CN111741531B - Optimization method for optimal operation state of communication equipment under 5G base station - Google Patents
Optimization method for optimal operation state of communication equipment under 5G base station Download PDFInfo
- Publication number
- CN111741531B CN111741531B CN202010863911.2A CN202010863911A CN111741531B CN 111741531 B CN111741531 B CN 111741531B CN 202010863911 A CN202010863911 A CN 202010863911A CN 111741531 B CN111741531 B CN 111741531B
- Authority
- CN
- China
- Prior art keywords
- state
- time
- real
- communication equipment
- optimization
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000006854 communication Effects 0.000 title claims abstract description 75
- 238000005457 optimization Methods 0.000 title claims abstract description 72
- 238000004891 communication Methods 0.000 title claims abstract description 71
- 238000000034 method Methods 0.000 title claims abstract description 59
- 230000008569 process Effects 0.000 claims abstract description 22
- 230000009471 action Effects 0.000 claims description 19
- 150000001875 compounds Chemical class 0.000 claims description 16
- 238000004146 energy storage Methods 0.000 claims description 7
- 230000005540 biological transmission Effects 0.000 claims description 4
- 230000002068 genetic effect Effects 0.000 claims description 3
- 238000013139 quantization Methods 0.000 claims description 3
- 239000000243 solution Substances 0.000 description 5
- 230000007246 mechanism Effects 0.000 description 3
- 239000012086 standard solution Substances 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000005562 fading Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/04—Wireless resource allocation
- H04W72/044—Wireless resource allocation based on the type of the allocated resource
- H04W72/0473—Wireless resource allocation based on the type of the allocated resource the resource being transmission power
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/18—TPC being performed according to specific parameters
- H04W52/20—TPC being performed according to specific parameters using error rate
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/18—TPC being performed according to specific parameters
- H04W52/26—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service]
- H04W52/265—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service] taking into account the quality of service QoS
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/18—TPC being performed according to specific parameters
- H04W52/26—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service]
- H04W52/267—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service] taking into account the information rate
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/50—Allocation or scheduling criteria for wireless resources
- H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/50—Allocation or scheduling criteria for wireless resources
- H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
- H04W72/542—Allocation or scheduling criteria for wireless resources based on quality criteria using measured or perceived quality
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/50—Allocation or scheduling criteria for wireless resources
- H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
- H04W72/543—Allocation or scheduling criteria for wireless resources based on quality criteria based on requested quality, e.g. QoS
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/04—Wireless resource allocation
- H04W72/044—Wireless resource allocation based on the type of the allocated resource
- H04W72/0446—Resources in time domain, e.g. slots or frames
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/04—Wireless resource allocation
- H04W72/044—Wireless resource allocation based on the type of the allocated resource
- H04W72/046—Wireless resource allocation based on the type of the allocated resource the resource being in the space domain, e.g. beams
Landscapes
- Engineering & Computer Science (AREA)
- Quality & Reliability (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention discloses an optimization method of the optimal running state of communication equipment under a 5G base station, wherein the communication equipment under the 5G base station is divided into communication equipment in a non-real-time updating state and communication equipment in a real-time updating state; for communication equipment with a non-real-time update state, knowing a time slot, a power supply quantity and an uncontrollable parameter group, constructing and solving an optimization problem, and realizing the operation state optimization of the communication equipment under the 5G base station according to a solving result; for communication equipment with a real-time updated state, the equipment can obtain the power supply electric quantity and the uncontrollable parameters only through real-time information, namely only through any time slot; and (3) by constructing a Markov decision process, then solving an optimal strategy of the Markov decision process, and realizing the operation state optimization of the communication equipment under the 5G base station according to the optimal strategy. The invention simultaneously aims at updating the communication equipment in two different running states in real time and non-real time, so that the overall performance of the communication equipment in the whole running process can be optimal.
Description
Technical Field
The invention relates to the field of network communication, in particular to an optimization method for the optimal running state of communication equipment under a 5G base station.
Background
Conventional communication mechanisms tend to default to a device having enough energy to perform corresponding operations during execution, and do not consider the energy factor of the device. When the power storage capacity of the device is weak, and the power supply amount is uncertain, the traditional communication mechanism can increase the risk of power failure of the device.
