to replace the inferior solution in the nest with the new solution or a potential superior solution. In addition, levy flight is a typical random walk mechanism, which represents a type of non-gaussian random process, and the steady increment of levy flight follows levy stable distribution. Because the levy flight second moment is divergent, the motion process always has large jump under the condition of small aggregation, and therefore, the introduction of the levy flight into the optimization technology is a strategy for effectively avoiding the algorithm from falling into local optimization.
In the cuckoo algorithm, when cuckoo i generates a new solution xt+1Then, a levy flight is executed, as shown in the following formula:
wherein alpha is>0 is the step size of the image to be displayed,
representing a multiplier, the random walk step of the levy flight follows a levy distribution,
where u obeys a mean of 0 and the variance is σ2The distribution of (a) to (b) is,
v obeys a distribution with a mean of 0 and a variance of 1.
In combination with the first objective function, since the channel fading coefficient is relatively fixed, the uplink capacity optimization problem takes only the transmission power of each of the K terminals accessing the network as a variable, so that there is one egg in the nest of the simplified cuckoo.
In the above, the first objective function has 0<Pk<PmaxThat is, the uplink capacity optimization problem is a constrained optimization problem. Here, it is considered that a penalty function is adopted to convert a constrained optimization problem into an unconstrained optimization problem so as to improve algorithm iteration efficiency in the uplink capacity optimization process.
Based on this, in an embodiment, the first objective function further includes a first penalty function and a second penalty function; wherein the content of the first and second substances,
the function value of the first penalty function represents the sum of the transmission power of all second terminals in the at least one first terminal; the function value of the second penalty function represents the number of all second terminals in the at least one first terminal; the second terminal is a terminal of which the corresponding transmitting power is greater than or equal to the rated maximum transmitting power in the at least one first terminal.
Here, the first objective function is composed of a function representing the uplink capacity of the network, a first penalty function, and a second penalty function, and when determining the first objective function, a penalty coefficient corresponding to each penalty function may be determined according to an actual deployment situation of the network.
Based on this, in an embodiment, before the optimizing the first objective function by using the first uplink capacity optimization model to obtain the optimal solution about the first objective function, the method further includes:
and determining a first weight value corresponding to the first penalty function, and determining a second weight value corresponding to the second penalty function.
When in actual application, the first objective function is set to be
F(P)＝f(P)+w1*(sum_viol)2+w2*num_viol
Wherein f (P) is based on
A function determined for characterizing the uplink capacity of the network, corresponding to the constraint condition in the above, sum _ viol characterizing the sum of the transmit powers of all terminals violating the constraint condition, i.e. the sum of the transmit powers of the terminals having a transmit power greater than or equal to the nominal maximum transmit power; w is a1A first weight value corresponding to the first penalty function sum _ viol, that is, a penalty coefficient corresponding to the first penalty function sum _ viol; num _ viol characterizes the number of terminals violating the constraint, w2Is a second weight value corresponding to the second penalty function num _ viol, i.e. a penalty coefficient corresponding to the second penalty function num _ viol.
In practical application, the number of terminals accessing the network is large, and considering that the uplink capacity of the network mainly depends on the transmitting power of the terminals accessing the network, the constraint conditions related to signal strength can be introduced in the uplink capacity optimization process, so that in the process that the cuckoo generates a new solution through levy flight, in order to further reduce the operation amount and improve the calculation efficiency, the terminals with the signal strength reaching a certain threshold value participate in the uplink capacity optimization process, and the terminals with the signal strength not reaching the certain threshold value do not participate in the uplink capacity optimization process.
Based on this, in an embodiment, the signal quality parameter of each of the at least one first terminal is greater than or equal to a first set threshold.
