CN105960005A - Power control method for ensuring user fairness in ultra-dense network - Google Patents

Abstract

This invention discloses a power control method for ensuring user fairness in an ultra-dense network. By adopting an uplink power control method, the uplink transmission power of a terminal is adjusted at a real time through overall considering the levels of the users of small and medium-sized base stations in the ultra-dense network, the channel gains of the users, and so on; and thus, the target for maximizing the weight sum of the energy efficiency of the uplink of a terminal is achieved. The method has the advantages for ensuring the high energy efficiency of the uplink of a terminal and ensuring the relative fair energy efficiency among different terminals.

Description

Power control method for guaranteeing user fairness in ultra-dense network

Technical Field

The invention belongs to the field of mobile communication, relates to terminal power control in a mobile communication network, and particularly relates to a small base station power control method capable of ensuring energy efficiency fairness of a mobile terminal uplink.

Background

With the explosive development of mobile communication technology, mobile terminals represented by smart phones are increasingly popularized, and various mobile applications are in a wide range, and both the overall mobile data traffic of a network and the mobile data traffic of a single user present a rapidly increasing situation. In order to meet the challenge of massive traffic, operators start to intensively deploy small base stations to form a heterogeneous dual-layer network so as to enhance the capacity of the network. Meanwhile, in the past, users acquire data from a network more, and the amount of uploaded data is relatively small, so that the data flow of a downlink from a base station to a terminal is often higher than that of an uplink from the terminal to the base station. However, with the popularity of mobile social networking, network gaming, cloud computing, and other applications in recent years, the amount of data uploaded by mobile users has increased and is becoming more frequent.

On the other hand, for the terminal, the transmission and processing of mobile data can cause serious energy consumption. Due to the limitation of battery technology, the capacity of the battery of the mobile terminal is relatively slowly increased in recent years, and the short endurance time of the mobile terminal has become a big pain point for consumers. Because the energy consumption of the radio frequency end related to data transceiving accounts for a high proportion of the whole energy consumption of the mobile terminal, the energy-saving potential of the mobile terminal is great by optimizing the power control mode of an uplink.

Meanwhile, for the uplink transmission of the mobile network, the uplink transmission efficiency of the mobile terminal is greatly affected by differences in the environment where the user is located, the service requirements and the like. For users with poor link conditions, the energy consumption is relatively large and the energy efficiency is relatively low, while for users with good link conditions and less interference, the energy consumption is relatively small and the energy efficiency is relatively high. However, it is necessary for mobile networks to allow users to enjoy mobile services in a relatively fair manner. In view of the great significance of the current energy efficiency index for mobile terminals, it is also reasonable and necessary to ensure that the mobile terminals transmit data with a relatively fair energy efficiency.

The uplink power control in the current mobile communication standard aims to guarantee the received signal-to-interference-and-noise ratio, and does not consider the optimization of energy efficiency. Meanwhile, most of the existing new methods aim at guaranteeing the fairness of the data rate of the mobile users, and the schemes for optimizing the fairness of the energy efficiency are few. In particular, there is a gap in the energy efficiency fairness guarantee method for the uplink of the mobile network terminal.

Disclosure of Invention

The invention aims to solve the problems and provides a power control method for guaranteeing user fairness in an ultra-dense network.

The invention discloses a power control method for guaranteeing user fairness in an ultra-dense network, which comprises the following steps:

step 1: the small base station carries out channel estimation on the uplink channel allocated to each user to obtain channel gain;

set the sub-channels of the small base station asN represents the number of subchannels; the set of users isK represents the number of users; user k is assigned a set of channels ofN_kRepresents the number of subchannels allocated to user k; channel gain of user k on uplink subchannel n is

