CN106330608B - The uplink user Throughput fairness optimization method in number energy integrated communication network - Google Patents
The uplink user Throughput fairness optimization method in number energy integrated communication network Download PDFInfo
- Publication number
- CN106330608B CN106330608B CN201610728778.3A CN201610728778A CN106330608B CN 106330608 B CN106330608 B CN 106330608B CN 201610728778 A CN201610728778 A CN 201610728778A CN 106330608 B CN106330608 B CN 106330608B
- Authority
- CN
- China
- Prior art keywords
- user
- energy
- downlink
- uplink
- power
- 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
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
- H04L43/0876—Network utilisation, e.g. volume of load or congestion level
- H04L43/0888—Throughput
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/391—Modelling the propagation channel
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- Environmental & Geological Engineering (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention discloses one kind number can uplink user Throughput fairness optimization method in integrated communication networks, specifically include: establishing the uplink and downlink network model in number energy integrated network；Determine base station transmitting power, noise power and energy transformation ratio；User's downlink business demand is defined, goal expression and its constraint are obtained；Calculate the expression formula for the energy that each user's downlink is gathered in；And then obtain the expression formula of user uplink data amount；The optimization of user uplink Throughput fairness is completed by the upstream and downstream time slot and power splitting factor of combined optimization user, obtains transmission strategy.Method of the invention is modeled by the physics scene to a kind of several energy integrated networks, and according to convex optimum theory to model solution, it solves in number energy integrated network, user's communication energy derives from the radiofrequency signal that base station is sent, and in the case that user has downlink data amount demand, the problem of strict guarantee user uplink transmits fairness, maximizes user uplink data amount.
Description
Technical field
The invention belongs to technical field of communication network, and in particular to a kind of uplink user in number energy integrated communication network
Throughput fairness optimization method.
Background technique
The energy of existing wireless communication system mostlys come from power grid power supply and battery power supply.Power grid is powered so that system
It is sustainable to obtain reliable energy, but deployment electric power networks are required, to keep systematic difference range limited；Battery power so that
Systematic difference is more portable, but the storage capacity of monocell is severely restricted the power of system and energy,
To constrain the service performance and life cycle of system, and the charge capacity of current battery has become the bottleneck of technology development.
Number energy integrated communication technology is on the basis of existing wireless power technology, by the technological means in a variety of forward positions, wireless
Information is transmitted while being realized collection of energy (Energy Harvesting, EH), thus realizing the same of high efficient and reliable information communication
When make full use of valuable energy resource, provide possibility to solve information and synchronous energy transmission in wireless communication,
Become an important directions of future communications development.
Research has been considered that the optimized throughput in number energy integrated communication network transmission, including uplink total throughout
The optimization of optimization and upstream data amount fairness, but it is all based on the physical field of time slot switching (Time Swiching, TS) technology
Scape is realized the simultaneous interpretation of number energy, is optimized to upstream data amount.Wherein also having research is to divide (Power based on power
Splitting, PS) technology realizes the optimization of uplink total throughout, but since number can be double " remote-close " in integration
Effect, channel quality is poor or the energy that gathers in apart from base station user farther out's downlink is small, and the power that uplink needs
Greatly, therefore user uplink data amount is difficult to be guaranteed.
Summary of the invention
It is an object of the invention to overcome in the prior art to gulp down multi-purpose amount energy integrated communication network transmission optimization
The defect that user uplink data amount fairness is not accounted for when the amount of spitting is proposed and a kind of is used under the scene of lower line number energy simultaneous interpretation
The transmission strategy that family uplink throughput fairness is optimal.
The purpose of the present invention is achieved through the following technical solutions: a kind of uplink in number energy integrated communication network
User throughput fairness optimization method, comprising the following steps:
S1, it determines network model, establishes the uplink and downlink network model in number energy integrated network；
S2, base station transmitting power, noise power and energy transformation ratio are determined；
S3, define user's downlink business demand, obtain about the goal expression of user's downlink business demand and its about
Beam；
S4, the expression formula for calculating the energy that each user's downlink is gathered in；
S5, the energy that user's downlink is collected into according to obtained in S4 expression formula obtain the expression of user uplink data amount
Formula；
S6, user uplink throughput fairness is completed by the upstream and downstream time slot and power splitting factor of combined optimization user
Property optimization, and obtain transmission strategy.
