CN107124755B - Double-layer network power control method based on unbiased broadcast Gossip algorithm - Google Patents
Double-layer network power control method based on unbiased broadcast Gossip algorithm Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/18—TPC being performed according to specific parameters
- H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
- H04W52/243—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account interferences
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/18—TPC being performed according to specific parameters
- H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
- H04W52/241—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account channel quality metrics, e.g. SIR, SNR, CIR, Eb/lo
Abstract
The invention discloses a double-layer network power control method based on an unbiased broadcast Gossip algorithm, and relates to a double-layer network power control method. The invention aims to solve the problem that the average signal-to-interference-and-noise ratio cannot be obtained in the power control process due to the fact that the signal-to-interference-and-noise ratio of the user to which the base station node belongs is collected in the directed graph network in the prior art is deviated. The specific process is as follows: step one, initialization, setting the transmitting power of each base station as the maximum transmitting power at the moment when t is 0; step two, acquiring SINR information of users to which each base station belongs in the step one; thirdly, distributed average consensus is carried out on the SINR information obtained in the second step by using an unbiased broadcast Gossip algorithm to obtain a reference SINR value of each base station at the time t; and step four, adjusting the transmitting power of each base station by using the reference SINR value until the SINR of the user to which each base station belongs is the same. The invention is used in the field of mobile communication network control.
Description
Technical Field
The invention relates to a power control method of a double-layer network.
Background
Distributed consensus: the Gossip algorithm mainly solves the distributed consensus problem in the aspect of distributed signal processing. The distributed consensus problem is that all nodes in the network can finally make the state values of all nodes the same through the exchange of local information with neighboring nodes. If the state value is the average value of the initial values of the nodes, the common consensus is called average. Suppose that the wireless sensor network has N nodes, and each node collects the parameter value of the position where the node is located. t is 0 as initial time, and the initial value of each node is x
i(0),i＝1,2,...,N。x
i(t) represents the parameter value of inode at time t. Writing all initial values into vector form with x (0) ═ x
1(0),x
2(0),...,x
N(0)]
T. Suppose inAt time t, the i node is randomly activated, and a neighbor node j is selected at the same time, the two nodes exchange information, x
i(t+1)＝x
j(t+1)＝(x
i(t)+x
j(t))/2. Intuitively, as long as the network is connected, each node in the network must eventually converge to the initial value mean value through a limited number of iterations, i.e.
Namely, the aim of distributed average consensus is achieved.
Unbiased broadcast Gossip algorithm: in wireless communication, the difference of transmission power among nodes often causes that the symmetry of network topology cannot be ensured. The existing method can control the transmitting power among nodes in an undirected graph network so as to achieve the consistency of the receiving signal to interference plus noise ratios of users, but the deviation exists in the signal to interference plus noise ratios of the users of the base station nodes collected in the undirected graph network, so that the users of all base stations can not obtain the same signal to interference plus noise ratio in the power control process.
Disclosure of Invention
The invention aims to solve the problem that users of base stations cannot obtain the same signal-to-interference-and-noise ratio in the power control process due to the fact that the signal-to-interference-and-noise ratios of the users to which the base station nodes belong are collected in a directed graph network in the prior art are deviated, and provides a double-layer network power control method based on an unbiased broadcast Gossip algorithm.
The method for controlling the power of the double-layer network based on the unbiased broadcast Gossip algorithm comprises the following specific processes:
step one, initialization, setting the transmitting power of each base station as the maximum transmitting power at the moment when t is 0;
each base station comprises 1 Macrocell base station and N-1 Femtocell base stations, wherein N is a positive integer;
setting the maximum transmitting power of N-1 Femtocell base stations and 1 Macrocell base station as a rated power; the Macrocell base station is an upper base station, and the Femtocell base station is a bottom base station; the Femtocell base station and the Macrocell base station form a double-layer network model, and the Femtocell base station and the Macrocell base station adopt a shared frequency band, namely frequency full multiplexing;
step two, acquiring SINR information of users to which each base station belongs in the step one;
the SINR is a signal to interference plus noise ratio;
step three, distributed average consensus is carried out on the SINR information obtained in the step two by utilizing an unbiased broadcast Gossip algorithm to obtain a reference SINR value gamma of each base station at the time t
ref(t)；
Step four, utilizing the reference SINR value gamma
refAnd (t) adjusting the transmitting power of each base station until the SINR of the users to which each base station belongs is the same.
