CN108260199B - Power control method in heterogeneous cellular network base station - Google Patents

Power control method in heterogeneous cellular network base station Download PDF

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CN108260199B
CN108260199B CN201810163592.7A CN201810163592A CN108260199B CN 108260199 B CN108260199 B CN 108260199B CN 201810163592 A CN201810163592 A CN 201810163592A CN 108260199 B CN108260199 B CN 108260199B
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base station
user
time slot
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power control
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CN108260199A (en
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吴青锋
李云
曹傧
吴广富
赵为粮
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Chongqing University of Post and Telecommunications
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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
    • H04W52/26—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service]
    • H04W52/267—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service] taking into account the information rate
    • 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
    • H04L67/568—Storing data temporarily at an intermediate stage, e.g. caching
    • 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
    • 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
    • H04W52/38—TPC being performed in particular situations
    • H04W52/42—TPC being performed in particular situations in systems with time, space, frequency or polarisation diversity
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/12—Wireless traffic scheduling
    • H04W72/121—Wireless traffic scheduling for groups of terminals or users

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Abstract

The invention belongs to the field of heterogeneous cellular network communication, in particular to a power control method in a heterogeneous cellular network base station; the method comprises the following steps: setting an optimal scheduling user of a t time slot, namely a user of data generated firstly or a user to be scheduled by the home base station; tstart is 0; determining queue backlog of an optimal scheduling user of the t time slot according to the optimal scheduling user of the t time slot, and calculating an optimal power control parameter of the t time slot according to the queue backlog of the optimal scheduling user of the t time slot; calculating the data transmission rate of the base station to the user in the t time slot; calculating the data amount allowed to be accessed into the cache by the user queue of the t time slot according to the data transmission rate to obtain the user queue backlog of the t +1 time slot; solving the optimal scheduling user of the base station in the t +1 time slot according to the optimal power control parameter of the t time slot and the user queue backlog of the t +1 time slot; continuously calculating the optimal power control parameter of the t +1 time slot; the invention can reduce the time delay of the user and effectively improve the energy efficiency of the system.

Description

Power control method in heterogeneous cellular network base station
Technical Field
The invention belongs to the technical field of wireless communication, and relates to a power control method in a heterogeneous cellular network base station.
Background
With the popularization of mobile intelligent terminals, the continuous improvement of wireless communication technology and the rapid development of mobile internet technology, users have made higher requirements on the accommodation capacity, coverage and communication quality of wireless networks. The homogeneous network covered by the traditional single-layer macro base station is difficult to meet the high-speed increase of the communication service demand and the characteristic difference of the service in the spatial distribution. In the fourth generation mobile communication technology, a heterogeneous cellular network becomes a core technology. The heterogeneous network meets the rapidly-increased traffic demand in an economic and effective manner, and improves the service quality of users, but the heterogeneous network also brings many problems, such as signal interference between a macro Base Station and a Femto Base Station (FBS), infinite resource management, vertical handover between heterogeneous networks, and the energy consumption of the whole wireless communication network is increased greatly, so that the heterogeneous network becomes a research hotspot in the field of wireless communication.
Although the FBS has low transmitting power, the energy consumption problem cannot be ignored with the sharp increase of the number. The FBS is installed by a user, network planning and deployment cannot be performed in advance, and the power control scheme suitable for a specific network topology cannot effectively solve the energy efficiency problem in the network, so that the research of the power control method of the heterogeneous cellular network is more necessary. By designing a power control scheme in a targeted manner, the system throughput of the whole network can be improved, the power consumption of transmitting and receiving signals can be reduced, and green communication is really realized.
Power control is a measure for effectively improving energy efficiency, and researchers have proposed many schemes, and the existing research mainly focuses on cell interference suppression and system capacity improvement. Wang W et al combine FBS deployment with a reduction in the transmit power of macro base stations, improving the energy efficiency of the system through appropriate power control. However, the traditional centralized power control method is used by the user, and the method is difficult to apply because the topological structure of the FBS is easily influenced by the installation and use behaviors of the user; in consideration of independence among FBS, Mao T and the like explore the power control problem in a heterogeneous network from the game angle, a macro base station and the FBS respectively solve the energy efficiency optimization problem of the macro base station and the FBS in a selfish way, signals of other base stations are used as interference, and the optimal distributed power of the macro base station and the FBS is obtained according to the interference brought by the other base stations.
