CN108156620B - Ultra-dense network small station sleeping method based on channel and queue sensing - Google Patents

Ultra-dense network small station sleeping method based on channel and queue sensing Download PDF

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CN108156620B
CN108156620B CN201810086759.4A CN201810086759A CN108156620B CN 108156620 B CN108156620 B CN 108156620B CN 201810086759 A CN201810086759 A CN 201810086759A CN 108156620 B CN108156620 B CN 108156620B
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CN108156620A (en
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潘志文
李沛
刘楠
尤肖虎
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White Box Shanghai Microelectronics Technology Co ltd
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Southeast University
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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
    • 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
    • H04W52/02—Power saving arrangements
    • H04W52/0203—Power saving arrangements in the radio access network or backbone network of wireless communication networks
    • H04W52/0206—Power saving arrangements in the radio access network or backbone network of wireless communication networks in access points, e.g. base stations
    • 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

Abstract

The invention provides a super-dense network small station sleeping method based on channel and queue sensing, which comprises the following steps: collecting network information; calculating the optimal sleep proportion according to a gradient descent method; calculating the number of small stations to be dormant; setting the base station switching ratio to be 0, and calculating the average queue length and the average transmission rate of each small station in each time interval within a certain time length; arranging the small stations in an ascending order according to the product of the average queue length of each small station and the average transmission rate of the user; obtaining the dormancy number; and closing the small stations in sequence according to the sequence. The base station dormancy method provided by the invention aims at the super-dense heterogeneous network, and the base station dormancy strategy is executed by collecting data traffic and combining the channel state between the base station and the user, so that the method is well suitable for an actual system, can bring better performance gain than the traditional method, and obviously reduces the energy consumption of the system under the condition of ensuring the time delay characteristic of the user.

Description

Ultra-dense network small station sleeping method based on channel and queue sensing
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a super-dense network small station sleeping method based on channel and queue sensing in a wireless communication system.
Background
The explosive growth of mobile traffic in 5g (the mobile generation) networks presents a significant challenge to mobile network operators. In order to meet the requirement of future mobile data and greatly improve the system capacity and the user experience quality, the ultra-dense low-power small stations are deployed in the coverage area of the traditional high-power macro station, particularly the high-service hot spot area, so that a huge throughput gain can be obtained. In order to solve the problem, operators transmit backhaul link information from a core network to a small station by using a wireless transmission technology. Wireless backhaul techniques, however, can create transmission delays for the backhaul links as compared to conventional optical fiber transmission techniques. In addition, the increasing number of base stations in the ultra-dense heterogeneous network inevitably consumes more power energy. Moreover, the energy consumption of the backhaul link also puts a great strain on system energy saving. One effective energy-saving technique is to implement a base station dormancy strategy based on the user's traffic to reduce system energy consumption. However, the base station sleep strategy reduces the number of available base stations to affect the delay characteristics of the user, and in order to ensure the service quality of the user and reduce the energy consumption of the system, the base station to be turned off is selected according to the well-designed base station sleep strategy to realize the balance between the energy consumption and the delay.
The existing base station dormancy technology selects a base station to be closed based on user service perception or channel state, the service state and the channel information are not combined, and the backhaul link information is transmitted by adopting an optical fiber technology without considering the influence of the time delay and the energy consumption of the backhaul link on the overall performance of the system, so that the existing dormancy strategy is difficult to be applied to an actual ultra-dense heterogeneous network based on wireless backhaul.
Disclosure of Invention
In order to solve the problems, the invention discloses a super-dense network small station dormancy method based on channel and queue sensing, which describes the balance problem of system energy consumption and time delay as the problem of minimizing a system cost function, and makes a channel and queue sensing base station dormancy strategy under the condition that user service and channel state dynamically change.
Existing base station dormancy techniques select a base station to be turned off based on user traffic awareness or channel conditions. We have found that the user delay in the system is not only related to the traffic but also to the channel conditions. The larger the traffic is, the longer the user queue waiting time is, and meanwhile, the better the channel state between the user and the base station is, the smaller the user transmission delay is.