Step 2, the small base station sets two K × 1-dimensional vectors lambda (t) as lambda (lambda)₁(t),…,λ_k(t),…,λ_K(t)]^T、γ(t)＝[γ₁(t),…,γ_k(t),…,γ_K(t)]^TAs an auxiliary variable for calculating the optimum transmit power, where t is an iteration count variable used to identify the number of iterations of vectors λ (t) and γ (t), λ₁(t)、λ_k(t)、λ_K(t) denotes the 1 st, kth and kth elements, respectively, of the vector λ (t), γ₁(t)、γ_k(t)、γ_K(t) denotes the 1 st, kth and kth elements of the vector γ (t), respectively, and two K-1-dimensional vectors β(s) [ β ]₁(s),…,β_k(s),…,β_K(s)]^TAnd μ(s) ═ μ₁(s),…,μ_k(s),…,μ_K(s)]^TAs an auxiliary variable for calculating the optimal transmit power, where s is an iteration count variable used to identify β(s) and μ(s) iterations, β₁(s)、β_k(s)、β_K(s) denotes the 1 st, kth and kth elements, respectively, of vector β(s) (. mu.)₁(s)、μ_k(s)、μ_K(s) represents the 1 st, kth and kth elements of the vector μ(s), respectively; the value of the iteration count variable t is initialized to 1, and the value of each element of the vectors λ (t) and γ (t) when t is 1 is set to 0, that is, λ (1) ═ γ (1) ═ 0, …,0, …,0]^T；

The value of the iteration count variable s is initialized to 1, and the value of each element of β(s) and μ(s) when s is 1 is set to 1, that is, β (1) ═ μ (1) ═ 1, [1, …,1 …,1]^T；

And step 3: the small base station calculates the optimal uplink transmission power value of each terminal on each allocated sub-channel in sequence;

if the calculated transmission power is less than zero, the optimal transmission power of the terminal on the subchannel is set to be 0; otherwise, the optimal transmitting power of the terminal on the subchannel is the calculated transmitting power;

and 4, step 4: the small base station adds 1 to the value of the iteration count variable t, updates each element of the parameter vectors lambda (t) and gamma (t), judges whether the parameter vectors lambda (t) and gamma (t) are converged, and jumps to the step 3 if the parameter vectors lambda (t) and gamma (t) are not converged, or jumps to the step 5 if the parameter vectors lambda (t) and gamma (t) are converged;

and 5: the small base station adds 1 to the value of the iteration count variable s, updates each element of the parameter vectors beta(s) and mu(s), judges whether the parameter vectors beta(s) and mu(s) are converged, and jumps to the step 6 if the parameter vectors beta(s) and mu(s) are converged, or jumps to the step 3 if the parameter vectors beta(s) and mu(s) are converged;

step 6: the small base station sends the uplink optimal transmitting power value of each terminal to each user terminal, and then waits for the next power control.

In step 3, the transmission power value of the small base station on each subchannel is:

$p_k^n=\frac{B\left({\mathrm{\μ}}_k\right(s)w_k+{\mathrm{\λ}}_k(t\left)\right)}{\left(ln2\right)\left({\mathrm{\μ}}_k\right(s\left){\mathrm{\β}}_k\right(s)+{\mathrm{\γ}}_k(t\left)\right)}-\frac{{\mathrm{\σ}}^2}{g_k^n},$

wherein,representing the optimum transmit power on subchannel n for user k occupying subchannel n in the small base station, B representing the bandwidth of the individual subchannels, σ²Representing the noise power of the small base station on each subchannel;representing the channel gain, w, of user k to the small cell on subchannel n_kRepresenting energy-efficient phase of user k uplinkWeight to other users, μ_k(s) is the kth element of the K × 1-dimensional vector μ(s), β_k(s) is the kth element of the K × 1 dimensional vector β(s) (. lambda.)_k(T) is the kth element of the K × 1-dimensional vector λ (T), γ_kW is the kth element of a vector gamma (T) of dimension K × 1_kRepresents the weight of the energy efficiency of the uplink of user k relative to other users, w_kCan be determined by the operator according to the needs of the operator as long as the guarantee is ensuredAnd (4) finishing. For example, the operator may determine the service level provided to the user, and if the operator can provide C service levels to the user, different service levels may be empirically determined to be different w^c，w^cThe larger the energy efficiency weight of the user representing the class is, the higher the priority for optimizing the energy efficiency of the user of the class is, otherwise, the lower the weight is, the lower the priority for optimizing the energy efficiency of the user of the class is. Specifically, each user k determines one according to the service classThen, the user under the same base station is normalized again to obtain