The conventional power control method has the following defects: 1) queuing delay of the base station for transmitting data to the user is not considered; 2) maximizing energy efficiency is not considered. In order to overcome the defects of the traditional power control method, the invention provides a base station power control method in a heterogeneous cellular network, which considers time delay and energy efficiency.
Disclosure of Invention
The invention provides a power control method in a heterogeneous cellular network base station; the method comprises the following steps:
s1, initializing the system, where t is 0, and setting a user of the first generated data or a user to be scheduled by the hnb as an optimal scheduling user of the t timeslot;
s2, determining the queue backlog of the optimal scheduling user of the t time slot according to the optimal scheduling user of the t time slot, and calculating the optimal power control parameter of the t time slot according to the queue backlog of the optimal scheduling user of the t time slot;
s3, calculating the data transmission rate of the base station to the user in the t time slot;
s4, calculating the data amount allowed to be accessed into the cache by the user queue of the t time slot according to the data transmission rate, and obtaining the backlog of the user queue of the t +1 time slot;
s5, solving the optimal scheduling user of the base station in the t +1 time slot according to the optimal power control parameter of the t time slot and the user queue backlog of the t +1 time slot;
s6, when t is t +1, the process returns to step S2, and the optimal power control parameter for the t +1 slot is calculated.
Further, the calculation formula of the multiple iterations includes:
Figure BDA0001583768560000021
wherein L represents the number of iterations; [ X ]]+Represents the maximum of 0 and X;
Figure BDA0001583768560000022
representing the base station fiOptimal scheduling of users u in t slotsI,J *(t) data backlog, uI,J *(t) denotes the J-th user of the I-th base station, uI,J *∈ui,j(ii) a W is the base station fiTo user ui,jV denotes a control parameter, β denotes the importance of energy consumption, NTRXRepresenting the number of transmitting and receiving antennas of the base station;
Figure BDA0001583768560000023
is the transmit power dependent power loss slope; pmaxRepresents the maximum transmit power of the femto base station; n is a radical of0Representing a noise power spectral density; i is(j)Denotes a macro base station and other femto base stations to a base station fiInterference of (2);
Figure BDA0001583768560000031
indicating base station f in the course of transmissioniTo user ui,jA large-scale fading coefficient;
Figure BDA0001583768560000032
indicating base station f in the course of transmissioniFor user ui,jSmall scale fading coefficients of (a); i {1,2,..., n }; n represents the total number of femto base stations.
Further, the I(J)The method comprises the following steps:
Figure BDA0001583768560000033
wherein the content of the first and second substances,
Figure BDA0001583768560000034
denotes base station f in the L-th iterationmThe power control parameter of (a);
Figure BDA0001583768560000035
representing the base station fmFor user ui,jLarge scale fading coefficients of;
Figure BDA0001583768560000036
representing the base station fmFor user ui,jSmall scale fading coefficients of (a); pMRepresenting the transmit power of the macro base station;
Figure BDA0001583768560000037
representing macro base station to user ui,jLarge scale fading coefficients of;
Figure BDA0001583768560000038
representing macro base station to user ui,jSmall scale fading coefficients.
Further, the data transmission rate includes:
Figure BDA0001583768560000039
wherein W is the base station fiTo user ui,jThe downlink bandwidth of (2);
Figure BDA00015837685600000310
representing user ui,jFrom base station f in the t-th time slotiThe signal to interference plus noise ratio of the received signal.
Further, the
Figure BDA00015837685600000311
The calculation formula of (2) is as follows:
Figure BDA00015837685600000312
wherein the content of the first and second substances,
Figure BDA00015837685600000313
indicating base station f after iteration is completediThe power control parameter of (a);
Figure BDA00015837685600000314
indicating base station f after iteration is completedmThe power control parameter of (a); n is a radical of0Representing a noise power spectral density; w is the base station fiTo user ui,jThe downlink bandwidth of (2); pmaxRepresents the maximum transmit power of the femto base station;
Figure BDA00015837685600000315
indicating base station f in the course of transmissioniTo user ui,jA large-scale fading coefficient;
Figure BDA00015837685600000316
indicating base station f in the course of transmissioniFor user ui,jSmall scale ofA fading coefficient;
Figure BDA00015837685600000317
representing the base station fiFor user ui,jThe scheduling parameter of (2).