Based on the method, the optimal base station dormancy ratio is calculated firstly, the number of base stations to be dormant is calculated according to the base station dormancy ratio, namely, the number of the base stations to be closed under the condition of meeting the user time delay characteristic is determined. Secondly, considering the average queue length of each base station and the average transmission rate obtained by the user associated with the base station, arranging the small stations in an ascending order according to the product of the average queue length and the average transmission rate obtained by the user, and sequentially selecting the base stations to be closed according to the order, thereby reducing the energy consumption of the system.
In order to achieve the purpose, the invention provides the following technical scheme:
a super-dense network small station sleeping method based on channel and queue sensing comprises the following steps:
step 1, collecting network information
Measuring the total number N of users, macro stations, small stations and gateways in the areau、Nm、Ns、NgObtaining the distribution density lambda of the gateway, the macro station, the small station and the user in the areag,λm,λsAnd λu；
When the user flow reaches the meeting berth process, counting the user flow use condition in a period of time to obtain the user flow arrival rate lambda and the average bit size l of each packet;
obtaining the bandwidth W of the backhaul link of the small station wireless deployed in the areabWireless access bandwidth W adopted by macro stationmWireless access bandwidth W adopted by small stationsMacro station transmission power PmtSmall station transmission power PstGateway transmission power Pgt；
Recording the average energy consumption of each gateway
Figure BDA0001562548490000025
Average energy consumption of sleeping small stationsEnergy consumption of macro and small station static links
Figure BDA0001562548490000027
And
Figure BDA0001562548490000028
obtaining a path loss coefficient alpha in a wireless channel by using a channel estimation method;
determining offset value A of user associated to small station according to network operation conditionbLoad-dependent energy consumption factor Δ p for macro and small stationsmAnd Δ psThe values of a signal-to-interference ratio threshold beta, a weight factor omega, an iterative search step length delta, iterative search accuracy xi, a time interval T and a time length T are determined according to needs;
all macro stations are in an activated state;
the base station dormancy ratio of the small station is marked as theta, and the initial value theta of the base station dormancy ratio0The optimal sleep ratio is determined according to the network operation condition*The value of the base station dormancy ratio in the nth iteration process is thetan；
And 2, iterating according to a gradient descent method, wherein the initial value of the iteration number is n-0, and calculating the sleep ratio theta-theta of the base station during the nth iterationnAverage packet delay for macro and small station users
Figure BDA0001562548490000021
And
Figure BDA0001562548490000022
and average time delay of the whole network
Figure BDA0001562548490000023
Firstly, the probability Pr of the user connecting to the small station is calculatedSUE(θ)
Figure BDA0001562548490000024
The gateway, the macro station and the small station are M/G/1 queues, so the time delay of a user comprises transmission time delay and queuing time delay;
the user is divided into two parts: the first part is a user connected with the macro station, and the second part is a user connected with the small station;
average time delay for user connected with macro station
Figure BDA0001562548490000031
Including average transmission delay for the delay of the radio access link
Figure BDA0001562548490000032
And average queuing delay
Figure BDA0001562548490000033
Namely, it is
Figure BDA00015625484900000330
Calculated by the following formula：
Figure BDA0001562548490000038
Wherein the content of the first and second substances,
Figure BDA0001562548490000039
represents an average transmission rate obtained when a user accesses the macro station;
Figure BDA00015625484900000310
and
Figure BDA00015625484900000311
the average transmission probability of the macro station and the small station is obtained by the following two equations through a dichotomy:
Figure BDA00015625484900000312
Figure BDA00015625484900000313
in the above formula, the first and second carbon atoms are,
Figure BDA00015625484900000314
Figure BDA00015625484900000315
Figure BDA00015625484900000316
for the users accessing the small station, the average time delay of each packet is the average time delay of the wireless access link
Figure BDA00015625484900000317
And wireless backhaul link average delay
Figure BDA00015625484900000318
