CN112543472B - Multi-relay time slot and power joint optimization method based on DTPS (delay tolerant packet switching) protocol in cognitive SWIPT (switched Power packet exchange protocol) - Google Patents
Multi-relay time slot and power joint optimization method based on DTPS (delay tolerant packet switching) protocol in cognitive SWIPT (switched Power packet exchange protocol) Download PDFInfo
- Publication number
- CN112543472B CN112543472B CN202011308251.8A CN202011308251A CN112543472B CN 112543472 B CN112543472 B CN 112543472B CN 202011308251 A CN202011308251 A CN 202011308251A CN 112543472 B CN112543472 B CN 112543472B
- Authority
- CN
- China
- Prior art keywords
- cognitive
- swipt
- node
- relay node
- time slot
- 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
- 230000001149 cognitive effect Effects 0.000 title claims abstract description 99
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000005457 optimization Methods 0.000 title claims abstract description 24
- 238000004891 communication Methods 0.000 claims abstract description 24
- 230000007423 decrease Effects 0.000 claims description 12
- 230000005540 biological transmission Effects 0.000 claims description 11
- 238000006243 chemical reaction Methods 0.000 claims description 2
- 230000009194 climbing Effects 0.000 claims description 2
- 238000013461 design Methods 0.000 claims description 2
- 230000008030 elimination Effects 0.000 claims description 2
- 238000003379 elimination reaction Methods 0.000 claims description 2
- 238000005562 fading Methods 0.000 claims description 2
- 238000003306 harvesting Methods 0.000 claims description 2
- RZWWGOCLMSGROE-UHFFFAOYSA-N n-(2,6-dichlorophenyl)-5,7-dimethyl-[1,2,4]triazolo[1,5-a]pyrimidine-2-sulfonamide Chemical compound N1=C2N=C(C)C=C(C)N2N=C1S(=O)(=O)NC1=C(Cl)C=CC=C1Cl RZWWGOCLMSGROE-UHFFFAOYSA-N 0.000 claims 1
- 238000011160 research Methods 0.000 abstract description 4
- 238000012546 transfer Methods 0.000 abstract description 2
- 238000001228 spectrum Methods 0.000 description 13
- 238000005516 engineering process Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000003247 decreasing effect Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000004576 sand Substances 0.000 description 1
- 230000001225 therapeutic effect Effects 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/06—Testing, supervising or monitoring using simulated traffic
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
-
- 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
- H04W52/38—TPC being performed in particular situations
- H04W52/46—TPC being performed in particular situations in multi hop networks, e.g. wireless relay networks
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention discloses a DTPS (delay tolerant packet switching) protocol-based multi-relay time slot and power joint optimization method in cognitive SWIPT (switched Power packet transfer). The cognitive SWIPT communication network comprises a plurality of cognitive SWIPT relay nodes provided with power division receivers, a multi-relay time slot and power joint optimization method based on a dynamic time slot power division protocol in cognitive SWIPT is researched, and power division factors, time slot distribution factors and power distribution coefficients are jointly optimized. The method establishes an optimization model by taking the maximum network and the rate of the cognitive SWIPT communication network as optimization targets, and enables the system performance to meet the required requirements through the selection of the optimal relay node and the combined optimization of the power division factor, the time slot distribution factor and the power distribution coefficient of the optimal relay node. Research shows that with the increase of the first time slot factor and the power division factor, the reachable rate of the cognitive SWIPT communication network is increased firstly and then reduced. The method can maximally recognize the reachable rate of the SWIPT communication network.
Description
Technical Field
The invention belongs to the technical field of Information and communication engineering, and provides a Dynamic Time Power Splitting (DTPS) protocol-based multi-relay Time slot and Power joint optimization method in cognitive Wireless energy-carrying communication (SWIPT).