Further, the calculation formula of the amount of data allowed to access the cache by the queue of the t slot is as follows:
Figure BDA0001583768560000041
wherein when
Figure BDA0001583768560000042
When the data is received, the data is allowed to be accessed into the buffer queue; when in use
Figure BDA0001583768560000043
When arriving data is prohibited from entering the stable queue
Figure BDA0001583768560000044
Figure BDA0001583768560000045
Indicating transmission to u in the t-th time sloti,jThe number of groups of (2);
Figure BDA0001583768560000046
representing the base station fiNeeds to be sent to user ui,jThe data buffer queue of (1).
Further, the backlog of the user queue of the t +1 th timeslot includes:
Figure BDA0001583768560000047
Figure BDA0001583768560000048
wherein Q (t +1) represents the user queue backlog of the t +1 time slot;
Figure BDA0001583768560000049
indicating base station f at t +1 time slotiNeeds to be sent to user ui,jThe data buffer queue of (2); [ X ]]+Represents the maximum value among 0 and X.
Further, the solving the optimal scheduled user of the base station in the t +1 time slot includes:
Figure BDA00015837685600000410
wherein u isI',J'*(t+1)Represents the best scheduling user of the base station in the t +1 time slot, i.e. the J 'th user of the I' th base station, and uI',J'*(t+1)∈ui,j；argmin[·]Expressing the value of the independent variable when the minimum value is obtained, V expressing the control parameter, β expressing the importance of energy consumption;
Figure BDA00015837685600000411
is the transmit power dependent power loss slope; pmaxRepresenting the maximum transmit power of the femto base station.
The invention has the beneficial effects that: the invention adopts a power control technology, provides a base station power control method in a heterogeneous cellular network, does not need to know prior knowledge such as channel state transition probability, and only makes real-time transmission decision and adjusts the sending power of the base station according to the current channel state and queue backlog, and can effectively improve the system energy efficiency while reducing the user time delay.
Drawings
FIG. 1 is a flow chart of a power control method in a heterogeneous cellular network base station of the present invention;
fig. 2 is a two-layer heterogeneous network model of a macro base station-femto base station;
FIG. 3 is a diagram of a conventional associated base station fiThe user data queue model of (1);
FIG. 4 is a graph of the average efficiency of the system of the present invention;
FIG. 5 is a diagram of the average queue backlog of the system of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clearly and completely apparent, the technical solutions in the embodiments of the present invention are described below with reference to the accompanying drawings, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
The power control method in the heterogeneous cellular network base station of the present invention includes as shown in fig. 1:
s1, initializing the system, where t is 0, and setting a user of the first generated data or a user to be scheduled by the hnb as an optimal scheduling user of the t timeslot;
s2, determining the queue backlog of the optimal scheduling user of the t time slot according to the optimal scheduling user of the t time slot, and calculating the optimal power control parameter of the t time slot according to the queue backlog of the optimal scheduling user of the t time slot;
s3, calculating the data transmission rate of the base station to the user in the t time slot;
s4, calculating the data amount allowed to be accessed into the cache by the user queue of the t time slot according to the data transmission rate, and obtaining the backlog of the user queue of the t +1 time slot;
s5, solving the optimal scheduling user of the base station in the t +1 th time slot according to the optimal power control parameter of the t th time slot and the user queue backlog of the t +1 th time slot, and returning to the step S1, wherein t is t + 1; continuously solving the optimal power control parameter of the base station;
s6, when t is t +1, the process returns to step S2, and the optimal power control parameter for the t +1 slot is calculated.