In order to solve the problems, the application researches an energy collection perception communication mechanism design, namely, a communication process and related parameters are dynamically adjusted according to the change of equipment storage and power supply energy. Selecting a proper modeling mode and an analysis method according to the power supply characteristics, the equipment energy storage characteristics and the data generation characteristics of different equipment: for the equipment with controllable power supply amount (such as the equipment is powered by a special energy source), known energy storage amount of the equipment and predictable data generation amount, simulating the communication process of the equipment by using known information under the online condition, constructing a proper mathematical model by combining service scene characteristics and related performance indexes, and optimizing the communication process by using a mathematical tool; and for some equipment with uncontrollable and unpredictable information, by means of models such as a Markov decision process and the like, operation steps and energy management of the equipment are optimized in real time in a communication process by using algorithms such as dynamic planning and the like.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an optimization method of the optimal operation state of communication equipment under a 5G base station.
The technical scheme adopted by the application is as follows: the invention provides an optimization method of an optimal operation state of communication equipment under a 5G base station, wherein the communication equipment under the 5G base station is divided into communication equipment in a non-real-time updating state and communication equipment in a real-time updating state;
for communication devices that update status in non-real time, time slots are knowniPower supply quantity
And uncontrollable parameter set
Constructing an optimization problem and solving the optimization problem, wherein the concrete steps are as follows:
a) determining an optimization objective: equipment performance index quantization under 5G base station
，
Is a controllable parameter set;
b) determining power demand guarantee constraints: the electric quantity contained in the equipment at the beginning of each time slot can guarantee the electric power requirement of the time slot;
c) modeling an optimization problem: under the constraint condition of power demand guarantee, obtaining the optimal performance index, and specifically modeling as follows:
in the formula (I), the compound is shown in the specification,Nthe number of the total time slots is,B 1the amount of power that the device contains at the beginning of the 1 st time slot,
to be in the uncontrollable parameter group
Selecting controllable parameter group
Energy consumed to perform the operation;
d) solving the optimization problem in the step c), and realizing the operation state optimization of the communication equipment under the 5G base station according to the solving result;
for communication equipment with a real-time update state, the equipment only has real-time information, namely, the power supply electric quantity and the uncontrollable parameter set can be obtained only when any time slot is reached; by constructing a Markov decision process and solving an optimal strategy of the Markov decision process, the running state optimization is realized, and the specific steps are as follows:
A) determining state space, action space and reward: in the Markov decision process, if the state of the communication equipment in the real-time update state is the power supply electric quantity
Battery energy storage
Uncontrollable parameter group
In the case of (1), the action taken is to select a controllable parameter set for the communication device that is updating the state in real time
If yes, the reward is the concerned equipment performance index;
B) determining decision rules and policies: if the current state-action history is
And t is denoted as the t-th time slot; in determining the rule
Next, the action is determined by the current state-action history; the strategy is expressed as
；
C) Determining an optimization target and modeling a problem: evaluating a policy by a desired alternate bonus sum of a bonus sum
Good or bad; when the initial state is
Expectation of bonus sum from 1 st slot to Nth slot
The following were used:
in the formula (I), the compound is shown in the specification,
is a reward for the t-slot(s),
as a policy
(iii) a desire;
and
respectively are elements in a state random sequence and an action random sequence; the ultimate goal is to find the optimal strategy
So that
In the formula (I), the compound is shown in the specification,
represents the set of all possible policies that may be applied,
is a state space;
D) and C), solving the optimization target of the step C) to obtain an optimal strategy, and realizing the operation state optimization of the communication equipment under the 5G base station according to the optimal strategy.
Further, the controllable parameter sets of the communication device with the non-real-time update state and the communication device with the real-time update state comprise the transmission power and the coding rate.
Further, the uncontrollable parameter sets of the communication device with the non-real-time updating state and the communication device with the real-time updating state comprise channel conditions, generated data amount, allocated time resources and space resources.
Further, the device performance indexes under the 5G base station include an error rate, throughput and quality of service (QoS).
Further, the step b) is specifically as follows: suppose iniThe device contains an amount of power at the beginning of a time slot of
Then obtain
In the formula (I), the compound is shown in the specification,
to be in the uncontrollable parameter group
Selecting controllable parameter group
Energy consumed to perform the operation; in order to guarantee the power demand of the equipment, the following energy constraint conditions are necessary:
in the formula (I), the compound is shown in the specification,Nto the total number of time slots, correspond toNAn energy constraint, i.e., the amount of power a device has for any time slot, guarantees the power requirements of that time slot.