Summing; likewise, the access link average delayAnd wireless backhaul link average delayAlso respectively comprise average transmission time delay
Figure BDA00015625484900000321
And average queuing delay
Figure BDA00015625484900000322
Namely, it is
Figure BDA00015625484900000323
Wherein the content of the first and second substances,
Figure BDA00015625484900000324
calculated by the following formula:
Figure BDA00015625484900000325
Figure BDA00015625484900000326
wherein the content of the first and second substances,
Figure BDA00015625484900000327
the average transmission rate of the wireless transmission link for the small station user,
Figure BDA00015625484900000328
representing the average transmission rate of the backhaul link from the gateway to the small station;
Figure BDA00015625484900000329
calculated by the following formula:
Figure BDA0001562548490000041
Figure BDA0001562548490000042
wherein the content of the first and second substances,the average transmission probability for a gateway is calculated by:
Figure BDA0001562548490000044
thereby obtaining the average time delay of the network
Figure BDA0001562548490000045
The following formula:
Figure BDA0001562548490000046
step 3, calculating the sleep ratio theta of the current base station by the following formulanLower system average energy consumption
Figure BDA0001562548490000047
And a cost function F (θ):
Figure BDA0001562548490000048
Figure BDA0001562548490000049
and 4, solving the current nth iteration through the following formula, wherein the sleep proportion theta of the cost function F (theta) to the base station is thetanDerivative function of
Figure BDA00015625484900000410
Figure BDA00015625484900000411
Wherein the content of the first and second substances,
Figure BDA00015625484900000412
Figure BDA00015625484900000413
Figure BDA00015625484900000414
Figure BDA00015625484900000415
Figure BDA0001562548490000051
Figure BDA0001562548490000052
Figure BDA0001562548490000053
and 5, updating the base station sleep ratio theta, wherein the base station sleep ratio theta is equal to theta in the n +1 th iterationn+1Updated by the following equation:
Figure BDA0001562548490000054
step 6, judging that the whole network cost function F (theta) is F (theta) under the current base station dormancy ration) Whether a minimum point is reached; when F (theta)n+1)-F(θn) When the value is less than ξ, the optimal point is reached, and the step 8 is executed to quit the iteration process; otherwiseExecuting step 7;
and 7: updating the current iteration number n +1, and executing the step 2-6;
and 8: quitting the iteration process to obtain the optimal sleep proportion theta of the base station*；
And step 9: making theta equal to 0, namely all the small stations are in an activated state, and updating the user connection state; counting the queue lengths of all the small stations and the average transmission rate of users at a time interval T, and further calculating the average queue length and the average transmission rate in the time length T;
step 10: arranging the small stations in an ascending order according to the product of the average queue length of each small station and the average transmission rate of the user;
step 11: obtaining the optimal dormancy ratio theta according to the step 8*Calculating the number N of base stations to be dormantoff＝[θ*Ns]；
Step 12: closing the top N in sequence according to the small station sequence obtained in the step 10offAnd (5) a small station.
Specifically, the minimum value of the base station sleep ratio θ of the small station in step 1 is θmin0, maximum value θmax＝1。
Specifically, the average transmission rate obtained when the user accesses the macro station in step 2
Figure BDA0001562548490000056
Average transmission rate of wireless transmission link of small station user
Figure BDA0001562548490000057
Average transmission rate of backhaul link from gateway to small station
Figure BDA0001562548490000055
And (4) solving according to a shannon formula.
Compared with the prior art, the invention has the following advantages and beneficial effects:
the base station dormancy method provided by the invention aims at the super-dense heterogeneous network, and the base station dormancy strategy is executed by collecting data traffic and combining the channel state between the base station and the user, so that the method is well suitable for an actual system, can bring better performance gain than the traditional method, and obviously reduces the energy consumption of the system under the condition of ensuring the time delay characteristic of the user. Compared with the existing business perception and channel perception base station dormancy scheme, the method can fully utilize the business change and channel information of the small station, select the base station set to be dormant, and flexibly control the balance problem between the energy saving of the system and the service quality of the user.