In the present invention, a two-layer heterogeneous cellular network downlink transmission model is considered, as shown in fig. 2; assuming that the macro base station and the femto base stations are in the same frequency band, one slot of each femto base station serves only one user. With F ═ F1,f2,......fnDenotes a set of femto base stations for convenience of description; the following base stations all denote femto base stations;
Figure BDA0001583768560000051
representing the base station fiI ∈ {1, 2.., n }, where n represents the number of base stations,
Figure BDA0001583768560000052
representing the base station fiM of lower correlationiIndividual user, miRepresenting the base station fiMaximum number of users of the lower association. Suppose that at the beginning of the t-th time slot, base station f is setiNeeds to be sent to user ui,jIs buffered in a queue
Figure BDA0001583768560000061
In (1),
Figure BDA0001583768560000062
the representation indicates that base station f is in t +1 time slotiNeeds to be sent to user ui,jThe data buffer queue of (1). By using
Figure BDA0001583768560000063
To indicate the queue status of all base stations in time slot t, i.e. the queue backlog of users in time slot t. By using
Figure BDA0001583768560000064
Indicating base station f in t time slotiIs sent to ui,jThe number of groups of (2);
Figure BDA0001583768560000065
the time average of (d) is:
Figure BDA0001583768560000066
wherein
Figure BDA0001583768560000067
Representative user ui,jAssociated base station fiAverage data arrival rate of time.
Figure BDA0001583768560000068
To represent data packets arriving throughout the network.
Figure BDA0001583768560000069
Representing the arrival of a packet into a queue
Figure BDA00015837685600000610
I.e. the queue of t slots, allows access to the amount of buffered data.
Figure BDA00015837685600000611
Representing f within t time slotiSent to user ui,jThe number of packets. Associated at base station fiThe user data queue model of (2) is shown in fig. 3. And converting the time delay of the data into the system stability by a Lyapunov optimization method for solving. The length of the queue is limited, as can be seen from the stability condition of the queue. The stability of the queue can therefore be defined as:
Figure BDA00015837685600000612
wherein T represents the total number of time slots, E {. denotes the expectation of · s; for the system model in fig. 2, two control parameters are set to improve the system energy efficiency and reduce the queue delay; the control parameters include: power control parameters and user scheduling parameters.
(1) Power control parameter
Figure BDA00015837685600000613
Suppose femto base station fiHas a maximum transmission power of PmaxDefining power control parameters
Figure BDA00015837685600000614
Representative base station fiAt the power transmission level of time slot t, the transmission power of the base station is
Figure BDA00015837685600000615
The total power consumption calculation formula of the base station is as follows:
Figure BDA00015837685600000616
wherein the content of the first and second substances,
Figure BDA00015837685600000617
is a base station fiTotal transmit power of; n is a radical ofTRXIs the number of base station transmit receive antennas;
Figure BDA00015837685600000618
is the fixed power consumption of the base station;
Figure BDA00015837685600000619
is the transmit power dependent power loss slope.
(2) User scheduling parameters
Figure BDA00015837685600000620
Indicating base station f at the t-th time slotiWhether to schedule user ui,j，
Figure BDA00015837685600000621
Representing the base station fiUser u is being scheduledi,j，
Figure BDA00015837685600000622
Representing the base station fiWithout scheduling user ui,j。
Constructing a utility function to express energy efficiency; defining the time-averaged power consumption of the network:
Figure BDA0001583768560000071
p represents the sum of the time-averaged power consumption of all femto base stations within the network.
According to the queue model in FIG. 3, user ui,jMay be expressed as the number of downstream packets allowed to enter the queue per unit time. Namely:
Figure BDA0001583768560000072
because high throughput and low energy consumption are mutually restricted, the energy efficiency function is constructed by considering the balance between the high throughput and the low energy consumption
Figure BDA0001583768560000073
Where g (═ log) is2(1 +), that is to say
Figure BDA0001583768560000074
Figure BDA0001583768560000075
Is a non-decreasing concave function representing the gain from throughput, α represents the importance of throughput, β represents the importance of energy consumption, and α + β is 1, the maximum energy efficiency model can be expressed as:
Figure BDA0001583768560000076
wherein the content of the first and second substances,
Figure BDA0001583768560000077
representing the base station fiSent to user ui,jThe maximum value of the number of packets of (1); variables in the above formula
Figure BDA0001583768560000078
The time average function is not in accordance with the framework requirement of a drift-penalty method of a weighting cost function in the Lyapunov optimization method, and the Lyapunov optimization method provides auxiliary variables to replace the time average function for solving. Function g (#) is a non-linear concave function that allows the amount of data in the queue to be accessed into the cache
