CN112543472A - 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 PDF

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CN112543472A
CN112543472A CN202011308251.8A CN202011308251A CN112543472A CN 112543472 A CN112543472 A CN 112543472A CN 202011308251 A CN202011308251 A CN 202011308251A CN 112543472 A CN112543472 A CN 112543472A
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许晓荣
孙明杭
姚英彪
冯维
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Hangzhou Dianzi University
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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 joint 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 maximize the reachable rate of the cognitive SWIPT communication network.

Description

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)
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:
step 1, recognizing scene assumption and modeling of a DTPS protocol-based multi-relay time slot and power joint optimization method in SWIPT:
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 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 unity 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:
Figure BDA0002788953240000031
wherein the content of the first and second substances,
Figure BDA0002788953240000032
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 of
Figure BDA0002788953240000033
White 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:
Figure BDA0002788953240000034
optimal relay node RopThe energy collected was:
Figure BDA0002788953240000035
wherein eta represents energy conversion efficiency and satisfies 0 ≦ eta ≦ 1. Thus optimal relayingNode RopThe transmission power of (a) is:
Figure BDA0002788953240000036
optimal relay node RopThe signals used for information decoding are:
Figure BDA0002788953240000037
therefore, the received snr of the best relay node is:
Figure BDA0002788953240000038
meanwhile, the signals received by the cognitive receiving node (SR) are:
Figure BDA0002788953240000039
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 of
Figure BDA00027889532400000310
White 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), namely
Figure BDA0002788953240000041
The cognitive SWIPT network data is forwarded by a decode-and-forward (DF) protocol.
In the second time slot, the signal broadcasted by the optimal relay node is
Figure BDA0002788953240000042
PR receives as a signal
Figure BDA0002788953240000043
Wherein the content of the first and second substances,
Figure BDA0002788953240000044
channel coefficient, n, for best relay node to primary user receiving node PRDIs a mean value of 0 and a variance of
Figure BDA0002788953240000045
White gaussian noise.
Therefore, the receiving signal-to-noise ratio of the primary user receiving node PR is:
Figure BDA0002788953240000046
meanwhile, the signals received by the cognitive receiving node SR are:
Figure BDA0002788953240000047
wherein the content of the first and second substances,
Figure BDA0002788953240000048
channel coefficient n for optimal relay node to cognitive receiving node SRCRIs a mean value of 0 and a variance of
Figure BDA0002788953240000049
White gaussian noise.
Signal x received in the first time slot due to SRSThus, is derived from
Figure BDA00027889532400000410
In elimination of xSObtaining:
Figure BDA00027889532400000411
therefore, the receiving signal-to-noise ratio of the cognitive receiving node SR is:
Figure BDA00027889532400000412
in summary, the achievable rate (unit band information rate) of each node is:
Figure BDA00027889532400000413
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 cognitive receiving node 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:
Figure BDA0002788953240000051
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.
Step 2, selecting a strategy for the optimal relay node:
according to channel state information between a master user sending node PT and a cognitive SWIPT relay node
Figure BDA0002788953240000052
Channel state information between cognitive SWIPT relay node and master user receiving node PR
Figure BDA0002788953240000053
Channel state information between cognitive SWIPT relay node and cognitive receiving node SR
Figure BDA0002788953240000054
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:
Figure BDA0002788953240000055
step 3, optimizing the power division factor and the time slot allocation factor of the optimal SWIPT relay node:
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, decreasing with increasing ρ and decreasing with increasing β. Therefore, the overall achievable rate for a cognitive SWIPT network is expressed as:
Figure BDA0002788953240000061
wherein the content of the first and second substances,
Figure BDA0002788953240000062
Figure BDA0002788953240000063
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.
(3) End of the cycle, at this point
Figure BDA0002788953240000064
β*ρ*Are the optimum variables.
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 (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.
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. In the phase, 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 division 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 decodes information and collects energy of received signals.
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, the factor t is allocated with the time slot1The 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) shows network reachable sum rate and time slot allocation factor t in cognitive SWIPT1And (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, the factor t is allocated with the time slot1The 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 exists
Figure BDA0002788953240000091
Power 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 (4)

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;
and 3, optimizing the power division factor and the time slot allocation factor of the optimal SWIPT relay node.
2. The DTPS protocol-based multi-relay timeslot and power joint optimization method in cognitive SWIPT as claimed in claim 1, wherein the scenario assumption and modeling 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 ═ 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:
Figure FDA0002788953230000011
wherein the content of the first and second substances,
Figure FDA0002788953230000012
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 of
Figure FDA0002788953230000014
White 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:
Figure FDA0002788953230000013
optimal relay node RopThe energy collected was:
Figure FDA0002788953230000021
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:
Figure FDA0002788953230000022
optimal relay node RopThe signals used for information decoding are:
Figure FDA0002788953230000023
therefore, the received snr of the best relay node is:
Figure FDA0002788953230000024
meanwhile, the signals received by the cognitive receiving node SR are:
Figure FDA0002788953230000025
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 of
Figure FDA0002788953230000029
White gaussian noise of (1);
second time slot, optimal relay node RopUsing the first stage to collectAccording to the power distribution factor beta, dividing the collected energy into two parts; 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, namely
Figure FDA0002788953230000026
The method comprises the steps of forwarding cognitive SWIPT network data by a decoding forwarding protocol;
in the second time slot, the signals broadcast by the best relay node are:
Figure FDA0002788953230000027
the signals received by the PR are:
Figure FDA0002788953230000028
wherein the content of the first and second substances,
Figure FDA0002788953230000031
channel coefficient, n, for best relay node to primary user receiving node PRDIs a mean value of 0 and a variance of
Figure FDA0002788953230000032
White gaussian noise of (1);
therefore, the receiving signal-to-noise ratio of the primary user receiving node PR is:
Figure FDA0002788953230000033
meanwhile, the signals received by the cognitive receiving node SR are:
Figure FDA0002788953230000034
wherein，
Figure FDA0002788953230000035
Channel coefficient n for optimal relay node to cognitive receiving node SRCRIs a mean value of 0 and a variance of
Figure FDA0002788953230000036
White gaussian noise of (1);
signal x received in the first time slot due to SRSThus, is derived from
Figure FDA0002788953230000037
In elimination of xSObtaining:
Figure FDA0002788953230000038
therefore, the receiving signal-to-noise ratio of the cognitive receiving node SR is:
Figure FDA0002788953230000039
in summary, the reachable rate of each node is:
Figure FDA00027889532300000310
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 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:
Figure FDA0002788953230000041
wherein P isCPower consumption of a system circuit;
the overall reachable rate of the cognitive SWIPT network is maximized, namely:
Figure FDA0002788953230000042
wherein eta0Representing the lowest energy efficiency required by the system.
3. The DTPS protocol-based multi-relay timeslot and power joint optimization method in cognitive SWIPT as claimed in claim 2, wherein the optimal relay node selection policy 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 node
Figure FDA0002788953230000043
Channel state information between cognitive SWIPT relay node and master user receiving node PR
Figure FDA0002788953230000044
Channel state information between cognitive SWIPT relay node and cognitive receiving node SR
Figure FDA0002788953230000045
Wherein i is 1,2, …, N; in order to guarantee system energy efficiency, the method is equivalent to maximizing the overall reachable rate of the cognitive SWIPT network, and therefore the most important rate is selected according to the following criteriaThe best relay node:
Figure FDA0002788953230000046
4. the method of claim 3, wherein the optimization of the optimal SWIPT relay node power division factor and slot allocation factor is implemented 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:
Figure FDA0002788953230000051
wherein the content of the first and second substances,
Figure FDA0002788953230000052
Figure FDA0002788953230000053
Figure 1
Figure FDA0002788953230000055
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:
(a) updating
Figure FDA0002788953230000056
(b) Calculating the gradient repeatedly;
(3) end of the cycle, at this point
Figure FDA0002788953230000057
Are the optimum variables.
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