Drawings
Fig. 1 is a flowchart of a sleeping method for a cell in an ultra-dense network based on channel and queue sensing according to the present invention.
Detailed Description
The technical solutions provided by the present invention will be described in detail below with reference to specific examples, and it should be understood that the following specific embodiments are only illustrative of the present invention and are not intended to limit the scope of the present invention.
The invention relates to a method for solving the problem of energy consumption and time delay balance, which is based on the point of energy consumption and time delay balance and corresponds to the problem of minimizing the cost function of a system. The operator may select a particular tradeoff factor based on the relative importance of energy savings and user quality of service to determine the number of base stations to hibernate.
According to the invention, the flow model of the user is considered to meet the Poisson arrival process, the time delay and the energy consumption of the wireless access network and the backhaul network are firstly analyzed according to the M/G/1 queuing model, the minimum value point of the cost function corresponding to the energy consumption and time delay balance problem is solved by iteration through a gradient descent method, the optimal base station dormancy proportion of the system is obtained, and then the set of the optimal dormancy base stations is selected according to the optimal base station dormancy proportion, so that the energy consumption of the system is minimized under the condition that the service quality of the user is ensured.
Specifically, the ultra-dense network small station sleep method based on channel and queue sensing, as shown in fig. 1, includes the following steps:
step 1: collecting network information
The operator measures the total number of users, macro stations, small stations and gateways in the area and respectively records the total number as Nu，Nm，Ns，NgThereby obtaining the regionDistribution density lambda of gateway, macro station, small station and user in domaing,λm,λsAnd λu. When the user traffic reaches the destination, the operator counts the user traffic usage within a period of time (the time can be set according to the situation) to obtain the user traffic arrival rate λ and the average bit size l of each packet. Obtaining the backhaul link bandwidth W of the small station wireless deployed in the area through an operatorbWireless access bandwidth W adopted by macro stationmWireless access bandwidth W adopted by small stationsMacro station transmission power PmtSmall station transmission power PstGateway transmission power Pgt. The operator records the average energy consumption of each gateway
Figure BDA0001562548490000061
Average energy consumption P of sleeping small stationsSEnergy consumption of static links of macro and small stations
Figure BDA0001562548490000062
And
Figure BDA0001562548490000063
and obtaining the path loss coefficient alpha in the wireless channel by using a channel estimation method. Offset value A for user association to small stationbLoad-dependent energy consumption factor Δ p for macro and small stationsmAnd Δ psThe values of the signal-to-interference ratio threshold beta, the weight factor omega, the iterative search step length delta, the iterative search accuracy xi, the time interval T and the time length T are automatically determined by an operator according to the network operation condition. All macro stations are all in an active state.
The sleep ratio of the base station of the small station is recorded as theta, and the minimum value of the sleep ratio is thetamin0, maximum value θ max1. Initial value theta of base station sleep ratio0The optimum sleep ratio is theta which is determined by an operator according to the network running condition*. The initial value of the iteration times is n-0, and the value of the dormancy proportion of the base station in the nth iteration process is thetan。
Then, the optimal base station dormancy proportion theta is solved by iteration according to a gradient descent method*。
Step 2: in the nth iteration, the dormancy ratio theta at the base station is calculatednThen, the average packet delay of the macro station and the small station users are respectively recorded as
Figure BDA0001562548490000071
And
Figure BDA0001562548490000072
and average time delay of the whole network
Figure BDA0001562548490000073
Firstly, the probability Pr of the user connecting to the small station is calculatedSUE(θ)
Figure BDA0001562548490000074
The gateway, the macro station and the small station are M/G/1 queues, so the time delay of the user comprises transmission time delay and queuing time delay. Average transmission time delay of users
Figure BDA0001562548490000075
And average queuing delay
Figure BDA0001562548490000076
Respectively expressed as:
Figure BDA0001562548490000077
Figure BDA0001562548490000078
here, the
Figure BDA0001562548490000079
The average transmission rate is expressed and can be obtained by a shannon formula. P represents the average transmission probability, the average transmission probability of the macro and the small stations, respectivelyAndthe following conditions are respectively satisfied:
Figure BDA00015625484900000712
where x denotes an integral variable, with no practical physical meaning.
Figure BDA00015625484900000713
Figure BDA00015625484900000714
Figure BDA00015625484900000715
The average transmission probability in the current traffic state can be obtained from equations (4) and (5) by using the dichotomyAnd
Figure BDA00015625484900000717
the user is divided into two parts: the first part is users connected with the macro station, and the second part is users connected with the small station. Average time delay for user connected with macro station
Figure BDA00015625484900000718
Including average transmission delay for the delay of the radio access link
Figure BDA00015625484900000719
And average queuing delay
Figure BDA00015625484900000720
Namely, it is
Figure BDA00015625484900000721
According to the formulas (2) and (3), the following results are obtained
Here, the
Figure BDA0001562548490000082
The average transmission rate obtained when the user accesses the macro station can be obtained according to the shannon formula.
For the users accessing the small station, the average time delay of each packet is the average time delay of the wireless access link
Figure BDA0001562548490000083
And wireless backhaul link average delay
Figure BDA0001562548490000084
And (4) summing. Likewise, the access link average delay
Figure BDA0001562548490000085
And wireless backhaul link average delay
Figure BDA0001562548490000086
Also respectively comprise average transmission time delay
Figure BDA00015625484900000827
And average queuing delayNamely, it is
Figure BDA0001562548490000088
Similarly, from equation (2) can be derived
Figure BDA0001562548490000089
Figure BDA00015625484900000810
Figure BDA00015625484900000811
Here, the first and second liquid crystal display panels are,
Figure BDA00015625484900000812
the average transmission rate of the wireless transmission link for the small station user,representing the average transmission rate of the gateway-to-small-station backhaul link.
Figure BDA00015625484900000814
And
Figure BDA00015625484900000815
can be obtained according to the Shannon formula.
From equation (3) can be obtained
Figure BDA00015625484900000816
Figure BDA00015625484900000817
Figure BDA00015625484900000818
Figure BDA00015625484900000819
Is the average transmission probability of the gateway,
Figure BDA00015625484900000820
thereby obtaining the average time delay of the network
Figure BDA00015625484900000821
And step 3: calculating the dormancy ratio theta of the current base stationnLower system average energy consumption
Figure BDA00015625484900000823
And a cost function F (theta)
Figure BDA00015625484900000824
Figure BDA00015625484900000825
Here, the value of the weighting factor ω is determined by the operator according to the network operation condition.
And 4, step 4: when the current nth iteration is solved, the sleep proportion theta of the cost function F (theta) relative to the base station is thetanDerivative function of
Figure BDA00015625484900000826
Figure BDA0001562548490000091
Here, the
Figure BDA0001562548490000092
Figure BDA0001562548490000093
Figure BDA0001562548490000094
Figure BDA0001562548490000096
Figure BDA0001562548490000098
And 5: updating the base station dormancy ratio theta, and when the (n + 1) th iteration is performed, the base station dormancy ratio theta is equal to thetan+1Is updated to
Figure BDA0001562548490000099
Here, the value of the iterative search step δ is determined by the operator according to the network operation conditions.
Step 6: under the condition of judging the dormancy ratio of the current base station, the cost function F (theta) of the whole network is equal to F (theta)n) Whether the minimum point is reached: when F (theta)n+1)-F(θn) When the value is less than ξ, the optimal point is reached, and the step 8 is executed to quit the iteration process; otherwise, step 7 is performed. Here, the value of the iterative search accuracy ξ is determined by the operator depending on the network operating conditions.
And 7: and updating the current iteration number n +1 and executing the step 2-6.