Figure BDA0001583768560000079
Defining auxiliary variables
Figure BDA0001583768560000081
Figure BDA0001583768560000082
Is a common variable and satisfies
Figure BDA0001583768560000083
Can be used forIt is understood that g (×) is both a non-decreasing concave function and a non-linear concave function; converting formula (5) to:
Figure BDA0001583768560000084
equation (6) is equivalent to the original problem (5) because g (×) is a continuous non-decreasing concave function. To solve equation (6), the inequality is constrained
Figure BDA0001583768560000085
Conversion into a queue stability model for each
Figure BDA0001583768560000086
Introducing virtual queues
Figure BDA0001583768560000087
And is
Figure BDA0001583768560000088
Virtual queues
Figure BDA0001583768560000089
Is a function of time of t, which is updated in the following manner:
Figure BDA00015837685600000810
define Θ (t) to be a set of vectors of queue q (t) and virtual queue h (t), where,
Figure BDA00015837685600000811
Figure BDA00015837685600000812
for time slot t, define the lyapunov function L (Θ (t)) as the sum of the squares of all queues in the current queue state:
Figure BDA00015837685600000813
defining the lyapunov drift as the delta of the lyapunov function between the current time slot and the next time slot:
Δ(Θ(t))＝E[L(Θ(t+1))-L(Θ(t))|Θ(t)](9)
according to lyapunov optimization, a drift-penalty method (dpp) needs to be defined to guide the selection of control actions. And (3) representing the optimized target in a cost function, converting the optimized target into the sum of the Lyapunov drift and the weighted cost function, and obtaining a control strategy which enables the network to be stable and has the highest energy efficiency by minimizing the drift-plus-penalty. The definition is as follows:
Figure BDA0001583768560000091
v denotes a control parameter, V is a non-negative control parameter and represents a trade-off between system utility and system stability, and adjusting the control parameter V can adjust the distance between the time-averaged cost function and the target cost function, and it can be understood that V is a trade-off point between system stability and the target function. Because the utility maximization problem needs to be translated into the minimization problem in lyapunov optimization, only the minimum value of the supremum of equation (10) is required. Assuming that there are constants B >0, >0 such that for any time slot t and any possible state variable Θ (t), the lyapunov drift satisfies the following condition:
Figure BDA0001583768560000092
in order to minimize the right side of equation (11), the right side of the inequality may be decomposed into several independent sub-problems, each of which may be solved separately. At each time slot t, the state of the current queue Θ (t) ═ q (t), h (t)) is obtained according to online observation; it will be appreciated that the user queue backlog q (t) for the t time slot can be obtained by online observation. For each user ui,j，
Figure BDA0001583768560000093
Is through a minimization formula
Figure BDA0001583768560000094
Obtained by
Figure BDA0001583768560000095
Can be obtained by on-line observation, and therefore the auxiliary variables are solved by
Figure BDA0001583768560000096
Figure BDA0001583768560000097
Because of the fact that
Figure BDA0001583768560000101
Is a concave function, so equation (12) is also a concave function. The peak of the function is obtained by taking the derivative of the objective function. By derivation to
Figure BDA0001583768560000102
The value of the variable can be obtained by taking into account the constraint of equation (12).
Figure BDA0001583768560000103
Step S2, determining the queue backlog of the optimal scheduling user of the t slot according to the optimal scheduling user of the t slot, and calculating the optimal power control parameter of the t slot according to the queue backlog of the optimal scheduling user of the t slot includes:
according to base station fiOptimal scheduling of users u in t slotsI,J *(t) solving the power control parameter
Figure BDA0001583768560000104
Definition of
Figure BDA0001583768560000105
Because only one user can transmit and receive data service in one time slot t.
Figure BDA0001583768560000106
A distributed iterative algorithm is used to allow each base station to minimize the utility of this iteration based on previous control parameters. By making the first derivative of the cost function equal to 0, one can obtain
Figure BDA0001583768560000107
The iterative formula of (2).
Figure BDA0001583768560000108
Wherein, L represents the iteration times and is an iteration mark; u. ofI,J *(t) represents the best scheduled user for the t time slot; is the best scheduling user in the above formula definition;
Figure BDA0001583768560000111
it represents interference to the base station by macro base stations and other FBS's. For the start of each time-slot t,
Figure BDA0001583768560000112
and each base station iterates according to the formula (15), and if the last iteration is reached, the result of the last iteration is used as the power control parameter of the current time slot.