The invention has the beneficial effects that:
adopting distributed average consensus: collecting SINR information of the whole network by using an unbiased broadcast Gossip algorithm, and enabling each base station to obtain average consensus reference SINR information gamma through iteration
ref(t); and adopting power control: using reference SINR information gamma
ref(t) adjusting the transmission power of each base station; the problem that in the prior art, the deviation exists in the signal to interference plus noise ratios of the users to which the base station nodes belong in the directed graph network, so that the users to which each base station belongs can not obtain the same signal to interference plus noise ratio in the power control process is solved. The method can self-adaptively realize that the base stations in the network adjust the respective transmitting power under the condition of not assuming the target SINR, thereby achieving the purpose that the users to which the base stations belong obtain the same SINR and obtaining the equal service. Compared with the existing power control method, the method has certain advantages in convergence speed, and can better reflect the condition that each user is interfered by other users by utilizing the consensus information of the whole network.
It can be seen from fig. 2 that the setting of the M parameter affects the convergence speed of the algorithm: when the value of M is smaller, the convergence speed is higher. When M is set to N
maxThe time algorithm has the fastest convergence speed, and the system is required to know a complete network topology (the known value of each base station) at the time; when M is N-1, convergence speed is sacrificed, but the method is easier to realize in engineering; when the iteration is carried out for 200 times, M takes N
maxThe Macrocell base station SINR, the Femtocell base station average SINR when M takes N-1, and M takes 0.5 (N + N)
max-1) Macrocell base station SINR, M taking 0.5 x (N + N)
max-1) Femtocell base station average SINR, Macr with M being N-1The SINR of the cell base station is equal to the average SINR of the Femtocell base station with the M of N-1, and the SINR and the average SINR are both 48 dB. The Macrocell base station is an upper base station, and the Femtocell base station is a bottom base station.
Drawings
FIG. 1 is a graph of the power control results of the present invention;
fig. 2 is a diagram of the result of the effect of the parameter M on the convergence rate in the present invention, where femto is Femtocell, Femtocell is the bottom base station, Ave is Average, and Average is Average.
Detailed Description
The first embodiment is as follows: the specific engineering of the double-layer network power control method based on the unbiased broadcast Gossip algorithm in the embodiment is as follows:
the unbiased broadcast Gossip algorithm is characterized by being capable of realizing distributed average consensus in a graph network.
Let G ═ (v, epsilon) represent a directed graph, where v ═ 1, 2.
Is a collection of directed edges. If the information i sent by j can be received, the representing direction of the edge between i and j is i ← j, namely a directed edge (i, j) epsilon in the network exists. In an undirected graph, two nodes can communicate without direction limitation as long as edges exist between i and j; the directed graph can only communicate in the direction of the directed edge. In the directed graph, let
And
respectively representing an inner neighbor set and an outer neighbor set of node i. The inner neighbor set represents the set of all nodes which can send information to the node i, and the outer neighbor set represents the set of all nodes which can receive the information sent by the node i. The sum of the number of neighbor nodes of node i is called degree. Corresponding to the neighbor set is called the degree of income, which is recorded as
Conversely, the corresponding out degree of the neighboring set is recorded as
And | N | represents the number of elements of the set N.
In the Gossip algorithm, nodes have initial state values and accompanying values, and each node assumes a poisson clock. When the node clock expires, the activated node broadcasts its current state value and accompanying values all over the network. Assuming that node k is activated at time t +1, node k broadcasts its state value x
k(t) and the accompanying value yk (t). All of
The node (c) receives the information broadcast by the node (k) and updates the information as follows:
the broadcast node k is updated as:
x
k(t+1)＝x
k(t)
y
k(t+1)＝0
others
The node (b) keeps the state value unchanged, i.e.:
x
i(t+1)＝x
i(t)
y
i(t+1)＝y
i(t)
parameter a
j,k,b
j,k,
And ξ, the generated broadcast Gossip algorithm is different
j,k,b
j,k,
The following method is adopted:
the network model adopts a double-layer network model, namely a double-layer network consisting of the Femtocell and the Macrocell, and the Femtocell at the bottom layer and the Macrocell at the upper layer work in a mode of sharing a frequency band. The system comprises N-1 Femtocell base stations deployed below 1 Macrocell base station, and the respective radiuses are R
fAnd R
m. Macrocell is placed in the center of a cell, and users are randomly distributed in the radius R of a base station
mAnd (4) the following steps. For Femtocell, users are randomly distributed at radius R of base station
fWithin.
Step one, initialization, setting the transmitting power of each base station as the maximum transmitting power at the moment when t is 0;
each base station comprises 1 Macrocell base station and N-1 Femtocell base stations, wherein N is a positive integer;
setting the maximum transmitting power of N-1 Femtocell base stations and 1 Macrocell base station as a rated power; the Macrocell base station is an upper base station, and the Femtocell base station is a bottom base station; the Femtocell base station and the Macrocell base station form a double-layer network model, and the Femtocell base station and the Macrocell base station adopt a shared frequency band, namely frequency full multiplexing;
step two, acquiring SINR information of users to which each base station belongs in the step one;
the SINR is a signal to interference plus noise ratio;
step three, distributed average consensus is carried out on the SINR information obtained in the step two by utilizing an unbiased broadcast Gossip algorithm to obtain a reference SINR value gamma of each base station at the time t
ref(t)；
Step four, utilizing the reference SINR value gamma
refAnd (t) adjusting the transmitting power of each base station until the SINR of the users to which each base station belongs is the same.
The second embodiment is as follows: the first difference between the present embodiment and the specific embodiment is: in the second step, SINR information of the user to which each base station belongs in the first step is collected; the specific process is as follows:
it is assumed that each base station uses an orthogonal manner when allocating downlink resources to users, that is, only one user is served in each resource block, so that the base stations correspond to users one-to-one when considering access in a single resource block.
Order to
The sequence number is a set of sequence numbers of a base station and a user; then the received signal to Interference plus Noise Ratio (SINR) of the ith user at time t is recorded as γ
i(t) is represented by
Wherein h is
iiTo represent the channel coefficients from the ith base station to the ith user, h
jiRepresenting the channel coefficient from the jth base station to the ith user; sigma
2Representing a noise variance of a received signal of a user; p is a radical of
i(t) is the transmission power rate of the ith base station at time t, namely, the transmission power of the base station i for receiving the user i is represented; p is a radical of
jAnd (t) is the transmission power of the jth base station at the time t, namely, the transmission power of the base station j for receiving the user j is represented.
Other steps and parameters are the same as those in the first embodiment.
The third concrete implementation mode: the present embodiment differs from the first or second embodiment in that: distributed average consensus in the third step: obtaining the result of the step two by using an unbiased broadcast Gossip algorithmThe SINR information is subjected to distributed average consensus to obtain a reference SINR value gamma of each base station at the time t
ref(t); expressed as:
other steps and parameters are the same as those in the first or second embodiment.
The fourth concrete implementation mode: the difference between this embodiment mode and one of the first to third embodiment modes is: the power control in the fourth step: using the reference SINR value gamma
ref(t) adjusting the transmitting power of each base station until the SINR of the users of each base station is the same; the specific process is as follows:
the power control algorithm is as follows
In the formula,. DELTA.p
i(t) is the variation of the transmission power of the ith base station at the time t +1 and the transmission power of the ith base station at the time t, b
i>0 is the power adjustment step size of the ith base station to control the update speed of the algorithm, p
i(t) the transmission power of the ith base station at time t, p
i(t +1) is the transmission power of the ith base station at time t + 1, γ
i(t) is the received signal-to-interference-and-noise ratio of the ith user at the time t, f
iiIs a local weight, f
jiConnecting weights for the jth base station and ith base station,
represents the neighbor set of the ith base station,
is a collection of directed edges that are,
when the transmission power of all base stations changes by Δ p (t) ([ Δ p ]) at time t +1 and time t
1(t),…,Δp
N(t)]Is 0When the power is not adjusted, stopping adjusting the transmitting power of each base station; when the variation delta p between the transmission power of the ith base station at the moment t +1 and the transmission power of the ith base station at the moment t
iIf (t) is not 0, executing step two, using p
i(t +1) substitution of p
iAnd (t) until the SINR of the users to which each base station belongs is the same.
Other steps and parameters are the same as those in one of the first to third embodiments.
The fifth concrete implementation mode: the difference between this embodiment and one of the first to fourth embodiments is: the connection weight satisfies the following condition at any time t:
1) arbitrary f
ii≥0，f
ji≥0；
2)
Other steps and parameters are the same as in one of the first to fourth embodiments.
The Macrocell-Femtocell double-layer network power control method based on the unbiased broadcast Gossip algorithm comprises two layers of iteration processes, which are respectively as follows: the first layer, unbiased broadcast Gossip algorithm process-realizing the distributed average consensus of the SINR information of each user in the network; and in the second layer, the power adjustment of each base station in the network is realized by adopting the proposed power control algorithm. The specific description is as follows:
the following examples were used to demonstrate the beneficial effects of the present invention:
the first embodiment is as follows:
the double-layer network power control method based on the unbiased broadcast Gossip algorithm is specifically prepared according to the following steps:
the connection weight of the network in the simulation is set as follows:
for out degree, M is the number of base stations, where N
maxM is less than or equal to N-1, where
The model considered during the network parameter setting is a Macrocell-Femtocell double-layer network. First assume a reference distance D
refThe path loss model is as follows, 1 meter:
wherein D
ijRepresents the distance between the jth user and the ith base station, and PL
ijIndicating the path loss between the jth user and the ith base station α and β indicate the signal propagation path loss exponent, respectively
The power adjustment procedure of the proposed algorithm is given in fig. 1. It can be seen that although the Macrocell base station initially uses the maximum transmit power, it cannot guarantee that its users get good signal quality. After the algorithm is used, the user feeds back the SINR value measured by the user to the base station to which the user belongs, after the base station compares the reference SINR obtained by the feedback information summarization received by other base stations, if the reference SINR is higher than the reference SINR, the transmission power of the user is reduced, and if the reference SINR is lower than the reference SINR, the transmission power of the user is increased within the power constraint range until each user in the system obtains the same SINR value. On one hand, the received signal power of the user can be improved due to the increase of the transmitting power, and on the other hand, the interference can be reduced due to the reduction of the power of the base station corresponding to the peripheral high SINR user. The two aspects act together to ensure that all users in the system can obtain consensus, that is, the received signals SINR of all users are equal.
Fig. 2 shows the effect of the parameter M on the convergence speed of the method.
The present invention is capable of other embodiments and its several details are capable of modifications in various obvious respects, all without departing from the spirit and scope of the present invention.
Claims (2)
1. The double-layer network power control method based on the unbiased broadcast Gossip algorithm is characterized by comprising the following specific processes:
step one, initialization, setting the transmitting power of each base station as the maximum transmitting power at the moment when t is 0;
each base station comprises 1 Macrocell base station and N-1 Femtocell base stations, wherein N is a positive integer;
setting the maximum transmitting power of N-1 Femtocell base stations and 1 Macrocell base station as a rated power; the Macrocell base station is an upper base station, and the Femtocell base station is a bottom base station; the Femtocell base station and the Macrocell base station form a double-layer network model, and the Femtocell base station and the Macrocell base station adopt a shared frequency band, namely frequency full multiplexing;
step two, acquiring SINR information of users to which each base station belongs in the step one;
the SINR is a signal to interference plus noise ratio;
step three, distributed average consensus is carried out on the SINR information obtained in the step two by utilizing an unbiased broadcast Gossip algorithm to obtain a reference SINR value gamma of each base station at the time t
ref(t); expressed as:
step four, utilizing the reference SINR value gamma
ref(t) adjusting the transmitting power of each base station until the SINR of the users of each base station is the same; the specific process is as follows:
in the formula,. DELTA.p
i(t) is the variation of the transmission power of the ith base station at time t +1 and the transmission power of the ith base station at time t β