Further, in the step d), if the optimization problem is a convex optimization problem, solving by using a standard solution of the convex optimization problem; if the optimization problem is not a convex optimization problem, a solution of a standard convex optimization problem is combined with a genetic algorithm to solve so as to reduce the occurrence of convergence to a suboptimal solution.
Further, the solution of the standard convex optimization problem is newton's method or interior point method.
Further, in the step a), the value set of the power supply capacity of the device is recorded as
And the set of values of the battery energy storage of the equipment is recorded as
The value set of the uncontrollable parameter set is recorded as
(ii) a The state space is represented as
(ii) a Status of state
Being an element of the state space,
(ii) a Value set of controllable parameter group
Namely the motion space; any set of controllable parameter sets is
An element of (1), referred to as an action; selecting a set of controllable parameters is selecting an action in the Markov decision process.
Further, in the step D), for the Markov decision process, the optimal strategy is obtained by using a dynamic programming, value iteration, strategy iteration or linear programming method.
The invention has the beneficial effects that: different communication equipment has different performance indexes which need to be concerned under different operation environments, and the conventional optimization scheme is difficult to solve various performance indexes in a unified and generalized way; the same communication equipment also has two different states of real-time update and non-real-time update during operation, and the performance optimization of the communication equipment in the real-time update state cannot be well solved. The present invention quantifies a device performance metric of interest as
The method is suitable for optimizing performance indexes under different conditions, and meanwhile, the overall performance of the communication equipment in the whole operation process can be optimal by aiming at updating the communication equipment in two different operation states, namely real-time operation state and non-real-time operation state.
Drawings
Fig. 1 is a flowchart of an optimization method for an optimal operating state of a communication device under a 5G base station according to the present invention.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
As shown in fig. 1, in the method for optimizing the optimal operating state of the communication device in the 5G base station, the communication device in the 5G base station is divided into two cases, namely a non-real-time update state communication device and a real-time update state communication device; the controllable parameter group of the communication device in the non-real-time updating state and the communication device in the real-time updating state comprises transmission power and coding rate. The uncontrollable parameter groups of the communication equipment in the non-real-time updating state and the communication equipment in the real-time updating state are external conditions of the communication equipment, and comprise channel conditions, generated data volume, allocated time resources and space resources.
For communication devices that update status in non-real time, time slots are knowniPower supply quantity
And uncontrollable parameter set
Based on the method, an optimization problem is constructed firstly, and then the optimization problem is solved: a) determining an optimization objective; b) depicting power demand guarantee constraints; c) modeling an optimization problem; d) and (5) solving an optimization problem. The method comprises the following specific steps:
a) determining an optimization objective;
quantization of concerned equipment performance index under 5G base station
(ii) a The goal of the optimization is to select the most suitable one
So that
And max.
The specific form and nature of (a) is related to the device performance indicators of interest, including bit error rate, throughput, quality of service, QoS. Wherein
Is an uncontrollable parameter determined by the external environment or service scenario, and
is a controllable parameter, and performance
Is that
And
are determined jointly according to
Adjustment of
So that
And max. In the embodiment of the invention, the time for sending a frame is set as the length of a time slot, and the information bit contained in the frame is set as
A frame error rate of
If the performance index concerned is the average number of correctly decoded information bits in a slot, then there is
In the formula (I), the compound is shown in the specification,
only with controllable parameters (coding rate, bandwidth, etc.), while the frame error rate is related to both controllable parameters (transmission power, etc.) and uncontrollable parameters (channel fading, etc.). Namely, it is
By
Determine whether or not to use
By
And
and (4) jointly determining.
b) Depicting power demand guarantee constraints;
suppose iniThe device contains an amount of power at the beginning of a time slot of
Then can obtain
In the formula (I), the compound is shown in the specification,
to be in the uncontrollable parameter group
Selecting controllable parameter group
Energy consumed to perform the operation; in order to guarantee the power demand of the equipment, the following energy constraint conditions are necessary:
in the formula (I), the compound is shown in the specification,Nto the total number of time slots, correspond toNThe energy constraint condition is that for any time slot, the electric quantity of the equipment can guarantee the power requirement of the time slot;
c) modeling an optimization problem;
in order to make the overall performance of the communication equipment under the 5G base station in the whole operation process best, namely, to depict the optimization problem of obtaining the optimal performance index under the constraint condition of power demand guarantee, the optimization problem is modeled as follows:
d) solving the optimization problem, if the optimization problem is a convex optimization problem, solving by using a standard solution (Newton method, interior point method and the like) of the convex optimization problem; if the optimization problem is not a convex optimization problem, one method is to convert the problem into the convex optimization problem through observation and then solve the convex optimization problem, and the other method is to combine the Newton method, the interior point method and other methods with other algorithms such as a genetic algorithm and the like to solve the convex optimization problem, for example, the R-genetic optimization algorithm of a derivative is used, so that the condition that the convex optimization problem converges to a suboptimal solution is reduced, and the operation state optimization of the communication equipment under the 5G base station is realized according to the solving result. Some non-convex optimization problems that fit a particular structure may also be solved directly, for example, using projection gradient descent, alternative minimization, expectation maximization algorithms, stochastic optimization, etc.
For communication equipment with a real-time updated state, the equipment can obtain the power supply electric quantity and the uncontrollable parameters only through real-time information, namely only through any time slot; based on the above, the Markov decision process is constructed, and then the optimal strategy of the Markov decision process is solved: the method comprises the following specific steps: a) depicting state space, action space and rewards; b) depicting decision rules and strategies; c) optimizing target depiction and problem modeling; d) and solving the optimal strategy in the Markov decision process.
a) Depicting state space, action space and rewards;
the value set of the electric quantity provided by the equipment is recorded as
And the value set of the electric quantity contained in the equipment is recorded as
The value set of the uncontrollable parameter set is recorded as
(ii) a The state space can be represented as
(ii) a Status of state
Being an element of the state space,
(ii) a Value set of controllable parameter set
Namely the motion space; it can be seen that any set of controllable parameter sets is
An element of (1), referred to as an action; selecting a set of controllable parameters is to select an action in the Markov decision process;
in state during Markov decision process
At the time of, adopt the actions
The resulting benefit is defined as the reward
(ii) a If the power supply capacity of the communication equipment in the real-time update state
Battery energy storage of communication equipment capable of updating state in real time
Uncontrolled parameter set for communication equipment capable of updating state in real time
In the case of selecting a controllable parameter set of a communication device which updates the state in real time
The reward is then the performance indicator of the device concerned at that time, i.e. the reward is
In the formula (I), the compound is shown in the specification,
updating the performance indicators of the state of the communication device in real time for interest;
b) depicting decision rules and strategies; the decision rule is in a certain time slotiSelecting a method of action; the method specifically comprises the following steps: if the current state-action history is
And t is denoted as the t-th time slot; in determining the rule
Next, the action is determined by the current state-action history; the strategy being a sequence of decision rules, using
Is shown, i.e.
，NThe number of the total time slots is; the decision rule has Markov property and certainty, namely the selection of the action is only related to the current state;
c) optimizing target depiction and problem modeling;
since the amount of power supplied to the device and the uncontrollable set of parameters are random in practice, a strategy is evaluated here by the expected substitute prize sum of the prize sum
Good and bad, use
Is shown, i.e.
In the formula (I), the compound is shown in the specification,
and
respectively a random sequence of states
And motion random sequence
The elements (A) and (B) in (B),
is a reward for the t-slot(s),
as a policy
(iii) a desire; the ultimate goal is to find the optimal strategy
So that
In the formula (I), the compound is shown in the specification,
represents the set of all possible policies;
d) solving an optimal strategy in a Markov decision process; for a standard Markov decision process, methods such as dynamic programming, value iteration, strategy iteration or linear programming can be used to find an optimal strategy, and in addition, greedy strategies have been largely proven to achieve a locally optimal solution. Therefore, when the performance loss is within an acceptable range, a local optimal strategy with low computational complexity similar to a greedy strategy can be adopted, and the operation state optimization of the communication equipment under the 5G base station can be realized according to the optimal strategy.
The above-described embodiments are intended to illustrate rather than to limit the invention, and any modifications and variations of the present invention are within the spirit of the invention and the scope of the appended claims.