Wherein the content of the first and second substances,
Figure BDA0001583768560000113
denotes base station f in the L-th iterationmThe power control parameter of (a);
Figure BDA0001583768560000114
representing the base station fmFor user ui,jLarge scale fading coefficients of;
Figure BDA0001583768560000115
representing the base station fmFor user ui,jSmall scale fading coefficients of (a); pMRepresenting the transmit power of the macro base station;
Figure BDA0001583768560000116
representing macro base station to user ui,jLarge scale fading coefficients of;
Figure BDA0001583768560000117
representing macro base station to user ui,jSmall scale fading coefficients.
S3, calculating the data transmission rate of the base station to the user in the t slot includes: calculating base station fiFor user ui,jInstantaneous data transmission rate of
Figure BDA0001583768560000118
According to the system model and the power level, the user u can be obtainedi,jFrom base station f in the t-th time slotiSignal to interference plus noise ratio of received signal:
Figure BDA0001583768560000119
wherein the content of the first and second substances,
Figure BDA00015837685600001110
representing the large scale fading coefficients during transmission.
Figure BDA00015837685600001111
Representing the small-scale channel fading coefficient, W being the base station fiTo user ui,jThe downlink bandwidth of (2). Base station fiFor user ui,jThe instantaneous data transmission rate of (c) is:
Figure BDA00015837685600001112
s4, calculating the data amount allowed to be accessed into the cache by the user queue of the t time slot according to the data transmission rate to obtain the user queue backlog of the t +1 time slot:
for each user ui,jTo do so
Figure BDA00015837685600001113
Figure BDA00015837685600001114
Can be obtained by on-line observation from
Figure BDA0001583768560000121
In the selection of
Figure BDA0001583768560000122
Figure BDA0001583768560000123
Is through a minimization formula
Figure BDA0001583768560000124
Obtained, by derivation:
Figure BDA0001583768560000125
this is a simple threshold-based access control strategy when
Figure BDA0001583768560000126
When arriving, the data is allowed to access the queue, which not only reduces
Figure BDA0001583768560000127
Is also given a value of
Figure BDA0001583768560000128
Is closer to
Figure BDA0001583768560000129
I.e., increases throughput, so that system utility is improved. When in use
Figure BDA00015837685600001210
When arriving data is prohibited from entering the stable queue
Figure BDA00015837685600001211
The overflow of the congested network due to the acceptance of the latest data is avoided, and the stability of the network is ensured.
S5, according to the power control parameter of the base station at the t-th time slot and the user queue backlog, solving the optimal scheduling user of the base station at the t +1 time slot comprises:
updating the association at base station fiUser u ofi,jA data queue at t +1 time slot.
Figure BDA00015837685600001212
Wherein, X+＝max{X,0}；
Power control parameter of base station according to t time slot
Figure BDA00015837685600001213
User queue backlog
Figure BDA00015837685600001214
Solving base station fiOptimal scheduling user u at t +1 time slotI'，J'*(t+1)；
Figure BDA00015837685600001216
uI',J'*(t+1)Represents the best scheduling user of the base station in the t +1 time slot, i.e. the J 'th user of the I' th base station, and uI',J'*(t+1)∈ui,j(ii) a Next, the base station updates the transmission power and the queue backlog of all users and calculates the scheduled user of the next time slot, and the process is repeated.
The invention firstly associates the base stations, because the base stations of the Time Division Duplex (TDD) series can only serve one user in one Time slot, one Time slot can only schedule one user. Then, power control is performed according to the data amount of the user, so that the power control parameter is iterated for multiple times because all base stations are influenced mutually, and the power control parameter (the value of 0-1) of the base station is iterated for multiple times to reach a stable value. That is, the power control parameter is iterated for many times in a time slot, and it is found that the power control parameter is not easy to converge to the range of 0-1 in the simulation, so that a boundary is set, and the power control parameter is iterated for many times to achieve the convergence effect.
To further illustrate the effectiveness of the power calculation method described in this patent, we simulate it, and the simulation parameters are set as follows: setting MBS to be 1, FBS to be 12, each FBS has 4 users, and the packet arrival rate of the users follows poisson distribution, where λ is 2 kb/s.
