CN109672997B - Industrial Internet of things multi-dimensional resource joint optimization algorithm based on energy collection - Google Patents

Industrial Internet of things multi-dimensional resource joint optimization algorithm based on energy collection Download PDF

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CN109672997B
CN109672997B CN201910051575.9A CN201910051575A CN109672997B CN 109672997 B CN109672997 B CN 109672997B CN 201910051575 A CN201910051575 A CN 201910051575A CN 109672997 B CN109672997 B CN 109672997B
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周振宇
张春天
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North China Electric Power University
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Abstract

The invention relates to a high-energy-efficiency resource allocation optimization algorithm applied to an industrial Internet of things scene based on energy collection, and aims at the problems of low energy efficiency, shortage of spectrum resources and the like in a machine-to-machine (M2M) communication network, the energy collection is considered to be combined with cognitive M2M communication, and an M2M transmitter (M2M-TX) transmits data to a corresponding receiver (M2M-RX) by utilizing radio frequency energy transmitted by a base station and multiplexing the spectrum resources of a cellular link. Firstly, establishing a three-dimensional matching relation between M2M-TX, M2M-RX and a Resource Block (RB); secondly, solving the sub-problems of joint power control and time distribution through an Alternative Optimization (AO), nonlinear fractional programming and linear programming algorithm to obtain a corresponding preference list; and finally, solving the sub-problems of joint channel selection and peer node discovery by a three-dimensional matching algorithm based on a pricing mechanism based on the acquired preference list so as to acquire an optimal spectrum resource allocation scheme.

Description

Industrial Internet of things multi-dimensional resource joint optimization algorithm based on energy collection
Technical Field
The invention belongs to the field of wireless communication, and particularly relates to a high-energy-efficiency resource allocation optimization algorithm applied to an industrial internet of things scene based on energy collection, aiming at the problems of low energy efficiency, shortage of spectrum resources and the like in a machine-to-machine (M2M) communication network, the combination of energy collection and cognitive M2M communication is considered, and an M2M transmitter (M2M-TX) transmits data to a corresponding receiver (M2M-RX) by utilizing radio frequency energy transmitted by a base station and multiplexing the spectrum resources of a cellular link. Firstly, establishing a three-dimensional matching relation between M2M-TX, M2M-RX and a Resource Block (RB); secondly, solving the sub-problems of joint power control and time distribution through an Alternative Optimization (AO), nonlinear fractional programming and linear programming algorithm to obtain a corresponding preference list; and finally, solving the sub-problems of joint channel selection and peer node discovery by a three-dimensional matching algorithm based on a pricing mechanism based on the acquired preference list so as to acquire an optimal spectrum resource allocation scheme.
Background art:
industrial internet of things (IIoT) has become the label for the next generation of new industrial revolution interconnecting the internet and industrial devices. The intelligent network creates an interconnected global network, and can support mutual information exchange and mutual cooperation among new applications such as smart cities, intelligent traffic systems and intelligent power grids. However, as the number of devices in the internet of things increases dramatically, it puts higher demands on communication technologies and protocols: the method is suitable for large-scale data exchange, can provide connection anytime and anywhere, and supports intelligent resource allocation. Machine-to-machine (M2M) communication is a key technology for implementing the vision of the internet of things, and autonomous mutual communication between machines can be realized without human intervention.
Despite the advantages of the M2M communication network, its wide deployment still faces some challenges. First, currently, spectrum resources are scarce, and most of the available spectrum resources are already occupied by supporting human-to-human (H2H) communication applications (e.g., voice and video). To alleviate spectrum usage pressures, spectrum may be reallocated for M2M communications by multiplexing spectrum resources of a legacy H2H communication system (e.g., a 3G/4G cellular network). However, sharing of spectrum resources between H2H Cellular Users (CUs) and M2M devices inevitably results in co-channel interference between them, which, unless managed well, may degrade system performance. Second, M2M devices have very limited installed battery capacity, and periodic replacement of the batteries of M2M devices deployed in large areas can be very expensive. Therefore, optimizing the energy efficiency of the M2M transmitter (M2M-TX) by intelligent resource allocation while guaranteeing QoS for the M2M communication link and the cellular communication link is of paramount importance.
The invention content is as follows:
the invention provides a high-energy-efficiency resource allocation optimization algorithm in an industrial internet of things scene based on energy collection, aiming at maximizing the energy efficiency of transmitters M2M-TXs in an M2M single-cell communication network scene based on energy collection. The algorithm firstly establishes a three-dimensional matching relationship between M2M-TX, M2M-RX and a resource block RB; secondly, solving the sub-problems of joint power control and time distribution through an Alternative Optimization (AO), nonlinear fractional programming and linear programming algorithm to obtain a corresponding preference list; and finally, solving the sub-problems of joint channel selection and peer node discovery by a three-dimensional matching algorithm based on a pricing mechanism based on the acquired preference list so as to acquire an optimal spectrum resource allocation scheme. The specific process is as follows:
1) FIG. 1 is a diagram of an energy harvesting-based M2M single-cell communication network system model, which has K CUs, N M2M-TXs, and M2M-RXs. Each M2M-TX has the functions of energy collection and data transmission, and adopts a channel mode with block fading characteristicsEach time block is divided into two time slots for energy collection and data transmission. In the energy harvesting slots, M2M-TXs harvests energy from adjacent wireless Radio Frequency (RF) signals; during the data transmission time slot, M2M-TXs uses the stored and collected energy to transmit information to the corresponding M2M-RXs. Transmitter TX considering Downlink energy harvesting and Spectrum multiplexing scenariosnBy multiplexing at most one cellular subscriber CkSpectrum resource block RBkTo a corresponding receiver RXm(at most one) transmission data, in sn,m,kThe three elements form a one-to-one matching relationship, as indicated by 1.
Cellular user C due to downlink spectrum reusekWill tolerate being transmitted by the transmitter TXnGenerated co-channel interference, receiver RXmCo-channel interference generated by the base station will be tolerated. Cellular user CkThe signal-to-noise-and-interference ratio (SINR) of (a) is:
Figure BDA0001950877150000021
wherein p is0And pnBase station and M2M transmitter TX, respectivelynTransmit power of g0,kAnd gn,kRespectively base station to cellular user CkAnd transmitter TXnTo cellular subscriber CkChannel gain of, N0Is additive white gaussian noise.
M2M receiver RXmThe expression of achievable SINR is:
Figure BDA0001950877150000031
gn,m,kand g0,m,kAre respectively TXnTo RXmChannel gain and base station to RXmThe channel gain of (1).
M2M communication pair (TX)n，RXm) The achievable spectral efficiency is:
Rn＝τn,ilog2(1+γn,m,k)
τn,iindicating the time of data transmission.
M2M transmitter TXnDuring the energy collection period taun,eThe energy collected in the device is as follows:
En＝τn,eλnp0g0,n,k
wherein λ isn(0＜λn< 1) refers to transmitter TXnEnergy collection efficiency factor of g0,n,kIs a base station to a transmitter TXnChannel gain of the link.
M2M transmitter TXnThe energy consumption of the M2M transmitter TX includes the energy consumption of data transmission and the energy consumption of circuitnDuring the energy collection period taun,eThe energy consumption is as follows:
Figure BDA0001950877150000032
wherein p iscIs the circuit power consumption.
M2M transmitter TXnIn a data transmission period taun,iThe energy consumption is as follows:
Figure BDA0001950877150000033
M2M transmitter TXnThe total energy consumption of (1) is:
Figure BDA0001950877150000034
then M2M transmitter TXnThe energy efficiency of (A) is as follows:
Figure BDA0001950877150000035
2) in the cognitive M2M communication network based on energy collection, the key research point is how to jointly optimize channel selection, peer node discovery (transmitter TX) from the energy efficiency perspectivenFinding corresponding receivers RXm) Power control and time allocation. While guaranteeing quality of service (QoS) for cellular users and users of the M2M communication link, energy constraints are taken into accountThe problem of maximizing the energy efficiency of the M2M transmitter is as follows:
P1:
Figure BDA0001950877150000041
s.t.C1:
Figure BDA00019508771500000412
C2:
Figure BDA00019508771500000413
C3:
Figure BDA00019508771500000414
C4:
Figure BDA0001950877150000045
C5:
Figure BDA00019508771500000415
C6:
Figure BDA00019508771500000416
C7:
Figure BDA00019508771500000417
C8:
Figure BDA0001950877150000049
C9:
Figure BDA00019508771500000410
C10:
Figure BDA00019508771500000411
wherein, C1And C2Guarantee the QoS requirements of the cellular link and the M2M link; c3Is a time allocation constraint; c4Guaranteed TXnThe total energy consumption does not exceed the sum of the collected energy and the stored energy; c5Define TXnTransmit power constraints of; c6Ensuring that the time allocation variable is non-negative; c7～C10One-to-one matching is guaranteed between RB, M2M-TX and M2M-RX.
3) Converting the three-dimensional matching problem between M2M-TX, M2M-RX and RB into a two-dimensional matching problem between M2M-TX and RXRB (RXRB pair formed by M2M-RX and RB); the formed optimization problem P1 is a mixed integer nonlinear programming (MINLP) problem, and an alternative optimization, a nonlinear fractional programming and a linear programming algorithm are adopted to solve the joint power control and time allocation sub-problem. The optimization problem P1 is transformed and decomposed, taking into account the M2M transmitter TXnWith M2M receiver RXmForming M2M communication pairs and multiplexing cellular subscriber CUskCommunication resource block RBkI.e. s n,m,k1, then the transmitter TXnThe energy efficiency optimization problem is as follows:
P2:
Figure BDA0001950877150000051
s.t.C1～C6
the problem P2 was transformed using a nonlinear fractional programming algorithm (a Dinkelbach algorithm is used herein) to:
P3:
Figure BDA0001950877150000052
s.t.C1～C6
wherein t is the number of Dinkelbach iterations in the t-th iteration
Figure BDA0001950877150000053
Can be varied by optimal power control and time allocation
Figure BDA0001950877150000054
And
Figure BDA0001950877150000055
to find, the Dinkelbach iteration will end up:
Figure BDA0001950877150000056
is a normal number of arbitrary size.
The problem P3 is decomposed into a power control sub-problem and a time distribution sub-problem, and the optimization is respectively carried out by adopting an alternative optimization method. The power control sub-problem is as follows:
P4:
Figure BDA0001950877150000057
s.t.C1,C2,C4,C5
considering the time allocation variable fixed, only the power control variable is optimized, where l is the number of iterations of the Alternating Optimization (AO). P4 is a standard convex optimization equation that can be optimized using standard convex optimization methods.
The time allocation sub-problem is:
P5:
Figure BDA0001950877150000058
s.t.C3,C4,C6
considering the power control variable is fixed, only the time allocation variable is optimized. It is easy to prove that at the first iteration, the optimal solution of P5
Figure BDA0001950877150000059
Must satisfy
Figure BDA00019508771500000510
Thus, by
Figure BDA00019508771500000511
Is replaced by
Figure BDA00019508771500000512
The optimized variables of P5 are only
Figure BDA00019508771500000513
The original time allocation problem is converted into a unitary linear optimization problem, and the problem P5 can be optimized by adopting a linear programming method.
The AO iteration will end up with:
Figure BDA0001950877150000061
is a normal number of arbitrary size.
4) Based on the obtained optimal power control and time allocation scheme, an optimal solution, i.e. s, to the problem P2 can be obtainedn,m,k1-hour transmitter TXnIs used as the preference value of each M2M-TX for RXRB, and the preference values are arranged in descending order to build the corresponding preference list. According to the established preference list, stable matching between M2M-TX, M2M-RX and RB is realized by the methods of 'applying for' and 'increasing price', and the steps are as follows:
TXnfor RXRBm,kThe preference value of (a) is defined as:
Figure BDA0001950877150000062
wherein,
Figure BDA0001950877150000063
is TXnCan be obtained by solving the problem P2, GmAnd GkThe price increase values of M2M-RX and RB respectively are set as the matching cost only in the matching process, have no practical significance, and the initial value is 0. In the matching process, according to the established preference list, M2M-TX applies a matching application to the favorite RXRB, and when only one M2M-TX applies to the RXRB, the two are directly matched; when a plurality of M2M-TXs make matching requests to the same RXRB, the matching cost is increased by deltaG each timem+ΔGkAnd finally only one proposed application M2M-TX. When all M2M-TX match to the corresponding RXRB or no available RXRB matches to M2M-TX, the match ends.
Description of the drawings:
fig. 1 is a diagram of an M2M single-cell communication network system model based on energy harvesting.
Fig. 2 is a simulation parameter diagram.
Fig. 3 is a scatter plot diagram of an M2M single-cell communication network system.
Fig. 4 is a graph of M2M versus transmission distance versus network average energy efficiency.
Fig. 5 is a graph of the number of M2M receivers versus the average energy efficiency of the network.
FIG. 6 is a graph of Dinkelbach iteration number versus network average energy efficiency.
Detailed Description
The implementation mode of the invention is divided into two steps, wherein the first step is the establishment of a model, and the second step is the implementation of an algorithm. The system model is shown in fig. 1, which corresponds to the description of the system model diagram of M2M single cellular communication network based on energy collection in the invention.
1) For a system model, each M2M-TX has functions of energy collection and data transmission, and the M2M-TX transmits information in a data transmission time slot and collects energy in an energy collection time slot in each time block by adopting a channel model with block fading characteristics. Transmitter TX considering Downlink energy harvesting and Spectrum multiplexing scenariosnBy multiplexing cellular subscribers CkSpectrum resource block RBkTo a corresponding receiver RXmAnd transmitting the data. But multiplexing the spectrum resources results in co-channel interference between them. Furthermore, the M2M devices have very limited installed battery capacity, and periodic replacement of the batteries of M2M devices deployed in large areas can be very expensive. Therefore, it is important to design an intelligent resource allocation scheme to optimize the energy efficiency of the M2M transmitter (M2M-TX) while guaranteeing QoS for the M2M communication link and the cellular communication link.
2) In order to solve the problems, a three-dimensional matching relation among M2M-TX, M2M-RX and RB is established; secondly, solving the sub-problem of joint power control and time distribution through AO, nonlinear fractional programming and linear programming algorithm to obtain a corresponding preference list; and finally, solving the sub-problems of joint channel selection and peer node discovery by a three-dimensional matching algorithm based on a pricing mechanism based on the acquired preference list so as to acquire an optimal spectrum resource allocation scheme.
For the present invention, we have performed a number of simulations. Specific parameters in the simulation are shown in the graph 2, and assuming that cell radius is 300M, CUs are randomly distributed in the cell, and M2M-TXs and M2M-RXs are randomly distributed in the cell with a distance not exceeding 35M. The proposed algorithm is compared with several other heuristic algorithms.
Fig. 3 is a scatter plot diagram of an M2M single-cell communication network system. The number of transmitters, receivers and cellular users is N-M-10 and K-12 respectively, and the maximum transmission distance of the M2M communication link is set as dmax＝35m。
Fig. 4 shows the relationship between M2M and the average energy efficiency of the network. It is observed that the average energy efficiency of the network decreases with the increase of the transmission distance by M2M, because as the transmission distance by M2M increases, M2M-TXs must use larger transmission power to ensure that each communication link meets the corresponding QoS requirement, which may decrease the average energy efficiency of the network. Furthermore, the proposed algorithm can approach the optimal energy value achieved by a robust scheme to the maximum extent with low computational complexity.
Fig. 5 shows a graph of the number of M2M receivers versus the average energy efficiency of the network. It was found that as the number of M2M receivers and RBs increases, the average energy efficiency of the network increases. Since an increase in the number of M2M receivers and RBs increases the probability that the M2M transmitter will match to a RXRB that prefers a higher value.
Fig. 6 shows the relationship between the number of Dinkelbach iterations and the average energy efficiency of the network. In the course of the successive iterations,
Figure BDA0001950877150000081
it gradually converges to an optimum value. It can be seen that the number of iterations is about 4. Furthermore, the number of pairs of M2M has little to no convergence performance of the proposed power control algorithmInfluence.
Although specific implementations of the invention are disclosed for illustrative purposes and the accompanying drawings, which are included to provide a further understanding of the invention and are incorporated by reference, those skilled in the art will appreciate that: various substitutions, changes and modifications are possible without departing from the spirit and scope of the present invention and the appended claims. Therefore, the present invention should not be limited to the disclosure of the preferred embodiments and the drawings, but the scope of the invention is defined by the appended claims.

Claims (3)

1. An industrial internet of things multidimensional resource joint optimization algorithm based on energy collection is characterized in that:
1) aiming at the problems of low energy efficiency and short spectrum resources in an M2M communication network, energy collection is combined with cognitive M2M communication, and a three-dimensional matching algorithm based on a pricing mechanism is provided;
2) solving the sub-problems of joint power control and time distribution through alternate optimization, nonlinear fractional programming and linear programming algorithms to obtain a corresponding preference list;
3) based on the obtained preference list, solving the sub-problems of joint channel selection and peer node discovery through a three-dimensional matching algorithm based on a pricing mechanism so as to obtain an optimal allocation scheme of the spectrum resources;
in step 1), under the conditions that the energy efficiency of the communication network is low and the spectrum resources are insufficient, the M2M transmitter considers combining energy collection with cognitive M2M communication to realize high-energy-efficiency communication, and needs to simultaneously consider the problems of insufficient energy consumption of M2M equipment and co-channel interference generated by multiplexing the M2M transmitter with a cellular user channel, so as to maximize the energy efficiency of the M2M transmitter:
(1) consider the M2M transmitter TXnWith M2M receiver RXmForming M2M communication pairs and multiplexing cellular subscriber CUskCommunication resource block RBkBy sn,m,k1, then M2M pairs (TX)n，RXm) The achievable spectral efficiency is:
Rn＝τn,ilog2(1+γn,m,k)
τn,iindicating the time of data transmission, gamman,m,kIs the M2M receiver RXmThe achievable signal-to-noise-and-interference ratio SINR is expressed as:
Figure FDA0002622026130000011
pnand p0Respectively M2M transmitter TXnTransmission power with base station, gn,m,kAnd g0,m,kAre respectively TXnTo RXmChannel gain and base station to RXmChannel gain of, N0Is additive white gaussian noise;
M2M transmitter TXnThe total energy consumption of (1) is:
Figure FDA0002622026130000012
τn,erepresenting the time of energy collection, pcIs the circuit power;
then M2M transmitter TXnThe energy efficiency of (A) is as follows:
Figure FDA0002622026130000013
(2) considering energy constraint while guaranteeing the service quality QoS of cellular users and users of the M2M communication link, the problem of maximizing the efficiency of the M2M transmitter is formed as follows:
P1:
Figure FDA0002622026130000021
s.t.C1:
Figure FDA0002622026130000022
C2:
Figure FDA0002622026130000023
C3:
Figure FDA0002622026130000024
C4:
Figure FDA0002622026130000025
C5:
Figure FDA0002622026130000026
C6:
Figure FDA0002622026130000027
C7:
Figure FDA0002622026130000028
C8:
Figure FDA0002622026130000029
C9:
Figure FDA00026220261300000210
C10:
Figure FDA00026220261300000211
wherein, C1And C2Guarantee the QoS requirements of the cellular link and the M2M link; c3Is a time allocation constraint; c4Guaranteed TXnThe total energy consumption does not exceed the sum of the collected energy and the stored energy; c5Define TXnTransmit power constraints of; c6Ensuring that the time allocation variable is non-negative; c7～C10One-to-one matching is guaranteed between RB, M2M-TX and M2M-RX.
2. The energy-harvesting-based industrial internet of things multidimensional resource joint optimization algorithm of claim 1, wherein the step 2) of obtaining the corresponding preference list specifically comprises: converting the three-dimensional matching problem between M2M-TX, M2M-RX and RB into a two-dimensional matching problem between M2M-TX and RXRB, wherein the RXRB is an RXRB pair formed by M2M-RX and RB; the formed optimization problem P1 is a mixed integer nonlinear programming MINLP problem, and the sub-problem of joint power control and time allocation needs to be solved by adopting an alternating optimization algorithm, a nonlinear fractional programming algorithm and a linear programming algorithm:
(1) the optimization problem P1 is transformed and decomposed, taking into account the M2M transmitter TXnWith M2M receiver RXmForming M2M communication pairs and multiplexing cellular subscriber CUskCommunication resource block RBkI.e. sn,m,k1, then the transmitter TXnThe energy efficiency optimization problem is as follows:
P2:
Figure FDA00026220261300000212
s.t.C1～C6
the Dinkelbach algorithm is adopted to convert the problem P2 into:
P3:
Figure FDA0002622026130000031
s.t.C1～C6
wherein t is the number of Dinkelbach iterations in the t-th iteration
Figure FDA0002622026130000032
Can be varied by optimal power control and time allocation
Figure FDA0002622026130000033
And
Figure FDA0002622026130000034
to find, the Dinkelbach iteration will end up:
Figure FDA0002622026130000035
is a normal number of any size;
(2) decomposing the problem P3 into a power control subproblem and a time distribution subproblem, and respectively optimizing by adopting an alternative optimization method; the power control sub-problem is as follows:
P4:
Figure FDA0002622026130000036
s.t.C1,C2,C4,C5
considering time distribution variable fixation, only optimizing a power control variable, wherein l is the iteration number of alternately optimizing AO; p4 is a standard convex optimization equation, which can be optimized by adopting a standard convex optimization method;
(3) the time allocation sub-problem is:
P5:
Figure FDA0002622026130000037
s.t.C3,C4,C6
considering the fixation of the power control variable, only the time distribution variable is optimized, and the P5 can be optimized by adopting a linear programming method.
3. The energy-collection-based industrial internet of things multidimensional resource joint optimization algorithm of claim 1, wherein the pricing mechanism-based three-dimensional matching algorithm of step 3) obtains the optimal allocation scheme of spectrum resources, and specifically comprises: acquiring preference values of each M2M-TX for RXRB based on the acquired optimal power control and time allocation scheme, and establishing a corresponding preference list in descending order of the preference values; according to the established preference list, stable matching between M2M-TX, M2M-RX and RB is realized by the methods of 'applying for' and 'increasing price', and the steps are as follows:
TXnfor RXRBm,kThe preference value of (a) is defined as:
Figure FDA0002622026130000038
wherein,
Figure FDA0002622026130000039
is TXnCan be obtained by solving the problem P2, GmAnd GkThe price increment values of M2M-RX and RB are respectively set as the matching cost only in the matching process, the matching cost has no practical significance, and the initial value is 0; in the matching process, according to the established preference list, M2M-TX applies a matching application to the favorite RXRB, and when only one M2M-TX applies to the RXRB, the two are directly matched; when a plurality of M2M-TXs make matching requests to the same RXRB, the matching cost is increased by deltaG each timem+ΔGkFinally, until only one of the filed applications, M2M-TX; when all M2M-TX match to the corresponding RXRB or no available RXRB matches to M2M-TX, the match ends.
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US7929478B2 (en) * 2007-03-13 2011-04-19 Nec Laboratories America, Inc. Optimal resource allocation in a multi-hop OFDMA wireless network with cooperative relaying
US8200266B2 (en) * 2007-06-29 2012-06-12 Telefonaktiebolaget Lm Ericsson (Publ) Method for allocation of communication parameters in a multiuser wireless communications system
CN107087305B (en) * 2017-01-10 2023-05-12 华北电力大学 Terminal through communication resource management scheme based on energy collection
CN107947878B (en) * 2017-11-22 2021-01-19 江苏理工学院 Cognitive radio power distribution method based on energy efficiency and spectrum efficiency joint optimization

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101951414A (en) * 2010-10-22 2011-01-19 无锡市云感科技股份有限公司 Information system of wireless sensing network combined with internet of things platform

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