CN104540170A - Transmission line monitoring network node combined congestion control and power control iterating method - Google Patents
Transmission line monitoring network node combined congestion control and power control iterating method Download PDFInfo
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- 230000005540 biological transmission Effects 0.000 title claims abstract description 83
- 238000012544 monitoring process Methods 0.000 title claims abstract description 20
- 238000000034 method Methods 0.000 title claims abstract description 13
- 230000006870 function Effects 0.000 claims abstract description 27
- 230000008901 benefit Effects 0.000 claims abstract description 10
- 238000005562 fading Methods 0.000 claims abstract description 9
- 230000003247 decreasing effect Effects 0.000 claims abstract description 4
- 230000003920 cognitive function Effects 0.000 claims description 7
- 238000005457 optimization Methods 0.000 claims description 6
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- 230000002035 prolonged effect Effects 0.000 abstract description 2
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Classifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/0215—Traffic management, e.g. flow control or congestion control based on user or device properties, e.g. MTC-capable devices
- H04W28/0221—Traffic management, e.g. flow control or congestion control based on user or device properties, e.g. MTC-capable devices power availability or consumption
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/0289—Congestion control
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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
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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
- H04W52/30—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
- H04W52/343—TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading taking into account loading or congestion level
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/12—Wireless traffic scheduling
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Abstract
The invention discloses a transmission line monitoring network node combined congestion control and power control iterating method. The method includes the following steps of firstly, establishing a transmission line state monitoring network CRN with a cognition function; secondly, establishing a generalized network benefit maximizing mathematic model so as to solve the combined congestion control and transmission power control problem of the CRN. In order to eliminate channel fading influences and improve the wireless signal transmission quality, a combined congestion control and power control iterating algorithm with overflow constraints is put forward. It can be seen from simulation results that it can be ensured that all users in a cognition wireless network meet the overflow probability requirement and the node transmission power loss can be reduced. It can be displayed from simulation results that the transmission speed and power of sensor nodes can be rapidly decreased to appropriate values through the algorithm to meet the overflow probability requirement of all the nodes. Thus, the network congestion is reduced, and the service life of the network is prolonged.
Description
Technical Field
The invention belongs to the field of power communication, and provides a power transmission line monitoring network with a cognitive function, and a combined congestion control and power control iterative algorithm with overflow constraint to determine the transmission rate and power of each node.
Background
The transmission line is an important component of the power system, and the state monitoring of the transmission line is very necessary, which is a necessary way for realizing the science and technology and the modernization of the power industry. Due to the characteristics of large layout range, various power requirements, long line distance and the like, once the power transmission line is influenced by severe weather such as strong wind, ice and snow, rainstorm, hail and the like or serious natural disasters such as torrential flood, earthquake, mountain landslide and the like, the line is likely to be damaged or even interrupted, and the operation and safety state of a power system are influenced. This requires that the power workers monitor the line running status in real time, and when a problem is found, the power transmission line is overhauled comprehensively at the first time, and the power running is recovered as soon as possible.
Due to the special layout of the transmission line, the communication network of the transmission line is required to have the characteristics of reliability, rapidness, flexibility, economy and the like, and the traditional wired network not only has complex wiring but also has higher cost. Although the wireless network has a slightly poorer stability on a link than a wired network, along with the continuous improvement of a bottom layer technology and the continuous optimization of an upper layer protocol and a networking technology, the wireless technology is greatly improved, and the reliability and the stability of the wireless network are better and better, so that the application of the wireless network in the monitoring of the state of the power transmission line not only has a wide market prospect, but also is technically feasible.
The cognitive wireless network refers to the fact that the network intelligently adapts to changes of the external environment by sensing the external environment and adjusting internal configuration of the communication network in real time through understanding and learning of the external environment. The traditional power transmission line monitoring network node is a common sensor node, and data transmission can be carried out only according to a preset receiving and transmitting rate and transmission power. This results in unnecessary power waste and data congestion.
Disclosure of Invention
The invention designs a novel power transmission line state monitoring network, and monitoring nodes are sensors with cognitive functions. By uniformly arranging the sensor nodes with the channel sensing function along the line, the novel network can automatically select the unoccupied wireless channels and automatically adjust the node transmission rate and the transmission power of the novel network. Through the dynamic self-adaptive process of perception-decision-behavior of each node, the overflow probability of each node can be ensured to meet the preset requirement, the data transmission of the node is prevented from being congested, and the survival time of the network is prolonged.
The invention designs a new node joint congestion power and power control iterative algorithm in a cognitive wireless network.
The joint congestion control and power control iteration method for the power transmission line monitoring network node comprises the following steps:
firstly, establishing a power transmission line state monitoring network CRN with a cognitive function: the network consists of the following two sensor nodes: a primary user PU and a secondary user SU; the primary user PU uses the wireless channel at any time, and the secondary user SU uses the channel only under the condition of not interfering the PU; the PU and the SU both have the capability of sensing whether a certain channel is occupied and adjusting the transmission rate and the transmission power of the PU and the SU;
secondly, establishing a generalized network benefit maximization mathematical model to solve the joint congestion control and transmission power control problems of the CRN:
i) for the main user PU, the overflow probability is as follows:
in the above formula, the first and second carbon atoms are,the probability of overflow is indicated and,andrespectively representing links l and l0Upper SINR minimum threshold, overflow probability threshold, N0Representing additive white gaussian noise at each receiving node; the transmission rate set of SU is x ═ x1,...,xS]TThe range of the rate isThe transmission power is set as p ═ p1,...,pL]TThe range of power isp0Representing PUs0Transmit power of the peripheral SU s at a rate xsA benefit U can be obtained during transmissions(xs) And in space xsAbove is the strictly non-decreasing concave function;
ii) optimizing the overflow probability:
iii) the Lagrangian function of the above equation can be expressed as:
wherein:
in the above formula, λ ═ λ1,...,λL],v＝[v1,...,vL]Both with μ are lagrange factors; the partial derivative of the above equation can be expressed as:
wherein D is1(λ) represents the congestion control algorithm of the transport layer, D2(λ, v, μ) represents a transmission power control algorithm of the physical layer; an iterative formula can be obtained by making the first derivative of the above two equations 0 to maximize the merit function:
wherein,an inverse function representing the number of first order bias layers of the benefit function, <math>
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vl(t+1)＝[vl(t)-α(t)hl(t)]+
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In the above three formulae, [ a ]]+Is max { a,0 }.
The lagrangian factor is initialized using any combination of natural number numbers greater than or equal to 0: i.e., { λ (0), v (0), μ (0) } ≧ 0.
Repeating the step II until the convergence of the node transmission rate and the power is achieved; the iterative process is as follows: the node transmission rate and power at time t are { x (t +1), p (t +1) }, the node transmission rate at time t +1 is formula (15), the transmission power is formula (16), and the three lagrangian factors are formulas (17), (18) and (19), respectively.
The channel adopts a Rayleigh fading channel model.
According to the invention, a cognitive wireless network is introduced into the power transmission line, and sensor nodes with cognitive functions are uniformly arranged into the cognitive wireless network along the line. Each node collects relevant information at any time and sends the information to the base station according to a certain route. Therefore, self parameters are adjusted through the dynamic self-adaptive process of perception-decision-behavior of each node, and optimization of network performance is realized.
However, due to the time-varying and uncertainty of the wireless channel, and the effects of shadow fading, interference between users, etc., the wireless signal fades during propagation. In order to overcome the influence of channel fading and improve the transmission quality of wireless signals, the invention provides a combined congestion control and power control iterative algorithm with overflow constraint. According to the simulation result, the method and the device can ensure that all users in the cognitive radio network meet the overflow probability requirement, and reduce the transmission power loss of the node.
Simulation results show that the algorithm can enable the transmission rate and the power of the sensor nodes to be converged to appropriate values rapidly, and the overflow probability requirements of all the nodes are met. Thereby reducing network congestion and extending network life.
Drawings
FIG. 1 is a schematic diagram of a power transmission line on-line monitoring network system model with a cognitive function;
FIG. 2 is a graph of link transmission power as a function of iteration number;
FIG. 3 is a graph of link flow rate as a function of iteration number;
FIG. 4 is a graph of overflow probability as a function of iteration number.
Detailed Description
The power transmission line state monitoring network with the cognitive function consists of the following two sensor nodes: primary User (PU) and Secondary User (SU). The PU may use a certain radio channel at any time, and the SU may use this channel only without interfering with the PU. The PU and the SU can sense whether a certain channel is occupied or not and adjust the transmission rate and the transmission power of the PU and the SU. The power transmission line state monitoring network composed of the PU and the SU is called Cognitive Radio Networks (CRN) for short.
Currently used G (S, L, S)0,l0) Denotes a CRN, where S { 1., S } denotes a set of SUs, L { 1., L } denotes a link for data transmission between SUs, and S0Denotes PU, l0Representing the links between PUs for data transmission. Let s (l) denote the set of SUs using link l, and l(s) denote the set of paths used by a SU s for data transmission. The transmission rate set of SU is x ═ x1,...,xS]TThe range of the rate isThe transmission power is set as p ═ p1,...,pL]TThe range of power isPeripheral SU s at a rate xsA benefit U can be obtained during transmissions(xs) And in space xsAbove is a strictly non-decreasing concave function. Here represented by the following α -interest function:
the capacity of the link L ∈ L is obtained by shannon's theorem:
cl(γl(p,p0))＝log(1+γl(p,p0)) (2)
wherein p is0Representing PUs0Assuming that the power is a constant value; gamma rayl(p,p0) Represents the instantaneous Signal-to-noise ratio (Signal to Interference) of the link L ∈ L plus Noise Ratio，SINR)
In the above formula, gliRepresenting a slow fading channel, f, transmitted from node i to node lliRepresents a fast fading channel transmitted from node i to node l; n is a radical of0Representing Additive White Gaussian Noise (AWGN) at each receiving node. Here we use the Rayleigh fading channel model, i.e. fliRepresenting independent identically distributed exponential random variables. So the PU is on link l0The instantaneous SINR at (d) is:
the normalized average SINR is:
for the CRN, the energy of the node is the most important resource, so the transmission power control of the node is also essential for the network. The invention designs a new cost function and designs the following Generalized Network Utility Maximization (GNUM) mathematical model to solve the Joint Congestion Control and transmission power Control problem (JCPC) of the CRN:
in the above formula, the first and second carbon atoms are,the probability of overflow is indicated and,andrespectively representing links l and l0Upper SINR minimum threshold, overflow probability threshold. The objective of the present invention is to design a distributed JCPC algorithm that maximizes the benefits of CRN with minimal power consumption. The overflow probability for the rayleigh channel is expressed as follows:
for a PU, the overflow probability is:
optimization algorithm
Simplification by using the following:
the lagrange function of the above equation can be expressed as:
wherein:
in the above formula, λ ═ λ1,...,λL],v＝[v1,...,vL]And μ are lagrange factors. The partial derivative of the above equation can be expressed as:
wherein D is1(λ) represents the congestion control algorithm of the transport layer, D2(λ, v, μ) denotes a transmission power control algorithm of the physical layer. An iterative formula can be obtained by making the first derivative of the above two equations 0 to maximize the merit function:
wherein,an inverse function representing the number of first order bias layers of the benefit function, <math>
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μ(t+1)＝[μ(t)-α(t)m(t)]+Wherein <math>
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In the above three formulae, [ a ]]+Is max { a,0 }.
Thus, the optimized JCPC algorithm with overflow constraint designed by the invention is as follows:
1. any combination of numbers greater than or equal to 0 is used to initialize the lagrangian factor: i.e., { λ (0), v (0), μ (0) } ≧ 0;
2. and repeating the iteration process until the convergence of the transmission rate and the power of the node is reached. The iterative process is as follows: the node transmission rate and power at time t are { x (t +1), p (t +1) }, the node transmission rate at time t +1 is formula (15), the transmission power is formula (16), and the three lagrangian factors are formulas (17), (18) and (19), respectively.
Now assume that a transmission line has 5 SU nodes and 2 PU nodes. Meanwhile, data transmission streams among 4 secondary users and data transmission streams among 1 primary user are set. The schematic diagram is shown in fig. 1. All the nodes are uniformly distributed at intervals of 100 meters. Let the slow fading gain of the channel beWherein A is 10-5Is a fixed value, dliRepresenting the distance between the transmitting node on link i and the receiving node on link i. Let the transmission power limit of the node beAndand setting overflow probability threshold values of 4 secondary user streams (0.20.30.30.2) respectively, wherein SINR threshold values are (0.60.20.20.6) dB respectively. Let the transmission power of the PU be, the overflow probability threshold and SINR threshold are 0.4 and 1.4dB, respectively. Let α be 2 in the α -interest function of all users (including primary users and secondary users). AWGN is taken as N in the text0＝10-13mW。
Fig. 2 and 3 show the transmission power versus rate for 4 secondary user streams as a function of the number of iterations. The converged SINRs are (0.24110.05840.12720.0713) dB, and the converged rates are (0.21600.05680.11970.0689) kbs.
Fig. 4 shows the overflow probability of the primary user stream and the 4 secondary user streams varying with the number of iterations, and the simulation result shows that the converged overflow probabilities are all lower than the preset overflow probability value.
Claims (4)
1. The joint congestion control and power control iteration method for the power transmission line monitoring network node is characterized by comprising the following steps of:
firstly, establishing a power transmission line state monitoring network CRN with a cognitive function: the network consists of the following two sensor nodes: a primary user PU and a secondary user SU; the primary user PU uses the wireless channel at any time, and the secondary user SU uses the channel only under the condition of not interfering the PU; the PU and the SU both have the capability of sensing whether a certain channel is occupied and adjusting the transmission rate and the transmission power of the PU and the SU;
secondly, establishing a generalized network benefit maximization mathematical model to solve the joint congestion control and transmission power control problems of the CRN:
i) for the main user PU, the overflow probability is as follows:
in the above formula, the first and second carbon atoms are,the probability of overflow is indicated and,andrespectively representing links l and l0Upper SINR minimum threshold, overflow probability threshold, N0Representing additive white gaussian noise at each receiving node; the transmission rate set of SU is x ═ x1,…，xS]TThe range of the rate isThe transmission power is set as p ═ p1,…,pL]TThe range of power isp0Representing PUs0Transmit power of the peripheral SU s at a rate xsA benefit U can be obtained during transmissions(xs) And in space xsAbove is the strictly non-decreasing concave function;
ii) optimizing the overflow probability:
iii) the Lagrangian function of the above equation can be expressed as:
wherein:
in the above formula, λ ═ λ1,…,λL],v＝[v1,…,vL]Both with μ are lagrange factors; the partial derivative of the above equation can be expressed as:
wherein D is1(λ) represents the congestion control algorithm of the transport layer, D2(λ, v, μ) represents a transmission power control algorithm of the physical layer; an iterative formula can be obtained by making the first derivative of the above two equations 0 to maximize the merit function:
wherein,an inverse function representing the number of first order bias layers of the benefit function, <math>
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due to D1(lambda) and D2(λ, v, μ) is a strictly concave function and is second order derivable; the following iterative formula can be used to solve both extrema;
λl(t+1)＝[λl(t)-α(t)gl(t)]+wherein <math>
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vl(t+1)＝[vl(t)-α(t)hl(t)]+
(18)
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μ(t+1)＝[μ(t)-α(t)m(t)]+Wherein <math>
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In the above three formulae, [ a ]]+Is max { a,0 }.
2. The power transmission line monitoring network node joint congestion control and power control iterative method of claim 1, characterized in that: the lagrangian factor is initialized using any combination of natural number numbers greater than or equal to 0: i.e., { λ (0), v (0), μ (0) } ≧ 0.
3. The power transmission line monitoring network node joint congestion control and power control iterative method of claim 1, characterized in that: repeating the step II until the convergence of the node transmission rate and the power is achieved; the iterative process is as follows: the node transmission rate and power at time t are { x (t +1), p (t +1) }, the node transmission rate at time t +1 is formula (15), the transmission power is formula (16), and the three lagrangian factors are formulas (17), (18) and (19), respectively.
4. The power transmission line monitoring network node joint congestion control and power control iterative method of claim 1, characterized in that: the channel adopts a Rayleigh fading channel model.
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