In step 4, the small base station updates each element in the parameter vector lambda thereof based on the following formula

Wherein,_k(t) represents the step size of each element of the vector λ (t) at the t-th iteration, and satisfies Representing the lowest data rate requested by user k.

The small base station updates each element in its parameter vector y (t) based on the following formula

Therein, ζ_k(t) represents the step size of each element of vector ζ (t) at the t-th iteration, and satisfies Representing the upper limit of the sum of the transmit powers of terminal k on the respective subchannels.

Each small base station judges whether the parameter vectors lambda and gamma are converged based on the basis that whether lambda is satisfied_k(t)＝λ_k(t-1) and γ_k(t)＝γ_k(t-1), if each element of λ (t), γ (t) satisfies the above condition, it means that λ (t), γ (t) have converged, otherwise, it means that λ (t), γ (t) have not converged.

In step 5, the small cell updates its own parameter vectors β(s) and μ(s) based on the following rule

$\left[\begin{array}{c}\mathrm{\β}\left(s\right)\\ \mathrm{\μ}\left(s\right)\end{array}\right]=\left[\begin{array}{c}\mathrm{\β}(s-1)\\ \mathrm{\μ}(s-1)\end{array}\right]+q\left(s\right),$

Wherein,is a 2K × 1-dimensional vector combined by β(s) and mu(s),

is a 2K × 1-dimensional vector combined by β (s-1) and mu (s-1),is a K × 1-dimensional auxiliary vector,is a 2K × 1-dimensional auxiliary vector,respectively represent 2K × 1-dimensional vectorsThe 1 st, kth and 2K-th dimensional elements of (a);

when K ∈ [1, K)]When the temperature of the water is higher than the set temperature,

when K ∈ [ K +1,2K ]]When the temperature of the water is higher than the set temperature, represents a 2K × 1-dimensional vectorWith respect to the 2K × 1 vectorThe jacobian matrix of (a) is,is the fixed circuit power consumption of terminal k.

The judgment of β(s), μ(s) has converged is based on the condition whether each element of β(s), μ(s) satisfies β_k(s)＝β_k(s-1) and μ_k(s)＝μ_k(s-1). if satisfied, it indicates β, μ has converged, otherwise, it indicates β(s), μ(s) has not converged.

The invention has the advantages that:

(1) the invention endows different weighted values for the energy efficiency of uplink transmission of different terminals, and optimizes the energy efficiency of uplink transmission of all users, thereby ensuring the high efficiency and fairness of the energy efficiency of uplink transmission of the users and enabling different users to realize the uplink transmission of data with relatively fair and higher energy efficiency; (2) (ii) a (3) (ii) a

Drawings

FIG. 1 is a flowchart illustrating the steps of a power control method for guaranteeing fairness among users in an ultra-dense network according to the present invention;

Detailed Description

The present invention will be described in further detail with reference to the accompanying drawings and examples.

The invention provides a small base station power control method capable of ensuring energy efficiency fairness of a mobile terminal uplink, which adopts an uplink power control mode, adjusts uplink transmission power of a terminal in real time by comprehensively considering factors such as grades of small base station users, channel gains of the users and the like in an ultra-dense network, achieves the effect of maximizing the weighted sum of the uplink energy efficiency of the terminal, and has the advantages of ensuring high energy efficiency of the terminal uplink and ensuring relatively fair energy efficiency among different terminals.