Here, the Signal Quality parameter of the first terminal is greater than the first set threshold, and the Signal Quality parameter includes, but is not limited to, a Reference Signal Receiving Power (RSRP) of the terminal, a Reference Signal Receiving Quality (RSRQ) Signal-to-Noise Ratio (SINR), or a Signal-to-Interference plus Noise Ratio (SINR) measured. For example, a first set threshold r is setthRSRP greater than or equal to rthThe terminal participates in the optimization process of the uplink capacity, and the RSRP is less than rthThe terminal(s) does not participate in the optimization process of the uplink capacity. Also for example, a first set threshold SINR is setthSINR greater than or equal to SINRthThe terminal participates in the optimization process of the uplink capacity, and the SINR is less than the SINRthThe terminal(s) does not participate in the optimization process of the uplink capacity.
With reference to fig. 3, the process of optimizing the first objective function by using the first uplink capacity optimization model is as follows:
step 1: initializing the population to generate n nest Pi；
Step 2: and (5) judging whether t is less than the maximum iteration number or a termination condition, if so, executing the step (3), otherwise, executing the step (5).
And step 3: the cuckoo generates a new solution through levy flight and evaluates the fitness value gamma corresponding to the new solution.
And 4, step 4: and generating a random number belonging to the group, judging whether the random number belonging to the group is smaller than pa, and updating the solution according to the judgment result.
Wherein is made of
Update the solution, H () representing a step function, i.e.When pa is larger than e, H () is 1, use
Updating the solution; when pa is less than e, H () is 0
And updating the solution.
And 5: and sorting the fitness values gamma corresponding to all the solutions to the current optimal solution.
Step 6: and outputting an uplink optimization result.
In practical application, in order to determine the optimal solution of uplink capacity optimization most accurately, a pareto optimal solution algorithm is adopted. Defining according to pareto optimal solution: assuming that for all targets of any two solutions S1 and S2, S1 is better than S2, which is called S1 dominated S2, if S1 is not dominated by other solutions, S1 is called non-dominated solution (also called non-dominated solution or Pareto solution), non-dominating sets in all solutions are used as the first layer, each layer of solutions is sequentially defined as dominating sets of the next layer of solutions, and the sequence numbers of each layer are numbered from 1.
Based on this, when the first objective function is optimized by using the first uplink capacity optimization model to obtain the optimal solution of the first objective function, the method includes:
and sorting all solutions based on the fitness of each solution in all solutions by adopting a pareto optimal solution algorithm, and determining the optimal solution according to a sorting result.
In practical application, even if the transmission power of the terminal changes, the interference between different terminals may change or may not change, so that in order to further distinguish the optimal solution from the non-optimal solution, the SINR is used to measure the distance between the optimal solutions in the process of determining the optimal solution, so as to more accurately reflect the influence of the optimal solution on the uplink capacity optimization result.
Based on this, in an embodiment, when the pareto optimal solution algorithm is used to rank all solutions based on the fitness of each solution in all solutions, and the optimal solution is determined according to a ranking result, the method includes:
and determining the fitness of the corresponding solution based on the Euclidean distance of the signal-to-noise ratio between any two first terminals.
Here, let the fitness be
Wherein q isiIndicating the sequence number of the layer in which the solution is located. For the first layer solution, define NiIs a distance factor, Ni＝∑j S(dij)，dijAnd the Euclidean distance of SINR between any two first terminals is represented. Therefore, by adopting the pareto optimal solution algorithm, the applicability of each solution is calculated based on the Euclidean distance of SINR between the terminals, all solutions are sorted based on the applicability, the optimal solution is selected, and the accuracy of the cuckoo algorithm can be effectively improved.
The present application will be described in further detail with reference to the following application examples.
Referring to FIG. 2, a CPU1～CPU3A centralized controller, which is characterized to be deployed in a decentralized network, may be understood as a base station. In the related art, the uplink capacity of the network is locally optimized by the base station. As shown in fig. 2, the CPU1For antenna array AP2、AP4～AP8、AP10The overlay network performs uplink capacity optimization, CPU2For antenna array AP9、AP11～AP14The overlay network performs uplink capacity optimization, CPU3For antenna array AP1、AP3、AP15～AP20The overlay network performs uplink capacity optimization. In the process of implementing the scheme of the embodiment of the present application, a Network Management System (NMS) is implemented by the Network node shown in fig. 2, and the overall optimization of the uplink capacity of the Network is performed by using the cuckoo algorithm and centering on the user to obtain the optimization result of the transmission power of each terminal, so as to implement the global optimization of the uplink capacity of the Network, and the method is suitable for a large-scale Network structure of the cellular Network.