Further, step S1 establishes uplink and downlink network model and specifically includes following sub-step:
S11, the integrated cellular network of number energy based on TDMA for considering single cell, base station and user are single antenna, base
The BS that stands sends the integrated signal of number energy with the forms of broadcasting to M user (U by down channel timesharing1,U2,U3,...,UM), it uses
Family sends information to base station with then passing through up channel timesharing；The communication of downlink is completed with constant power in base station；If base station with
Channel between user remains unchanged in a duty cycle T,WithIt is expressed as the down channel and uplink of user j
The channel power of channel declines, if channel is awgn channel, and channel noise power is Pn；
S12,Period in, base station is with power PBSIt is communicated in the form of broadcast with user j, at this point, user j
By power cutting techniques with splitting factor μjBy downlink reception to signal energy be divided into two parts, a part is used as energy
It collects, another part received signal energy is decoded acquisition corresponding information, and other users are then complete by the signal received
Portion is acquired as energy.
Beneficial effects of the present invention: method of the invention is counted by the physics scene to a kind of several energy integrated networks
Modeling is learned, and according to convex optimum theory to model solution, solved in number energy integrated network, user's communication energy derives from
The radiofrequency signal that base station is sent, and in the case that user has downlink data amount demand, strict guarantee user uplink transmits fairness,
The problem of maximizing user uplink data amount.
Detailed description of the invention
Fig. 1 is dynamic resource optimal method flow chart of the invention；
Fig. 2 is the network model schematic diagram of number energy integrated communication network of the invention；
Fig. 3 is to carry out the method schematic diagram that power divides energy after user of the invention receives signal；
Fig. 4 is network model time slot allocation figure of the invention.
Specific embodiment
Technical solution of the present invention is further illustrated in the following with reference to the drawings and specific embodiments.
As shown in Figure 1, a kind of dynamic resource optimal method of several energy integrated communication networks, comprising the following steps:
S1, it determines network model, establishes the uplink and downlink network model in number energy integrated network；Concrete model such as Fig. 2 institute
Show, including following sub-step:
S11, the integrated cellular network of number energy based on TDMA for considering single cell, base station and user are single antenna, base
The BS that stands sends the integrated signal of number energy with the forms of broadcasting to M user (U by down channel timesharing1,U2,U3,...,UM), it uses
Family sends information to base station with then passing through up channel timesharing；The communication of downlink is completed with constant power in base station；If base station with
Channel between user remains unchanged in a duty cycle T,WithIt is expressed as the down channel and uplink of user j
The channel power of channel declines, if channel is awgn channel；
S12,Period in, base station is with power PBSIt is communicated in the form of broadcast with user j.At this point, user j
By power cutting techniques with splitting factor μjBy downlink reception to signal energy be divided into two parts, a part is used as energy
It collects, another part received signal energy is decoded acquisition corresponding information, and other users are then complete by the signal received
Portion is acquired as energy；
S2, base station transmitting power, noise power and energy transformation ratio are determined；Specifically include following sub-step:
S21, base station transmitting power P is determined according to actual hardware and base station ambient environmental conditionsBS；
S22, according to actual scene situation, determine user's j upstream and downstream channel noise powerUser's j equipment
Energy conversion efficiency is βj。
S3, define user's downlink business demand, obtain about the goal expression of user's downlink business demand and its about
Beam；Its concrete methods of realizing are as follows: according to the demand of practical application scene, determine the Minimum requirements D of user's j downlink data amountj
(bit/Hz)；Two parts are partitioned into according to the signal power that power segmentation principle as shown in Figure 3 will receive, wherein for believing
Ceasing decoded power isThe data volume expression formula that user's j downlink reception arrives are as follows:
Available downlink data amount constraint are as follows:
Wherein, j=1,2,3...M.
S4, the expression formula for calculating the energy that each user's downlink is gathered in；Here with specific reference to the original of power cutting techniques
Reason obtains.