The power control method for guaranteeing user fairness in the ultra-dense network of the invention has a flow as shown in fig. 1, and comprises the following steps:

step 1: and the small base station performs channel estimation on the uplink channel allocated to each user to acquire channel gain.

If the set of sub-channels available for the small base station is denoted asThe user set is represented asThe set of channels to which user k is assigned is denoted asThe channel gain for user k on uplink subchannel n is expressed as

Step 2, the small base station sets two K × 1-dimensional vectors lambda (t) as lambda (lambda)₁(t),…,λ_k(t),…,λ_K(t)]^T、γ(t)＝[γ₁(t),…,γ_k(t),…,γ_K(t)]^TAs an auxiliary variable for calculating the optimum transmit power, where t is an iteration count variable used to identify the number of iterations of vectors λ (t) and γ (t), λ₁(t)、λ_k(t)、λ_K(t) denotes the 1 st, kth and kth elements, respectively, of the vector λ (t), γ₁(t)、γ_k(t)、γ_K(t) denotes the 1 st, kth and kth elements of the vector γ (t), respectively, two K × 1-dimensional vectors β(s) [ β ]₁(s),…,β_k(s),…,β_K(s)]^TAnd μ(s) ═ μ₁(s),…,μ_k(s),…,μ_K(s)]^TAs an auxiliary variable for calculating the optimal transmit power, where s is an iteration count variable used to identify β(s) and μ(s) iterations, β₁(s)、β_k(s)、β_K(s) denotes the 1 st, kth and kth elements, respectively, of vector β(s) (. mu.)₁(s)、μ_k(s)、μ_K(s) represents the 1 st, kth and kth elements of the vector μ(s), respectively; the value of the iteration count variable t is initialized to 1, and the value of each element of the vectors λ (t) and γ (t) when t is 1 is set to 0, that is, λ (1) ═ γ (1) ═ 0, …,0, …,0]^T；

The value of the iteration count variable s is initialized to 1, and the value of each element of β(s) and μ(s) when s is 1 is set to 1, that is, β (1) ═ μ (1) ═ 1, [1, …,1 …,1]^T。

And step 3: the small base station calculates the best uplink transmission power value of each terminal on each sub-channel allocated to the small base station in turn.

If the calculated transmission power is less than zero, the optimal transmission power of the terminal on the subchannel is 0; otherwise, the transmitting power of the terminal on the subchannel is the calculated transmitting power.

The invention is suitable for the environment that the macro base station and the small base station use different frequency bands, and meanwhile, an effective interference avoidance mechanism is arranged between the small base stations. Therefore, the interference between the neighboring small base stations is also at a negligible level. By usingRepresenting the uplink transmission power of a user k occupying a subchannel n in a small base station on the subchannel n, B representing the bandwidth of a single subchannel, sigma²Representing the noise power of the small base station on each subchannel,representing the set of sub-channels assigned to user k,representing the fixed circuit consumption of terminal k,representing the lowest data rate limit for terminal k,representing the upper limit of the sum of the transmit powers of terminal k on all sub-channels, the uplink optimum transmit power is obtained based on solving the following problem P1:

problem P1 is that there is a minimum data rate QoSGuaranteed user uplink transmission energy efficiency weighting and maximization problem, wherein w_kWeight, w, representing energy efficiency of uplink transmission of user k_kCan be determined by the operator according to the needs of the operator as long as the guarantee is ensuredAnd (4) finishing. For example, the operator may determine the service level provided to the user, and if the operator can provide C service levels to the user, different service levels may be empirically determined to be different w^c，w^cThe larger the energy efficiency weight of the user representing the class is, the higher the priority for optimizing the energy efficiency of the user of the class is, otherwise, the lower the weight, the lower the priority for optimizing the energy efficiency of the user of the class is. Each user k is assigned one according to the service classThen, the user under the same base station is normalized again to obtain the user