In order to implement the method according to the embodiment of the present application, an uplink capacity optimization apparatus is further provided in the embodiment of the present application, and is disposed on a first network node, as shown in fig. 4, and the apparatus includes:
a calculating unit 401, configured to optimize a first objective function by using a first uplink capacity optimization model, so as to obtain an optimal solution for the first objective function; the function value of the first objective function represents the uplink capacity of the network; the first objective function takes the transmission power of each first terminal in at least one first terminal accessing the network as a variable;
a configuring unit 402, configured to configure a corresponding first power value for the transmission power of each of the at least one first terminal; wherein the content of the first and second substances,
the first uplink capacity optimization model represents an uplink capacity optimization model obtained based on a cuckoo algorithm; and the first power value represents the transmitting power of the corresponding terminal when the first objective function takes the optimal solution.
Wherein, in an embodiment, the first objective function further comprises a first penalty function and a second penalty function; wherein the content of the first and second substances,
the function value of the first penalty function represents the sum of the transmission power of all second terminals in the at least one first terminal; the function value of the second penalty function represents the number of all second terminals in the at least one first terminal; the second terminal is a terminal of which the corresponding transmitting power is greater than or equal to the rated maximum transmitting power in the at least one first terminal.
In one embodiment, the apparatus further comprises:
and the determining unit is used for determining a first weight value corresponding to the first penalty function and determining a second weight value corresponding to the second penalty function.
In an embodiment, the signal quality parameter of each of the at least one first terminal is greater than or equal to a first set threshold.
In one embodiment, the signal quality parameter comprises one of:
a reference signal received power;
a reference signal reception quality;
signal to noise ratio.
In an embodiment, when the calculating unit 401 optimizes the first objective function by using the first uplink capacity optimization model to obtain an optimal solution about the first objective function, it is configured to:
and sorting all solutions based on the fitness of each solution in all solutions by adopting a pareto optimal solution algorithm, and determining the optimal solution according to a sorting result.
In an embodiment, the computing unit 401 is configured to:
and determining the fitness of the corresponding solution based on the Euclidean distance of the signal-to-noise ratio between any two first terminals.
In practical applications, the calculating unit 401 and the determining unit may be implemented by a processor in the uplink capacity optimizing apparatus, and the configuring unit 402 may be implemented by a processor in the uplink capacity optimizing apparatus in combination with a communication interface.
It should be noted that: in the uplink capacity optimization apparatus provided in the above embodiment, when performing uplink capacity optimization, only the division of the program modules is described as an example, and in practical applications, the processing allocation may be completed by different program modules according to needs, that is, the internal structure of the apparatus is divided into different program modules to complete all or part of the processing described above. In addition, the uplink capacity optimization device provided in the above embodiment and the uplink capacity optimization method embodiment belong to the same concept, and specific implementation processes thereof are described in the method embodiment and are not described herein again.
Based on the hardware implementation of the program module, and in order to implement the method on the first network node side in the embodiment of the present application, an embodiment of the present application further provides a first network node, as shown in fig. 5, the first network node 500 includes:
a first communication interface 501, which is capable of performing information interaction with other network nodes;
the first processor 502 is connected to the first communication interface 501 to implement information interaction with other network nodes, and is configured to execute the method provided by one or more technical solutions of the first network node side when running a computer program. And the computer program is stored on the first memory 503.
Specifically, the first processor 502 is configured to:
optimizing a first objective function by adopting a first uplink capacity optimization model to obtain an optimal solution about the first objective function; the function value of the first objective function represents the uplink capacity of the network; the first objective function takes the transmission power of each first terminal in at least one first terminal accessing the network as a variable; configuring a corresponding first power value for the transmission power of each of the at least one first terminal; wherein the content of the first and second substances,
the first uplink capacity optimization model represents an uplink capacity optimization model obtained based on a cuckoo algorithm; and the first power value represents the transmitting power of the corresponding terminal when the first objective function takes the optimal solution.