Concrete methods of realizing are as follows: according to the available collected energy of user j of the illustraton of model 2 and Fig. 4 that are proposed in S1
Expression formula are as follows:
S5, the energy that user's downlink is collected into according to obtained in S4 expression formula obtain the expression of user uplink data amount
Formula；Specifically include following sub-step: according to the available user j upstream data of network model Fig. 2 and Fig. 4 proposed in S1
Measure expression formula are as follows:
That is,
S6, user uplink throughput fairness is completed by the upstream and downstream time slot and power splitting factor of combined optimization user
Property optimization, and obtain transmission strategy；Specifically include following sub-step:
S61, meet downlink business demand consumption while, be expected that by under the uplink for reasonably distributing each user
The power splitting factor of row transmission time slot and each user, so that user uplink data amount fairness is optimal, then it is corresponding
Mathematical model can be such that
0≤uj≤1
J=1,2 ..., M
S62, the problem are not a convex optimization problem, need to carry out it variable replacement, are enabledThen obtain
New optimization problem is as follows:
J=1,2 ..., M
New optimization problem is that the proof of convex problem is as follows:
Objective functionIt is functionPerspective
Function it is easy to show that by the basic conception of convex optimum theory (affine function, logarithmic function and min function)It is one
Stringent concave function.Since perspective function and original function keep identical convexity, so objective function is a stringent concave function.
And remaining constraint condition can prove convexity by seeking Hessian matrix, therefore the problem after variable replacement is one convex asks
Topic.
S63, have been proven that Solve problems are a convex optimization problems due to S62, it is possible to right by Lagrange
Even method obtains optimal solution.Due to the particularity of Solve problems, if solved to problem direct iteration, complexity is too high, then to S62
The problems in converted to obtain new problem it is as follows:
J=1,2 ..., M
Wherein R is the variable newly introduced.
S64, setting RmaxFor a biggish value, R is setminIt is 0；It takesPass through Lagrange duality
Method solves the problems in S63；If optimum results are greater than T, R is takenmax=R, on the contrary take Rmin=R, then be brought into S63 and ask
Solution, until Rmax-Rmin< ε (ε is preset error margin), finally obtains optimal solution.
S65, the solving result obtained according to S64 determine transmission strategy.
Method of the invention carries out mathematical modeling by the physics scene to a kind of several energy integrated networks, and according to convex excellent
Change theory to model solution, solves in number energy integrated network, the radio frequency letter that user's communication energy is sent from base station
Number, and in the case that user has downlink data amount demand, strict guarantee user uplink transmits fairness, maximizes user uplink number
The problem of according to amount.
Those of ordinary skill in the art will understand that the embodiments described herein, which is to help reader, understands this hair
Bright principle, it should be understood that protection scope of the present invention is not limited to such specific embodiments and embodiments.This field
Those of ordinary skill disclosed the technical disclosures can make according to the present invention and various not depart from the other each of essence of the invention
The specific variations and combinations of kind, these variations and combinations are still within the scope of the present invention.
Claims (6)
1. a kind of uplink user Throughput fairness optimization method in number energy integrated communication network, comprising the following steps:
S1, it determines network model, establishes the uplink and downlink network model in number energy integrated network；
Specifically include following sub-step:
S11, the integrated cellular network of number energy based on TDMA for considering single cell, base station and user are single antenna, base station BS
The integrated signal of number energy is sent to M user (U by down channel timesharing with the forms of broadcasting1,U2,U3,...,UM), user is then
Base station is sent information to by up channel timesharing；The communication of downlink is completed with constant power in base station；If base station and user
Between channel remained unchanged in a duty cycle T,WithIt is expressed as the down channel and up channel of user j
Channel power decline, if channel is awgn channel, and channel noise power is Pn；
S12,Period in, base station is with power PBSIt is communicated in the form of broadcast with user j, at this point, user j passes through
Power cutting techniques are with splitting factor μjBy downlink reception to signal energy be divided into two parts, a part is received as energy
Collection, another part received signal energy is decoded acquisition corresponding information, and other users are then by the signal received whole
It is acquired as energy；
S2, base station transmitting power, noise power and energy transformation ratio are determined；
S3, user's downlink business demand is defined, obtained about the goal expression of user's downlink business demand and its constraint；
S4, the expression formula for calculating the energy that each user's downlink is gathered in；
S5, the energy that user's downlink is collected into according to obtained in S4 expression formula obtain the expression formula of user uplink data amount；
S6, to complete user uplink Throughput fairness by the upstream and downstream time slot and power splitting factor of combined optimization user excellent
Change, and obtains transmission strategy.