And 8: quitting the iteration process to obtain the optimal sleep proportion theta of the base station*。
And step 9: let θ be 0, i.e. all the small stations are in active state, and update the user connection state. The operator determines the time length T and the time interval T according to the network operation condition, counts the queue lengths of all the small stations and the average transmission rate of the users in the time interval T, and further calculates the average queue length and the average transmission rate in the time length T.
Step 10: the small stations are arranged in ascending order according to the product of the average queue length of each small station and the average transmission rate of the user.
Step 11: obtaining the optimal dormancy ratio theta according to the step 8*Calculating the number N of base stations to be dormantoff＝[θ*Ns]。
Step 12: closing the top N in sequence according to the small station sequence obtained in the step 10offAnd (5) a small station.
The technical means disclosed in the invention scheme are not limited to the technical means disclosed in the above embodiments, but also include the technical scheme formed by any combination of the above technical features. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and such improvements and modifications are also considered to be within the scope of the present invention.

Claims (3)

1. A super-dense network small station sleeping method based on channel and queue sensing is characterized by comprising the following steps:
step 1, collecting network information
Measuring the total number N of users, macro stations, small stations and gateways in the areau、Nm、Ns、NgObtaining the distribution density lambda of the gateway, the macro station, the small station and the user in the areag,λm,λsAnd λu；
When the user traffic reaches the state meeting the Poisson process, counting the user traffic use condition in a period of time to obtain the user traffic arrival rate lambda and the average size l of each packet;
obtaining the bandwidth W of the backhaul link of the small station wireless deployed in the areabWireless access bandwidth W adopted by macro stationmWireless access bandwidth W adopted by small stationsMacro station transmission power PmtSmall station transmission power PstGateway transmission power Pgt；
Recording the average energy consumption of each gateway
Figure FDA0002189619620000011
Average energy consumption of sleeping small stations
Figure FDA0002189619620000012
Energy consumption of macro and small station static links
Figure FDA0002189619620000013
And
Figure FDA0002189619620000014
obtaining a path loss coefficient alpha in a wireless channel by using a channel estimation method;
determining offset value A of user associated to small station according to network operation conditionbLoad-dependent energy consumption factor Δ p for macro and small stationsmAnd Δ psThe values of a signal-to-interference ratio threshold beta, a weight factor omega, an iterative search step length delta, iterative search accuracy xi, a time interval T and a time length T are determined according to needs;
all macro stations are in an activated state;
the base station dormancy ratio of the small station is marked as theta, and the initial value theta of the base station dormancy ratio0The optimal sleep ratio is determined according to the network operation condition*The value of the base station dormancy ratio in the nth iteration process is thetan；
And 2, iterating according to a gradient descent method, wherein the initial value of the iteration number is n-0, and calculating the sleep ratio theta-theta of the base station during the nth iterationnAverage packet delay for macro and small station users
Figure FDA0002189619620000015
Andand average time delay of the whole network
Figure FDA0002189619620000017
Firstly, the probability Pr of the user connecting to the small station is calculatedSUE(θ)
Figure FDA0002189619620000018
The gateway, the macro station and the small station are M/G/1 queues, so the time delay of a user comprises transmission time delay and queuing time delay;
the user is divided into two parts: the first part is a user connected with the macro station, and the second part is a user connected with the small station;
average time delay for user connected with macro station
Figure FDA0002189619620000019
Including average transmission delay for the delay of the radio access link
Figure FDA00021896196200000110
And average queuing delay
Figure FDA0002189619620000021
Namely, it is
Figure FDA0002189619620000023
Calculated by the following formula:
Figure FDA0002189619620000024
Figure FDA0002189619620000025
wherein the content of the first and second substances,
Figure FDA0002189619620000026
represents an average transmission rate obtained when a user accesses the macro station;
Figure FDA0002189619620000027
and
Figure FDA0002189619620000028