Base station fiTo user ui,jThe downlink bandwidth of the channel is 5MHz, and the Gaussian white noise power density N0＝4*10-20W/Hz. The number of antennas of the macro base station is 6, the maximum transmission power is 20W, the fixed power loss is 130W, the power loss slope of the base station is 4.7, and the large-scale fading coefficient
Figure BDA0001583768560000131
The number of the FBS antennas is 2, and the maximum transmission power is
Figure BDA0001583768560000132
Fixed power loss of base station
Figure BDA0001583768560000133
Power loss slope of base station
Figure BDA0001583768560000134
Large scale fading coefficient in transmission process
Figure BDA0001583768560000135
d represents the path length of the signal fading; l iswallRepresents the through-wall loss of the signal; small scale fading coefficient
Figure BDA0001583768560000136
Subject to rayleigh fading.
According to the simulation, when the beta is 0.1 and the alpha is 0.9, the energy efficiency of the system is the highest.
As can be seen from FIGS. 4 and 5, as V increases, the system average utility function increases at approximately the rate of O (1/V), while the average queue backlog increases at approximately the rate of O (V); o represents the system complexity. According to different requirements of different users on time delay, when a proper V value is selected, the algorithm can ensure that the queue backlog is small, namely the time delay of the users is low, and meanwhile, good energy efficiency can be obtained.
It will be appreciated that the lyapunov process employed in the present invention may employ existing techniques, such as: cheng A, Jin H, Li J, et al. Joint discontinuous transmission and power control for high performance in heterologous small cell networks [ C ]// IEEE, International symposium on Personal, Indor, and Mobile Radio communication. IEEE,2015: 970. in the constraint of queue delay, the document uses the Lyapunov optimization method to ensure the stability of the queue, and designs a power control algorithm based on non-cooperative game to reduce the energy consumption of the network.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: ROM, RAM, magnetic or optical disks, and the like.
The above-mentioned embodiments, which further illustrate the objects, technical solutions and advantages of the present invention, should be understood that the above-mentioned embodiments are only preferred embodiments of the present invention, and should not be construed as limiting the present invention, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (7)

1. A method for power control in a heterogeneous cellular network base station, comprising the steps of:
s1, initializing the system, and setting the optimal scheduling user of the t time slot, namely the user generating data firstly or the user to be scheduled by the femtocell; tstart is 0;
s2, determining the data buffer queue of the optimal scheduling user of the t time slot according to the optimal scheduling user of the t time slot, and calculating the optimal power control parameter of the t time slot according to the data buffer queue of the optimal scheduling user of the t time slot;
s3, calculating the data transmission rate of the base station to the user in the t time slot;
s4, calculating the data amount allowed to be accessed into the cache by the user queue of the t time slot according to the data transmission rate to obtain a data cache queue of the t +1 time slot;
s5, solving the optimal scheduling user of the base station at the t +1 time slot according to the optimal power control parameter of the t time slot and the data buffer queue of the t time slot;
s6, when t is t +1, returning to the step S2, and calculating the optimal power control parameter of the t +1 time slot;
the calculation formula of the optimal power control parameter of the t slot of step S2 is:
Figure FDA0002564965880000011
wherein L represents the number of iterations; [ X ]]+Represents the maximum of 0 and X;
Figure FDA0002564965880000015
representing the base station fiOptimal scheduling of users u in t slotsI,J *(t) data backlog, uI,J *(t) denotes the J-th user of the I-th base station, uI,J *∈ui,j(ii) a W is the base station fiTo user ui,jV denotes a control parameter, β denotes the importance of energy consumption, NTRXRepresenting the number of transmitting and receiving antennas of the base station;
Figure FDA0002564965880000012
is the transmit power dependent power loss slope; pmaxRepresents the maximum transmit power of the femto base station; n is a radical of0Representing a noise power spectral density; i is(L)Denotes a macro base station and other femto base stations to a base station fiOfDisturbing;
Figure FDA0002564965880000013
indicating base station f in the course of transmissioniTo user ui,jA large-scale fading coefficient;
Figure FDA0002564965880000014
indicating base station f in the course of transmissioniFor user ui,jI ∈ {1, 2.., n }, n representing the total number of femto base stations.
2. The method of claim 1, wherein the macro base station and other femtocells are paired with a base station fiThe interference of (a) includes:
Figure FDA0002564965880000021
wherein the content of the first and second substances,
Figure FDA0002564965880000022
denotes base station f in the L-th iterationmThe power control parameter of (a);
Figure FDA0002564965880000023
representing the base station fmFor user ui,jLarge scale fading coefficients of;
Figure FDA0002564965880000024
representing the base station fmFor user ui,jSmall scale fading coefficients of (a); pMRepresenting the transmit power of the macro base station;
Figure FDA0002564965880000025
representing macro base station to user ui,jLarge scale fading coefficients of;
Figure FDA0002564965880000026
representing macro base station to user ui,jSmall scale fading coefficients.
3. The method of claim 1, wherein the data transmission rate comprises:
Figure FDA0002564965880000027
wherein the content of the first and second substances,
Figure FDA0002564965880000028
representing the data transmission rate of the user of the t time slot; w is the base station fiTo user ui,jThe downlink bandwidth of (2);
Figure FDA0002564965880000029
representing user ui,jFrom base station f in the t-th time slotiThe signal to interference plus noise ratio of the received signal.
4. The method as claimed in claim 3, wherein the user u is a base station in a heterogeneous cellular networki,jFrom base station f in the t-th time slotiThe calculation formula of the signal-to-interference-and-noise ratio of the received signal is as follows:
Figure FDA00025649658800000210
wherein the content of the first and second substances,
Figure FDA00025649658800000211
indicating base station f after iteration is completediThe power control parameter of (a);
Figure FDA00025649658800000212
indicating base station f after iteration is completedmThe power control parameter of (a); n is a radical of0Representing a noise power spectral density; w is the base station fiTo user ui,jThe downlink bandwidth of (2); pmaxRepresents the maximum transmit power of the femto base station;
Figure FDA00025649658800000213
indicating base station f in the course of transmissioniTo user ui,jA large-scale fading coefficient;
Figure FDA00025649658800000214
indicating base station f in the course of transmissioniFor user ui,jSmall scale fading coefficients of (a);
Figure FDA00025649658800000215
representing the base station fiScheduling parameters for the user;
Figure FDA00025649658800000216
representing the base station fmFor user ui,jLarge scale fading coefficients of;
Figure FDA00025649658800000217
representing the base station fmFor user ui,jSmall scale fading coefficients of (a);
Figure FDA00025649658800000218
representing macro base station to user ui,jLarge scale fading coefficients of;
Figure FDA00025649658800000219
representing macro base station to user ui,jSmall scale fading coefficients.
5. The method as claimed in claim 1, wherein the t-slot queue in step S4 allows the calculation formula of the data amount of the buffer to be accessed is:
Figure FDA0002564965880000031
wherein when
Figure FDA0002564965880000032
When the data is received, the data is allowed to be accessed into the buffer queue; when in use
Figure FDA0002564965880000033
When arriving, the data will be prohibited from entering the queue
Figure FDA0002564965880000034
Figure FDA0002564965880000035
Indicating base station f in t time slotiIs sent to ui,jThe number of groups of (2);
Figure FDA0002564965880000036
indicating base station f at t time slotiNeeds to be sent to user ui,jThe data buffer queue of (2);
Figure FDA0002564965880000037
is a base station fiSent to user ui,jThe virtual queue of (1).
6. The method as claimed in claim 5, wherein the calculation formula of the data buffer queue of t +1 timeslot in step S4 is:
Figure FDA0002564965880000038
wherein the content of the first and second substances,
Figure FDA0002564965880000039
indicating base station f at t +1 time slotiNeeds to be sent to user ui,jThe data buffer queue of (2); [ X ]]+Represents the maximum value among 0 and X.
7. The method as claimed in claim 6, wherein the step of solving the best scheduled user of the base station in t +1 slot comprises:
Figure FDA00025649658800000310
wherein u isI',J'*(t+1)Represents the best scheduling user of the base station in the t +1 time slot, i.e. the J 'th user of the I' th base station, and uI',J'*(t+1)∈ui,j；argmin[·]Expressing the value of the independent variable when the minimum value is obtained, V expressing the control parameter, β expressing the importance of energy consumption;
Figure FDA00025649658800000311
is the transmit power dependent power loss slope; pmaxRepresenting the maximum transmit power of the femto base station.
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