CN110149648A - Distributed agent repeater system and optimization method under power constraint - Google Patents
Distributed agent repeater system and optimization method under power constraint Download PDFInfo
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- CN110149648A CN110149648A CN201910427669.1A CN201910427669A CN110149648A CN 110149648 A CN110149648 A CN 110149648A CN 201910427669 A CN201910427669 A CN 201910427669A CN 110149648 A CN110149648 A CN 110149648A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/309—Measuring or estimating channel quality parameters
- H04B17/336—Signal-to-interference ratio [SIR] or carrier-to-interference ratio [CIR]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/382—Monitoring; Testing of propagation channels for resource allocation, admission control or handover
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L1/00—Arrangements for detecting or preventing errors in the information received
- H04L1/0001—Systems modifying transmission characteristics according to link quality, e.g. power backoff
- H04L1/0014—Systems modifying transmission characteristics according to link quality, e.g. power backoff by adapting the source coding
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/56—Provisioning of proxy services
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
- H04W40/22—Communication route or path selection, e.g. power-based or shortest path routing using selective relaying for reaching a BTS [Base Transceiver Station] or an access point
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- 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
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/50—Allocation or scheduling criteria for wireless resources
- H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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Abstract
The invention discloses distributed agent repeater system and optimization methods under a kind of power constraint, within the system, what information source node emitted is the analog signal of Gaussian Profile, received destination node is digital signal, multiple relay nodes are responsible for carrying out distributed source coding to the analog signal received, are forwarded after being converted into digital signal.For such system, Framework of Theoretical Analysis is proposed: it is theoretical with CEO, it is deduced after considering channel fading, each relay node transmission rate, then in conjunction with shannon capacity theory, wireless simulation Sensor Network and the foundation of radio digital communication net is contacted.It proposes corresponding optimum design method: under conditions of general power is limited, power distribution is carried out between information source node and relay node, so that destination node signal-to-noise performance reaches best.Theory analysis and simulation result all show that the optimization method that the present invention is mentioned can make this act on behalf of repeater system and provide signal-to-noise performance more better than amplification forwarding system.
Description
Technical field
The invention belongs to the technical fields of wireless communication, and in particular to distributed agent repeater system and excellent under power constraint
Change method.
Background technique
Wireless relaying technique is a kind of very important technology in wireless communication, has been widely used for all kinds of channel radios
In letter system, such as satellite relay communication, microwave radio relay communication and mobile communication system.It can be effective against channel and decline
The capacity for falling bring influence, the coverage area of expansion wireless signal, raising communication system, increases the diversity gain of receiving end,
The reliability of information transmission is improved, so as to improve communication quality.
Currently, be concentrated mainly on digital junction network facet to the research of relaying technique, such as amplification forwarding, decoding forwarding and
Compression forwarding etc..In amplification forwarding, the source signal received is only simply carried out power amplification by relay node, then
It is forwarded directly to destination node；In decoding forwarding, relay node is that the signal that will be received is decoded, and recovers original letter
After breath, then is recompiled by certain coding mode and be sent to destination node；In compression forwarding, relay node is first by information source
Node signal is demodulated, and is then quantified again, estimated and is compressed, is finally transmitted to destination node.Destination node will come from
The signal of information source node carries out combined decoding with the signal from relay node.Unlike decoding forwarding, compression forwarding mould
Relay node in type does not need to decode the information received completely, and only extracts from the signal received and mesh
The maximally related part of signal that receives of node.
Under amplification forwarding strategy, information source node S sends analog signal X (t), transmits through awgn channel, then in
After node RiThe signal received is
Yri(t)=hriX(t)+Nri(t), i=1,2 ..., L
In formula, hriFor the fading channel factor in i-th of information source node-repeated link, NriIt (t) is that zero-mean, variance areIndependent identically distributed white Gaussian noise.Then relay node RiThe power of the signal received is
The signal received is carried out power amplification by each relay node, it is assumed that the relay node power amplification factor is βi,
Transmission power is Pri, then have
In formula
Then amplified signal is transmitted to destination node by additive white Gaussian noise channel by each relay node.Assuming that
Relaying-destination node link fading channel factor is hdi, then destination node receive corresponding signal from each channel and
Power is respectively as follows:
In addition to this, wireless simulation Sensor Network is scouted also in real life there is also being widely applied, such as in military situation,
Environmental monitoring, smart home, detection and monitoring of medical treatment & health situation etc., this to grind wireless simulation junction network
Study carefully and has great importance.
But it is very rare for research work of the analog signal in the transmission of more junction networks at present.
When destination node directly can not observe and receive information source node transmission information, L relay node can only be passed through
To wonder the feelings that certain specifying informations can not but observe directly similar to Mr. Yu CEO when realizing the estimation to information source information
Condition.If the CEO employs the L agents that can be directly monitored by information, report by the way that they are independent come indirect gain
Information, and agent needs to weigh in reported lengths and distortion program.Problems are known as CEO problem.
The case where CEO problem of distributed source coding is Gaussian distributed equal with noise for information source, describes
Rate distortion zone and code rate under the conditions of certain distortion constraints, after relay node progress distributed source coding.For information source
The case where information X (t) can not be directly observed by information processing centre, processing center pass through L mutually independent relay nodes
Respectively information source information is observed.Relay node observes to be source signal Y after noise pollutionri(t)=hriX(t)
+Nri(t), then i=1,2 ..., L carry out distributed source coding and compression to it each independently, are then uniformly sent to
Processing center, total rate of transmission are R.Processing center obtains information source information X (t) after the signal received is carried out joint decoding
Estimation
By X (t) and Yri(t) it is described with n sampled point, are as follows:
Definition side, which is distorted, is
If there are distributed coding schemes at relay node, felt in processes in corresponding decoding scheme, so that
Then the relationship of transmission rate R total at all relay nodes and distortion factor d is reachable to (R, d).If withTable
Show the set of all achievable pairs (R, d), then rate distortion function is defined as
The CEO problem of distributed source coding only has studied in wireless simulation sensor network, and relay node is distributed
Corresponding rate distortion zone and code rate problem after formula compressed encoding, there is no the transmission performances to entire sensed communication network
It is studied and is designed.
Summary of the invention
The present invention aiming at the shortcomings in the prior art, proposes more relay nodes under a kind of power constraint to analog source
The system model that information is compressed and forwarded, the system is by the wireless simulation Sensor Network of information source to more junction networks and without line number
Word communication network two parts composition.It is theoretical according to the CEO theory of distributed source coding and classical shannon capacity, it establishes
Act on behalf of the theoretical analysis method of repeater system.Under general power confined condition, to wireless simulation Sensor Network and radio digital communication
Net carries out optimal power allocation, reaches the signal-to-noise performance at destination node most preferably, compared to traditional amplification forwarding system
With better signal-to-noise performance.
To achieve the above object, the invention adopts the following technical scheme:
Distributed agent repeater system under power constraint characterized by comprising an information source node S, 2L item decline letter
Road, L relay node Ri(i=1,2 ... L) and a destination node D；Without the chain that direct transfers between information source node S and destination node D
Road, signal pass through multiple relay node RiBe sampled, encode, compress after forward again, to realize information source node S and purpose section
Communication between point D；Wireless simulation Sensor Network, multiple relay node R are formed between information source node S to multiple relay node Rii
To formation radio digital communication net between destination node D；
Relay node RiPlace is provided with source encoder, source encoder by it is receiving, by fading channel and mix
The analog signal Y of white Gaussian noiseri(t) it is sampled, encodes and compresses, be converted to digital signal Y 'ri(t), each relaying
Node RiAgain with identical transmission power PrBy digital signal Y 'ri(t) it is sent to destination node D, realizes relay node RiTo purpose
The Digital Transmission of node D.
To optimize above-mentioned technical proposal, the concrete measure taken further include:
Further, it to the distributed source coding problem of sensor network, is managed using the CEO in distributed source coding
By the rate distortion function for establishing more junction networks, and combine shannon capacity theoretical, by wireless simulation Sensor Network and no line number
Word communication network establishes connection, thus under general power confined condition, between wireless simulation Sensor Network and radio digital communication net
Power distribution is carried out, signal-to-noise performance is made to reach maximum.
Further, information source node S sends the analog signal X (t), relay node R of its sensor acquisitioniReceive decaying
And the analog signal Y that is mixed with white Gaussian noiseri(t)=hriX(t)+Nri(t), i=1,2 ..., L, Nri(t) it indicates
I-th of relay node RiThe white Gaussian noise at place, variance arehriFor channel fading coefficient；
In distortion factor d allowed band, relay node RiAfter carrying out distributed source coding, total rate distortion function of system
Are as follows:
WhereinFor the mean power of Gaussian source X (t), α=E [| hr|2], E indicates mathematic expectaion.
Further, the power P for the signal that each relay node receivesxiEqual, the transmission of each relay node respectively
Power PriAlso after mutually the same therefore quantified, coding and compression, the transmission rate for the digital signal being converted into is also identical, then
Each relay node RiCorresponding rate distortion function are as follows:
Wherein γDIndicate the quantitative graphs of signal at destination node, RiIt (d) is each relay node RiTo reception signal
Quantified, encoded and compressed minimal information rate.
Further, in conjunction with shannon capacity theory, wireless simulation Sensor Network and radio digital communication net is established and joined
System, relay node RiTransmission rate should be equal to relay node RiTo the channel capacity C of destination node Di, i.e.,
Wherein μ PriIndicate the power for the signal from each relay node that destination node D is received；If relay node Ri
It is with the destination node D total power constraint for receiving signalWherein P indicates that relay node and destination node D receive
The general power of signal.
Further, under general power confined condition, meet the information rate of each relay node of wireless simulation Sensor Network
Equal to the channel capacity in radio digital communication net, so that quantitative graphs γDMaximum optimization problem is excellent using constraining as follows
Change model:
Max: γD
The optimal solution of Constraint Anchored Optimization meets following two element equations:
Known relay node quantity L and total power constraint P, solving equations can be obtained so that quantitative graphs γDMost
When big, information source node S and relay node RiPower allocation schemeAnd PrAnd the maximum value of corresponding quantitative graphs
(γD)max。
Further, the antenna of destination node D will come from each relay node RiDigital signal Y 'ri(t) it carries out maximum
It is received than merging, signal Y will be obtainedd(t) it is transmitted to the joint decoder being located at destination node D and carries out combined decoding, it can be extensive
It appears again information source information
The beneficial effects of the present invention are: distributed agent repeater system is compared to simulation under power constraint proposed by the present invention
Relay forwarding system, it is far better in the signal-to-noise performance of low power section, there is very strong interference free performance.In addition, in height
Power region, with the increase of total power constraint, improving multiple can be also gradually increased.This is for analog source in more trunk networks
Transmission in network gives better forwarding scheme, and in the case where sending the limited situation of power resource, in traditional
After forwarding, better transmission performance can be obtained.
Detailed description of the invention
Fig. 1 is distributed agent repeater system schematic diagram of the present invention.
Fig. 2 is to act on behalf of the destination node signal-to-noise performance of repeater system and amplification forwarding system with the change of relay node quantity
Change contrast schematic diagram.
Fig. 3 is to act on behalf of the destination node signal-to-noise performance of repeater system and amplification forwarding system with the variation of total power constraint
Contrast schematic diagram.
Specific embodiment
In conjunction with the accompanying drawings, the present invention is further explained in detail.
More relaying distributed agent repeater systems as shown in Figure 1, system structure include an information source node S, 2L item
Fading channel, L relay node Ri(i=1,2 ... L) and a destination node D.Without the chain that direct transfers between information source node S and purpose D
Road, signal pass through multiple relay node RiBe sampled, encode, compress after forward again, to realize information source node and purpose section
Communication between point.
Relay node RiPlace is provided with source encoder, source encoder by it is receiving, by fading channel and mix
The analog signal Y of white Gaussian noiseri(t) it is sampled, encodes and compresses, be converted to digital signal Y 'ri(t), each relaying
Node RiAgain with identical transmission power PrBy digital signal Y 'ri(t) it is sent to destination node D, realizes relay node RiTo purpose
The Digital Transmission of node D；The antenna of destination node D will come from each relay node RiDigital signal Y 'ri(t) it carries out maximum
It is received than merging, signal Y will be obtainedd(t) it is transmitted to the joint decoder being located at destination node D and carries out combined decoding, it can be extensive
It appears again information source information
To the distributed source coding problem of sensor network, established using the CEO theory in distributed source coding more
The rate distortion function of junction network, and combine shannon capacity theoretical, analog sensed network and digital communications network are established
Connection, to carry out power distribution between sensor network and communication network under general power confined condition, make signal-to-noise ratio
It can reach maximum.
Relay node RiThat receive is analog signal Y that is having decayed and being mixed with white Gaussian noiseri(t)=hriX
(t)+Nri(t), i=1,2 ..., L, Nri(t) i-th of relay node R is indicatediThe white Gaussian noise at place, variance arehriFor letter
Road attenuation coefficient.
In distortion factor d allowed band, relay node RiAfter carrying out distributed source coding, total rate distortion function of system
Are as follows:
WhereinFor the mean power of Gaussian source X (t), α=E [| hr|2], E indicates mathematic expectaion.
The power P for the signal that each relay node receivesxiEqual, the transmission power P of each relay node respectivelyri?
After mutually the same therefore quantified, coding and compression, the transmission rate for the digital signal being converted into is also identical, then each relaying
Node RiCorresponding rate distortion function is
Wherein γDIndicate the quantitative graphs of signal at destination node, RiIt (d) is each relay node RiTo reception signal
Quantified, encoded and compressed minimal information rate.
In conjunction with shannon capacity theory, analog sensed network and digital communications network foundation are contacted, relay node Ri
Transmission rate should be equal to relay node RiTo the channel capacity C of destination node Di, i.e.,
μPriIndicate the power for the signal from each relay node that destination node D is received.If relay node RiAnd mesh
Node D receive signal total power constraint beWherein P indicates that relay node and destination node D receive signal
General power.
Under general power confined condition, meet the information rate of each relay node of wireless simulation Sensor Network equal to no line number
Channel capacity in word communication network, so that quantitative graphs γDFollowing Constraint Anchored Optimization can be used in maximum optimization problem:
Max: γD
The optimal solution of Constraint Anchored Optimization meets following two element equations:
Known relay node quantity L and total power constraint P, solving equations can be obtained so that quantitative graphs γDMost
When big, information source node S and relay node RiPower allocation schemeAnd PrAnd the maximum value of corresponding quantitative graphs
(γD)max。
In conclusion being mentioned the invention proposes the distributed agent repeater system models relayed under a kind of power constraint more
The Framework of Theoretical Analysis for having gone out the distributed agent repeater system proposes the Optimization Theory method of system.In general power
Under confined condition, power distribution is carried out.As shown in Figure 2,3, compared to amplification forwarding system, the noise at destination node can be made
Reach maximum than performance.In optimization process, it is contemplated that the influence of fading channel has carried out function in terms of information rate maximizes
Rate optimization can realize the most fast rate of information throughput, sufficiently extension power service life under limited total power constraint,
It is worth with more actual use.
It should be noted that the term of such as "upper", "lower", "left", "right", "front", "rear" cited in invention, also
Only being illustrated convenient for narration, rather than to limit the scope of the invention, relativeness is altered or modified, in nothing
Under essence change technology contents, when being also considered as the enforceable scope of the present invention.
The above is only the preferred embodiment of the present invention, protection scope of the present invention is not limited merely to above-described embodiment,
All technical solutions belonged under thinking of the present invention all belong to the scope of protection of the present invention.It should be pointed out that for the art
For those of ordinary skill, several improvements and modifications without departing from the principles of the present invention should be regarded as protection of the invention
Range.
Claims (7)
1. distributed agent repeater system under power constraint characterized by comprising an information source node S, 2L item decline letter
Road, L relay node Ri(i=1, a 2...L) and destination node D；Without the chain that direct transfers between information source node S and destination node D
Road, signal pass through multiple relay node RiBe sampled, encode, compress after forward again, to realize information source node S and purpose section
Communication between point D；Information source node S to multiple relay node RiBetween formed wireless simulation Sensor Network, multiple relay node RiIt arrives
Radio digital communication net is formed between destination node D；
Relay node RiPlace is provided with source encoder, source encoder by it is receiving, by fading channel and be mixed with Gauss
The analog signal Y of white noiseri(t) it is sampled, encodes and compresses, be converted to digital signal Y 'ri(t), each relay node Ri
Again with identical transmission power PrBy digital signal Y 'ri(t) it is sent to destination node D, realizes relay node RiTo destination node D
Digital Transmission.
2. the optimization method of distributed agent repeater system under power constraint as described in claim 1, it is characterised in that: to biography
The distributed source coding problem of sensor network establishes the rate of more junction networks using the CEO theory in distributed source coding
Distortion function, and combine shannon capacity theoretical, wireless simulation Sensor Network and the foundation of radio digital communication net are contacted, thus
Under general power confined condition, power distribution is carried out between wireless simulation Sensor Network and radio digital communication net, makes signal-to-noise ratio
Performance reaches maximum.
3. the optimization method of distributed agent repeater system under power constraint as claimed in claim 2, it is characterised in that: information source
Node S sends the analog signal X (t), relay node R of its sensor acquisitioniIt receives having decayed and is mixed with white Gaussian
The analog signal Y of noiseri(t)=hriX(t)+Nri(t), i=1,2 ..., L, Nri(t) i-th of relay node R is indicatediThe height at place
This white noise, variance arehriFor channel fading coefficient；
In distortion factor d allowed band, relay node RiAfter carrying out distributed source coding, total rate distortion function of system are as follows:
WhereinFor the mean power of Gaussian source X (t), α=E [| hr|2], E indicates mathematic expectaion.
4. the optimization method of distributed agent repeater system under power constraint as claimed in claim 3, it is characterised in that: each
The power P for the signal that relay node receivesxiEqual, the transmission power P of each relay node respectivelyriAlso mutually the same, therefore
After quantified, coding and compression, the transmission rate for the digital signal being converted into is also identical, then each relay node RiCorresponding
Rate distortion function are as follows:
Wherein γDIndicate the quantitative graphs of signal at destination node, RiIt (d) is each relay node RiIt is carried out to signal is received
Quantization, coding and compressed minimal information rate.
5. the optimization method of distributed agent repeater system under power constraint as claimed in claim 4, it is characterised in that: in conjunction with
Shannon capacity is theoretical, wireless simulation Sensor Network and the foundation of radio digital communication net is contacted, relay node RiTransmission speed
Rate should be equal to relay node RiTo the channel capacity C of destination node Di, i.e.,
Wherein μ PriIndicate the power for the signal from each relay node that destination node D is received；If relay node Ri and mesh
Node D receive signal total power constraint beWherein P indicates that relay node and destination node D receive signal
General power.
6. the optimization method of distributed agent repeater system under power constraint as claimed in claim 5, it is characterised in that: total
Under power-limited condition, the information rate for meeting each relay node of wireless simulation Sensor Network is equal in radio digital communication net
Channel capacity, so that quantitative graphs γDMaximum optimization problem uses following Constraint Anchored Optimization:
Max: γD
The optimal solution of Constraint Anchored Optimization meets following two element equations:
Known relay node quantity L and total power constraint P, solving equations can be obtained so that quantitative graphs γDWhen maximum,
Information source node S and relay node RiPower allocation schemeAnd PrAnd maximum value (the γ of corresponding quantitative graphsD)max。
7. the optimization method of distributed agent repeater system under power constraint as claimed in claim 6, it is characterised in that: purpose
The antenna of node D will come from each relay node RiDigital signal Y 'ri(t) maximum-ratio combing reception is carried out, signal will be obtained
Yd(t) it is transmitted to the joint decoder being located at destination node D and carries out combined decoding, information source information can be recovered
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