the average transmission probability of the macro station and the small station is obtained by the following two equations through a dichotomy:
Figure FDA0002189619620000029
in the above formula, the first and second carbon atoms are,
Figure FDA00021896196200000211
Figure FDA00021896196200000212
Figure FDA00021896196200000213
for the users accessing the small station, the average time delay of each packet is the average time delay of the wireless access link
Figure FDA00021896196200000214
And wireless backhaul link average delay
Figure FDA00021896196200000215
Summing; likewise, the access link average delay
Figure FDA00021896196200000216
And wireless backhaul link average delay
Figure FDA00021896196200000217
Also respectively comprise average transmission time delay
Figure FDA00021896196200000218
And average queuing delay
Figure FDA00021896196200000219
Namely, it is
Figure FDA00021896196200000220
Wherein the content of the first and second substances,
Figure FDA00021896196200000221
calculated by the following formula:
Figure FDA00021896196200000222
Figure FDA00021896196200000223
wherein the content of the first and second substances,
Figure FDA00021896196200000224
the average transmission rate of the wireless transmission link for the small station user,
Figure FDA00021896196200000225
representing the average transmission rate of the backhaul link from the gateway to the small station;
Figure FDA0002189619620000031
calculated by the following formula:
Figure FDA0002189619620000032
Figure FDA0002189619620000033
wherein the content of the first and second substances,
Figure FDA0002189619620000034
the average transmission probability for a gateway is calculated by:
Figure FDA0002189619620000035
thereby obtaining the average time delay of the network
Figure FDA0002189619620000036
The following formula:
Figure FDA0002189619620000037
step 3, calculating the sleep ratio theta of the current base station by the following formulanLower system average energy consumption
Figure FDA0002189619620000038
And a cost function F (θ):
Figure FDA0002189619620000039
Figure FDA00021896196200000310
wherein, ω is a weight factor;
and 4, solving the current nth iteration through the following formula, wherein the sleep proportion theta of the cost function F (theta) to the base station is thetanDerivative function of
Figure FDA00021896196200000311
Wherein the content of the first and second substances,
Figure FDA00021896196200000313
Figure FDA00021896196200000314
Figure FDA0002189619620000041
Figure FDA0002189619620000042
Figure FDA0002189619620000043
Figure FDA0002189619620000044
Figure FDA0002189619620000045
and 5, updating the base station sleep ratio theta, wherein the base station sleep ratio theta is equal to theta in the n +1 th iterationn+1Updated by the following equation:
Figure FDA0002189619620000046
step 6, judging that the whole network cost function F (theta) is F (theta) under the current base station dormancy ration) Whether a minimum point is reached; when F (theta)n+1)-F(θn) When the value is less than xi, the optimal point is reached, and step 8 is executed to quitAn iteration process is output; otherwise, executing step 7;
and 7: updating the current iteration number n +1, and executing the step 2-6;
and 8: quitting the iteration process to obtain the optimal sleep proportion theta of the base station*；
And step 9: making theta equal to 0, namely all the small stations are in an activated state, and updating the user connection state; counting the queue lengths of all the small stations and the average transmission rate of users at a time interval T, and further calculating the average queue length and the average transmission rate in the time length T;
step 10: arranging the small stations in an ascending order according to the product of the average queue length of each small station and the average transmission rate of the user;
step 11: obtaining the optimal dormancy ratio theta according to the step 8*Calculating the number N of base stations to be dormantoff＝[θ*Ns]；
Step 12: closing the top N in sequence according to the small station sequence obtained in the step 10offAnd (5) a small station.
2. The ultra-dense network cell sleeping method based on channel and queue awareness according to claim 1, characterized in that: the minimum value of the base station sleep ratio theta of the small station is thetamin0, maximum value θmax＝1。
3. The ultra-dense network cell sleeping method based on channel and queue awareness according to claim 1, characterized in that: average transmission rate obtained when a user accesses a macro station
Figure FDA0002189619620000051
Average transmission rate of wireless transmission link of small station user
Figure FDA0002189619620000052
Average transmission rate of backhaul link from gateway to small stationAccording to the Shannon formula。
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