KR102243033B1 - Method of Joint User Association and Power Allocation for Millimeter-Wave Ultra-dense Networks - Google Patents

Abstract

The present invention relates to a user association and power allocation method for a millimeter wave ultra-high density network, comprising: setting an initial value of an achievable power allocation variable and an association variable representing an association between a specific base station and a terminal; Identifying a viable point of a power allocation variable for optimizing energy efficiency from all viable initial points; Calculating a next linkage variable by using an executable point of the identified power allocation variable; And calculating a next power allocation variable by using the calculated next linkage variable.

Description

Method of Joint User Association and Power Allocation for Millimeter-Wave Ultra-dense Networks}

The present invention relates to a mobile communication system, and more particularly, to a user linkage and power allocation method for a millimeter wave ultra-high density network in a mobile communication system.

In order to not only provide services to exponentially increasing mobile devices, but also to meet the demands of various high-speed data rate services, millimeter wave (mmWave) communication of ultra-dense network (UDN) is for future wireless communication systems. It is considered a promising technology.

A high-density topology that combines mmWave of a high frequency band by arranging multiple small-cell base stations (SBSs) in a certain space increases energy efficiency (EE) as well as user terminal (UE) throughput of the entire network. It is expected to increase.

There is a need for a method of maximizing system EE (energy efficiency) and ensuring quality of service (QoS) constraints for each UE by jointly optimizing SBS-UE linkage and power allocation by utilizing the advantages of mmWave and UDN. Specifically, throughput fairness between UEs can also be considered by formulating the UE throughput maxmin problem. However, since the SBS-UE association problem is reflected as a complex mixed-integer non-convex problem, there is a problem that it is difficult to solve. On the other hand, the power allocation problem is a non-convex structure, and it is difficult to deal with the SBS-UE linkage problem at the same time.

[Document 1] Registered Patent Publication No. 10-1722330 3GPP LTE-A Cooperative scheduling and hybrid spectrum access method and system based on QoS priority for load balancing in ultra-high density networks (Inha University Industry-Academic Cooperation Foundation) 2017.03.27.

In order to solve the above problem, in the embodiment of the present invention, the SBS-UE linkage and SBS-UE linkage are performed by separating the initial optimization problem into two subproblems and applying an alternating descent method that can be processed one by one in each iteration. It is possible to provide a user linkage and power allocation method for a millimeter-wave ultra-high density network that can jointly optimize power allocation.

According to various embodiments, the SBS-UE linkage problem in the present invention is reconstructed using a penalty scheme. Then, the non-convex problem is processed by successive convex programming to transform it into a simple convex quadratic function at each iteration.

A user association and power allocation method for a millimeter-wave ultra-high density network to achieve the above object includes: setting an association variable representing an association between a specific base station and a terminal and an initial value of the achievable power allocation variable; Identifying a viable point of a power allocation variable for optimizing energy efficiency from all viable initial points; Calculating a next linkage variable by using an executable point of the identified power allocation variable; And calculating a next power allocation variable by using the calculated next linkage variable.

Preferably, the method is characterized in that the step of calculating the linkage variable and the power allocation variable is repeatedly performed until it is determined that the linkage variable or the power allocation variable converges based on a specified condition.

Preferably, the method is characterized in that the linkage variable is defined by the following equation.

Preferably, the linkage variable is characterized in that it is reset to the box constraint condition according to the following equation.

Preferably, the method further comprises: calculating a positive penalty factor for downgrading the difference between the square of the linkage variable and the linkage variable to zero.

Preferably, the positive penalty factor is characterized in that it is calculated by the following equation.

및

는 하기 수학식으로 정의된다.Where, above

And

Is defined by the following equation.

Preferably, the method is characterized in that it is repeatedly performed until a minimum achievable transmission rate of each terminal among a plurality of terminals satisfies a quality of service (QoS) requirement.

Preferably, the method is characterized in that it is repeatedly performed so that the minimum achievable transmission rate of each terminal among a plurality of terminals is maximized.

Preferably, the method is characterized in that the non-convex problem is converted into quadratic convex optimization and processed using a path-following procedure.

According to the present invention, it is possible to maximize system energy efficiency and guarantee quality of service (QoS) constraints for each UE by jointly optimizing SBS-UE linkage and power allocation by utilizing the advantages of mmWave and UDN.

In addition, according to the present invention, by separating the initial optimization problem into two subproblems and alternately applying a descent algorithm in each iteration, the primitive problem is divided into two suboptimization problems, and It is possible to provide low computational complexity and efficiency by reconstructing a non-convex optimization problem into a low complexity quadratic convex optimization problem while ensuring the increase. Accordingly, an increase in the objective function is guaranteed in each iteration.