Wherein, in an embodiment, the first objective function further comprises a first penalty function and a second penalty function; wherein the content of the first and second substances,
the function value of the first penalty function represents the sum of the transmission power of all second terminals in the at least one first terminal; the function value of the second penalty function represents the number of all second terminals in the at least one first terminal; the second terminal is a terminal of which the corresponding transmitting power is greater than or equal to the rated maximum transmitting power in the at least one first terminal.
In one embodiment, the first processor 502 is further configured to:
and determining a first weight value corresponding to the first penalty function, and determining a second weight value corresponding to the second penalty function.
In an embodiment, the signal quality parameter of each of the at least one first terminal is greater than or equal to a first set threshold.
In one embodiment, the signal quality parameter comprises one of:
a reference signal received power;
a reference signal reception quality;
signal to noise ratio.
In one embodiment, the first processor 502 is further configured to:
and sorting all solutions based on the fitness of each solution in all solutions by adopting a pareto optimal solution algorithm, and determining the optimal solution according to a sorting result.
In one embodiment, the first processor 502 is further configured to:
and determining the fitness of the corresponding solution based on the Euclidean distance of the signal-to-noise ratio between any two first terminals.
It should be noted that: the specific processing procedures of the first processor 502 and the first communication interface 501 may be understood with reference to the above-described methods.
Of course, in practice, the various components in the first network node 500 are coupled together by a bus system 504. It is understood that the bus system 504 is used to enable communications among the components. The bus system 504 includes a power bus, a control bus, and a status signal bus in addition to a data bus. For clarity of illustration, however, the various buses are labeled as bus system 504 in fig. 5.
The first memory 503 in embodiments of the present application is used to store various types of data to support the operation of the first network node 500. Examples of such data include: any computer program for operating on the first network node 500.
The method disclosed in the embodiment of the present application can be applied to the first processor 502, or implemented by the first processor 502. The first processor 502 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be implemented by an integrated logic circuit of hardware or an instruction in the form of software in the first processor 502. The first Processor 502 may be a general purpose Processor, a Digital Signal Processor (DSP), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc. The first processor 502 may implement or perform the methods, steps and logic blocks disclosed in the embodiments of the present application. A general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed in the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium located in the first memory 503, and the first processor 502 reads the information in the first memory 503 to complete the steps of the foregoing method in combination with the hardware thereof.
In an exemplary embodiment, the first network node 500 may be implemented by one or more Application Specific Integrated Circuits (ASICs), DSPs, Programmable Logic Devices (PLDs), Complex Programmable Logic Devices (CPLDs), Field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, Micro Controllers (MCUs), microprocessors (microprocessors), or other electronic components for performing the aforementioned methods.
It is understood that the first memory 503 of the embodiments of the present application may be a volatile memory or a nonvolatile memory, and may also include both volatile and nonvolatile memories. Among them, the nonvolatile Memory may be a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a magnetic random access Memory (FRAM), a Flash Memory (Flash Memory), a magnetic surface Memory, an optical disk, or a Compact Disc Read-Only Memory (CD-ROM); the magnetic surface storage may be disk storage or tape storage. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of illustration and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Enhanced Synchronous Dynamic Random Access Memory (Enhanced DRAM), Synchronous Dynamic Random Access Memory (SLDRAM), Direct Memory (DRmb Access), and Random Access Memory (DRAM). The memories described in the embodiments of the present application are intended to comprise, without being limited to, these and any other suitable types of memory.
In an exemplary embodiment, the present application further provides a storage medium, specifically a computer storage medium, which is a computer readable storage medium, for example, including a first memory 503 storing a computer program, where the computer program is executable by the first processor 502 of the first network node 500, so as to complete the steps of the foregoing first network node side method. The computer readable storage medium may be Memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface Memory, optical disk, or CD-ROM.