2. the uplink user Throughput fairness optimization method according to claim 1 in number energy integrated communication network,
It is characterized in that, step S2 specifically includes following sub-step:
S21, base station transmitting power P is determined according to actual hardware and base station ambient environmental conditionsBS；
S22, according to actual scene situation, determine user's j upstream and downstream channel noise powerUser's j plant capacity
Transfer efficiency is βj。
3. the uplink user Throughput fairness optimization method according to claim 2 in number energy integrated communication network,
It is characterized in that, step S3 concrete methods of realizing are as follows: according to the demand of practical application scene, determine user's j downlink data amount
Minimum requirements Dj；Divide principle according to power to obtain, the data volume expression formula that user's j downlink reception arrives are as follows:
Available downlink data amount constraint are as follows:
Wherein, j=1,2,3...M.
4. the uplink user Throughput fairness optimization method according to claim 2 in number energy integrated communication network,
It is characterized in that, step S4 calculates the expression formula for the energy that each user's downlink is gathered in；With specific reference to power cutting techniques
Principle obtains the collected derivation of energy formula of user j are as follows:
5. the uplink user Throughput fairness optimization method according to claim 4 in number energy integrated communication network,
It is characterized in that, step S5 obtains user's j upstream data amount expression formula specifically:
That is,
6. the uplink user Throughput fairness optimization method according to claim 5 in number energy integrated communication network,
It is characterized in that, step S6 specifically includes following sub-step:
S61, meet downlink business demand consumption while, be expected that by reasonably distribute each user upstream and downstream pass
The power splitting factor of defeated time slot and each user, so that user uplink data amount fairness is optimal, then corresponding mathematics
Model can be such that
0≤uj≤1
J=1,2 ..., M
S62, variable replacement is carried out to it, enableIt is as follows then to obtain new optimization problem:
J=1,2 ..., M
New optimization problem is that the proof of convex problem is as follows:
Objective functionIt is functionPerspective function,It is a stringent concave function,
Since perspective function and original function keep identical convexity to pass through Hessian matrix so objective function is a stringent concave function
Orthotropicity prove functionIt is a stringent convex function, and it is remaining
Constraint condition is all affine constraint, therefore the problem after variable replacement is a convex problem；
S63, have been proven that Solve problems are a convex optimization problems due to S62, it is possible to pass through Lagrange duality method
Optimal solution is obtained, the problems in step S62 is converted to obtain new problem as follows:
J=1,2 ..., M
Wherein, R is the variable newly introduced；
S64, setting RmaxFor a biggish value, R is setminIt is 0；It takesPass through Lagrange duality method pair
The problems in S63 is solved；If optimum results are greater than T, R is takenmax=R, on the contrary take Rmin=R, then be brought into S63 and solve, directly
To Rmax-Rmin< ε (ε is preset error margin), finally obtains optimal solution；
S65, the solving result obtained according to S64 determine transmission strategy.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610728778.3A CN106330608B (en) | 2016-08-25 | 2016-08-25 | The uplink user Throughput fairness optimization method in number energy integrated communication network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610728778.3A CN106330608B (en) | 2016-08-25 | 2016-08-25 | The uplink user Throughput fairness optimization method in number energy integrated communication network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106330608A CN106330608A (en) | 2017-01-11 |
| CN106330608B true CN106330608B (en) | 2019-03-26 |
Family
ID=57791013
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610728778.3A Active CN106330608B (en) | 2016-08-25 | 2016-08-25 | The uplink user Throughput fairness optimization method in number energy integrated communication network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106330608B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110167106B (en) * | 2019-05-30 | 2020-06-26 | 电子科技大学 | Multi-user resource allocation method based on base station selection under fog architecture |
| CN111245484B (en) * | 2020-01-13 | 2022-01-25 | 电子科技大学中山学院 | Multidimensional resource joint scheduling optimization method for wireless energy transmission network |