Background
With the development of wireless communication technology, the amount of wireless traffic data is increasing explosively. Various wireless devices require certain spectrum resources to transmit data, which brings great challenges to scarce spectrum resources. The lack of spectrum resources is not caused by the lack of spectrum physics, but is caused by the low utilization rate of the spectrum and the strict spectrum allocation policy. Therefore, how to improve the spectrum efficiency has become one of the problems that needs to be solved in the current wireless communication. Currently, a Cognitive Radio (CR) technology, which is one of the 5G key technologies, is considered as one of effective means for improving spectrum efficiency. At present, most electronic equipment is powered by a battery with limited capacity, but the electronic equipment in some special scenes cannot be powered continuously by the battery, such as electronic therapeutic instruments built in a human body. Therefore, how to collect renewable energy sources such as solar energy, wind energy, geothermal energy, vibration energy and the like from the environment is always a research hotspot, although natural energy sources are taken as sources for collecting energy to further save the energy consumption of the network, the natural energy sources are greatly influenced by external factors such as weather and the like, the collection efficiency is very limited, and the stability of a communication network is influenced.
The information transmission by using electromagnetic waves has been successfully carried out, and the energy of the electromagnetic waves is a potential green energy source, but the energy transmission by using the electromagnetic waves has not been greatly developed, wherein the low energy collection efficiency is a key problem to be solved urgently. In recent years, researchers have noticed that Radio Frequency (RF) signals can carry information and energy as electromagnetic waves, and along with the development of RF signal energy collecting circuits, the RF signal energy collecting efficiency is greatly improved, and people see the possibility of simultaneous transmission of information and energy. Wireless portable communication (SWIPT) technology has emerged as a combination of Wireless Information Transmission (WIT) and Wireless Power Transmission (WPT).
The SWIPT provides a new wireless energy collection method for nodes in the energy-limited network, and simultaneous transmission of information and energy is achieved. The CR provides a new solution for the contradiction between the scarcity of spectrum resources and the low spectrum utilization rate. By combining the two, the cognitive SWIPT can fully utilize the characteristic that CR opportunity utilizes spectrum resources and SWIPT information and Energy to transmit simultaneously, and is expected to improve the Spectrum Efficiency (SE) and the Energy Efficiency (EE) of a wireless network at the same time. The cognitive SWIPT can guarantee the energy efficiency of a system while ensuring the spectrum effect, and is one of the development directions of the future high-energy-efficiency cognitive wireless communication technology.
Disclosure of Invention
The invention provides a DTPS (delay tolerant packet switched) protocol-based multi-relay time slot and power joint optimization method in cognitive SWIPT (switched Power packet transfer) aiming at the optimization target of maximizing network and rate (unit frequency band information rate) in a cognitive SWIPT relay network with a plurality of power division receivers, and provides a specific process of the method. The method relates to a selection strategy of an optimal relay node in a cognitive SWIPT network, a cognitive SWIPT time slot allocation factor, an optimal relay node power division factor and a joint optimization of an optimal relay node power allocation coefficient.
The technical scheme of the invention comprises the following steps:
without loss of generality, before describing the design strategy in detail, the following assumptions are made:
(1) assuming that the entire communication slot is normalized to 1, the first slot is t1The first time slot is t2And t is1+t2=1;
(2) Legal nodes can obtain state information of all channels, and all channels in the system are subject to Rayleigh flat fading;
(3) in the SWIPT network, a master user sending node and a master user target node cannot directly communicate and need to be forwarded through a relay node.
In the first time slot, Primary Transmitter (PT) is transmitted by Primary user with power PSBroadcasting a signal x of unit powerS. N cognitive SWIPT relays Ri(i-1, 2, …, N) can receive the signal xSThe cognitive SWIPT network selects the optimal relay node R from the N relay nodes according to the optimal relay node selection strategyopThe best relay node RopThe signal received in the first time slot is:
wherein,sending a node PT to an optimal relay node R for a primary useropOf the channel coefficient nRIs a mean of 0 and a variance ofWhite gaussian noise.
Optimal relay node RopThe receiver divides the received signal into two parts with a power division factor ρ (0 ≦ ρ ≦ 1): one part is used for information decoding and the other part is used for energy collection.
Optimal relay node RopThe signals for energy harvesting were:
optimal relay node RopThe energy collected was:
wherein eta represents energy conversion efficiency and satisfies 0 ≦ eta ≦ 1. Thus the best relay node RopThe transmission power of (a) is:
optimal relay node RopThe signals used for information decoding are:
therefore, the received snr of the best relay node is:
meanwhile, the signals received by the cognitive receiving node (SR) are:
wherein h isSCChannel coefficient, n, for a primary user sending node PT to a cognitive receiving node SRCIs a mean of 0 and a variance ofWhite gaussian noise.
Second time slot, optimal relay node RopUsing the energy collected in the first stage, the collected energy is divided into two portions according to a power division factor β. One part is used for sending main information to a Primary user receiving node (PR), and the other part is used for sending cognitive information to a cognitive receiving node (SR), namelyIt forwards cognitive SWIPT network data in a decode-and-forward (DF) protocol.
In the second time slot, the signal broadcasted by the optimal relay node is
PR receives as a signal
Wherein,channel coefficient, n, for the best relay node to the primary user receiving node PRDIs a mean value of 0 and a variance ofWhite gaussian noise.
Therefore, the receiving signal-to-noise ratio of the primary user receiving node PR is:
meanwhile, the signals received by the cognitive receiving node SR are:
wherein,channel coefficient n for optimal relay node to cognitive receiving node SRCRIs a mean value of 0 and a variance ofWhite gaussian noise.
Signal x received in the first time slot due to SRSThus, is derived fromIn elimination of xSAnd obtaining:
therefore, the receiving signal-to-noise ratio of the cognitive receiving node SR is:
in summary, the achievable rate (unit band information rate) of each node is:
γD=(1-t1)log2(1+SNRD) (15)
γC=(1-t1)log2(1+SNRC) (16)
wherein gamma isRIndicating the best relay node RopAchievable rate, gammaDIndicating the reach rate, gamma, of the primary user receiving node PRCAnd the reachable rate of the SR is shown.
Defining the main network reachable rate as:
RS=min(γD,γR) (17)
the overall reachable rate of the cognitive SWIPT network is defined as follows:
R=min(RS,γC) (18)
defining a system energy efficiency ηEEIs represented as follows:
wherein P isCThe power consumption of the system circuit.
Under the condition of guaranteeing the energy efficiency of the system, the overall reachable rate of the cognitive SWIPT network is maximized, namely:
max R
s.t.0≤t1≤1
0≤ρ≤1 (20)
0≤β≤1
ηEE≥η0
wherein eta0Representing the lowest energy efficiency required by the system.
according to channel state information between a master user sending node PT and a cognitive SWIPT relay nodeChannel state information between cognitive SWIPT relay node and master user receiving node PRChannel state information between cognitive SWIPT relay node and cognitive receiving node SRIn order to guarantee system energy efficiency, the overall reachable rate of the cognitive SWIPT network is maximized, and therefore the optimal relay node is selected according to the following criteria:
the optimal SWIPT relay node forwards the signal using the DF protocol. As can be seen from the above, γRWith t1Increases and decreases with increasing ρ. Gamma rayDWith t1Increases of (b) increase first and then decrease, increases with increasing ρ, and increases with increasing β. Gamma rayCWith t1Increases of (b) increase first and then decrease, decreases with increasing ρ, and decreases with increasing β. Therefore, the overall achievable rate for a cognitive SWIPT network is expressed as:
finally, R can be expressed as a function of beta, and the optimal solution is found by using a gradient climbing method. The solving method is as follows:
(1) initialization: randomly giving an initial value beta0The margin δ is 0.001 and the step v is 0.01.
(2) Calculating a gradient with respect to β, and if the gradient is greater than a tolerance and the energy efficiency is greater than a required minimum energy efficiency, performing the following loop:
(a) updating beta0=β0+v▽β0;
(b) The gradient is calculated again.
The invention has the following beneficial effects:
the cognitive wireless energy-carrying communication (SWIPT) is taken as a research background, a plurality of cognitive SWIPT relay nodes provided with power division receivers exist in a cognitive SWIPT communication network, a multi-relay time slot and power joint optimization method based on a dynamic time slot power Division (DTPS) protocol in the cognitive SWIPT is researched, and power division factors, time slot distribution factors and power distribution coefficients are jointly optimized. The method establishes an optimization model by taking the maximum network and the rate of the cognitive SWIPT communication network as optimization targets, and enables the system performance to meet the required requirements through the selection of the optimal relay node and the joint optimization of the power division factor, the time slot distribution factor and the power distribution coefficient of the optimal relay node. The method analyzes the influence of the power division factor of the optimal relay node, the power division factor of the sending node and the transmission time slot of the cognitive SWIPT communication network on the network reachable rate. Research shows that with the increase of the first time slot factor and the power division factor, the reachable rate of the cognitive SWIPT communication network is increased firstly and then reduced. Under the condition of a plurality of cognitive SWIPT relay nodes provided with power dividing receivers, the method can maximize the reachable rate of the cognitive SWIPT communication network.
Drawings
Fig. 1(a) is a communication model diagram of a first stage of a DTPS protocol-based multi-relay timeslot and power joint optimization method in cognitive SWIPT.
Fig. 1(b) is a second-stage communication model diagram of a DTPS protocol-based multi-relay timeslot and power joint optimization method in cognitive SWIPT.
Fig. 2 shows a transmission protocol of a DTPS protocol-based multi-relay timeslot and power joint optimization method in cognitive SWIPT.
FIG. 3(a) shows the reachable rate of each node and the time slot allocation factor t in the cognitive SWIPT1A graph of the relationship (c).
Fig. 3(b) is a graph of the relationship between the reachable rate of each node and the power division factor ρ in the cognitive SWIPT.
Fig. 3(c) is a graph of the relationship between the reachable rate of each node and the power distribution coefficient β in the cognitive SWIPT.
FIG. 4(a) shows network reachable sum rate and time slot allocation factor t in cognitive SWIPT1And (4) a relational graph.
Fig. 4(b) is a graph of the relationship between the network reachable sum rate and the power division factor ρ in the cognitive SWIPT.
Fig. 4(c) is a graph of the relationship between the network reachable sum rate and the power distribution coefficient beta in the cognitive SWIPT.
Detailed Description
Fig. 1(a) is a communication model diagram of a first stage of a DTPS protocol-based multi-relay timeslot and power joint optimization method in cognitive SWIPT. At this stage, a master user sending node broadcasts data signals to a plurality of cognitive SWIPT relay nodes and cognitive receiving nodes which are provided with power dividing receivers. The cognitive SWIPT communication network selects an optimal cognitive SWIPT relay node according to an optimal relay node selection strategy, and the optimal cognitive SWIPT relay node performs information decoding and energy collection on a received signal.
Fig. 1(b) is a second-stage communication model diagram of a DTPS protocol-based multi-relay timeslot and power joint optimization method in cognitive SWIPT. At this stage, the optimal cognitive SWIPT relay node receives data forwarded by the target node and the cognitive receiving node to the master user.
Fig. 2 shows a transmission protocol of a DTPS protocol-based multi-relay timeslot and power joint optimization method in cognitive SWIPT. In the first time slot, a master user sending node broadcasts information to a cognitive relay and a cognitive receiving node. The cognitive SWIPT relay adopts a PS receiver to divide information into two parts, namely information decoding and energy collection by a power division factor rho; in the second time slot, the optimal cognitive SWIPT relay respectively receives the collected energy to a target node and a cognitive receiving node for forwarding information by a power distribution coefficient beta.
FIG. 3(a) shows the reachable rate of each node and the time slot allocation factor t in the cognitive SWIPT1And (5) a relational graph. As can be seen, with the slot allocation factor t1The information rate of the optimal cognitive SWIPT relay node is gradually increased, the information rate of the target node received by the master user is increased and then decreased, and the information rate of the cognitive receiving node is also increased and then decreased. Time slot distribution factor t of three intersection points1Is 0.35.
Fig. 3(b) is a graph of the relationship between the reachable rate of each node and the power division factor ρ in the cognitive SWIPT. As can be seen from the figure, as the power division factor ρ increases, the information rate of the optimal cognitive SWIPT relay node gradually decreases, the information rate of the target node received by the master user gradually increases, and the information rate of the cognitive receiving node also gradually increases. The power division factor ρ at the intersection of the three is 0.78.
Fig. 3(c) shows that in the cognitive SWIPT, the reachable rate of each node is related to the power distribution coefficient β. The information rate of the optimal relay node is irrelevant to the power distribution coefficient, so that the information rates of the primary user receiving the target node and the cognitive receiving node are only given. As can be seen from the graph, as the power distribution coefficient β increases, the information rate of the primary user receiving the destination node gradually decreases, and the information rate of the cognitive receiving node gradually increases. The power distribution coefficient β at the intersection of the two is 0.9.
FIG. 4(a) For cognizing the SWIPT, the network reachable rate and the time slot are allocated with a factor t1And (5) a relational graph. Fig. 4(b) is a graph of the relationship between the network reachable sum rate and the power division factor ρ in the cognitive SWIPT. Fig. 4(c) is a graph of the relationship between the network reachable sum rate and the power distribution coefficient beta in the cognitive SWIPT. As can be seen, with the slot allocation factor t1The power division factor rho and the power distribution coefficient beta are increased, the network reachable rate is increased and then decreased, and therefore the optimal time slot distribution factor existsPower division factor p*0.78, power distribution coefficient beta*0.9, the network can reach the maximum rate of 1.4 bps/Hz.
It should be understood by those skilled in the art that the above embodiments are only used for illustrating the present invention and are not to be taken as limiting the present invention, and the changes and modifications of the above embodiments are within the scope of the present invention.
Claims (1)
1. A DTPS protocol-based multi-relay time slot and power joint optimization method in cognitive SWIPT is characterized by comprising the following steps:
step 1, scene assumption and modeling;
step 2, selecting a strategy for the optimal relay node;
step 3, optimizing a power division factor and a time slot allocation factor of the optimal SWIPT relay node;
the scene assumption and modeling described in step 1 are specifically implemented as follows:
without loss of generality, before describing the design strategy in detail, the following assumptions are made:
(1) assuming that the entire communication time slot is normalized to 1, the first time slot is t1The first time slot is t2And t is1+t2=1;
(2) Legal nodes can obtain state information of all channels, and all channels in the system are subject to Rayleigh flat fading;
(3) in the SWIPT network, a master user sending node and a master user receiving node cannot directly communicate and need to be forwarded through a relay node;
in the first time slot, the master user sends the node PT with power PSBroadcasting a signal x of unity powerS(ii) a N cognitive SWIPT relay nodes RiCan receive signal xSWherein i is 1,2, …, N; the cognitive SWIPT network selects the optimal relay node R from the N relay nodes according to the optimal relay node selection strategyopThe best relay node RopThe signal received in the first time slot is:
wherein,sending a node PT to an optimal relay node R for a primary useropOf the channel coefficient nRIs a mean of 0 and a variance ofWhite gaussian noise of (1);
optimal relay node RopThe receiver divides the received signal into two parts with a power division factor p: one part is used for information decoding, and the other part is used for energy collection;
optimal relay node RopThe signals for energy harvesting were:
optimal relay node RopThe energy collected was:
wherein eta represents energy conversion efficiency, and satisfies 0 ≤ eta ≤ 1 and 0 ≤ ρ ≤ 1; thus the best relay node RopThe transmission power of (a) is:
optimal relay node RopThe signals used for information decoding are:
therefore, the received snr of the best relay node is:
meanwhile, the signals received by the cognitive receiving node SR are:
wherein h isSCChannel coefficient, n, for a primary user sending node PT to a cognitive receiving node SRCIs a mean of 0 and a variance ofWhite gaussian noise of (1);
second time slot, optimal relay node RopDividing the collected energy into two parts according to a power distribution factor beta by using the energy collected in the first stage; one part is used for sending main information to the main user receiving node PR, and the other part is used for sending cognitive information to the cognitive receiving node SR, namelyIt forwards the protocol with decodingTransmitting cognitive SWIPT network data;
in the second time slot, the signals broadcast by the best relay node are:
the signals received by the PR are:
wherein,channel coefficient, n, for best relay node to primary user receiving node PRDIs a mean value of 0 and a variance ofWhite gaussian noise;
therefore, the receiving signal-to-noise ratio of the primary user receiving node PR is:
meanwhile, the signals received by the cognitive receiving node SR are:
wherein,channel coefficient n for optimal relay node to cognitive receiving node SRCRMean value of 0 and variance ofWhite gaussian noise of (1);
signal x received in the first time slot due to SRSThus coming fromMiddle elimination of xSObtaining:
therefore, the receiving signal-to-noise ratio of the cognitive receiving node SR is:
in summary, the reachable rate of each node is:
γD=(1-t1)log2(1+SNRD) (15)
γC=(1-t1)log2(1+SNRC) (16)
wherein gamma isRIndicating the best relay node RopAchievable rate, gammaDIndicating the reachable rate, y, of the primary user receiving node PRCRepresenting the SR reachable rate of the cognitive receiving node;
defining the main network reachable rate as:
RS=min(γD,γR) (17)
the overall reachable rate of the cognitive SWIPT network is defined as follows:
R=min(RS,γC) (18)
defining a system energy efficiency ηEEIs represented as follows:
wherein P isCPower consumption of a system circuit;
the overall reachable rate of the cognitive SWIPT network is maximized, namely:
wherein eta0Represents the lowest energy efficiency required by the system;
the optimal relay node selection strategy described in step 2 is specifically implemented as follows:
according to channel state information between a master user sending node PT and a cognitive SWIPT relay nodeChannel state information between cognitive SWIPT relay node and master user receiving node PRChannel state information between cognitive SWIPT relay node and cognitive receiving node SRWherein i is 1,2, …, N; in order to guarantee system energy efficiency, the overall reachable rate of the cognitive SWIPT network is maximized, and therefore the optimal relay node is selected according to the following criteria:
the optimization of the power division factor and the time slot allocation factor of the optimal SWIPT relay node in the step 3 is specifically realized as follows:
the optimal SWIPT relay node forwards the signal by using a DF protocol; as can be seen from the above, γRWith t1Increases with increasing ρ and decreases with increasing ρ; gamma rayDWith t1Increases of (b) increase first and then decrease, increases with increasing ρ, and increases with increasing β; gamma rayCWith t1Increases of (b) increase first and then decrease, decreases with increasing ρ, and decreases with increasing β; therefore, the overall achievable rate for a cognitive SWIPT network is expressed as:
finally, R is expressed as a function of beta, and an optimal solution is searched by using a gradient climbing method; the solving method is as follows:
(1) initialization: randomly giving an initial value beta0Margin delta is 0.001, step length v is 0.01;
(2) calculating a gradient with respect to β, and if the gradient is greater than a tolerance and the energy efficiency is greater than a required minimum energy efficiency, performing the following loop:
(b) Calculating the gradient repeatedly;
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202011308251.8A CN112543472B (en) | 2020-11-20 | 2020-11-20 | Multi-relay time slot and power joint optimization method based on DTPS (delay tolerant packet switching) protocol in cognitive SWIPT (switched Power packet exchange protocol) |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202011308251.8A CN112543472B (en) | 2020-11-20 | 2020-11-20 | Multi-relay time slot and power joint optimization method based on DTPS (delay tolerant packet switching) protocol in cognitive SWIPT (switched Power packet exchange protocol) |
Publications (2)
Publication Number | Publication Date |
---|---|
CN112543472A CN112543472A (en) | 2021-03-23 |
CN112543472B true CN112543472B (en) | 2022-06-21 |
Family
ID=75014821
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202011308251.8A Active CN112543472B (en) | 2020-11-20 | 2020-11-20 | Multi-relay time slot and power joint optimization method based on DTPS (delay tolerant packet switching) protocol in cognitive SWIPT (switched Power packet exchange protocol) |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN112543472B (en) |
Families Citing this family (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113518359B (en) * | 2021-04-29 | 2023-02-07 | 东北大学 | Optimization method for combining multi-relay selection and power division factor based on speed |
CN113490277B (en) * | 2021-07-07 | 2024-02-02 | 杭州电子科技大学 | SWIPT-based energy allocation and time slot switching coefficient joint optimization method in H-CRAN |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108601042A (en) * | 2018-03-27 | 2018-09-28 | 杭州电子科技大学 | Relaying auxiliary information based on time slot switching and energy transmission method |
CN109787828A (en) * | 2019-01-18 | 2019-05-21 | 杭州电子科技大学 | Recognize the selection of SWIPT optimal node and beam forming co-design method |
CN110062377A (en) * | 2019-03-22 | 2019-07-26 | 杭州电子科技大学 | Power splitting factor and beam forming combined optimization method in capable of communicating are taken safely |
US10454320B1 (en) * | 2018-08-29 | 2019-10-22 | King Fahd University Of Petroleum And Minerals | SWIPT network system with single antenna destination nodes |
CN111884688A (en) * | 2020-06-23 | 2020-11-03 | 杭州电子科技大学 | OPS structure-based R-E domain optimization method for multi-node multi-antenna SWIPT network |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11405873B2 (en) * | 2017-05-01 | 2022-08-02 | Lg Electronics Inc. | Device and method for performing authentication in wireless power transmission system |
-
2020
- 2020-11-20 CN CN202011308251.8A patent/CN112543472B/en active Active
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108601042A (en) * | 2018-03-27 | 2018-09-28 | 杭州电子科技大学 | Relaying auxiliary information based on time slot switching and energy transmission method |
US10454320B1 (en) * | 2018-08-29 | 2019-10-22 | King Fahd University Of Petroleum And Minerals | SWIPT network system with single antenna destination nodes |
CN109787828A (en) * | 2019-01-18 | 2019-05-21 | 杭州电子科技大学 | Recognize the selection of SWIPT optimal node and beam forming co-design method |
CN110062377A (en) * | 2019-03-22 | 2019-07-26 | 杭州电子科技大学 | Power splitting factor and beam forming combined optimization method in capable of communicating are taken safely |
CN111884688A (en) * | 2020-06-23 | 2020-11-03 | 杭州电子科技大学 | OPS structure-based R-E domain optimization method for multi-node multi-antenna SWIPT network |
Non-Patent Citations (4)
Title |
---|
Optimization of power allocation and outage probability of cooperative relay networks;Jiawen Wu;《2016 8th International Conference on Wireless Communications & Signal Processing (WCSP)》;20161124;全文 * |
Simultaneous wireless information and power transfer for cognitive two-path successive relaying networks;Zihe Zhang;《2017 IEEE/CIC International Conference on Communications in China (ICCC)》;20180405;全文 * |
无线携能网络中一种基于时隙切换的中继辅助信能同传协议;洪鑫龙;《信号处理》;20181230;第34卷(第12期);全文 * |
沈霖晖.无线携能网络中一种非线性能量收集与信息传输均衡策略.《信号处理》.2020, * |
Also Published As
Publication number | Publication date |
---|---|
CN112543472A (en) | 2021-03-23 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN105610485B (en) | A kind of wireless relay communication system is taken can transmission method | |
CN109041196B (en) | Resource joint allocation method based on energy efficiency maximization in NOMA energy-carrying communication system | |
CN107508628B (en) | Cooperative transmission method in radio frequency energy collection relay network | |
CN108601042B (en) | Relay auxiliary information and energy transmission method based on time slot switching | |
CN107948983B (en) | Energy acquisition small base station resource allocation method based on alliance game | |
CN105451343B (en) | A kind of more junction network resource allocation methods based on energy acquisition | |
CN110519848B (en) | Joint resource allocation method of cognitive relay wireless sensor network | |
CN112543472B (en) | Multi-relay time slot and power joint optimization method based on DTPS (delay tolerant packet switching) protocol in cognitive SWIPT (switched Power packet exchange protocol) | |
CN108811025B (en) | Wireless energy-carrying communication cooperative transmission scheme based on relay energy storage | |
CN107332602B (en) | Full duplex relaying energy under the conditions of energy constraint reclaims communication means and system certainly | |
CN109714806A (en) | A kind of wireless power junction network optimization method of non-orthogonal multiple access | |
CN110602758B (en) | Cognitive energy-carrying relay communication method based on multi-slot wireless energy collection | |
CN112887042B (en) | Energy-carrying communication network user pairing method based on non-orthogonal multiple access | |
CN105307271A (en) | Multi-antenna communication system circulating energy collection method with maximum throughput capacity | |
CN110278576A (en) | A kind of wireless energy acquisition non-orthogonal multiple access system resource allocation methods | |
CN106304111A (en) | Isomery cellular network power allocation method based on energy acquisition relay station | |
CN105451315A (en) | Serial energy acquisition method with characteristic of throughput maximization | |
CN111542109A (en) | User peer-to-peer cooperation method based on power division under non-orthogonal multiple access | |
Tanabe et al. | Energy-aware receiver-driven medium access control protocol for wireless energy-harvesting sensor networks | |
CN114390652A (en) | Trapped user terminal equipment energy acquisition and information transmission method | |
CN110012526A (en) | It is a kind of that the node sleep dispatching method that can be communicated wirelessly is taken based on time slot switching | |
CN112261662B (en) | Transmission method for improving energy efficiency of NOMA cooperative communication system | |
CN109275149B (en) | Resource allocation method based on energy accumulation in cognitive wireless power supply network | |
CN113490238B (en) | Relay selection optimization method for FD multi-relay cooperative SWIPT network | |
CN113490277B (en) | SWIPT-based energy allocation and time slot switching coefficient joint optimization method in H-CRAN |
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 |