1 is a diagram illustrating a network including base stations and user terminals randomly distributed in a circular cell according to the present invention.
2 is a diagram illustrating a high-density network scenario including 5 base stations and 20 user terminals randomly distributed in a circular cell according to the present invention.
3 is a graph of achievable energy efficiency versus a transmission rate threshold compared to an SBS-UE linkage scenario according to the present invention.
4 is a graph of achievable energy efficiency versus a transmission rate threshold compared to other technologies according to the present invention.
5 is a graph of achievable energy efficiency versus a transmission rate threshold compared to other technologies according to the present invention.
6 is a graph of achievable energy efficiency versus a transmission rate threshold compared to other performance optimization techniques according to the present invention.
7A is a graph showing the convergence of the proposed energy efficiency optimization according to the present invention.
7B is a graph showing the convergence of the proposed maximum and minimum UE throughput according to the present invention.

Hereinafter, specific details for carrying out the present invention will be described based on embodiments with reference to the drawings. These embodiments are described in detail sufficient to enable a person skilled in the art to practice the present invention. It should be understood that the various embodiments of the present invention are different from each other, but need not be mutually exclusive. For example, specific shapes, structures, and characteristics described herein may be implemented in other embodiments without departing from the spirit and scope of the present invention in relation to one embodiment. In addition, it should be understood that the location or arrangement of individual components in each disclosed embodiment may be changed without departing from the spirit and scope of the present invention. Accordingly, the detailed description to be described below is not intended to be taken in a limiting sense, and the scope of the present invention, if properly described, is limited only by the appended claims, along with all scopes equivalent to those claimed by the claims. Like reference numerals in the drawings refer to the same or similar functions over several aspects.

Unless otherwise defined, all terms (including technical and scientific terms) used in the present specification may be used with meanings that can be commonly understood by those of ordinary skill in the art to which the present invention belongs. In addition, terms defined in a commonly used dictionary are not interpreted ideally or excessively unless explicitly defined specifically.

An embodiment of the present invention maximizes system EE (energy efficiency) by jointly optimizing SBS-UE association and power allocation by utilizing the advantages of mmWave and UDN, and reducing quality of service (QoS) constraints for each UE. A method is proposed to ensure. To this end, in an embodiment of the present invention, it is solved by formulating a UE throughput maximum minimum (maxmin) problem for throughput fairness between UEs.

In this case, in an embodiment of the present invention, SBS-UE linkage and power allocation are jointly optimized by separating the initial optimization problem into two subproblems and applying an alternating descent method that can be processed one by one in each iteration. do. In addition, the SBS-UE linkage problem in the present invention is reconstructed using a penalty method, and is processed by continuous convex programming in order to convert the non-convex problem into a simple convex quadratic function at each iteration.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings in order to enable those of ordinary skill in the art to easily implement the present invention.

By deploying multiple small cell base stations (SBS) such as micro cells, pico cells, and micro cell operators distributed to existing cells, the ultra-high density network (UDN) It can improve coverage and network performance for all of the UEs.

In particular, deploying SBS in UDN can save more power and budget than deploying existing macro cell base stations. In addition, these SBSs can be flexibly configured to process transmit/receive signals because they have the potential to have a high-speed central processing unit. As a result, the processing load of UDN can be effectively shared among SBSs instead of centralizing it, which can put a lot of burden on the system in a large-scale network. However, due to the limited capacity and scheduling scheme, these deployed SBSs cannot serve multiple UEs at the same time. Therefore, the SBS-UE linkage problem becomes very important, especially in a high-peak traffic network with many SBSs and UEs.

On the other hand, in order to reinforce the lack of spectrum, SBSs of UDN can share the same spectrum. Therefore, the necessity of an inter-cell interference management scheme requires excellent design of resource allocation and interference mitigation. Considering the interference cancellation in UDNs, several schemes aiming to adjust the transmit power to maximize the spectral efficiency (SE) and energy efficiency (EE) of the entire system in relation to a fixed SBS-UE allocation set. Can be considered.

However, since the random SBS-UE linkage method significantly degrades achievable performance, a serious load balancing problem occurs in the considered UDNs. To overcome this problem, joint design of SBS-UE linkage and resource allocation system is required. Numerical results of applying the method according to the present invention to be described later demonstrate that the achievable performance is greatly improved. On the other hand, previous work on UDNs can improve system performance, but the achievable data rate is still limited to the upper threshold. Therefore, finding new approaches to achieving high throughput is essential to meeting new short-range communication requirements.

