CN106921413B - Low-voltage power line communication local area network performance optimization method based on dynamic game - Google Patents
Low-voltage power line communication local area network performance optimization method based on dynamic game Download PDFInfo
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Abstract
The invention provides a dynamic game-based performance optimization method for a low-voltage power line communication local area network, which adaptively changes channel access parameters and improves the throughput, the bandwidth utilization rate and the time slot utilization rate of the power line carrier communication local area network, and belongs to the field of communication. The method comprises the following steps: the method comprises the following steps: when the time slot of the low-voltage power line communication local area network is started, turning to the second step; step two: the node judges whether the channel is in an idle state at present, if so, the step III is carried out, if not, other channels are selected, and the step is repeated; step three: the node predicts the judgment result of the competitive node on the channel state by using a hidden Markov prediction algorithm to obtain the number n of active nodes participating in channel competition; step four: obtaining the optimal access probability p of the channel according to the obtained n; step five: and requesting to access the channel according to the obtained optimal access probability p, if the channel is successfully accessed, sending a data packet to the target node according to the access probability p by the node under the asymmetric channel environment, and if the channel is unsuccessfully accessed, turning to the step two.
Description
Technical Field
The invention relates to a performance optimization method for a low-voltage power line communication local area network, in particular to a performance optimization method for a low-voltage power line communication local area network based on a dynamic game, and belongs to the field of communication.
Background
The energy internet is considered as an important mark of the third industrial revolution and has now received wide attention all over the world. As the future development direction of the power system, the power energy internet is influenced by the trends of industrial 4.0 and internet +, and becomes one of the research hotspots of the academic world at present. The energy router serves as a core interface of an energy internet and is one of feasible schemes for power energy internet networking, so that a network constructed by the energy router serves as a research object, and improvement of QoS performance of a constructed low-voltage Power Line Communication (PLC) local area network is realized by researching a low-voltage power line carrier communication MAC layer access control protocol, so that important theoretical significance and engineering value are provided for performance such as power energy internet flow optimization and network congestion.
The low-voltage power line communication MAC layer protocol plays an important role in the whole protocol stack, and the performance of the protocol directly influences the comprehensive performance such as the throughput of the bandwidth resource limited PLC network, the frequency band utilization rate, the time slot utilization rate and the like. At present, the MAC protocol mainly adopts a TDMA or CSMA protocol. TDMA requires strict synchronization of the clocks of the nodes, and to some extent the application of the protocol will be limited; some researchers have intensively studied the CSMA channel access protocol, but the protocol cannot solve the collision per se, resulting in relatively low network throughput, bandwidth utilization, and other performances. Based on this, the problems existing at the present stage include the following:
1. the access of a node to a channel directly affects the access performance of a neighbor node.
2. Due to the randomness of the node access channel, it is relatively difficult to obtain the number of active nodes participating in channel competition. Although some documents propose node number estimation methods associated with the methods, the algorithms are complex and have great difficulty in implementation.
3. When the network access load is heavy, the collision probability of the data packet sent by the node is high, network blockage is easy to occur, and the performance such as bandwidth utilization rate, throughput and time slot utilization rate is reduced.
Disclosure of Invention
Aiming at the defects, the invention provides a dynamic game-based performance optimization method for a low-voltage power line communication local area network, which adaptively changes channel access parameters and improves the throughput, the bandwidth utilization rate and the time slot utilization rate of the power line carrier communication local area network.
The invention discloses a performance optimization method of a low-voltage power line communication local area network based on a dynamic game, which comprises the following steps:
the method comprises the following steps: when the time slot of the low-voltage power line communication local area network is started, turning to the second step;
step two: the node judges whether the channel is in an idle state at present, if so, the step III is carried out, if not, other channels are selected, and the step is repeated;
step three: the node predicts the judgment result of the competitive node on the channel state by using a hidden Markov prediction algorithm to obtain the number n of active nodes participating in channel competition;
step four: obtaining the optimal access probability p of the channel according to the obtained n;
step five: and requesting to access the channel according to the obtained optimal access probability p, if the channel is successfully accessed, sending a data packet to the target node according to the access probability p by the node under the asymmetric channel environment, and if the channel is unsuccessfully accessed, turning to the step two.
Preferably, the third step includes the following steps:
calculating the prior probability of the current t time slot by utilizing the posterior probability of the t-1 time slot, and then correcting the prior probability according to the observation channel state sequence of the current t time slot to obtain the posterior probability of the current t time slot:
wherein the content of the first and second substances,
Strepresenting a variable;
sithe channel state of a competitive node decision to be predicted by a tth time slot node is represented, and i is 0 and 1;
Otrepresenting a variable;
otthe channel state of the self judgment of the tth time slot node is shown, and t is 0 and 1;
S＝{s0＝0,s11, representing a hidden state in a hidden Markov prediction algorithm;
n has an initial value of 1 when siWhen the value of N is 1, i is 1, … N, and N is the number of nodes participating in channel contention.
Preferably, in the fourth step, the obtained optimal access probability p of the channel is:
wherein, TcDetecting the busy time of a channel by a non-collision node when a data packet collides in the channel; t isIIndicating the time the channel is free in the current time slot.
The features mentioned above can be combined in various suitable ways or replaced by equivalent features as long as the object of the invention is achieved.
The invention has the advantages that by combining the advantages of the game theory, the nodes can ensure that the active nodes can send data packets with the optimal access probability in any time slot when any node of the current network is unknown to compete for channel information, and the problem of relatively low QoS performance caused by serious conflict in the transmission process of the data packets in the network at the present stage is solved. The invention can realize the dynamic blind estimation of the number of active nodes participating in channel competition in an asymmetric channel environment, improve the accuracy of game information and provide conditions for the nodes to send data packets with the optimal access probability. And the throughput and the bandwidth utilization rate of the power line carrier communication local area network are improved. Meanwhile, the time slot utilization rate of the power line carrier communication local area network is improved.
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Fig. 1 is a schematic flowchart of a performance optimization method for a low-voltage power line communication local area network based on dynamic gaming according to an embodiment.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The invention is further described with reference to the following drawings and specific examples, which are not intended to be limiting. The embodiment is described with reference to fig. 1, and the method for optimizing the performance of the low-voltage power line communication local area network based on the dynamic game of the embodiment includes the following steps:
the method comprises the following steps: when the time slot of the low-voltage power line communication local area network is started, turning to the second step;
step two: the node judges whether the channel is in an idle state at present, if so, the step III is carried out, if not, other channels are selected, and the step is repeated;
step three: the node predicts the judgment result of the competitive node on the channel state by using a hidden Markov prediction algorithm to obtain the number n of active nodes participating in channel competition;
step four: obtaining the optimal access probability p of the channel according to the obtained n;
step five: and requesting to access the channel according to the obtained optimal access probability p, if the channel is successfully accessed, sending a data packet to the target node according to the access probability p by the node under the asymmetric channel environment, and if the channel is unsuccessfully accessed, turning to the step two.
In view of the characteristic that nodes in a low-voltage PLC local area network compete for a shared channel in a distributed environment, the method takes network performance maximization as an expected target, characterizes the process of node channel competition as a non-complete information dynamic game, and provides a self-adaptive p-adherence CSMA protocol based on hidden Markov prediction. Under the condition that the node has incomplete information, a hidden Markov prediction algorithm is used for the current game state, namely: and estimating the number of active nodes competing for the channel, adjusting the optimal access probability of the channel, and finally improving the network throughput, the bandwidth utilization rate and other performances through the limited dynamic game.
The access probability of the data packet sent by the node is directly related to the number n of the network active nodes, but under the asymmetric power line channel environment, the node cannot directly obtain the judgment result of a competitor on the channel state. The node can only predict the judgment result of a competitor according to the judgment result of the channel state of the node, and the number of active nodes participating in channel competition is obtained.
The hidden Markov model is a kind of Markov chain, which includes two kinds of hidden state and observed state, and its hidden state can not be directly observed by entity, and the observed result of entity, i.e. observed state, is the information reflected by hidden state. Each observation state is represented as various hidden states through some probability distribution, and the statistical law of the hidden state transition law also has the Markov characteristic. Therefore, the hidden markov model is a double stochastic process.
Based on the above, in the present embodiment, the node uses the hidden markov prediction algorithm to predict the decision result of the competitor on the channel state in the asymmetric channel environment, so as to obtain the number n of active nodes participating in channel competition, and predicts the decision of the competitor on the channel state by calculating the maximum posterior probability method, so as to obtain the number of active nodes quantitatively. The specific process comprises the following steps:
calculating the prior probability of the current t time slot by utilizing the posterior probability of the t-1 time slot, correcting the prior probability according to the observation channel state sequence of the current t time slot to obtain the posterior probability of the current t time slot, and obtaining the posterior probability of the current t time slot by a Bayesian formula:
let Qt(si,ot)＝p(St＝si,Ot＝ot) Substituting into Bayes' formula to obtain
And is
In the formula: from Q1(s1,o1)＝p(S1＝sj,O1＝o1)＝πjp(o1|sj) Recursion to obtain Q2(sj,o2)，Q3(sj,o3),...,Qt(sj,ot). Thus, find out
S iniThe value of (d) may represent the result of the contention node's decision channel predicted by the unauthorized user at time t.
StRepresenting a variable;
sithe channel state of a competitive node decision to be predicted by a tth time slot node is represented, and i is 0 and 1;
Otrepresenting a variable;
otthe channel state of the self judgment of the tth time slot node is shown, and t is 0 and 1;
S＝{s0＝0,s11, representing a hidden state in a hidden Markov prediction algorithm;
n is set to 1, and at the beginning of each time slot, if the node wants to participate in channel competition and predicts the decision result s of competitoriIf the value is 1, the value of n is added by 1. According to the node communication requirement in the network, the number of nodes participating in competition in each time slot is changed, and N is more than or equal to 1 and less than or equal to N.
In the fourth step of the present embodiment, the optimal access probability p of the channel is calculated according to the acquired n;
as known to those skilled in the art, when the bandwidth of the wideband PLC is in the range of [1.8,20] MHz, the network bandwidth utilization can be quantitatively expressed as:
in formula (4), bandwidth is communication bandwidth, S is network throughput, and E [ DATA ]]Length, T, representing the amount of data packet payload information sent by a nodesThe channel is detected as a busy time, T, for the time when the node successfully sends the data packetcNon-colliding nodes detect channel busy time, T, when data packets collide in the channelIIndicating the time during which the channel is free in the time slot, PtrFor at least one node to transmit probability, P, in a given time slotsIs the conditional probability if and only if only one node successfully sends a packet.
Wherein the content of the first and second substances,
Ts＝PRS0+PRS1+TFRAME+RIFS+TACK+CIFS (5)
Tc＝PRS0+PRS1+TFRAME+CIFS (6)
Ptr＝1-(1-p0)n(7)
Ps＝np0(1-p0)n-1/Ptr(8)
PRS0represents Priority Resolution Slots0 (PRS 0); PRS1Priority Resolution Slots1 (PRS 1); t isFRAMEIndicating the transmission time of the data packet; RIFS denotes the Inter Frame Space (RIFS) of the Response; t isACKIndicates the transmission time of an ACK (acknowledgement) response frame, CIFS p0A Frame Space (CIFS) indicating Contention; p is a radical of0Representing the probability of the node accessing the network;
in order to ensure the maximum bandwidth utilization rate of the network, the node access probability relation is deduced by taking the maximum bandwidth utilization rate as an objective function:
max{η} (9)
and the number of the first and second electrodes,
to maximize the network spectrum utilization, according to the formula (4), when
When taking the minimum value, η will take the maximum value, for the variable p in equation (11)0After calculating the partial derivative, making it equal to 0 to obtain
(1-p0)n(TI-Tc)+Tc(1-np0)＝0 (12)
The formula (12) belongs to the transcendental equation, and can be known from the Taylor formula
By bringing formula (13) into formula (12)
According to the results, when the node sends the data packet according to the probability of the formula (14), the network bandwidth utilization rate can be maximized. Accordingly, network throughput can be maximized. The mathematical expression of the network time slot utilization rate related to the network time slot utilization rate is different from the throughput when the transmission time of the data packet is used for replacing the information quantity length of the data packet payload. According to the derivation result, the network time slot utilization rate can be maximized.
The embodiment can realize the optimization of the network QoS performance in the low-voltage power line carrier communication local area network by the method.
Although the invention herein has been described with reference to particular embodiments, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present invention. It is therefore to be understood that numerous modifications may be made to the illustrative embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that features described in different dependent claims and herein may be combined in ways different from those described in the original claims. It is also to be understood that features described in connection with individual embodiments may be used in other described embodiments.
Claims (1)
1. A performance optimization method for a low-voltage power line communication local area network based on a dynamic game is characterized by comprising the following steps:
the method comprises the following steps: when the time slot of the low-voltage power line communication local area network is started, turning to the second step;
step two: the node judges whether the channel is in an idle state at present, if so, the step III is carried out, if not, other channels are selected, and the step is repeated;
step three: the node predicts the judgment result of the competitive node on the channel state by using a hidden Markov prediction algorithm to obtain the number n of active nodes participating in channel competition;
step four: obtaining the optimal access probability p of the channel according to the obtained n;
step five: requesting to access the channel according to the obtained optimal access probability p, if successful access is available, sending a data packet to a target node according to the access probability p by the node under the asymmetric channel environment, and if unsuccessful access is available, turning to the second step;
the third step comprises:
calculating the prior probability of the current t time slot by utilizing the posterior probability of the t-1 time slot, and then correcting the prior probability according to the observation channel state sequence of the current t time slot to obtain the posterior probability of the current t time slot:
wherein the content of the first and second substances,
Strepresenting a variable; siThe channel state of a competitive node decision to be predicted by a tth time slot node is represented, and i is 0 and 1;
Otrepresenting a variable; otThe channel state of the self judgment of the tth time slot node is shown, and t is 0 and 1;
S＝{s0＝0,s11, representing a hidden state in a hidden Markov prediction algorithm;
n has an initial value of 1 when siWhen the value of N is 1, the value of N is added with 1, i is 1, … N, and N is the number of nodes participating in channel competition;
in the fourth step, the obtained optimal access probability p of the channel is as follows:
wherein, TcDetecting the busy time of a channel by a non-collision node when a data packet collides in the channel; t isIIndicating the time the channel is free in the current time slot.
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