CN105007585B - Power distribution method based on outage probability efficiency maximum - Google Patents
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Abstract
The invention discloses a kind of power distribution methods based on outage probability efficiency maximum, and efficiency maximization problems can not be obtained by mainly solving existing cognitive radio power distribution method.Implementation step is：1. arrange parameter simultaneously initializes it；2. the Lagrange multiplier τ for meeting average transmitting power constraints and the Lagrange multiplier μ for meeting average interference power constraints is obtained；3. the transmission power P based on outage probability after nth iteration is calculated according to the multiplier τ and μn；4. it is P to be calculated respectively in transmission powernWhen efficiency function fn(η) and efficiency ηn ；5. couple efficiency function fn(η) makes decisions, if meeting iteration stopping condition, obtains the best transmission power under best efficiency and best efficiency, otherwise continues cycling through, and until meeting condition or reaching maximum iteration, obtains best efficiency at this time and best transmission power.There is the present invention efficiency to maximize, and step number needed for iteration stopping is few, it is easy to accomplish the advantages of, available for wirelessly communicating.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a power distribution method based on maximum interruption probability energy efficiency, which can be used for power distribution of maximum secondary user energy efficiency in a green cognitive radio system.
Background
With the rapid development of wireless and mobile communications, the contradiction between the increasing demand of wireless spectrum and the limited spectrum resources has become a prominent contradiction in the current wireless communication industry, but at the same time, a large amount of authorized spectrum is idle or has an extremely low utilization rate. In order to improve the current situation of low spectrum utilization rate, J.Mitola et al propose a concept of cognitive radio, and the main idea is to allow a secondary user to access to a current frequency band in an authorized frequency band on the premise of not influencing normal communication of a master user, so that the spectrum utilization rate is greatly improved. In order to maximize the transmission rate of the secondary users and protect the authorized users as much as possible, the secondary users must optimally allocate the transmission power to minimize the interference to the normal communication of the authorized users, so that the power allocation research in cognitive radio is widely focused by scholars at home and abroad.
At the same time, ubiquitous wireless services and the drastically increasing number of mobile devices result in a large amount of energy consumption and emission of greenhouse gases. Green communication networks are an inevitable trend in future wireless network design. The idea of a green communication network is to provide the best user experience while maximizing network energy efficiency.
The existing optimal power allocation strategy is mainly designed for the following two mechanisms:
1) an opportunistic spectrum access mechanism. The opportunistic spectrum access mechanism is characterized in that a secondary user uses a primary user frequency band to transmit when detecting that a primary user does not exist. Under the mechanism, the secondary user needs to accurately and quickly detect the frequency band of the primary user. Because the existing spectrum sensing technology cannot achieve a completely accurate detection effect, when a primary user does not exist but a secondary user misjudges that the primary user exists, the secondary user gives up using the frequency band to protect the primary user; and when the primary user exists but the secondary user misjudges that the primary user does not exist, the secondary user uses the frequency band of the primary user for transmission, so that interference is generated on the primary user. Therefore, the design of the optimal power allocation strategy can not only play a role in protecting the primary user in the mechanism, but also provide the maximum transmission rate of the secondary user.
2) A spectrum sharing mechanism. Under a frequency spectrum sharing mechanism, a secondary user and a main user share the same frequency band, and the secondary user does not need to detect the state of the main user. Under this mechanism, in order to guarantee the quality of service of the primary user, the secondary user needs to optimally design its transmission power. Since the spectrum efficiency is higher and the secondary users can obtain better service quality under the spectrum sharing mechanism, it is more important to design the optimal power allocation strategy under the spectrum sharing mechanism.
Under the conventional cognitive radio spectrum sharing mechanism, the existing power allocation method (1: x.kang, y.c. liang, a.nalalanthan, h.k.garg, r.zhang, "Optimal power allocation for communicating channels in a cognitive radio network: error Capacity and output Capacity" IEEE trans. wireless communication, vol.8, No.2, 940. 950,2009.2: l.musavian and s.aissa, "Capacity and power allocation for communicating channels" trans. wireless communication, vol.8, No. 8, 1, No. 148-156, jan.2009) is adjusted to minimize the secondary user transmission probability under the given condition, and the secondary user transmission is achieved by adjusting the secondary user transmission probability according to the given condition. The power distribution method is only related to the constraint condition, the channel gain from the secondary user sending end to the secondary user receiving end, the channel gain from the secondary user sending end to the primary user receiving end, the channel gain from the primary user sending end to the secondary user receiving end, the primary user sending power and the noise power of the secondary user receiving end, and is not related to the energy efficiency and the power amplification factor of the secondary user sending end. The power distribution method does not consider the influence of the energy efficiency obtained by the secondary user on the power distribution, so that the secondary user cannot be guaranteed to obtain the maximum energy efficiency, a large amount of extra energy consumption and greenhouse gas emission are generated, and unnecessary energy waste is caused.
Disclosure of Invention
The invention aims to provide a power distribution method based on maximum interruption probability energy efficiency to improve the energy efficiency of secondary users and reduce energy waste aiming at the defects of the prior art.
In order to achieve the above object, the technical method of the present invention comprises the steps of:
(1) setting the energy efficiency function fault-tolerant error xi > 0 and the interruption capacity r by the secondary user according to the required fault-tolerant error, the Lagrange iteration effect, the expected interruption capacity and the maximum required iteration timessbit/complex dimension, maximum number of iterations N, convergence error ξ corresponding to average transmit power constraint1more than 0, and the convergence error xi corresponding to the average interference power constraint2The iteration step length t of the Lagrange multiplier corresponding to the average transmission power constraint is more than 01The iteration step length t of the Lagrange multiplier corresponding to the average interference power constraint is larger than 02＞0；
(2) the secondary user initialization energy efficiency η is 0, and the Lagrange multiplier tau corresponding to the average transmission power constraint is tau0The lagrange multiplier mu corresponding to the average interference power constraint is mu0Secondary user transmission power Pnand energy efficiency ηnThe iteration number n of (1) is 0;
(3) the secondary user iteratively calculates the optimal transmission power P:
(3.1) the secondary user calculates the capacity r to meet the interruption of the secondary user in each fading conditionsTransmission power y of (a):
wherein g isssTransmit-to-receive channel power gain, h, for secondary userspsGain of channel power from primary user transmitting end to secondary user receiving end, PmThe transmission power is constant for the primary user,representing the noise variance of the secondary user receiving end;
(3.2) the secondary user calculates the transmission power P in each fading staten：
Where ρ is the power amplification factor, gspchannel power gain, η, from the sending end of the secondary user to the receiving end of the primary usern-1Energy efficiency obtained for the (n-1) th iteration of the secondary user;
(3.3) Secondary user based on average Transmission Power constraintAnd average interference power constraintCalculating Lagrange multiplier tau corresponding to average transmission power constraint through iteration of k being more than or equal to 1 time by using a subvariant iteration algorithmkLagrange multiplier mu corresponding to average interference power constraintk：
WhereinAndrespectively restricting the maximum average sending power of a secondary user and the maximum average interference power of the secondary user to a main user, wherein E {. is expressed as mathematical expectation of the solution;
(3.4) according to the calculated Lagrange multiplier taukAnd mukCalculating the transmission power
(3.6) respectively calculating the nth iteration energy efficiency function f by the secondary usersn(η) and nth iteration energy efficiency ηn：
Wherein P isCIn order to fix the consumed power of the circuit,the calculation expression of (a) is:
(3.7) Secondary user energy efficiency function fn(η) making a decision if | fn(eta) less than or equal to ξ, the nth transmission poweroptimum transmission power P, energy efficiency eta for secondary user energy efficiencynotherwise, judging whether the iteration number reaches the maximum iteration number N less than or equal to N, if so, then the sending power is obtainedIs a second leveloptimum user energy efficiency transmission power P, energy efficiency etanand obtaining the maximum energy efficiency η for the secondary user, otherwise, continuing the iteration until the constraint condition of iteration termination is met.
The invention has the following advantages:
1. under the constraint conditions of the average transmitting power and the average interference power of the secondary users, the maximum energy efficiency which is higher than that obtained by the traditional method for sharing the optimal transmitting power of the cognitive radio based on the frequency spectrum can be obtained.
2. The invention can rapidly obtain the optimal power distribution of energy efficiency under the condition of maximum energy efficiency.
3. The invention can ensure the service quality of the primary user and ensure the service quality of the secondary user under the condition of maximum energy efficiency.
4. The method has low calculation complexity and can be widely applied in practice.
Drawings
FIG. 1 is a flow chart of an implementation of the present invention;
FIG. 2 is a comparison graph of the maximum energy efficiency obtained by the secondary user using the present invention and two prior art transmission power allocation methods;
FIG. 3 is a comparison graph of the probability of interruption of the secondary users using the present invention and the two prior art methods of transmitting power allocation;
FIG. 4 is a diagram of the maximum energy efficiency obtained by a secondary user under different channel models using the present invention;
fig. 5 is a comparison graph of the maximum energy efficiency obtained by the secondary users under different transmission powers by using the two transmission power methods of the present invention and the prior art.
Detailed Description
Referring to fig. 1, the implementation steps of the invention are as follows:
step 1, a cognitive user sets target parameters.
setting the energy efficiency function fault-tolerant error xi > 0 and the interruption capacity r by the secondary user according to the required fault-tolerant error, the Lagrange iteration effect, the expected interruption capacity and the maximum required iteration timessbit/complex dimension, maximum number of iterations N, convergence error ξ corresponding to average transmit power constraint1more than 0, and the convergence error xi corresponding to the average interference power constraint2The iteration step length t of the Lagrange multiplier corresponding to the average transmission power constraint is more than 01The iteration step length t of the Lagrange multiplier corresponding to the average interference power constraint is larger than 02＞0；
The smaller the energy efficiency function fault tolerance error is, the more iterations are likely to be required, and the iterations also depend on the average interference constraint condition, the average transmission power constraint condition, the channel fading state and the energy efficiency optimum power level. The selection of the iteration step is a key influence factor of the number of steps required by the iteration stop, the proper iteration step is selected according to the loose of the constraint condition, the fast obtaining of the optimal solution can be ensured, and when the iteration step is set to be a constant, the obtained dissociation optimal solution can be very close to each other by the subgradent algorithm;
and 2, initializing secondary user parameters.
The selection of the initial value of the Lagrange multiplier has a large influence on the number of steps required by iteration, when the selected initial value of the Lagrange multiplier is close to the Lagrange multiplier meeting the constraint condition, the number of steps required by iteration stop is small, and if the selected initial value is not ideal, the subvariant algorithm needs to iterate for many times to obtain the final Lagrange multiplier. Therefore, the selection of the Lagrangian initial value is very important, the selection is properly performed according to the loose of the constraint condition, when the constraint condition is tighter, the selection of the Lagrangian initial value is relatively larger, and on the contrary, the selection of the Lagrangian multiplier initial value is relatively smaller;
secondary users in this examplethe initialized energy efficiency η is 0, and the Lagrange multiplier tau corresponding to the average transmission power constraint is tau0The lagrange multiplier mu corresponding to the average interference power constraint is mu0Secondary user transmission power Pnand energy efficiency ηnThe iteration number n of (1) is 0;
and 3, iteratively calculating the optimal transmitting power P by the secondary user.
(3.1) secondary user calculates guaranteed secondary user outage capacity r under each fading conditionsMinimum transmission power y:
wherein g isssTransmit-to-receive channel power gain, h, for secondary userspsGain of channel power from primary user transmitting end to secondary user receiving end, PmThe transmission power is constant for the primary user,representing the noise variance of the secondary user receiving end;
(3.2) the secondary user calculates the transmission power P in each fading staten：
Where ρ is the power amplification factor, gspchannel power gain, η, from the sending end of the secondary user to the receiving end of the primary usern-1Energy efficiency obtained for the (n-1) th iteration of the secondary user;
according to the calculation expression of the transmission power in each fading state, the transmission power in each fading state can be self-adaptively adjusted according to the condition of the channel state, so that the optimal average energy efficiency can be achieved in various fading states;
(3.3) Secondary user based on average Transmission Power constraintAnd average interference power constraintCalculating Lagrange multiplier tau corresponding to average transmission power constraint through iteration of k being more than or equal to 1 time by using a subvariant iteration algorithmkLagrange multiplier mu corresponding to average interference power constraintk：
WhereinAndrespectively restricting the maximum average sending power of a secondary user and the maximum average interference power of the secondary user to a main user, wherein E {. is expressed as mathematical expectation of the solution;
the looseness of the constraint conditions of the maximum average transmitting power and the maximum average interference power has larger influence on the number of steps required by iteration stop, when the constraint conditions of the maximum average transmitting power and the maximum average interference power are looser, the number of steps required by iteration stop is less, otherwise, the number of steps required by iteration stop is larger;
(3.4) according to the calculated Lagrange multiplier taukAnd mukCalculating the transmission power
(3.6) respectively calculating the nth iteration energy efficiency function f by the secondary usersn(η) and nth iteration energy efficiency ηn：
Wherein P isCIn order to consume power for a fixed circuit,the calculation expression of (a) is:
the energy efficiency function can reflect the acquired state capacity of the secondary user under each unit of joule power, namely the mathematical expectation of the energy efficiency acquired by the user under various fading conditions of a channel, thereby embodying the average energy efficiency of the secondary user;
as can be seen from the energy efficiency calculation expression, the maximization of the energy efficiency is not equal to the maximization of the experience state capacity under the traditional cognitive radio, so that the optimal sending power under the maximization of the experience state capacity under the traditional cognitive radio cannot ensure that the secondary user obtains the maximum energy efficiency;
(3.7) Secondary user energy efficiency function fn(η) making a decision if | fn(eta) less than or equal to ξ, the nth transmission poweroptimum transmission power P, energy efficiency eta for secondary user energy efficiencynotherwise, judging whether the iteration number reaches the maximum iteration number N less than or equal to N, if so, then the sending power is obtainedoptimum transmission power P, energy efficiency eta for secondary user energy efficiencynif not, continuing the iteration until the constraint condition of iteration termination is met;
the maximum iteration times can be selected according to the fault tolerance error required by the secondary user, if the fault tolerance error required by the secondary user is small, the maximum iteration times are selected to be larger, otherwise, the secondary user can select the relatively smaller maximum iteration times, and therefore the energy efficiency and power sending strategy can be obtained quickly.
The performance effects of the present invention can be further illustrated by the following simulations:
A. simulation conditions
Power amplification factor rho and circuit fixed power consumption P of secondary user transmitting endCSet to 0.2 and 0.05 watts, respectively, the secondary user received noise variance is set to 0.01, and the primary user transmit power PmSet to 60 milliwatts, lagrange iteration step t1、t2are all set to 0.1, and the fault tolerance errors xi and xi are set1,ξ2Are all set to 0.0001, the number of channel realizations is 100000, gss、gspAnd hpsFor power gain under Rayleigh channel, the average values are respectively set to be 2, 1.5 and the target interrupt capacity r of the secondary usersSet to 1 bit/complex.
The average transmit power constraint for simulations 1 and 2 was set to 100 milliwatts and the average interference power was set to 0 to 100 milliwatts. M of the nagakami-m fading channel of simulation 3 is set to 0.5, the average transmit power constraint is set to 100 milliwatts, and the average interference power is set to 0 to 100 milliwatts. The average interference power of simulation 4 was set to 10 mw and 50 mw, and the average transmission power was set to 0 mw to 100 mw.
B. Emulated content
Simulation 1: by adopting the invention and the existing transmission power distribution method based on the minimum interruption probability, the maximum energy efficiency obtained by the secondary user is compared, and the result is shown in fig. 2. In fig. 2, "energy efficiency maximization" represents the maximum energy efficiency obtained by the secondary user under the constraint of 100 mw average transmission power, and "interruption probability minimization" represents the maximum energy efficiency obtained by the secondary user under the constraint of 100 mw average transmission power by using the conventional spectrum sharing interruption probability-based optimal transmission power method.
Simulation 2: comparing the probability of interruption of the secondary user with the existing transmission power distribution method based on the minimum probability of interruption, the result is shown in fig. 3. In fig. 3, "energy efficiency maximization" represents the outage probability obtained by sampling the secondary user under the constraint of the average transmission power of 100 mw, and "outage probability minimization" represents the outage probability obtained by the secondary user under the constraint of the average transmission power of 100 mw by using the conventional spectrum sharing outage probability-based optimal transmission power method.
Simulation 3: the maximum energy efficiency obtained by the secondary user by using the method is simulated under the condition that the average transmission power is constrained to 100 milliwatts under different channel model models, and the result is shown in fig. 4. In fig. 4, there are four curves in total, where:
curve 1 represents the maximum energy efficiency obtained by the secondary user under the channel model that the secondary user transmitting end to the secondary user receiving end is a Gaussian channel, the primary user transmitting end to the secondary user receiving end is a Rayleigh channel, and the secondary user transmitting end to the primary user receiving end is a Rayleigh channel;
curve 2 represents the maximum energy efficiency obtained by the secondary user under the channel model that the channel from the secondary user sending end to the primary user receiving end is a rayleigh channel, the channel from the primary user sending end to the secondary user receiving end is a nakagami-m fading channel when m is 0.5, and the channel from the secondary user sending end to the primary user receiving end is a rayleigh channel;
curve 3 represents the maximum energy efficiency obtained by the secondary user under the nakagami-m fading channel model when the channel from the secondary user sending end to the secondary user receiving end is the rayleigh channel, the channel from the primary user sending end to the secondary user receiving end is the rayleigh channel, and the channel from the secondary user sending end to the primary user receiving end is m 0.5;
curve 4 represents the maximum energy efficiency obtained by the secondary user under the channel model that the channel from the secondary user sending end to the secondary user receiving end is a rayleigh channel, the channel from the primary user sending end to the secondary user receiving end is a rayleigh channel, and the channel from the secondary user sending end to the primary user receiving end is a rayleigh channel.
And (4) simulation: by adopting the invention and the existing transmission power distribution method based on the minimum interruption probability, the maximum energy efficiency obtained by the secondary users under different transmission powers is compared, and the result is shown in fig. 5.
C. Simulation result
As can be seen from fig. 2, under the constraint conditions of the average interference power and the average transmission power, the existing optimal transmission power method based on the spectrum sharing outage probability cannot ensure that the secondary user obtains the maximum energy efficiency, but the present invention can ensure that the secondary user obtains the maximum energy efficiency. Compared with the constraint condition of average transmission power, the constraint condition of average interference power is loose, that is, when the average interference power does not play a constraint role, the maximum energy efficiency obtained by the secondary user only depends on the average transmission power, and the more loose the constraint of the average transmission power is, the larger the maximum energy efficiency obtained by the secondary user is.
As can be seen from fig. 3, although the present invention does not guarantee the minimum probability of transmission interruption for the secondary user, it guarantees the maximum energy efficiency for the secondary user.
As can be seen from fig. 4, the channel fading from the secondary user sending end to the secondary user receiving end may not be beneficial to the maximum energy efficiency obtained by the secondary user, the channel fading from the secondary user sending end to the primary user receiving end and from the primary user sending end to the secondary user receiving end may be beneficial to the maximum energy efficiency obtained by the secondary user, and the fading from the primary user sending end to the secondary user receiving end may be more beneficial to the secondary user obtaining higher maximum energy efficiency. The reason is that the fading from the secondary user sending end to the secondary user receiving end can increase the interruption probability of the secondary user, the fading from the secondary user sending end to the primary user receiving end can play a role in reducing the interference to the primary user, and the channel fading from the primary user sending end to the primary user receiving end can play a role in reducing the interference to the secondary user, so that the interruption probability of the secondary user is reduced, and the maximum energy efficiency of the secondary user is improved.
As can be seen from fig. 5, the present invention can ensure that the secondary user obtains the maximum energy efficiency, whereas the existing method cannot ensure that the secondary user obtains the maximum energy efficiency. Under the condition that the constraint condition of the average transmission power is tighter and the constraint of the average interference power is looser, the optimal transmission power only depends on the average transmission power, and the two optimal transmission powers are the same, so that the two power distribution methods can obtain the maximum energy efficiency; in the situation that the constraint condition of the average transmission power is looser and the constraint of the average interference power is tighter, the optimal transmission power only depends on the average interference power, and the invention can obtain higher energy efficiency.
By integrating the simulation results and analysis, the optimal power distribution method based on the interrupt probability energy efficiency provided by the invention can enable the secondary user to obtain the maximum energy efficiency, and the number of steps required by iteration stop is small, so that the method is easy to implement, and the method can be better applied in practice.
Claims (2)
1. A power distribution method based on maximum interruption probability energy efficiency comprises the following steps:
(1) setting the energy efficiency function fault-tolerant error xi > 0 and the interruption capacity r by the secondary user according to the required fault-tolerant error, the Lagrange iteration effect, the expected interruption capacity and the maximum required iteration timessbit/complex dimension, maximum number of iterations N, convergence error ξ corresponding to average transmit power constraint1more than 0, and the convergence error xi corresponding to the average interference power constraint2Lagrange multiplier iteration step corresponding to average transmission power constraint higher than 0Length t1The iteration step length t of the Lagrange multiplier corresponding to the average interference power constraint is larger than 02＞0；
(2) the secondary user initialization energy efficiency η is 0, and the Lagrange multiplier tau corresponding to the average transmission power constraint is tau0The lagrange multiplier mu corresponding to the average interference power constraint is mu0Secondary user transmission power Pnand energy efficiency ηnThe iteration number n of (1) is 0;
(3) the secondary user iteratively calculates the optimal transmission power P:
(3.1) the secondary user calculates the capacity r to meet the interruption of the secondary user in each fading conditionsMinimum transmission power y:
wherein g isssTransmit-to-receive channel power gain, h, for secondary userspsGain of channel power from primary user transmitting end to secondary user receiving end, PmThe transmission power is constant for the primary user,representing the noise variance of the secondary user receiving end;
(3.2) the secondary user calculates the transmission power P in each fading staten：
Where ρ is the power amplification factor, gspchannel power gain, η, from the sending end of the secondary user to the receiving end of the primary usern-1Energy efficiency obtained for the (n-1) th iteration of the secondary user;
(3.3) the secondary user iterates for more than or equal to 1 time through a subvariant iteration algorithm according to the constraint condition of the average transmission power and the constraint condition of the average interference power, and the Lagrange multiplier tau corresponding to the constraint of the average transmission power is calculatedkLagrange multiplier mu corresponding to average interference power constraintk；
The average transmit power constraint condition and the average interference power constraint condition have the following formulas:
the average transmit power constraint is:
the average interference power constraint conditions are as follows:
wherein,andrespectively limiting the maximum average transmission power of the secondary users and the maximum average interference power of the secondary users to the primary users,e {. is expressed as the mathematical expectation of solving the question for the sending power after computing Lagrange multiplier for the kth time;
(3.4) according to the calculated Lagrange multiplier taukAnd mukCalculating the transmission power
(3.5) judging the Lagrange multiplier tau calculated by each iterationkAnd mukWhether an iteration termination condition is satisfied:if yes, executing the step (3.6), otherwise, returning to the step (3.3);
(3.6) Respectively calculating the nth iteration energy efficiency function f by the secondary usern(η) and nth iteration energy efficiency η n:
wherein P isCIn order to fix the consumed power of the circuit,for the interrupt indication function, the computational expression is:
(3.7) Secondary user energy efficiency function fn(η) making a decision if | fn(eta) less than or equal to ξ, the nth transmission poweroptimum transmission power P, energy efficiency eta for secondary user energy efficiencynotherwise, judging whether the iteration number reaches the maximum iteration number N less than or equal to N, if so, then the sending power is obtainedoptimum transmission power P, energy efficiency eta for secondary user energy efficiencynand obtaining the maximum energy efficiency η for the secondary user, otherwise, continuing the iteration until the constraint condition of iteration termination is met.
2. The method according to claim 1, wherein the lagrangian multiplier τ corresponding to the average transmit power constraint is calculated in step (3.3) by a subgradent iterative algorithmkCorresponding to an average interference power constraintLagrange multiplier mukCalculated by the following formula:
wherein,andrespectively limiting the maximum average transmission power of the secondary users and the maximum average interference power of the secondary users to the primary users, wherein E {. is the mathematical expectation of the solution, t1For Lagrange multiplier iteration step length, t, corresponding to average transmit power constraint2And constraining corresponding Lagrange multiplier iteration step length for the average interference power.
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| CN104168638A (en) * | 2013-10-31 | 2014-11-26 | 南京邮电大学 | Multi-relay-selection and power distribution method based on system interrupt probability |
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| CN103166695A (en) * | 2013-03-26 | 2013-06-19 | 北京邮电大学 | Relay device for combined optimization of capacity and bit error rate |
| CN103298084A (en) * | 2013-05-17 | 2013-09-11 | 山东大学 | Coordinated multi-relay selection and power distribution method based on energy efficiency criteria |
| CN104168638A (en) * | 2013-10-31 | 2014-11-26 | 南京邮电大学 | Multi-relay-selection and power distribution method based on system interrupt probability |
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