Due to the rapid movement of high-speed broadband wireless access, applications of gigabit services such as real-time streaming and gigabyte file transfer have been developed extensively. The core of these services is mainly based on millimeter wave (mmWave) communication, which is one of the powerful technologies that enable high data rate services. This mmWave technology uses a frequency band of 30 to 300 GHz, which corresponds to a wavelength of 10 to 1 mm. The main feature of mmWave is that it relies on a large bandwidth, so the interference between the frequency bands is much smaller than that of the 2-3 GHz frequency band. The maximum attenuation of mmWave can be achieved in the 60GHz, 120GHz and 180GHz frequency bands.

The application of mmWave in UDNs has been extensively studied to take advantage of short-range communication for high-speed data transmission services. In particular, the UE linkage scheme was developed to improve load balancing in UDNs. Since the radio channels of mmWave networks are very unstable due to high frequency operation, resource allocation schemes have also been studied. In the present invention, joint optimization for both UE association and power allocation is considered.

On the other hand, although various optimization goals have been examined in the prior art, the solution proposed in this system is still heuristic. In general, relaxation mehtod is applied to reconstruct the binary linkage problem, and the traditional Lagrangian and gradient methods are used to optimize the linkage problem. According to the prior art, the problem can become very difficult as more UEs join the system. Therefore, it is difficult to achieve a local optimization, so many iterations must be performed for convergence, resulting in high complexity.

In an embodiment of the present invention, it is possible to provide a maximum minimum (maxmin) UE throughput optimization aiming at throughput fairness between UEs in a mmWave network. In particular, a client linkage problem is proposed in order to optimize the number of transmitters involved according to each user speed requirement. That is, in the present invention, a new approach is proposed for jointly optimizing SBS-UE linkage and power allocation in order to increase the performance of mmWave in UDN.

Specifically, the present invention derives an important problem divided into two in UDNs. First, the EE of all networks is considered according to the quality of service (QoS) requirements of each UE. Second, throughput fairness between UEs is considered by formulating the UE throughput maximum minimum problem. Since the SBS-UE linkage problem proposed according to the present invention is a very difficult mixed integer non-convex optimization problem, it is very complex for a large-scale network. Therefore, it is impossible to apply existing methods such as binary search and full search in the context of UDN. On the other hand, the connection problem and the non-convex power allocation problem are closely related. Therefore, it is very impossible to solve this at the same time. According to the conventional proposed algorithm, the effectiveness has been proven in large-scale UDNs with low computational complexity. In particular, the alternating descent method was first derived so that the original problem can be divided into two separate sub-problems so that it can be easily handled on the same scale. However, in the conventional method, the connection problem and the power allocation problem are separated, but the structure is still not convex. In particular, since a very large calculation is required to solve a binary linkage variable, the linkage problem is a very difficult problem.

In the embodiment of the present invention, since the focus is also on large-scale UDNs, a penalty method is introduced to mitigate such a binary variable and convert the square into a unique form. Then, a low-complexity algorithm using a computer is implemented by converting a non-convex problem into a simple quadratic convex optimization problem using path-following procedures.

Hereinafter, in an embodiment of the present invention, a system model and problem formula will be described, and an alternating descent algorithm for an EE optimization problem will be described. Then, it expands for the problem of optimizing the maximum UE throughput and describes the numerical results. On the other hand, in the following description, boldface symbols are used for optimization variables, whereas nonbold symbols are used in deterministic terms irrespective of a vector or scalar value.

<System model and problem formulation>

A. System model

{1, ...., K}) 개의 UE 세트와 통신하며, 60 GHz mmWave 대역을 사용하는 M(m∈Μ

{1, ...., M})개의 SBS들의 세트를 포함함을 가정한다. 여기서 이들의 위치는 도 1에 도시된 바와 같이 반지름 R의 원형 셀내에 랜덤하게 분포된 것으로 가정한다. 이때, SBS들의 세트는 마이크로 셀, 피코 셀, 및 마이크로 셀 오퍼레이터들을 포함한다. 실제 시나리오에서, 상기 SBS 설정 및 정책들은 채용된 SBS의 타입에 의존한다. 모든 SBS들과 UE들에는 단일 안테나가 장착되어 있으며 반이중 모드로 작동한다. 한편, 60GHz 주파수 대역으로 통신함을 가정하며, SBS m과 UE k 사이의 채널 이득은 하기 <수학식 1>의 Friis 전송 방정식으로 모델링된다.UDN is K(k∈Κ

{1, ...., K}) communicates with a set of UEs and uses M(m∈Μ) using the 60 GHz mmWave band.

It is assumed that a set of {1, ...., M}) SBSs is included. Here, it is assumed that their positions are randomly distributed within a circular cell of radius R as shown in FIG. 1. At this time, the set of SBS includes a micro cell, a pico cell, and a micro cell operator. In a real scenario, the SBS settings and policies depend on the type of SBS employed. All SBSs and UEs are equipped with a single antenna and operate in half-duplex mode. Meanwhile, it is assumed that communication is performed in the 60GHz frequency band, and the channel gain between SBS m and UE k is modeled by the Friis transmission equation of Equation 1 below.

는 SBS m에서 UE k까지의 송신 안테나 이득이고,

는 SBS m에서 UE k까지의 수신 안테나 이득이며, ξ는 파장이고, d_mk는 SBS m과 UE k 사이의 거리, d₀ 는 원거리 필드 기준 거리이고, η는 경로 손실 지수이다.(η∈[2, 6]).In the above <Equation 1>

Is the transmit antenna gain from SBS m to UE k,

Is the receive antenna gain from SBS m to UE k, ξ is the wavelength, d _mk is the distance between SBS m and UE k, d ₀ is the far field reference distance, and η is the path loss index. 2, 6]).

Without general loss, it is assumed that each UE can associate only one SBS at each interval time, and each SBS can serve a plurality of UEs by performing a scheduling algorithm to avoid intra-cell interference. . There are two main reasons to suggest the above linkage problem. First, UEs can select the closest SBS or related SBS with the best channel gain so that their minimum transmission rate is guaranteed. Second, the load balancing of the entire system can be improved, where the processing load is shared evenly among the SBSs.

는 아래와 같이 표현되는 연계 변수이다.x is _{defined as [x 1} , ...., x _M ] ^T , and x _m is defined as [x _m,1 , ...., x _M,K ] ^T. At this time,

Is a linked variable expressed as follows.

Due to transmission scheduling between UEs in the associated SBS m, each UE may be assigned one time slot identical to other UEs. According to Shannon's capacity formula, the achievable rate of UE k through SBS m is as shown in Equation 2 below.

은 부가 백색 가우스 잡음(additive white Gaussian noise; AWGN)의 분산,

는 SBS m과 연계된 UE 수를 의미한다. 상기 <수학식 2>에서 알 수 있는 바와 같이, UE k는 다른 SBS들(n≠m)로부터 셀 간 간섭을 받는다.In the above <Equation 2>, W is the total bandwidth, p _m is the transmission power of the SBS,

Is the variance of additive white Gaussian noise (AWGN),

Denotes the number of UEs associated with SBS m. As can be seen from Equation 2, UE k receives inter-cell interference from other SBSs (n≠m).

B. Formulating the problem

In an embodiment of the present invention, it is possible to maximize the achievable throughput of the entire system while minimizing the total power consumption used for transmission. At this time, the optimization problem can be expressed as in Equation 3 below.

은 비-전송 전력이며, p_a는 안테나 회로 전력이다. 상기 <수학식 3>에 표시된 각각의 제약 조건들 중 (3b)는 각 UE가 하나의 SBS에 연결되어야 함을 나타내고, (3d)는 각 SBS의 최대 전송 전력(P_m ^max)을 나타내며, (3e)는 각 UE의 달성 가능한 전송률(R)이 QoS 요구 사항보다 높아야 한다는 것을 나타낸다.Where Ψ is a positive weighting parameter for optimizing _{P 0 (x, p),}

Is the non-transmit power and p _a is the antenna circuit power. Of each of the constraints indicated in Equation 3, (3b) indicates that each UE must be connected to one SBS, (3d) indicates the maximum transmission power (P _m ^max ) of each SBS, and ( 3e) indicates that the achievable transmission rate (R) of each UE should be higher than the QoS requirements.

Alternatively, the <Equation 3> is an EE maximization problem exactly in terms of the number of bits transmitted per Joule according to the QoS rate threshold for each UE, and can be derived as in Equation 4 below. .

In Equation 4, the objective function is composed of a difficult class of mixed integer problems as well as a non-convex power allocation problem, which is a very difficult optimization problem. On the other hand, the constraint (3e) of the above <Equation 3> also has a non-convex structure. Therefore, it is impossible to simultaneously process the binary linkage variable x and the continuous transmission power variable p. Especially in large networks, it becomes more difficult to solve the connection problem. Hereinafter, an embodiment of the present invention proposes an alternating descent algorithm capable of dividing the initial demotion problem into two subproblems and processing them one by one on the same time scale. In addition, the low-complexity path-following method is induced to transform the non-convex problem into a simple quadratic convex problem at each iteration.