Claims (7)
1. The optimization method of the optimal running state of the communication equipment under the 5G base station is characterized in that the communication equipment under the 5G base station is divided into communication equipment in a non-real-time updating state and communication equipment in a real-time updating state;
for communication devices that update status in non-real time, time slots are knowniPower supply quantity
And uncontrollable parameter set
Constructing an optimization problem and solving the optimization problem, wherein the concrete steps are as follows:
a) determining an optimization objective: equipment performance index quantization under 5G base station
，
Is a controllable parameter set;
b) determining power demand guarantee constraints: the electric quantity contained in the equipment at the beginning of each time slot can guarantee the electric power requirement of the time slot; the method specifically comprises the following steps: suppose iniThe device contains an amount of power at the beginning of a time slot of
Then obtain
In the formula (I), the compound is shown in the specification,
to be in the uncontrollable parameter group
Selecting controllable parameter group
Energy consumed to perform the operation; in order to guarantee the power demand of the equipment, the following energy constraint conditions are necessary:
in the formula (I), the compound is shown in the specification,Nto the total number of time slots, correspond toNThe energy constraint condition is that for any time slot, the electric quantity of the equipment can guarantee the power requirement of the time slot;
c) modeling an optimization problem: under the constraint condition of power demand guarantee, obtaining the optimal performance index, and specifically modeling as follows:
in the formula (I), the compound is shown in the specification,Nthe number of the total time slots is,B 1the amount of power that the device contains at the beginning of the 1 st time slot,
to be in the uncontrollable parameter group
Selecting controllable parameter group
Energy consumed to perform the operation;
d) solving the optimization problem in the step c), and realizing the operation state optimization of the communication equipment under the 5G base station according to the solving result;
for communication equipment with a real-time update state, the equipment only has real-time information, namely, the power supply electric quantity and the uncontrollable parameter set can be obtained only when any time slot is reached; by constructing a Markov decision process and solving an optimal strategy of the Markov decision process, the running state optimization is realized, and the specific steps are as follows:
A) determining state space, action space and reward: recording the value set of the power supply electric quantity of the equipment as
And the set of values of the battery energy storage of the equipment is recorded as
The value set of the uncontrollable parameter set is recorded as
(ii) a The state space is represented as
(ii) a Status of state
Being an element of the state space,
(ii) a Value set of controllable parameter group
Namely the motion space; any ofA set of controllable parameter sets are all
An element of (1), referred to as an action; selecting a set of controllable parameters is to select an action in the Markov decision process; in the Markov decision process, if the state of the communication equipment in the real-time update state is the power supply electric quantity
Battery energy storage
Uncontrollable parameter group
In the case of (1), the action taken is to select a controllable parameter set for the communication device that is updating the state in real time
If yes, the reward is the concerned equipment performance index;
B) determining decision rules and policies: if the current state-action history is
And t is denoted as the t-th time slot; in determining the rule
Next, the action is determined by the current state-action history; the strategy is expressed as
；
C) Determining an optimization target and modeling a problem: evaluating a policy by a desired alternate bonus sum of a bonus sum
Good or bad; when the initial state is
Expectation of bonus sum from 1 st slot to Nth slot
The following were used:
in the formula (I), the compound is shown in the specification,
is a reward for the t-slot(s),
as a policy
(iii) a desire;
and
respectively are elements in a state random sequence and an action random sequence; the ultimate goal is to find the optimal strategy
So that
In the formula (I), the compound is shown in the specification,
represents the set of all possible policies that may be applied,
is a state space;
D) and C), solving the optimization target of the step C) to obtain an optimal strategy, and realizing the operation state optimization of the communication equipment under the 5G base station according to the optimal strategy.
2. The method of claim 1, wherein the set of controllable parameters for the communication device in the non-real-time update state and the communication device in the real-time update state includes a transmission power and a coding rate.
3. The method of claim 1, wherein the uncontrollable parameter set of the communication device in the non-real-time update state and the communication device in the real-time update state comprises channel conditions, generated data amount, allocated time resources and space resources.
4. The method of claim 1, wherein the device performance indicators under the 5G base station include bit error rate, throughput, and quality of service (QoS).
5. The method according to claim 1, wherein in step d), if the optimization problem is a convex optimization problem, the solution of a standard convex optimization problem is used to solve; if the optimization problem is not a convex optimization problem, a solution of a standard convex optimization problem is combined with a genetic algorithm to solve so as to reduce the occurrence of convergence to a suboptimal solution.