| CN111246560B (en) * | 2020-03-25 | 2022-08-12 | 中国电子科技集团公司第五十四研究所 | Wireless energy-carrying communication time slot and power joint optimization method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104883741A (en) * | 2015-04-09 | 2015-09-02 | 南京邮电大学 | Resource allocation algorithm for cognitive radio network |
| CN105744628A (en) * | 2016-02-02 | 2016-07-06 | 电子科技大学 | Resource allocation optimization method of data and energy integrated communication networks |
| WO2016111687A1 (en) * | 2015-01-08 | 2016-07-14 | Hewlett-Packard Development Company, L.P. | Beamforming to a harvesting device |
| CN105848219A (en) * | 2016-05-28 | 2016-08-10 | 辽宁大学 | Wireless sensor network routing protocol for building load-balancing tree based on energy harvesting |
-
2016
- 2016-08-25 CN CN201610728778.3A patent/CN106330608B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016111687A1 (en) * | 2015-01-08 | 2016-07-14 | Hewlett-Packard Development Company, L.P. | Beamforming to a harvesting device |
| CN104883741A (en) * | 2015-04-09 | 2015-09-02 | 南京邮电大学 | Resource allocation algorithm for cognitive radio network |
| CN105744628A (en) * | 2016-02-02 | 2016-07-06 | 电子科技大学 | Resource allocation optimization method of data and energy integrated communication networks |
| CN105848219A (en) * | 2016-05-28 | 2016-08-10 | 辽宁大学 | Wireless sensor network routing protocol for building load-balancing tree based on energy harvesting |
Non-Patent Citations (2)
| Title |
|---|
| "无线携能通信系统中基于能量获取比例公平的波束成形设计";张海洋等;《物理学报》;20150228;说明书第[0040]-[0077]段 * |
| "无线系统中信息与功率联合传输技术的研究";张诚诚;《北京邮电大学硕士学位论文》;20150311;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106330608A (en) | 2017-01-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Han et al. | Powering mobile networks with green energy | |
| CN107087305B (en) | Terminal through communication resource management scheme based on energy collection | |
| CN105744628B (en) | A kind of resource allocation optimal method of several energy integrated communication networks | |
| CN107171701B (en) | Power distribution method of MassiveMIMO system based on hybrid energy acquisition | |
| CN110213826A (en) | Take can communication network robust resource allocation methods for isomery under a kind of non-ideal communication channel | |
| Ge et al. | Energy efficient optimization of wireless-powered 5G full duplex cellular networks: A mean field game approach | |
| CN104796900B (en) | D2D communication resource allocation methods in cellular network based on Game Theory | |
| CN110312269B (en) | Wireless energy-carrying communication system and method based on energy-information balance transmission | |
| CN106330608B (en) | The uplink user Throughput fairness optimization method in number energy integrated communication network | |
| CN106131939B (en) | A kind of power control optimal method of several energy integrated communication networks | |
| CN109088686A (en) | One kind being based on wireless messages and energy transmission method while 5G height frequency range | |
| Zaidi et al. | Solar energy empowered 5G cognitive metro-cellular networks | |
| CN105722179A (en) | Wireless energy transmission method for maximizing information throughput of cooperative relay system | |
| CN104640219A (en) | Interference coordinated sub-frame configuration method and device | |
| CN104796993A (en) | Stackelberg game-based cross-layer resource allocation method of heterogeneous network | |
| CN106304111A (en) | Isomery cellular network power allocation method based on energy acquisition relay station | |
| Jiang et al. | Q-learning based task offloading and resource allocation scheme for internet of vehicles | |
| CN109451569A (en) | A kind of resource allocation methods wirelessly taken in energy heterogeneous network | |
| CN103905106A (en) | Method for calculating multi-antenna and multicast beam forming vectors | |
| CN106357315B (en) | A kind of large-scale multiple antennas network energy efficiency resource allocation methods with incomplete CSI | |
| CN110139282A (en) | A kind of energy acquisition D2D communication resource allocation method neural network based | |
| CN106102173A (en) | Wireless backhaul based on multicast beam shaping and base station sub-clustering combined optimization method | |
| CN108521672A (en) | A kind of resource allocation methods of distributed wireless energy and the information transmission system | |
| CN105722203B (en) | Extensive high energy efficiency power distribution method of the antenna system based on particle swarm algorithm | |
| CN106376011B (en) | A kind of maximization uplink throughput method of several energy integrated communication networks |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |