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

A power control method to ensure user fairness in ultra-dense networks

technical field

The invention belongs to the field of mobile communication, relates to terminal power control in a mobile communication network, in particular to a small base station power control method capable of ensuring fair energy efficiency of uplinks of mobile terminals.

Background technique

With the explosive development of mobile communication technology, mobile terminals represented by smart phones are becoming more and more popular, and various mobile applications emerge in an endless stream. Both the overall mobile data traffic of the network and the mobile data traffic of a single user are showing a trend of rapid growth. To cope with the challenge of massive traffic, operators have begun to densely deploy small base stations to form a heterogeneous two-tier network to enhance network capacity. At the same time, in the past, users mostly obtained data from the network, and the amount of uploaded data was relatively small. Therefore, the data traffic of the downlink from the base station to the terminal was often higher than the uplink from the terminal to the base station. However, in recent years, with the popularity of applications such as mobile social networks, online games, and cloud computing, the amount of data uploaded by mobile users has become larger and more frequent.

On the other hand, for the terminal, the transmission and processing of mobile data will bring serious energy consumption. Due to the limitations of battery technology, the battery capacity of mobile terminals has grown relatively slowly in recent years, and the short battery life of mobile terminals has become a major pain point for consumers. Since the energy consumption of the radio frequency terminal related to data transmission and reception accounts for a high proportion of the overall energy consumption of the mobile terminal, the energy saving potential of the mobile terminal is huge by optimizing the power control of the uplink.

At the same time, for the uplink transmission of the mobile network, the differences in various aspects such as the user's environment and service requirements will have a huge impact on the uplink transmission efficiency of the mobile terminal. To transmit the same data flow, 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 low. higher. However, it is necessary for mobile networks to allow users to enjoy mobile services in a relatively fair manner. Considering the great significance of the current energy efficiency index for mobile terminals, it is also reasonable and necessary to ensure that mobile terminals transmit data with relatively fair energy efficiency.

The power control of the uplink in the current mobile communication standard is aimed at ensuring the received signal-to-interference-noise ratio, without considering the optimization of energy efficiency. At the same time, most of the existing new methods aim to ensure the fairness of the data rate of mobile users, and there are few schemes to optimize the fairness of energy efficiency. In particular, there is still a gap in the energy efficiency fairness guarantee method for the uplink of the mobile network terminal.

Contents of the invention

The purpose of the present invention is to solve the above problems and propose a power control method to ensure user fairness in an ultra-dense network. Factors such as channel gain, adjust the uplink transmission power of the terminal in real time, and achieve the goal of maximizing the weighted sum of the uplink energy efficiency of the terminal, which not only ensures the high energy efficiency of the uplink of the terminal, but also ensures the energy efficiency between different terminals relatively fair advantage.

The power control method for ensuring user fairness in the ultra-dense network of the present invention is divided into the following steps:

Step 1: The small base station performs channel estimation on the uplink channel allocated to each user to obtain channel gain;

Let the subchannel set of the small base station be N represents the number of sub-channels; the user set is K represents the number of users; the channel set assigned to user k is N _k represents the number of subchannels allocated to user k; the channel gain of user k on uplink subchannel n is

Step 2: The small base station sets two K×1-dimensional vectors λ(t)=[λ ₁ (t),…,λ _k (t),…,λ _K (t)] ^T , γ(t)=[γ ₁ (t),…,γ _k (t),…,γ _K (t)] ^T is used as an auxiliary variable to calculate the optimal transmit power, where t is the number of iteration count variables, used to identify the vector λ(t) and The number of iterations of γ(t), λ ₁ (t), λ _k (t), and λ _K (t) represent the 1st, kth and Kth elements of the vector λ(t), respectively, γ ₁ (t), γ _k (t), γ _K (t) represent the 1st, kth and Kth elements of vector γ(t) respectively; introduce two K=1-dimensional vectors β(s)=[β ₁ (s), …,β _k (s),…,β _K (s)] ^T and μ(s)=[μ ₁ (s),…,μ _k (s),…,μ _K (s)] ^T as the most Auxiliary variable of optimal transmit power, where s is the number of iteration count variable, used to identify the number of iterations of β(s) and μ(s), β ₁ (s), β _k (s), β _K (s) represent the 1st, kth and Kth elements of the vector β(s), respectively, and μ ₁ (s), μ _k (s), μ _K (s) represent the 1st, kth and Kth elements of the vector μ(s) respectively The Kth element; the value of the iteration count variable t is initialized to 1, and the value of each element of the vector λ(t) and γ(t) is set to 0 when t=1, that is, λ(1)= γ(1)=[0,...,0,...,0] ^T ;

Initialize the value of the number of iteration count variable s to 1, and set the value of each element of β(s) and μ(s) to 1 when s=1, that is, β(1)=μ(1)=[ 1,...,1...,1] ^T ;

Step 3: The small base station sequentially calculates the optimal uplink transmission power value of each terminal on each allocated subchannel;

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

Step 4: The small base station adds 1 to the value of the iteration count variable t, updates each element of the parameter vectors λ(t), γ(t), and judges whether the parameter vectors λ(t), γ(t) have converged, If the parameter vectors λ(t), γ(t) have not converged, then go to step 3, if the parameter vectors λ(t), γ(t) have converged, then go to step 5;

Step 5: The small base station adds 1 to the value of the iteration count variable s, the small base station updates each element of the parameter vectors β(s), μ(s), and judges whether the parameter vectors β(s), μ(s) have Convergence, if the parameter vectors β(s), μ(s) have converged, then go to step 6, otherwise, go to step 3;

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

In step 3, the transmit power value of the small base station on each sub-channel is:

${\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; l</span>}\mathrm{<span\; class="notranslate">\; no</span>}\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; \&beta;</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&gamma;</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>\frac{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}}<span\; class="notranslate">\; ,</span>$

in, Indicates the optimal transmission power of user k occupying sub-channel n in the small base station on sub-channel n, B represents the bandwidth of a single sub-channel, and ^σ2 represents the noise power of the small base station on each sub-channel; Indicates the channel gain from user k to the small base station on subchannel n, w _k indicates the weight of user k’s uplink energy efficiency relative to other users, μ _k (s) is the kth of the K×1-dimensional vector μ(s) elements, β _k (s) is the kth element of the K×1-dimensional vector β(s), λ _k (t) is the kth element of the K×1-dimensional vector λ(T), γ _k (t) is the kth element of the K×1-dimensional vector γ(T). w _k represents the weight of user k’s uplink energy efficiency relative to other users, w _k can be determined by operators according to their own needs, as long as it is ensured that That's it. For example, the operator can determine according to the service level provided to users. If the operator can provide users with ^C service levels, different service levels can be determined based on experience ^. The higher the energy efficiency weight of users of a class, the higher the priority of optimizing the energy efficiency of users of this class, otherwise, the lower the weight, the lower the priority of optimizing the energy efficiency of users of this class. Specifically, each user k determines a After that, the users under the same base station can be normalized again to obtain

In step 4, the small base station updates each element in its own parameter vector λ based on the following formula

Among them, Γ _k (t) represents the step size of each element of the vector λ(t) in the t-th iteration, and satisfies Indicates the lowest data rate requested by user k.

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

Among them, ζ _k (t) represents the step size of each element of the vector ζ (t) in the t iteration, and satisfies Indicates the upper limit of the sum of transmit power of terminal k on each subchannel.

Each small base station judges whether the parameter vectors λ and γ have converged based on the following basis, that is, whether λ _k (t)=λ _k (t-1) and γ _k (t)=γ _k (t-1) are satisfied, if λ( If each element of t) and γ(t) satisfies the above conditions, it means that λ(t) and γ(t) have converged, otherwise, it means that λ(t) and γ(t) have not converged.

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

$\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; \&beta;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span>\\ \mathrm{<span\; class="notranslate">\; \&mu;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span>\end{array}\right]<span\; class="notranslate">\; =</span>\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; \&beta;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>\\ \mathrm{<span\; class="notranslate">\; \&mu;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>\end{array}\right]<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; q</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>$

in, is a 2K×1-dimensional vector composed of β(s) and μ(s),

is a 2K×1-dimensional vector composed of β(s-1) and μ(s-1), is the K×1 dimensional auxiliary vector, is a 2K×1-dimensional auxiliary vector, represent 2K×1-dimensional vectors respectively The 1st, Kth and 2Kth dimension elements of ;

When k∈[1,K],

When k∈[K+1,2K], Represents a 2K×1-dimensional vector About 2K×1 vectors The Jacobian matrix of is the fixed circuit power consumption of terminal k.

Judging whether β(s) and μ(s) have converged is based on the following conditions, that is, whether each element of β(s) and μ(s) satisfies β _k (s)=β _k (s-1) and μ _k (s) ₌ μk(s-1). . If it is satisfied, it means that β and μ have converged, otherwise, it means that β(s) and μ(s) have not converged.

The advantages of the present invention are:

(1) The present invention assigns different weight values to the energy efficiencies of uplink transmissions of different terminals, and optimizes the energy efficiencies of uplink transmissions of all users, thereby ensuring high efficiency and high energy efficiency of uplink transmissions of users. Fairness, enabling different users to achieve data uplink transmission with relatively fair and high energy efficiency; (2); (3);

Description of drawings

Fig. 1 is a flow chart of the arrangement steps of the power control method for ensuring user fairness in the ultra-dense network of the present invention;

detailed description

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

The present invention provides a small base station power control method that can ensure the fair energy efficiency of the uplink of the mobile terminal. The uplink power control method is adopted, and by comprehensively considering factors such as the user level of the small base station in the ultra-dense network, the user's channel gain, etc., The uplink transmission power of the terminal is adjusted in real time to achieve the effect of maximizing the weighted sum of the uplink energy efficiency of the terminal, which has the advantages of not only ensuring high energy efficiency of the terminal uplink, but also ensuring relatively fair energy efficiency among different terminals.

A power control method for ensuring user fairness in an ultra-dense network of the present invention, as shown in Figure 1, includes the following steps:

Step 1: The small base station performs channel estimation on the uplink channel allocated to each user to obtain channel gain.

If the set of subchannels available to the small base station is expressed as A collection of users is represented as The channel set allocated to user k is expressed as Then the channel gain of user k on the uplink sub-channel n is expressed as

Step 2: The small base station sets two K×1-dimensional vectors λ(t)=[λ ₁ (t),…,λ _k (t),…,λ _K (t)] ^T , γ(t)=[γ ₁ (t),…,γ _k (t),…,γ _K (t)] ^T is used as an auxiliary variable to calculate the optimal transmit power, where t is the number of iteration count variables, used to identify the vector λ(t) and The number of iterations of γ(t), λ ₁ (t), λ _k (t), and λ _K (t) represent the 1st, kth and Kth elements of the vector λ(t), respectively, γ ₁ (t), γ _k (t), γ _K (t) represent the 1st, kth and Kth elements of vector γ(t) respectively; introduce two K×1-dimensional vectors β(s)=[β ₁ (s), …,β _k (s),…,β _K (s)] ^T and μ(s)=[μ ₁ (s),…,μ _k (s),…,μ _K (s)] ^T as the most Auxiliary variable of optimal transmit power, where s is the number of iteration count variable, used to identify the number of iterations of β(s) and μ(s), β ₁ (s), β _k (s), β _K (s) represent the 1st, kth and Kth elements of the vector β(s), respectively, and μ ₁ (s), μ _k (s), μ _K (s) represent the 1st, kth and Kth elements of the vector μ(s) respectively The Kth element; the value of the iteration count variable t is initialized to 1, and the value of each element of the vector λ(t) and γ(t) is set to 0 when t=1, that is, λ(1)= γ(1)=[0,...,0,...,0] ^T ;

Initialize the value of the number of iteration count variable s to 1, and set the value of each element of β(s) and μ(s) to 1 when s=1, that is, β(1)=μ(1)=[ 1,...,1...,1] ^T .