i>0 is the power adjustment step size of the ith base station to control the update speed of the algorithm, p
i(t) the transmission power of the ith base station at time t, p
i(t +1) is the transmission power of the ith base station at time t +1, γ
i(t) is the received signal-to-interference-and-noise ratio of the ith user at the time t, f
iiIs a local weight, f
jiConnecting weights for the jth base station and ith base station,
represents the neighbor set of the ith base station,
is a collection of directed edges that are,
when the transmission power of all base stations changes by Δ p (t) ([ Δ p ]) at time t +1 and time t
1(t),…,Δp
N(t)]When the power is 0, stopping adjusting the transmitting power of each base station; when the variation delta p between the transmission power of the ith base station at the moment t +1 and the transmission power of the ith base station at the moment t
iIf (t) is not 0, executing step two, using p
i(t +1) substitution of p
i(t), until SINR of each base station belonging user is the same;
the connection weight satisfies the following condition at any time t:
1) arbitrary f
ii≥0，f
ji≥0；
2)
2. The two-tier network power control method based on unbiased broadcast Gossip algorithm as claimed in claim 1, wherein: in the second step, SINR information of the user to which each base station belongs in the first step is collected; the specific process is as follows:
order to
The sequence number is a set of sequence numbers of a base station and a user; the signal to interference plus noise ratio of the received signal of the ith user at the time t is recorded as gamma
i(t) is represented by
Wherein h is
iiTo represent the channel coefficients from the ith base station to the ith user, h
jiRepresenting the channel coefficient from the jth base station to the ith user; sigma
2Representing a noise variance of a received signal of a user; p is a radical of
i(t) the transmission power of the ith base station at time t, p
jAnd (t) is the transmission power of the jth base station at the moment t, i is more than or equal to 1 and less than or equal to N, and j is more than or equal to 1 and less than or equal to N.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103152817A (en) * | 2013-03-27 | 2013-06-12 | 哈尔滨工业大学 | Distributed clock synchronizing method based on broadcast Gossip algorithm |
| CN104010329A (en) * | 2014-06-17 | 2014-08-27 | 哈尔滨工业大学 | Distributed load balancing method based on quantified unbiased broadcast Gossip algorithm |
| CN104254084A (en) * | 2013-06-26 | 2014-12-31 | 上海贝尔股份有限公司 | Method for performing distributed channel detection and sequential access |
| CN105334400A (en) * | 2015-09-24 | 2016-02-17 | 哈尔滨工业大学 | Distributed electromagnetic field received signal power intensity detection method based on unbiased broadcast Gossip algorithm |
| CN105353341A (en) * | 2015-10-16 | 2016-02-24 | 温州大学 | Wireless sensor network positioning method based on unmanned autonomous aircraft |
| CN105491588A (en) * | 2015-12-28 | 2016-04-13 | 哈尔滨工业大学 | Distributed Kalman consensus moving target tracking method on the basis of unbiased broadcast gossip algorithm |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012050913A1 (en) * | 2010-09-28 | 2012-04-19 | The Ohio State University | Predictive network system and method |
-
2017
- 2017-04-26 CN CN201710283940.XA patent/CN107124755B/en not_active Expired - Fee Related
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103152817A (en) * | 2013-03-27 | 2013-06-12 | 哈尔滨工业大学 | Distributed clock synchronizing method based on broadcast Gossip algorithm |
| CN104254084A (en) * | 2013-06-26 | 2014-12-31 | 上海贝尔股份有限公司 | Method for performing distributed channel detection and sequential access |
| CN104010329A (en) * | 2014-06-17 | 2014-08-27 | 哈尔滨工业大学 | Distributed load balancing method based on quantified unbiased broadcast Gossip algorithm |
| CN105334400A (en) * | 2015-09-24 | 2016-02-17 | 哈尔滨工业大学 | Distributed electromagnetic field received signal power intensity detection method based on unbiased broadcast Gossip algorithm |
| CN105353341A (en) * | 2015-10-16 | 2016-02-24 | 温州大学 | Wireless sensor network positioning method based on unmanned autonomous aircraft |
| CN105491588A (en) * | 2015-12-28 | 2016-04-13 | 哈尔滨工业大学 | Distributed Kalman consensus moving target tracking method on the basis of unbiased broadcast gossip algorithm |
Non-Patent Citations (2)
| Title |
|---|
| Broadcast Gossip Algorithms for Consensus on Strongly Connected Digraphs;Shaochuan Wu等;《IEEE TRANSACTIONS ON SIGNAL PROCESSING》;20130520;全文 * |
| Gossiping Along the Path: A Direction-Biased Routing Scheme for Wireless Ad Hoc Networks;Yaowei Yan等;《2015 IEEE Global Communications Conference (GLOBECOM)》;20151110;全文 * |
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