Problem P1 is a non-convex optimization problem, and in order to solve the above non-convex optimization problem relatively efficiently, two sets of auxiliary variable vectors β(s) ═ β are introduced₁(s),…,β_k(s),…,β_K(s)]^TAnd μ(s) ═ μ₁(s),…,μ_k(s),…,μ_K(s)]^TBoth are K × 1 dimensional vectors, it can be shown that there is, and only exists, one setAndso that the optimal solution of the problem P1Optimization with the following problem P2Solution of phase equality

And satisfies the following formula

Therefore, the solution of the best uplink transmit power value of each terminal on each sub-channel allocated to it by the small base station can be solved by two layers of loop iterations, when the inner loop is used, β(s) ═ β₁(s),…,β_k(s),…,β_K(s)]^TAnd μ(s) ═ μ₁(s),…,μ_k(s),…,μ_K(s)]^TIt has been given that β(s), μ(s) is substituted for β in problem P2^*(s),μ^*(s), then the problem P2 becomes a typical convex optimization problem, introducing two sets of lambertian multipliers λ (t) [ [ λ ] ]₁(t),…,λ_k(t),…,λ_K(t)]^T、γ(t)＝[γ₁(t),…,γ_k(t),…,γ_K(t)]^TThrough the classical Lagrange dual relaxation thought in convex optimization, the lambda (t) gamma (t) can be continuously updated through a secondary gradient method until the lambda (t) gamma (t) converges, and therefore the optimal solution can be simply and quickly obtained

$p_k^n=\frac{B\left({\mathrm{\μ}}_k\right(s)w_k+{\mathrm{\λ}}_k(t\left)\right)}{\left(ln2\right)\left({\mathrm{\μ}}_k\right(s\left){\mathrm{\β}}_k\right(s)+{\mathrm{\γ}}_k(t\left)\right)}-\frac{{\mathrm{\σ}}^2}{g_k^n}.$

To obtain inner layer circulationAfter the optimal solution, the outer loop may be paired with β ═ β₁,β₂,…,β_K],μ＝[μ₁,μ₂,…,μ_K]And carrying out iterative updating convergence on the two groups of vectors. In particular, based on the equationAndthe following formula may be constructed:

when K ∈ [1, K)]When the temperature of the water is higher than the set temperature,

when K ∈ [ K +1,2K ]]When the temperature of the water is higher than the set temperature,

it can be shown that if and only if β(s) ═ β^*(s),u(s)＝u^*(s) in the case of(s),thus β may be obtained iteratively by the quasi-newton method^*(s),u^*(s)。

And 4, step 4: the small base station adds 1 to the value of the iteration number counter t. Based on the secondary gradient method, the small base station updates each element in the lambertian multiplier λ (t) by the following formulaWherein,_k(t) represents the step size of each element of the vector λ (t) at the t-th iteration, and satisfies Represents the lowest data rate requested by user k;

the small cell updates each element in the parameter vector γ (t) based on the following formula

Therein, ζ_k(t) represents the step size of each element of vector ζ (t) at the t-th iteration, and satisfies Representing the upper limit of the sum of the transmit powers of terminal k on the respective subchannels.

Each small base station judges whether lagrangian multipliers λ (t), γ (t) have converged based on the following criteria. If each element of λ (t), γ (t) satisfies λ_k(t)＝λ_k(t-1) and γ_k(t)＝γ_k(t-1), it means λ (t), γ (t) have converged, otherwise, it means λ (t), γ (t) have not converged.

If the Lagrange multipliers lambda (t) and gamma (t) do not converge, skipping to the step 3; otherwise, jump to step 5.

And 5: the small base station adds 1 to the value of the iteration number counter s. Based on quasi-Newton method, the small base station updates beta(s) and mu(s) by the following formula,

$\left[\begin{array}{c}\mathrm{\β}\left(s\right)\\ \mathrm{\μ}\left(s\right)\end{array}\right]=\left[\begin{array}{c}\mathrm{\β}(s-1)\\ \mathrm{\μ}(s-1)\end{array}\right]+q\left(s\right),$

wherein,is a K × 1-dimensional auxiliary vector,represents a 2K × 1-dimensional vectorWith respect to the 2K × 1 vectorThe jacobian matrix of. .

When each element of β(s), μ(s) satisfies the following two equations,

β_k(s)＝β_k(s-1)

μ_k(s)＝μ_k(s-1)。

then β(s), μ(s) converge, otherwise β(s), μ(s) do not converge.

If β(s), μ(s) have converged, go to step 6, otherwise, go to step 3.

Step 6: the small base station sends the uplink optimal transmitting power value of each terminal to each user terminal, and then waits for the next power control.

Claims (7)

1. The power control method for guaranteeing user fairness in the ultra-dense network comprises the following steps:

step 1: the small base station carries out channel estimation on the uplink channel allocated to each user to obtain channel gain;

set the sub-channels of the small base station asN represents the number of subchannels; the set of users isK represents the number of users; user k is assigned a set of channels ofN_kRepresents the number of subchannels allocated to user k; channel gain of user k on uplink subchannel n is

Step 2, the small base station sets two K × 1-dimensional vectors lambda (t) as lambda (lambda)₁(t)，…，λ_k(t)，…，λ_K(t)]^T、γ(t)＝[γ₁(t)，…，γ_k(t)，…，γ_K(t)]^TAs an auxiliary variable for calculating the optimum transmit power, where t is an iteration count variable used to identify the number of iterations of vectors λ (t) and γ (t), λ₁(t)、λ_k(t)、λ_K(t) denotes the 1 st, kth and kth elements, respectively, of the vector λ (t), γ₁(t)、γ_k(t)、γ_K(t) denotes the 1 st, kth and kth elements of the vector γ (t), respectively, two K × 1-dimensional vectors β(s) [ β ]₁(s)，…，β_k(s)，…，β_K(s)]^TAnd μ(s) ═ μ₁(s)，…，μ_k(s)，…，μ_K(s)]^TAs an auxiliary variable for calculating the optimal transmit power, where s is an iteration count variable used to identify β(s) and μ(s) iterations, β₁(s)、β_k(s)、β_K(s) denotes the 1 st, kth and kth elements, respectively, of vector β(s) (. mu.)₁(s)、μ_k(s)、μ_K(s) represents the 1 st, kth and kth elements of the vector μ(s), respectively; the value of the iteration count variable t is initialized to 1, and the value of each element of the vectors λ (t) and γ (t) when t is 1 is set to 0, that is, λ (1) ═ γ (1) ═ 0, …,0, …,0]^T；

The value of the iteration count variable s is initialized to 1, and the value of each element of β(s) and μ(s) when s is 1 is set to 1, that is, β (1) ═ μ(1)＝[1，…，1…，1]^T；

And step 3: the small base station calculates the optimal uplink transmission power value of each terminal on each allocated sub-channel in sequence;

if the calculated transmission power is less than zero, the optimal transmission power of the terminal on the subchannel is set to be 0; otherwise, the optimal transmitting power of the terminal on the subchannel is the calculated transmitting power;

and 4, step 4: the small base station adds 1 to the value of the iteration count variable t, updates each element of the parameter vectors lambda (t) and gamma (t), judges whether the parameter vectors lambda (t) and gamma (t) are converged, and jumps to the step 3 if the parameter vectors lambda (t) and gamma (t) are not converged, or jumps to the step 5 if the parameter vectors lambda (t) and gamma (t) are converged;

and 5: the small base station adds 1 to the value of the iteration count variable s, updates each element of the parameter vectors beta(s) and mu(s), judges whether the parameter vectors beta(s) and mu(s) are converged, and jumps to the step 6 if the parameter vectors beta(s) and mu(s) are converged, or jumps to the step 3 if the parameter vectors beta(s) and mu(s) are converged;

step 6: the small base station sends the uplink optimal transmitting power value of each terminal to each user terminal, and then waits for the next power control.

2. The power control method for guaranteeing fairness of users in ultra-dense network as claimed in claim 1, wherein in step 3, the best uplink transmission power value of the small base station on each sub-channel is:

$p_k^n=\frac{B\left({\mathrm{\μ}}_k\right(s)w_k+{\mathrm{\λ}}_k(t\left)\right)}{\left(\ln 2\right)\left({\mathrm{\μ}}_k\right(s\left){\mathrm{\β}}_k\right(s)+{\mathrm{\γ}}_k(t\left)\right)}-\frac{{\mathrm{\σ}}^2}{g_k^n}$

wherein,representing the optimum transmit power on subchannel n for user k occupying subchannel n in the small base station, B representing the bandwidth of the individual subchannels, σ²Representing the noise power of the small base station on each subchannel;representing the channel gain, w, of user k to the small cell on subchannel n_kRepresents the weight of the energy efficiency of the uplink of user k relative to other users, mu_k(s) is the kth element of the K × 1-dimensional vector μ(s), β_k(s) is the kth element of the K × 1 dimensional vector β(s) (. lambda.)_k(T) is the kth element of the K × 1-dimensional vector λ (T), γ_kAnd (T) is the kth element of the vector gamma (T) with the dimension K × 1.

3. Power control for guaranteeing user fairness in ultra-dense networks as claimed in claim 2Method of, said w_kAs determined by the operator, it is possible to,

4. the power control method for guaranteeing fairness among users in ultra-dense network as claimed in claim 1, wherein in said step 4, the small cell updates each element in the parameter vector λ (t) based on the following formula

Wherein,_k(t) represents the step size of each element of the vector λ (t) at the t-th iteration, and satisfies Represents the lowest data rate requested by user k;

the small cell updates each element in the parameter vector γ (t) based on the following formula

Therein, ζ_k(t) represents the step size of each element of vector ζ (t) at the t-th iteration, and satisfies Representing the upper limit of the sum of the transmit powers of terminal k on the respective subchannels.

5. The power control method for guaranteeing fairness among users in ultra-dense network as claimed in claim 1, wherein in step 4, λ (t) and γ (t) converge when each element of λ (t) and γ (t) satisfies the following two equations:

λ_k(t)＝λ_k(t-1)

γ_k(t)＝γ_k(t-1)。

6. the power control method for guaranteeing fairness among users in ultra-dense network as claimed in claim 1, wherein in step 5, the method for updating vectors β(s) and μ(s) by the small cell comprises:

$\left[\begin{array}{c}\mathrm{\β}\left(s\right)\\ \mathrm{\μ}\left(s\right)\end{array}\right]=\left[\begin{array}{c}\mathrm{\β}(s-1)\\ \mathrm{\μ}(s-1)\end{array}\right]+q\left(s\right),$

wherein,is a 2K × 1-dimensional vector combined by β(s) and mu(s),is a 2K × 1-dimensional vector combined by β (s-1) and mu (s-1),is a K × 1-dimensional auxiliary vector,is a 2K × 1-dimensional auxiliary vector,respectively represent 2K × 1-dimensional vectorsThe 1 st, kth and 2K-th dimensional elements of (a);

when K ∈ [1, K)]When the temperature of the water is higher than the set temperature,

when K ∈ [ K +1,2K ]]When the temperature of the water is higher than the set temperature,represents a 2K × 1-dimensional vectorWith respect to the 2K × 1 vectorThe jacobian matrix of (a) is,is the fixed circuit power consumption of terminal k.

7. The power control method for guaranteeing fairness among users in ultra-dense network as claimed in claim 1, wherein in said step 5, when each element of β(s) and μ(s) satisfies the following two equations, β(s) and μ(s) converge:

β_k(s)＝β_k(s-1)

μ_k(s)＝μ_k(s-1)。