Step 3: The small base station sequentially calculates the optimal uplink transmission power value of each terminal on each sub-channel allocated to it.

If the calculated transmit power is less than zero, the optimal transmit power of the terminal on the sub-channel is 0; otherwise, the transmit power of the terminal on the sub-channel is the calculated transmit power.

The present invention is applicable to the environment where the macro base station and the small base station use different frequency bands, and at the same time, there is an effective interference avoidance mechanism between the small base stations. Therefore, the interference between adjacent small base stations is also at a negligible level. use Indicates the uplink transmission power of user k occupying subchannel n in the small base station on subchannel n, B indicates the bandwidth of a single subchannel, ^σ2 indicates the noise power of the small base station on each subchannel, Indicates the set of subchannels allocated to user k, denotes the fixed circuit consumption at terminal k, Indicates the minimum data rate limit for terminal k, Represents the upper limit of the sum of the transmission power of terminal k on all sub-channels, then the optimal transmission power of uplink is obtained based on solving the following problem P1:

Problem P1 is a weighted and maximized problem of user uplink transmission energy efficiency with the lowest data rate QoS guarantee, where w _k represents the weight of energy efficiency of user k uplink transmission, w _k can be determined by the operator according to its own needs, just make sure That's it. For example, the operator can determine according to the service level provided to users. If the operator can provide users with ^C service levels, then different service levels can be determined based on experience. A different ^wc , the larger wc, indicates the level If the user's energy efficiency weight is high, the priority of optimizing the energy efficiency of users of this level is high; otherwise, the weight is low, and the priority of energy efficiency optimization of users of this level is low. Each user k is assigned a After that, the users under the same base station can be normalized again to get

Problem P1 is a non-convex optimization problem. In order to solve the above non-convex optimization problems relatively efficiently, two sets of auxiliary variable vectors β(s)=[β ₁ (s),…,β _k (s),…,β _K (s)] ^T and μ(s)=[μ ₁ (s),…,μ _k (s),…,μ _K (s)] ^T , both are K×1-dimensional vectors. It can be shown that there is one and only one set of and such that the optimal solution to problem P1 is Equivalent to the optimal solution of the following problem P2

and satisfy the following formula

Therefore, the small base station can solve the optimal uplink transmission power value of each terminal on each subchannel allocated to it through two layers of cyclic iterations. In inner loop, β(s)=[β ₁ (s),…,β _k (s),…,β _K (s)] ^T and μ(s)=[μ ₁ (s),…,μ _k (s),…, μ _K (s)] ^T has been given, replace β ^* (s), μ ^* (s) in problem P2 with β (s), μ (s), then problem P2 becomes A typical convex optimization problem, introducing two sets of Langrange multipliers λ(t)=[λ ₁ (t),…,λ _k (t),…,λ _K (t)] ^T , γ(t) ＝[γ ₁ (t),…,γ _k (t),…,γ _K (t)] ^T , through the idea of classical Lagrange dual relaxation in convex optimization, λ( t)γ(t) until the two converge, so that the optimal solution can be obtained simply and quickly

${\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; l</span>}\mathrm{<span\; class="notranslate">\; no</span>}\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; \&beta;</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&gamma;</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>\frac{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}}<span\; class="notranslate">\; .</span>$

get the inner loop After the optimal solution of , the outer loop can iteratively update and converge the two sets of vectors β=[β ₁ ,β ₂ ,…,β _K ], μ=[μ ₁ ,μ ₂ ,…,μ _K ]. Specifically, based on the equation and The following formula can be constructed:

When k∈[1,K],

When k∈[K+1,2K],

It can be proved that if and only if β(s)=β ^* (s), u(s)=u ^* (s), Therefore, β ^* (s), u ^* (s) can be obtained iteratively by the quasi-Newton method.

Step 4: The small base station adds 1 to the value of the iteration count counter t. Based on the subgradient method, the small base station uses the following formula to update each element in the Langrange multiplier λ(t) Among them, Γ _k (t) represents the step size of each element of the vector λ(t) in the t-th iteration, and satisfies Indicates the minimum data rate requested by user k;

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

Among them, ζ _k (t) represents the step size of each element of the vector ζ (t) in the t iteration, and satisfies Indicates the upper limit of the sum of transmit power of terminal k on each subchannel.

Each small base station judges whether the Lagrangian multipliers λ(t) and γ(t) have converged based on the following basis. If each element of λ( _t ) and γ(t) satisfies λk(t)=λk(t-1) and _γk (t)= _γk ( _t -1), then λ(t) , γ(t) have converged, otherwise, it means λ(t), γ(t) have not converged.

If the Lagrangian multipliers λ(t), γ(t) do not converge, go to step 3; otherwise, go to step 5.

Step 5: The small base station adds 1 to the value of the iteration count counter s. Based on the quasi-Newton method, the small base station uses the following formula to update β(s) and μ(s),

$\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; \&beta;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span>\\ \mathrm{<span\; class="notranslate">\; \&mu;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span>\end{array}\right]<span\; class="notranslate">\; =</span>\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; \&beta;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>\\ \mathrm{<span\; class="notranslate">\; \&mu;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>\end{array}\right]<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; q</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>$

in, is the K×1 dimensional auxiliary vector, Represents a 2K×1-dimensional vector About 2K×1 vectors The Jacobian matrix of . .

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

β _k (s) = β _k (s-1)

μ _k (s) = μ _k (s-1).

Then β(s) and μ(s) converge, otherwise, β(s) and μ(s) do not converge.

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

Step 6: The small base station sends the best uplink transmit power value obtained for each terminal to each user terminal, and then waits for the next power control.

Claims (7)

1. A power control method for ensuring user fairness in an ultra-dense network, comprising the following steps: Step 1: The small base station performs channel estimation on the uplink channel allocated to each user to obtain channel gain; Let the subchannel set of the small base station be N represents the number of sub-channels; the user set is K represents the number of users; the channel set assigned to user k is N _k represents the number of subchannels allocated to user k; the channel gain of user k on uplink subchannel n is Step 2: The small base station sets two K×1-dimensional vectors λ(t)=[λ ₁ (t),...,λ _k (t),...,λ _K (t)] ^T , γ(t)=[γ ₁ (t), ..., γ _k (t), ..., γ _K (t)] ^T as an auxiliary variable for calculating the optimal transmit power, where t is the number of iteration count variables, used to identify the vector λ(t) and The number of iterations of γ(t), λ ₁ (t), λ _k (t), and λ _K (t) represent the 1st, kth and Kth elements of the vector λ(t), respectively, γ ₁ (t), γ _k (t), γ _K (t) represent the 1st, kth and Kth elements of vector γ(t) respectively; introduce two K×1-dimensional vectors β(s)=[β ₁ (s), ..., β _k (s), ..., β _K (s)] ^T and μ (s) = [μ ₁ (s), ..., μ _k (s), ..., μ _K (s)] ^T as the calculation of the most Auxiliary variable of optimal transmit power, where s is the number of iteration count variable, used to identify the number of iterations of β(s) and μ(s), β ₁ (s), β _k (s), β _K (s) represent the 1st, kth and Kth elements of the vector β(s), respectively, and μ ₁ (s), μ _k (s), μ _K (s) represent the 1st, kth and Kth elements of the vector μ(s) respectively The Kth element; the value of the iteration count variable t is initialized to 1, and the value of each element of the vector λ(t) and γ(t) is set to 0 when t=1, that is, λ(1)= γ(1)=[0,...,0,...,0] ^T ; Initialize the value of the number of iteration count variable s to 1, and set the value of each element of β(s) and μ(s) to 1 when s=1, that is, β(1)=μ(1)=[ 1,...,1...,1] ^T ; Step 3: The small base station sequentially calculates the optimal uplink transmission power value of each terminal on each allocated subchannel; If the calculated transmit power is less than zero, the optimal transmit power of the terminal on the subchannel is set to 0; otherwise, the optimal transmit power of the terminal on the subchannel is the calculated transmit power; Step 4: The small base station adds 1 to the value of the iteration count variable t, updates each element of the parameter vectors λ(t), γ(t), and judges whether the parameter vectors λ(t), γ(t) have converged, If the parameter vectors λ(t), γ(t) have not converged, then go to step 3, if the parameter vectors λ(t), γ(t) have converged, then go to step 5; Step 5: The small base station adds 1 to the value of the iteration count variable s, the small base station updates each element of the parameter vectors β(s), μ(s), and judges whether the parameter vectors β(s), μ(s) have Convergence, if the parameter vectors β(s), μ(s) have converged, then go to step 6, otherwise, go to step 3; Step 6: The small base station sends the optimal uplink transmission power value of each terminal to each user terminal, and then waits for the next power control. 2. The power control method for ensuring user fairness in the ultra-dense network according to claim 1, in the step 3, the optimal uplink transmit power value of the small base station on each sub-channel is: ${\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; ln</span>}\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; \&beta;</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&gamma;</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>\frac{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}}$ in, Indicates the optimal transmission power of user k occupying sub-channel n in the small base station on sub-channel n, B represents the bandwidth of a single sub-channel, and ^σ2 represents the noise power of the small base station on each sub-channel; Indicates the channel gain from user k to the small base station on subchannel n, w _k indicates the weight of user k’s uplink energy efficiency relative to other users, μ _k (s) is the kth of the K×1-dimensional vector μ(s) elements, β _k (s) is the kth element of the K×1-dimensional vector β(s), λ _k (t) is the kth element of the K×1-dimensional vector λ(T), γ _k (t) is the kth element of the K×1-dimensional vector γ(T). 3. the power control method that guarantees user fairness in the ultra-dense network according to claim 2, described w _k is determined by operator, 4. The power control method for ensuring user fairness in an ultra-dense network according to claim 1, wherein in step 4, the small base station updates each element in the parameter vector λ(t) based on the following formula Among them, Γ _k (t) represents the step size of each element of the vector λ(t) in the t-th iteration, and satisfies Indicates the minimum data rate requested by user k; The small base station updates each element in the parameter vector γ(t) based on the following formula Among them, ζ _k (t) represents the step size of each element of the vector ζ (t) in the t iteration, and satisfies Indicates the upper limit of the sum of transmit power of terminal k on each subchannel. 5. The power control method for ensuring user fairness in the ultra-dense network according to claim 1, in said step 4, when each element of λ(t), γ(t) satisfies the following two formulas , then λ(t), γ(t) converge: λ _k (t) = λ _k (t-1) γ _k (t)=γ _k (t−1). 6. The power control method for ensuring user fairness in the ultra-dense network according to claim 1, in the step 5, the method for the small base station to update vectors β(s) and μ(s) is: $\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; \&beta;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span>\\ \mathrm{<span\; class="notranslate">\; \&mu;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span>\end{array}\right]<span\; class="notranslate">\; =</span>\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; \&beta;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>\\ \mathrm{<span\; class="notranslate">\; \&mu;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>\end{array}\right]<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; q</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>$ in, is a 2K×1-dimensional vector composed of β(s) and μ(s), is a 2K×1-dimensional vector composed of β(s-1) and μ(s-1), is the K×1 dimensional auxiliary vector, is a 2K×1-dimensional auxiliary vector, represent 2K×1-dimensional vectors respectively The 1st, Kth and 2Kth dimension elements of ; When k ∈ [1, K], When k ∈ [K+1, 2K], Represents a 2K×1-dimensional vector About 2K×1 vectors The Jacobian matrix of is the fixed circuit power consumption of terminal k. 7. The power control method for ensuring user fairness in the ultra-dense network according to claim 1, in said step 5, when each element of β(s), μ(s) satisfies the following two formulas , then β(s), μ(s) converge: β _k (s) = β _k (s-1) μ _k (s) = μ _k (s-1).