It should be noted that: "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
The technical means described in the embodiments of the present application may be arbitrarily combined without conflict.
The above description is only a preferred embodiment of the present application, and is not intended to limit the scope of the present application.
Claims (11)
1. An uplink capacity optimization method applied to a first network node includes:
optimizing a first objective function by adopting a first uplink capacity optimization model to obtain an optimal solution about the first objective function; the function value of the first objective function represents the uplink capacity of the network; the first objective function takes the transmission power of each first terminal in at least one first terminal accessing the network as a variable;
configuring a corresponding first power value for the transmission power of each of the at least one first terminal; wherein the content of the first and second substances,
the first uplink capacity optimization model represents an uplink capacity optimization model obtained based on a cuckoo algorithm; and the first power value represents the transmitting power of the corresponding terminal when the first objective function takes the optimal solution.
2. The method of claim 1, wherein the first objective function further comprises a first penalty function and a second penalty function; wherein the content of the first and second substances,
the function value of the first penalty function represents the sum of the transmission power of all second terminals in the at least one first terminal; the function value of the second penalty function represents the number of all second terminals in the at least one first terminal; the second terminal is a terminal of which the corresponding transmitting power is greater than or equal to the rated maximum transmitting power in the at least one first terminal.
3. The method of claim 2, wherein before the optimizing a first objective function using a first uplink capacity optimization model to obtain an optimal solution for the first objective function, the method further comprises:
and determining a first weight value corresponding to the first penalty function, and determining a second weight value corresponding to the second penalty function.
4. The method of claim 1, wherein the signal quality parameter for each of the at least one first terminal is greater than or equal to a first set threshold.
5. The method of claim 4, wherein the signal quality parameter comprises one of:
a reference signal received power;
a reference signal reception quality;
signal to noise ratio.
6. The method of claim 1, wherein when the first objective function is optimized by using the first uplink capacity optimization model to obtain an optimal solution for the first objective function, the method comprises:
and sorting all solutions based on the fitness of each solution in all solutions by adopting a pareto optimal solution algorithm, and determining the optimal solution according to a sorting result.
7. The method of claim 6, wherein the pareto optimal solution algorithm is used to rank all solutions based on their fitness for each solution, and when determining the optimal solution according to the ranking result, the method comprises:
and determining the fitness of the corresponding solution based on the Euclidean distance of the signal-to-noise ratio between any two first terminals.
8. An uplink capacity optimizing apparatus, comprising:
the calculating unit is used for optimizing a first objective function by adopting a first uplink capacity optimization model to obtain an optimal solution about the first objective function; the function value of the first objective function represents the uplink capacity of the network; the first objective function takes the transmission power of each first terminal in at least one first terminal accessing the network as a variable;
a configuration unit, configured to configure a corresponding first power value for the transmission power of each of the at least one first terminal; wherein the content of the first and second substances,
the first uplink capacity optimization model represents an uplink capacity optimization model obtained based on a cuckoo algorithm; and the first power value represents the transmitting power of the corresponding terminal when the first objective function takes the optimal solution.
9. A network node, comprising: a first processor and a first communication interface; wherein the content of the first and second substances,
the first processor to:
optimizing a first objective function by adopting a first uplink capacity optimization model to obtain an optimal solution about the first objective function; the function value of the first objective function represents the uplink capacity of the network; the first objective function takes the transmission power of each first terminal in at least one first terminal accessing the network as a variable;
configuring a corresponding first power value for the transmission power of each of the at least one first terminal; wherein the content of the first and second substances,
the first uplink capacity optimization model represents an uplink capacity optimization model obtained based on a cuckoo algorithm; and the first power value represents the transmitting power of the corresponding terminal when the first objective function takes the optimal solution.
10. A network node, comprising: a first processor and a first memory for storing a computer program capable of running on the processor,
wherein the first processor is adapted to perform the steps of the method of any one of claims 1 to 7 when running the computer program.
11. A storage medium having a computer program stored thereon, the computer program, when being executed by a processor, performing the steps of the method of any one of claims 1 to 7.
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