<On the energy efficiency optimization problem alternation Alternating descent algorithm>

In the following description, methods for solving Equation 4 will be described. First, it can be seen that the binary constraint (3c) in Equation 3 is a discrete variable. Therefore, finding a binary solution in a large network requires a very complex task and can grow exponentially. Therefore, in such a scenario, it is difficult to apply a heuristic system such as a binary search or a full search. In order to solve the above problem, the present invention focuses not only on handling binaries with reasonable complexity, but also on high-level networks. Accordingly, in the present invention, binary relaxation in the constraint condition (3c) in Equation 3 can be reset as a box constraint ([]) as shown in Equation 5 below.

임을 쉽게 알 수 있다. 이항 변수와 그 제곱이 박스 범위에 있기 때문에 등식은 0 또는 1에 접근할 때만 발생한다. 이러한 특성을 이용하여 x_m,k와 제곱 사이의 차(substraction)를 제로-강제(zero-force)하기 위해 패널티 접근법(penalty approach)을 도입한다. 따라서, 새로운 EE 최적화 문제는 하기 <수학식 6>과 같이 재구성 될 수 있다.Considering the properties of binary variables,

It is easy to see that it is. Since the binomial variable and its square are in the box range, the equation only occurs when approaching 0 or 1. Using this characteristic, we introduce a penalty approach to zero-force the substraction between _{x m,k and the square.} Therefore, the new EE optimization problem can be reconstructed as shown in Equation 6 below.

의 갭을 0으로 다운 그레이드하는 양의 패널티 인자이다.Where Θ is the minus term

It is a positive penalty factor that downgrades the gap of to zero.

Although Equation 6 is relaxed with continuous association variables, the optimization problem is still complex with a non-convex structure. Therefore, finding the optimal solution for Equation 6 above can still be difficult due to the nonconcavity of the objective function and the non-convexity of the viable set.

Accordingly, in an embodiment of the present invention to be described later, the initial problem can be separated into two separate sub-problems by introducing an alternating descent method, and problems can be easily processed simultaneously in the same repetition. Specifically, in the embodiment of the present invention, the SBS-UE linkage problem is handled while maintaining the transmission power constant. Next, the transmit power is optimized based on certain optimal linkage variables found in the previous step. In this way, an increase in the objective function is guaranteed at each iteration until convergence. Since these subproblems are still in the non-convex structure, continuous convex programming proceeds to provide a computationally low complexity algorithm by solving a simple quadratic convex problem at each iteration.

A. SBS- UE Linkage problem

로 정의한다.

로 표시한다. 이에 따라, 상기 최적화 문제인 <수학식 6>은 하기 <수학식 7>로 간략화하여 나타낼 수 있다.According to an embodiment of the present invention, by using an alternating descent approach, power allocation variables are first ignored and the SBS-UE linkage problem is focused. At this time,

It is defined as

Denoted by Accordingly, the optimization problem <Equation 6> can be simplified and represented by the following <Equation 7>.

에 대해 <수학식 31>을 적용하고, 패널티 항

에 대해 <수학식 33>을 적용함으로써, 하기 <수학식 8>과 같이 하한(lower bound)을 얻을 수 있다.UE achievable transmission rate with reference to the equation attached to the second half of the <Equation 7>

<Equation 31> is applied to and the penalty term

By applying <Equation 33> to <Equation 33>, a lower bound can be obtained as shown in Equation 8 below.

는 하기 <수학식 9>와 같이 정의될 수 있다.Where, above

May be defined as in Equation 9 below.

In Equation 8, the positive penalty factor Θ can be found in the initial stage as shown in Equation 10 below.

On the other hand, if the attached <Equation 32> is applied to (3e) of <Equation 3>, which is a non-convex QoS constraint, it can be calculated as in <Equation 11>.

Therefore, the SBS-UE linkage optimization problem can be expressed as shown in Equation 12 below.

를 개선하기 위해 실현 가능한 지점(feasible point)

을 생성한다. 일반적으로, 초기 지점 x⁽⁰⁾에서, 최적화 문제인 상기 <수학식 12>는 일련의 시퀀스

를 생성할 것이고, 그 결과 하기 <수학식 13>이 도출될 수 있다.According to an embodiment of the present invention, instead of finding the global optimum for Equation 7 above, it is possible to solve the maximization of the lower limit of Equation 12. Accordingly, the objective function

Feasible point to improve

Is created. In general, at the initial point x ⁽⁰⁾ , the <Equation 12>, which is an optimization problem, is a series of sequences

Will be generated, and as a result, the following <Equation 13> can be derived.

는 수렴할 때까지

를 얻을 수 있는 실현 가능한 지점으로 사용된다.Especially,

Until convergence

Is used as a feasible point to obtain.

B. Power allocation problem

로 정의하고, 이는 이전 단계에서 확인된 최적의 x^* 이다. 따라서 전력 할당 문제는 하기 <수학식 14>로 나타낼 수 있다.In the power allocation problem

Is defined as, which is the optimal x ^* identified in the previous step. Therefore, the power allocation problem can be expressed by Equation 14 below.

의 하한을 구할 수 있다.By applying <Equation 30> attached to the above <Equation 6>, as shown in <Equation 15>

You can find the lower limit of

The <Equation 15> is a concave function, and α _m,k and β _m,k can be expressed by the following <Equation 16>.

를 찾는 것과 같다.Therefore, solving the above <Equation 14> can be realized in the following <Equation 17>

It's like looking for

는 각 반복에서 하기 <수학식 18>과 같이 얻어진다.In <Equation 17> above

Is obtained as in Equation 18 below in each iteration.

는 반복적으로 계산되는 <수학식 17>에 의해 초기 지점

으로부터 알 수 있다. 따라서, 최적 변수

가 (k) 번째 반복에서 목적 함수보다 (k+1) 번째 반복에서 목적 함수를 더 잘 향상시키기 때문에 목적 함수의 증가가 보장된다.Similar to the previous step

Is the initial point by <Equation 17>, which is repeatedly calculated

It can be seen from Therefore, the optimal variable

The increase of the objective function is guaranteed because a better improves the objective function in the (k+1) th iteration than the objective function in the (k) th iteration.

C. Initialization

In order to solve the above-described problem of <Equation 6>, it is necessary to find a possible point p for (3e) of <Equation 3>, which is a QoS constraint. Meanwhile, the initialization problem can be defined as shown in Equation 19 below in consideration of (3d) in Equation 3.

를 만족할 때까지 상기 <수학식 19>를 반복해서 실행한다. 그런 다음,

로 리셋한다. 상기 <수학식 19>에서 UE들 중 최소 달성 가능한 전송률이 그들의 QoS 요건이 충족될 때까지 각 반복마다 증가되는 것을 알 수 있다. 따라서, 전술한 <수학식 6>을 해결하기 위해 실행 가능한 초기 지점 p가 제공된다.Now, for every k

The above <Equation 19> is repeatedly executed until is satisfied. after that,

Reset to In Equation 19, it can be seen that the minimum achievable transmission rate among UEs is increased for each iteration until their QoS requirements are satisfied. Thus, an initial point p that is feasible to solve the above-described Equation 6 is provided.

From the initial points x ⁽⁰⁾ and p ⁽⁰⁾ , the above procedure improves the objective function of Equation 6 above. Specifically, the SBS-UE connection problem of <Equation 7> and the power allocation problem of <Equation 14> are solved alternately to improve the above-described <Equation 6>, which means (x, p) is finite repetition. Converge to the optimum point according to. The incremental procedure of Equation 6 may be expressed as Equation 20 below.

On the other hand, when the condition of Equation 21 below is triggered, convergence is activated with a small ε value.

The above-described alternating descent method to solve the EE maximization problem can be summarized with the following algorithm.

< UE Performance max min( maxmin ) Problem>

In UDN, UE fairness is an important issue because UE locations are randomly distributed across cells. In the following description, a maximum and minimum UE throughput problem is proposed in order to provide fairness of throughput between UEs. In other words, the minimum achievable transmission rate between UEs is maximized. The UE throughput maximum and minimum problem can be expressed as in Equation 22 below.

According to an embodiment of the present invention, by applying the same procedure as described above, the maximum and minimum problem of using the penalty method can be represented by the following <Equation 23>.

를 정의함으로써, UE 처리량 최대 최소 문제에 대한 SBS-UE 연계 문제는 하기 <수학식 24>와 같이 표현된다.Thus, the solution to the above problem is a combination of two subproblems and is solved by an alternating descent method. Transmit power

By defining, the SBS-UE association problem for the UE throughput maximum and minimum problem is expressed as shown in Equation 24 below.

및

는 전술한 <수학식 9>에서 확인될 수 있다.here,

And

Can be confirmed in the above-described <Equation 9>.

를 설정한다.Then, in order to solve the power allocation for the UE throughput maximum and minimum problem as shown in Equation 25 below,

Is set.

는 전술한 <수학식 15>에 의해 유도되는 하한 근사치이다.In the above <Equation 25>

Is an approximation of the lower limit derived by the above-described <Equation 15>.

The optimal value of Θ can be obtained in the initial stage as shown in Equation 26 below.

Similar to <Equation 21>, when the condition of the following <Equation 27> is satisfied, the convergence of the UE throughput maximum and minimum problem is satisfied.

In addition, the UE throughput maximum and minimum algorithm may be implemented as Algorithm 2 below.

Experiment result

, ξ=5 mm, η=3, d₀ =1m로 설정할 수 있다. 일반적인 손실 없이 P_max는

및

로 가정할 수 있다. 수렴 임계 값은 ε = 1e-4로 설정된다. 다른 매개 변수는 하기 <표 1>과 같이 요약될 수 있다.Monte Carlo simulation is implemented to show the efficiency of the algorithm proposed by the embodiment according to the present invention. Referring to FIG. 2, it is possible to assume an ultra-high density network consisting of M = 5 SBSs and K = 20 UEs randomly distributed in a cell. to the next,

, ξ=5 mm, η=3, d ₀ =1m. Without typical losses, P _max is

And

Can be assumed. The convergence threshold is set to ε = 1e-4. Other parameters can be summarized as shown in Table 1 below.

parameter value Cell radius 500 m Bandwidth (B) 1200 MHz Maximum transmission power 4.7 dBm Antenna power consumption 5.6 mW Noise power density -134 dBM/MHz

The graph of FIG. 3 shows the impact of UE QoS requirements on achievable EE. Referring to FIG. 3, the integrated SBS-UE linkage and power allocation scheme, that is, the achievable EE of power optimization, is compared with any SBS-UE linkage scheme of alternating descent. In the power allocation scheme, each UE selects the SBS with the largest channel gain to associate, and the power allocation scheme is applied to optimize the transmit power in achieving EE maximization. It can be seen that the achievable EE significantly decreases without the SBS-UE linkage system at each plot of the graph of FIG. 3, while the proposed algorithm exceeds the power optimization system. On the other hand, as R ^min increases, it can be seen that the alternative descent curve continuously decreases. This is due to the fact that when the UE increases the QoS threshold, all SBSs have to increase the transmit power to meet the requirements. On the other hand, the total throughput has been slightly reduced, thus reducing the achievable EE. 4 and 5, the achievable EE and throughput of the optimization problem proposed in Equation 4 are compared with the other two optimization approaches below.

As a first optimization method, a total rate maximization scheme is as shown in Equation 28 below.

As the next optimization method, a transmit power minimization scheme is as shown in Equation 29 below.

4 and 5, it can be seen that the EE maximization method for all QoS requirements is superior to other methods in terms of EE. On the other hand, the method of maximizing the total transmission rate shows an advantage in terms of achieving network throughput as shown in FIG. 5. The transmit power minimization method can save more transmit power to obtain the minimum QoS rate, but cannot achieve the EE and throughput as high as the other two methods.

6 shows a throughput that can be achieved compared to a UE transmission rate threshold for the total transmission rate optimization method of Equation 28 and the maximum and minimum UE optimization method of Equation 22. As can be seen from FIG. 6, the maximum and minimum UE optimization method clearly shows excellent performance in terms of fairness between UEs. In other words, according to an embodiment of the present invention, the minimum UE throughput according to <Equation 22> may be achieved higher than the minimum UE speed according to <Equation 28>. However, the total throughput of the maximum and minimum UE optimization method is much lower than the total achieved throughput of the total throughput maximization method. Thus, an optimization target can be selected according to the demand for UE throughput.

7 shows that the computational complexity of the proposed algorithm is low and convergence. Solving these optimization methods is generally based on the existing Lagrangian and sub-gradient methods. Therefore, the solution is very complex and requires large iterations to converge. Referring to FIG. 7, it can be seen that the method proposed according to the present invention rapidly converges to an optimal performance requiring about 12 repetitions. Thus, the results show the effectiveness of a low complexity solution where the increment of the target is guaranteed for each iteration.

As described above, in the present invention, a new method for jointly optimizing SBS-UE linkage and power allocation in relation to a 60 GHz millimeter wave ultra-high density network has been proposed. As described above, the proposed method aims at two different optimization problems, namely, the problem of maximizing the overall network EE for QoS requirements of each UE, and the problem of maximum and minimum UE throughput for providing fairness between UEs. Since the SBS-UE linkage problem is one of the difficult classes of mixed integer non-convex optimization problems, it is very difficult to solve with the non-convex power allocation scheme. Accordingly, in the embodiment of the present invention, by alternately applying the descent algorithm, the primitive problem is divided into two suboptimization problems, which are processed one by one in each iteration. More specifically, the penalty approach is applied to reconstruct the fairly difficult SBS-UE linkage problem. Finally, a continuous optimization method is applied to reconstruct the non-convex optimization problem into a low-complex quadratic convex optimization problem while ensuring the increase of the target at each iteration. As can be seen from the experimental results according to the present invention, the proposed method shows low computational complexity and efficiency.

Additional Equation

The present invention has been described above with the purpose of method steps representing the performance of specific functions and their relationships. The boundaries and order of these functional components and method steps have been arbitrarily defined herein for convenience of description. Alternative boundaries and orders may be defined as long as the specific functions and relationships are properly performed. Any such alternative boundaries and sequences are therefore within the scope and spirit of the claimed invention. In addition, the boundaries of these functional components have been arbitrarily defined for convenience of description. Alternative boundaries can be defined as long as certain important functions are performed properly. Likewise, flowchart blocks may also have been arbitrarily defined herein to indicate any significant functionality. For extended use, the flow diagram block boundaries and order may have been defined and still perform some important function. Alternative definitions of both functional elements and flowchart blocks and sequences are therefore within the scope and spirit of the claimed invention.

The present invention may also have been described, at least in part, in terms of one or more embodiments. Embodiments of the invention are used herein to represent the invention, its aspects, its features, its concepts, and/or examples thereof. A physical embodiment of an apparatus, article of manufacture, machine, and/or process embodying the present invention refers to one or more aspects, features, concepts, examples, etc. described with reference to one or more embodiments described herein. Can include. Moreover, in the overall drawing, embodiments may incorporate the same or similarly named functions, steps, modules, etc., which may use the same or different reference numbers, and as such, the functions, Steps, modules, etc. may be the same or similar functions, steps, modules, etc. or others.

As described above, in the present invention, specific matters such as specific components, etc., and limited embodiments and drawings have been described, but these are provided only to help a more general understanding of the present invention, and the present invention is not limited to the above embodiments. , If a person of ordinary skill in the field to which the present invention belongs, various modifications and variations are possible from these descriptions.

Therefore, the spirit of the present invention is limited to the described embodiments and should not be defined, and all things that are equivalent or equivalent to the claims as well as the claims to be described later belong to the scope of the present invention. .

Claims (10)

In the user linkage and power allocation method for a millimeter wave ultra-high density network,
Setting an initial value of an achievable power allocation variable and an association variable representing an association between a specific base station and a terminal;
Identifying a viable point of a power allocation variable for optimizing energy efficiency from all viable initial points;
Calculating a next linkage variable by using an executable point of the identified power allocation variable;
Calculating a next power allocation variable by using the calculated next linkage variable; And
And calculating a positive penalty factor for downgrading the difference between the square of the linkage variable and the linkage variable to zero.
The method of claim 1, wherein the method comprises:
A user for a millimeter wave ultra-high density network, characterized in that the step of calculating the linkage variable and the power allocation variable is repeatedly performed until it is determined that the linkage variable or the power allocation variable converges based on a specified condition Linkage and power allocation method.
The method of claim 1, wherein the method comprises:
The linkage variable is characterized in that defined by the following equation, user linkage and power allocation method for a millimeter wave ultra-high density network.

The method of claim 3, wherein the linkage variable is reset to a box constraint condition according to the following equation.

delete The method of claim 1, wherein the positive penalty factor is
User linkage and power allocation method for a millimeter wave ultra-high density network, characterized in that calculated by the following equation.

Where, above

And

Is defined by the following equation.

The method of claim 1, wherein the method comprises:
A method for user association and power allocation for a millimeter-wave ultra-high density network, characterized in that repeatedly performing until a minimum achievable transmission rate of each terminal among a plurality of terminals meets a quality of service (QoS) requirement.
The method of claim 1, wherein the method comprises:
A method for linking users and allocating power for a millimeter wave ultra-high density network, characterized in that it repeatedly performs so that the minimum achievable transmission rate of each terminal among a plurality of terminals is maximized.
The method of claim 1, wherein the method comprises:
A method for user linkage and power allocation for a millimeter wave ultra-high density network, characterized in that converting and processing a non-convex problem into a second-order convex optimization using a path-following procedure.
A computer-readable recording medium storing instructions set to cause the at least one processor to perform at least one operation when executed by at least one processor of an electronic device, comprising:
The at least one operation,
Setting an initial value of an achievable power allocation variable and an association variable indicating an association between a specific base station and a terminal;
Identifying a viable point of a power allocation variable for optimizing energy efficiency from all viable initial points;
Calculating a next linkage variable by using an executable point of the identified power allocation variable;
Calculating a next power allocation variable by using the calculated next linkage variable; And
And calculating a penalty factor for downgrading the difference between the square of the linkage variable and the linkage variable to zero.

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