6. The method as claimed in claim 5, wherein the solution of the convex optimization problem is Newton's method or interior point method.
7. The method as claimed in claim 1, wherein in step D), for the markov decision process, the optimal strategy is obtained by using a dynamic programming, value iteration, strategy iteration or linear programming method.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2020108090162 | 2020-08-12 | ||
| CN202010809016 | 2020-08-12 |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111741531A CN111741531A (en) | 2020-10-02 |
| CN111741531B true CN111741531B (en) | 2020-11-24 |
Family
ID=72658804
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010863911.2A Active CN111741531B (en) | 2020-08-12 | 2020-08-25 | Optimization method for optimal operation state of communication equipment under 5G base station |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111741531B (en) |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107809764A (en) * | 2017-09-21 | 2018-03-16 | 浙江理工大学 | A kind of multiple affair detection method based on Markov chain |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106713346B (en) * | 2017-01-13 | 2021-01-12 | 电子科技大学 | WLAN protocol design and analysis method based on wireless radio frequency energy transmission |
| CN107105438B (en) * | 2017-04-20 | 2020-06-26 | 成都瑞沣信息科技有限公司 | QoS-based data and energy integrated transmission strategy design method |
| CN108880893B (en) * | 2018-06-27 | 2021-02-09 | 重庆邮电大学 | Mobile edge computing server combined energy collection and task unloading method |
| CN110113195B (en) * | 2019-04-26 | 2021-03-30 | 山西大学 | Method for joint unloading judgment and resource allocation in mobile edge computing system |
-
2020
- 2020-08-25 CN CN202010863911.2A patent/CN111741531B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107809764A (en) * | 2017-09-21 | 2018-03-16 | 浙江理工大学 | A kind of multiple affair detection method based on Markov chain |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111741531A (en) | 2020-10-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ortiz et al. | Reinforcement learning for energy harvesting point-to-point communications | |
| CN111491358B (en) | Adaptive modulation and power control system based on energy acquisition and optimization method | |
| CN112788629B (en) | Online combined control method for power and modulation mode of energy collection communication system | |
| CN113438315A (en) | Internet of things information freshness optimization method based on dual-network deep reinforcement learning | |
| CN113395723B (en) | 5G NR downlink scheduling delay optimization system based on reinforcement learning | |
| Hua et al. | GAN-based deep distributional reinforcement learning for resource management in network slicing | |
| CN115065678A (en) | Multi-intelligent-device task unloading decision method based on deep reinforcement learning | |
| Ghosh et al. | Achieving sub-linear regret in infinite horizon average reward constrained mdp with linear function approximation | |
| CN111741531B (en) | Optimization method for optimal operation state of communication equipment under 5G base station | |
| Liu et al. | POMDP-based energy cooperative transmission policy for multiple access model powered by energy harvesting | |
| Zhao et al. | Joint computing resource and bandwidth allocation for semantic communication networks | |
| CN109246787B (en) | Relay selection method combined with predictive control | |
| Wu et al. | Q-learning based link adaptation in 5G | |
| CN112953666B (en) | Spectrum prediction switching method based on channel quality in cognitive wireless network | |
| Zhu et al. | Minimizing age of information in the uplink multi-user networks via dynamic bandwidth allocation | |
| TWI607665B (en) | Method for assigning backhaul links in cooperative wireless network | |
| Suljović et al. | Leveraging outage probability in systems limited by BX fading and Nakagami-m co-channel interference for classification-based QoS estimation | |
| Masadeh | Enhancing the performance of energy harvesting wireless communications using optimization and machine learning | |
| Bodin et al. | Energy harvesting communication system with a finite set of transmission rates | |
| Liu et al. | Machine learning based adaptive modulation scheme for energy harvesting cooperative relay networks | |
| CN112235131B (en) | Data center network service configuration method based on clean energy time window | |
| Ly | Reinforcement Learning-based Methods for Wireless Access Optimization and Multi-Interface Connectivity | |
| CN114339892B (en) | DQN and joint bidding based two-layer slice resource allocation method | |
| Fu et al. | A new theoretic foundation for cross-layer optimization | |
| Zhang et al. | Online learning for wireless video transmission with limited information |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |