CN109348532B - Cognitive Internet of vehicles efficient combined resource allocation method based on asymmetric relay transmission - Google Patents

Cognitive Internet of vehicles efficient combined resource allocation method based on asymmetric relay transmission Download PDF

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CN109348532B
CN109348532B CN201811292632.4A CN201811292632A CN109348532B CN 109348532 B CN109348532 B CN 109348532B CN 201811292632 A CN201811292632 A CN 201811292632A CN 109348532 B CN109348532 B CN 109348532B
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CN109348532A (en
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宋晓勤
谈雅竹
金慧
雷磊
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Nanjing University of Aeronautics and Astronautics
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
    • H04W52/243—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account interferences
    • H04W52/244—Interferences in heterogeneous networks, e.g. among macro and femto or pico cells or other sector / system interference [OSI]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/26—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service]
    • H04W52/267—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service] taking into account the information rate
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/30—TPC using constraints in the total amount of available transmission power
    • H04W52/34—TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading
    • H04W52/346—TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading distributing total power among users or channels
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0453—Resources in frequency domain, e.g. a carrier in FDMA
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0473—Wireless resource allocation based on the type of the allocated resource the resource being transmission power

Abstract

The invention provides a resource allocation method for maximizing system capacity of a cognitive vehicle network based on asymmetric relay transmission. The method adopts a distributed algorithm to model an objective function, takes the maximized system capacity as an optimization objective, and adds the requirements of the cognitive system such as the lowest communication rate, interference, power and the like. In the approximate optimal solution solving process, the power distribution is carried out after the optimal link selection is completed by the distribution of advanced subcarriers and relays. For the problem that the performance and complexity balance cannot be achieved by a common algorithm, the solution of the Lagrange multiplier is completed by adopting an alternative optimization mechanism in a power distribution part, and the distribution of the approximate optimal power is obtained. Finally, resource allocation is accomplished that maximizes system capacity. Simulation verification in MATLAB communication simulation environment proves that the method can balance performance and complexity.

Description

Cognitive Internet of vehicles efficient combined resource allocation method based on asymmetric relay transmission
Technical Field
The invention relates to a cognitive internet of vehicles technology, in particular to a resource allocation method of a cognitive internet of vehicles, and more particularly to a resource allocation method of cognitive internet of vehicles efficient combination based on asymmetric relay transmission.
Background
The Internet of vehicles (IoV) refers to a huge Internet network consisting of Vehicle location, speed and route information. With the rapid development of information technology, people have increasingly higher communication requirements, and the problem of reasonably and efficiently allocating communication resources remains a hotspot. Resource Allocation (RA) in IoV now faces many challenges, one of which is spectrum Resource shortage. With the ever increasing number of IoV and related application infrastructures, the amount of communication traffic has increased significantly, resulting in insufficient Dedicated Short Range Communication (DSRC) spectrum resources. At the same time, the higher real-time requirements also present another challenge. Since IoV is the basis of the Intelligent Transportation System (ITS), the requirements for real-time performance and reliability of the System are strict in order to ensure the safety of the Transportation System. In addition, the coverage of IoV is also a focus of consideration because of the long inter-vehicle distances and the constantly changing vehicle densities.
A Cognitive Radio Network (CRN) solves the problem of spectrum scarcity through intelligent sensing and dynamic spectrum access. And the Opportunistic Spectrum Access (OSA) is adopted to reduce the conflict and competition delay caused by dense nodes and improve the real-time performance of communication. Cooperative relaying may provide reliable end-to-end communication and reduce fading effects, which may be used to increase IoV coverage. Therefore, a novel IoV system-relay cooperative cognitive vehicle networking is emerging in combination with IoV, CR and relay technologies.
In cognition IoV, it is very difficult to establish and maintain end-to-end communication due to fast vehicle speed, long inter-vehicle distance, large vehicle density variations, etc., and therefore, many researchers consider using store-and-forward technology in vehicle communication systems. There are documents that propose a vehicle ad hoc network (VANT) predictive routing based on a hidden markov model (PRHMM) that improves transmission performance using regularity of vehicle motion behavior, but they do not consider the RA problem; there is a proposed power allocation algorithm based on energy efficiency priority in cognitive relay networks, which studies a worst-case robust power allocation scheme to improve the energy efficiency of an uplink cooperative relay Orthogonal Frequency Division Multiple access (OFDM) link, and a paper considers a Channel between a Primary User (PU) and a Secondary User (SU) and imperfect Channel State Information (CSI) of the Channel between the SU and a corresponding relay, but does not consider application to a IoV system. Furthermore, all the above works assume symmetric RA, i.e. equal transmission duration between nodes, which is difficult to achieve for real channels. Further, a cross-layer asymmetric RA based on the cognitive relay network is proposed in the literature, considering requirements such as queue stability and Quality of Service (QoS), but it is not studied IoV. Therefore, the invention provides an efficient combined RA algorithm in the cognitive Internet of vehicles based on asymmetric relay transmission so as to maximize the system throughput, and compared with the algorithms proposed by the prior scholars, the algorithm proposed by the scholars can well balance the complexity and the performance.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems in the prior art, the efficient joint resource allocation method of the cognitive Internet of vehicles based on the asymmetric relay transmission is provided, and the method can realize the maximization of throughput under the condition of the asymmetric relay transmission time slot of the cognitive Internet of vehicles by using the advantages of low complexity and high performance.
The technical scheme is as follows: in the cognitive Internet of vehicles system of asymmetric relay transmission, resources are reasonably and efficiently distributed to achieve the purpose of maximizing throughput. And under the constraint conditions of a cognitive source, cognitive relay power constraint, interference constraint, system minimum rate and the like, an objective function is established according to a system model, and optimization solution is carried out. And (3) adopting a step algorithm, firstly carrying out subcarrier and relay allocation, and then carrying out power allocation. In the sub-carrier and relay allocation process, a method of averagely allocating the total power available at a source and a relay is adopted to quickly obtain the sub-carrier allocation and the optimal relay selection which maximize the system capacity, and an optimal link is selected; and the power distribution part is used for constructing a Lagrangian function, solving the optimal solution expression according to the KKT condition, and eliminating the defects of more iteration times and high complexity of the traditional algorithm when calculating the Lagrangian multiplier, and reducing the algorithm complexity and completing the power distribution by adopting an alternative optimization mechanism. The invention is realized by the following technical scheme: a cognitive Internet of vehicles efficient combined resource allocation method based on asymmetric relay transmission comprises the following steps:
(1) respectively considering constraint conditions such as transmission power limit of a cognitive source and a cognitive relay, interference limit of a first time slot and a second time slot, minimum transmission rate of a cognitive system and the like, determining a system model for maximizing capacity, and obtaining an objective function;
(2) the method of evenly distributing the available total power is adopted for the cognitive source and the cognitive relay, the subcarrier distribution and the optimal relay selection which enable the system capacity to be maximized are quickly obtained, and the optimal link is established;
(3) updating a target function, and taking the power value and the time slot length as variables of the target function;
(4) constructing a Lagrangian function, and obtaining an optimal solution according to a KKT condition;
(5) and obtaining an optimal Lagrange multiplier by adopting an alternative optimization mechanism, substituting the optimal Lagrange multiplier into an optimal solution, and completing the approximate optimal power distribution.
Further, the step (1) comprises the following specific steps:
(1a) calculating the interference of the ith subcarrier on the secondary user to the primary user and the interference of the primary user to the secondary user;
(1b) and calculating the communication rate of the first time slot and the second time slot.
(1c) And modeling and writing a system objective function by jointly considering various constraints and communication rates.
Further, the step (2) comprises the following specific steps:
(2a) assuming that the available total power of the cognitive relay is averagely distributed to all subcarriers by the cognitive source, the interference caused by each subcarrier to the primary user and the interference caused by the primary user to each subcarrier on the secondary user are equal, so that the transmitting power distributed to the jth subcarrier by the source node and the transmitting power of the cognitive relay m on the kth subcarrier are obtained
Figure BSA0000173018920000021
(2b) Finding the best cognitive relay for the cognitive source subcarrier j
Figure BSA0000173018920000022
And matched cognitive destination subcarrier k*Satisfy (m)*，k*)＝argmaxm，k(Pm，kHm，k) And calculate the time
Figure BSA0000173018920000023
A value of (d);
(2c) in order to ensure reliable communication of the system, the requirement that the link communication rate is not lower than a preset threshold value is met, so that the rate is discussed in two cases;
(2d) if, if
Figure BSA0000173018920000031
Not distributing and identifying the source subcarrier j and the target subcarrier k*Respectively removed from the respective subcarrier sets if
Figure BSA0000173018920000032
Sub-carriers and relays are allocated to let psi (j, m)*)＝1，ω(m*，k*) And remove the subcarriers from the respective set, repeat this step until the subcarrier set is empty, at which point the relay and subcarrier allocation is complete.
Further, the step (5) comprises the following specific steps:
(5a) initializing relay transmit power
Figure BSA0000173018920000033
Searching for a minimum Lagrangian variable λ*，μ*Satisfy the constraint condition
Figure BSA0000173018920000034
(cognitively sourced power constraints) and
Figure BSA0000173018920000035
(first slot interference constraint);
(5b) updating the Lagrange variable λ ═ λ*，μ＝μ*Until the iteration error of the Lagrange multiplier is smaller than a preset error value, the algorithm is converged to obtain the Lagrange multiplier, and the Lagrange multiplier is substituted into an optimal solution formula to obtain the optimal power distribution of the cognitive source;
(5c) initializing cognitive source transmitting power for cognitive relay optimal power allocation
Figure BSA0000173018920000036
Searching for lagrange variables
Figure BSA0000173018920000037
Gamma satisfies the constraint condition
Figure BSA0000173018920000038
(cognitive relay power constraints),
Figure BSA0000173018920000039
(second slot interference constraint),
Figure BSA00001730189200000310
and (system rate constraint), updating Lagrange variables to obtain a value when the algorithm converges, and substituting the value into a cognitive relay optimal power allocation formula and a time slot allocation formula to obtain an approximate optimal solution.
Has the advantages that: according to the efficient joint resource allocation method in the cognitive Internet of vehicles based on the asymmetric relay transmission, the constraint conditions such as the transmission power limit of a cognitive source and a cognitive relay, the interference limit of a first time slot and a second time slot, the minimum transmission rate of a cognitive system and the like are considered respectively, and the rationality of resource allocation is ensured. And the complicated objective function is solved step by step, so that the difficulty and the calculated amount of the algorithm are greatly reduced. Given interference, power is evenly distributed, subcarriers and relays can be quickly allocated with the goal of maximizing capacity. When the Lagrange multiplier is solved in an iterative mode, an alternating mechanism is adopted, and iteration times are greatly reduced. Simulation results show that the algorithm can well balance system performance and computational complexity.
In conclusion, under the conditions of ensuring reasonable resource allocation, good system performance and low computation complexity, the efficient joint resource allocation method in the cognitive internet of vehicles based on the asymmetric relay transmission provided by the invention is superior in the aspect of maximizing the system throughput.
Drawings
FIG. 1 is a flow chart of an algorithm for efficiently allocating joint resources of cognitive Internet of vehicles based on asymmetric relay transmission;
FIG. 2 is a graph of system capacity as a function of distance from a cognitive source to a cognitive relay;
FIG. 3 is a graph of system capacity as a function of total power available for cognitive sources and cognitive relays under different algorithms;
FIG. 4 is a graph of system capacity as a function of interference threshold for different algorithms;
Detailed Description
The core idea of the invention is that: in the cognitive internet of vehicles with the cooperation of asymmetric relays, the defects of multiple iteration times and high calculation complexity of a traditional algorithm are overcome in the Lagrange multiplier solving process in the power distribution part, and corresponding Lagrange multiplier values are solved respectively by innovatively adopting an alternative optimization mechanism to obtain the approximate optimal power distribution of the cognitive source and the cognitive relay.
The present invention is described in further detail below.
Step (1), determining a system model for maximizing capacity under the constraint conditions of cognitive source and cognitive relay transmission power limitation, interference limitation of a first time slot and a second time slot, the lowest transmission rate of a cognitive system and the like, and obtaining an objective function, wherein the method comprises the following steps:
considering a downlink of asymmetric relay transmission in cognitive Internet of vehicles, the relay adopts a decoding forwarding mode. The system comprises a primary user node, a pair of secondary user nodes (a cognitive source and a cognitive destination), and M relay nodes, wherein a set M belongs to [1]It is shown that the total authorized bandwidth B is equally divided into N subcarriers, where the subcarriers are represented by the set N ═ {1, 2.., N }, each subcarrier bandwidth is Vf, the source-aware subcarrier is j, and the destination subcarrier is k, so the whole link is represented by (j, m, k). Considering the disparity of two transmission slots in the system, the 1 st slot T1The cognitive source transmits information to the relay node in the 2 nd time slot T2The relay collaboratively forwards information to the destination node, and the total transmission time slot T is T1+T2. Defining the interference of the ith subcarrier on the secondary user to the primary user as JjThe interference of the primary user to the ith subcarrier on the secondary user is IiSource to relay power, path loss, channel gain are Pj，m，Lj，m，hj，mSimilarly, the power, path loss and channel gain of the relay to the destination are respectively Pm，k，Lm，k，hm，k. Therefore the system speedThe rate can be defined as:
Figure BSA0000173018920000041
wherein Hj，m，Hm，kThe channel gains squared for source to relay, relay to destination,
Figure BSA0000173018920000042
the method is the sum of additive white Gaussian noise and main user interference, and similarly,
Figure BSA0000173018920000043
is the sum of additive white gaussian noise and secondary user interference.
Thus, taking the proposed constraints into account, the system model can be expressed as:
Figure BSA0000173018920000044
Figure BSA0000173018920000045
C8：T＝T1+T2(time slot constraint)
Wherein, Ps，PmMaximum transmission power, I, at cognitive source and cognitive relay nodes, respectivelythAs interference threshold, RthIs the rate threshold, ρjIs the interference factor, rho, of the jth subcarrier on the secondary user to the primary userm，kIs the interference factor of the mth relay on the subcarrier k to the primary user. If the jth subcarrier of the source node matches the relay, ψ (j, m) is 1, otherwise ψ (j, m) is 0, if the mth relay selection matches the kth subcarrier of the destination node, ω (m, k) is 1, otherwise ω (m, k) is 0.
And (2) further optimizing an objective function:
Figure BSA0000173018920000051
in addition to the constraints of expression 2, it is also necessary to satisfy
Rj，m＝Rm，kExpression 4
And (3) the scheme of subcarrier and relay allocation comprises the following steps:
(3a) assuming that the available total power of the cognitive relay is averagely distributed to all subcarriers by the cognitive source, the interference caused by each subcarrier to the primary user and the interference caused by the primary user to each subcarrier on the secondary user are equal, and thus the transmitting power distributed to the jth subcarrier by the source node is obtained
Figure BSA0000173018920000052
Similarly, the transmission power of the cognitive relay on the subcarrier is
Figure BSA0000173018920000053
(3b) Knowing the source subcarrier j, finding the best relay
Figure BSA0000173018920000054
And matched sub-carrier k*Satisfy the requirement of
(m*，k*)＝argmaxmk(Pm，kHm，k) Expression 7
And calculate the time
Figure BSA0000173018920000055
A value of (d);
(3c) if the communication rate of the link is not lower than the preset threshold value, the two conditions are discussed, and if the communication rate of the link is not lower than the preset threshold value, the two conditions are divided into two conditions
Figure BSA0000173018920000056
Do not allocate and assign the sub-carriers j, k*Respectively from their respective childrenRemove from the carrier set if
Figure BSA0000173018920000057
Sub-carriers and relays are allocated to let psi (j, m)*)＝1，ω(m*，k*) And remove the subcarriers from the respective set, repeat this step until the subcarrier set is empty, at which point the relay and subcarrier allocation is complete.
And (4) constructing a Lagrangian function, and obtaining an approximate optimal solution according to a KKT condition, wherein the method comprises the following steps:
(4a) the Lagrangian function is expressed as
Figure BSA0000173018920000061
Wherein P ═ { P ═ Pj，m，Pm，kDenotes a power allocation set, T ═ T1，T2Denotes a set of time slot allocations,
Figure BSA0000173018920000062
is a lagrange multiplier set;
(4b) according to the KKT condition, the optimal solution is obtained as
Figure BSA0000173018920000063
And (5) obtaining Lagrange multiplier values by adopting an alternative optimization mechanism, and performing power distribution, wherein the method comprises the following steps:
(5a) initializing relay transmit power
Figure BSA0000173018920000064
Searching for a minimum Lagrangian variable λ*，μ*The constraints C1 and C3 are satisfied;
(5b) updating the Lagrange variable λ ═ λ*，μ＝μ*Until the Lagrange multiplier iteration error is smaller than the preset error value, the algorithm is converged to obtain the LagrangeSubstituting the multipliers into an optimal solution formula to obtain the optimal power distribution of the cognitive source;
(5c) initializing cognitive source transmitting power for cognitive relay optimal power allocation
Figure BSA0000173018920000065
Searching for lagrange variables
Figure BSA0000173018920000066
And gamma meets constraint conditions C2, C4 and C5, the Lagrangian variable is updated to obtain a value when the algorithm is converged, and the value is substituted into the cognitive relay optimal power distribution formula and the time slot distribution formula to obtain an approximate optimal solution.
And finally, simulating, analyzing and comparing results. Different performances obtained by different resource allocation algorithms are analyzed and compared through the average result of 1000 Monte Carlo experiments. An algorithm for solving Lagrange multipliers by adopting traditional iteration to obtain an optimal solution is 'algorithm 1'; the average power distribution is adopted, the channel interference is equal to obtain the power distribution, and the algorithm with greatly reduced complexity is 'algorithm 2'; the algorithm we propose is called "algorithm 3". Considering a pair of transceiving nodes, M relays, users are randomly allocated in a range of 1000M × 1000M, all communication channels are set to be Rayleigh fading channels, the channel gain is an independent random value with an average value of 1 obeying Rayleigh distribution, wherein B is 20MHz, N is 64, M is 5, T iss＝5μs，σ2＝10-4。
In fig. 2, the impact of relay location on system throughput is depicted, where the distance between the cognitive source and destination is fixed at 2000m and the PL index is 3.5. The distance between the source and the relay is from 400m to 1600m, and Ps＝P m5 dBm. "asymmetric RA" refers to the algorithm proposed herein, while "symmetric RA" refers to the algorithm in the literature. From the simulation result, it is obvious that the asymmetric optimization algorithm has superior performance, good robustness and larger system capacity, namely, the influence of the relay position on the asymmetric resource allocation is smaller than that on the symmetric resource allocation.
In fig. 3, the system throughput and available work for different algorithm implementations are depictedRelationship of rate, wherein Ith0 dBm. Obviously, as the power budget increases, the system capacity increases because the system can allocate more power to different subcarriers. The method can also find that the performance of the algorithm provided by the user is better than that of the algorithm 2, and meanwhile, the optimal solution close to the algorithm 1 can be obtained, so that the scheme provides a good approximate optimization, the complexity is lower than that of the traditional algorithm, and the method has a good application value. Moreover, in the high power budget region, the gap between the proposed algorithm and the other two schemes is very close, since when the SU's transmit power is large, more interference will be generated to the PU, and the interference should be kept below a pre-specified threshold to ensure the QoS of the PU.
In fig. 4, the relationship of system throughput to interference constraints for different algorithm implementations is depicted, where Ps＝P m0 dBm. As can be seen from the simulation results, the algorithm proposed by the method is superior to the algorithm 2, is close to the optimal solution algorithm 1, achieves approximately optimal performance, and has much lower computational complexity. In addition, we can observe that system throughput increases with increasing interference threshold, and that all algorithms have similar performance in the low interference threshold region. In addition, as the interference threshold increases, the gap between the proposed algorithm and the other two schemes also increases.
The key point of the efficient joint resource allocation algorithm for asymmetric relay transmission is in a power allocation part, and when a Lagrange multiplier is solved, an alternate optimization mechanism is adopted, so that the iteration times of the algorithm are reduced, the complexity of the algorithm is greatly reduced, and meanwhile, the system performance can be well ensured not to be greatly influenced.
Based on the description of the present invention, it should be apparent to those skilled in the art that the efficient joint resource allocation algorithm of the present invention can greatly reduce the complexity of the algorithm and ensure the system performance.
Details not described in the present application are well within the skill of those in the art.

Claims (2)

1. An efficient joint allocation method based on asymmetric relay transmission in cognitive Internet of vehicles is characterized by comprising the following steps:
(1) considering a downlink of asymmetric relay transmission in a cognitive Internet of vehicles and a mode that two transmission time slots are different under the constraint conditions of cognitive source and cognitive relay transmission power limitation, interference limitation of a first time slot and a second time slot, the lowest transmission rate of a cognitive system and link matching; defining the system rate:
Figure FSB0000193240630000011
determining a system model for maximizing capacity to obtain an objective function:
Figure FSB0000193240630000012
(2) the method for evenly distributing the available total power is adopted for the cognitive source and the cognitive relay, the subcarrier distribution and the optimal relay selection which maximize the system capacity are quickly obtained, and the optimal link is established, wherein the method comprises the following specific steps:
(2a) assuming that the available total power of the cognitive relay is averagely distributed to all subcarriers by the cognitive source, the interference caused by each subcarrier to the primary user and the interference caused by the primary user to each subcarrier on the secondary user are equal, so that the transmitting power distributed to the jth subcarrier by the source node and the transmitting power of the cognitive relay m on the kth subcarrier are obtained
Figure FSB0000193240630000013
(2b) Finding the best cognitive relay for the cognitive source subcarrier j
Figure FSB0000193240630000014
And matched cognitive destination subcarrier k*Satisfy (m)*，k*)＝argmaxm，k(Pm，kHm，k) And calculate the time
Figure FSB0000193240630000015
A value of (d);
(2c) in order to ensure reliable communication of the system, the requirement that the link communication rate is not lower than a preset threshold value is met, so that the rate is discussed in two cases;
(2d) if, if
Figure FSB0000193240630000016
Not distributing and identifying the source subcarrier j and the target subcarrier k*Respectively removed from the respective subcarrier sets if
Figure FSB0000193240630000017
Sub-carriers and relays are allocated to let psi (j, m)*)＝1，ω(m*，k*) 1, and removing the sub-carriers from the respective sets, and repeating the steps until the sub-carrier sets are empty, at which time the relay and sub-carrier allocation is completed;
(3) updating a target function, and taking the power value and the time slot length as variables of the target function;
(4) obtaining an optimal solution of power and time slot distribution by constructing a Lagrange function and according to a KKT condition;
(5) and updating to obtain an optimal Lagrange multiplier by adopting an alternate optimization mechanism according to the cognitive source power constraint and the 1 st time slot interference constraint, and substituting into a cognitive relay optimal power allocation formula and a time slot allocation formula:
Figure FSB0000193240630000021
and completing the approximate optimal power distribution.
2. The efficient joint allocation method based on asymmetric relay transmission according to claim 1, wherein the step (5) comprises the following specific steps:
(5a) initializing relay transmit power
Figure FSB0000193240630000022
Searching for a minimum Lagrangian variable λ*，μ*Satisfy the constraint condition
Figure FSB0000193240630000023
(cognitively sourced power constraints) and
Figure FSB0000193240630000024
(first slot interference constraint);
(5b) updating the Lagrange variable λ ═ λ*，μ＝μ*Until the iteration error of the Lagrange multiplier is smaller than a preset error value, the algorithm is converged to obtain the Lagrange multiplier, and the Lagrange multiplier is substituted into an optimal solution formula to obtain the optimal power distribution of the cognitive source;
(5c) initializing cognitive source transmitting power for cognitive relay optimal power allocation
Figure FSB0000193240630000025
Searching for lagrange variables
Figure FSB0000193240630000026
Gamma satisfies the constraint condition
Figure FSB0000193240630000027
(cognitive relay power constraints),
Figure FSB0000193240630000028
(second slot interference constraint),
Figure FSB0000193240630000029
(system rate constraint), updating Lagrange variable to obtain the value of algorithm convergence, substituting into the cognitive relay optimal power allocation formula and time slotAnd (5) allocating a formula to obtain an approximate optimal solution.
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