TWI581653B - Decentralized multi-antenna network power distribution device, system, method and computer program product with iterative configuration antenna grouping operation - Google Patents

Description

Decentralized multi-antenna network power distribution device, system, method and computer program product thereof with iteratively configured antenna grouping operation

The invention relates to a wireless network power distribution technical solution, in particular to an iterative grouping and power allocation of an antenna device in a distributed multi-antenna network to optimize the transmission efficiency of the communication system.

In order to provide users with faster wireless network services, in the new generation of telecommunications services, the number of antenna devices providing wireless network services is increased in a single service area. With the increasing number of antennas in each service area, the power allocation for each antenna device has gradually become an important technical issue.

The prior art (Thesis name: Energy-Efficient Power Allocation for Distributed Large-Scale MIMO Cloud Radio Access Networks; publication name: Wireless Communications and Networking Conference (WCNC), 2014 IEEE) proposes a decentralized multi-antenna power distribution scheme due to the system The complexity is proportional to the number of antenna devices and the number of user devices. Therefore, when the power distribution of each antenna is performed in a high-density communication environment with a large number of user devices and distributed antennas (for example, a metropolitan communication environment) The huge amount of computation needs to be processed in real time, so that the system cannot effectively allocate power to the distributed multi-antenna in the wireless network service area during actual operation. In addition, because the wireless channel environment is quite variable, even the power system used in the communication system. The plan also does not guarantee that it will maintain excellent performance in the future.

In summary, how to provide a technical solution for power distribution of an antenna device according to the current communication environment is a technical problem that needs to be solved in the field.

In order to solve the problems disclosed above, it is an object of the present invention to provide an apparatus, system, method and computer program product for power distribution of a distributed multi-antenna network.

To achieve the above object, the present invention provides a decentralized multi-antenna network power distribution apparatus with an iteratively configured antenna grouping operation. The foregoing device comprises a control interface and a controller, and the control interface is used for externally distributing a plurality of distributed antenna devices, wherein the antenna device is used for providing wireless network services to a plurality of external user devices. The controller is connected to the control interface, and groups the antenna devices, and performs power allocation on the wireless network service area provided by the grouped antenna devices. To optimize the power configuration of the antenna device, the controller further evaluates the antenna more iteratively. The energy efficiency of the power distribution of the device to determine whether to reconfigure the grouping operation.

To achieve the above object, the present invention provides a decentralized multi-antenna network power distribution system with an iteratively configured antenna grouping operation, comprising a plurality of distributed antenna devices and the aforementioned distributed multi-antenna network power distribution device. A plurality of antenna devices are used to provide wireless network services to a plurality of external user devices. The distributed multi-antenna network power distribution device groups the antenna devices, performs power allocation on the wireless network service areas provided by the grouped antenna devices, and then iteratively evaluates the energy efficiency of the power distribution of the antenna devices. Determine whether to reconfigure the grouping operation.

To achieve the above objective, the present invention provides a decentralized multi-antenna network power allocation method with an iteratively configured antenna grouping operation, the foregoing method comprising the following steps: a plurality of decentralized antenna devices, the antenna device for providing wireless network services to a plurality of external user devices; grouping the antenna devices and providing wireless network service areas for the completed antenna devices Power allocation is performed; then, iteratively evaluates the energy efficiency of the power distribution of the antenna device to determine whether to reconfigure the grouping operation.

In order to achieve the above object, the present invention further proposes an antenna grouping operation with iterative configuration. The distributed multi-antenna network power distribution computer program product can complete the steps described in the foregoing method when the computer device loads and executes the computer program product.

In summary, the distributed multi-antenna network power distribution system and device of the present invention, The method and the computer program product group the antenna device, and then perform power allocation to effectively reduce the amount of calculation of the control computer, and can instantaneously configure the power of the antenna device in the service area to improve the wireless in the service area. Network quality.

1‧‧‧Distributed multi-antenna network power distribution system

11‧‧‧Distributed multi-antenna network power distribution device

111‧‧‧Control interface

112‧‧‧ Controller

12‧‧‧Antenna device

2‧‧‧User device

C1, C2‧‧‧ cluster

FIG. 1 is a schematic diagram of a system of a distributed multi-antenna network power distribution system with an iteratively configured antenna grouping operation according to an embodiment of the present invention.

2 is a flow chart of a method for decentralized multi-antenna network power allocation method with iteratively configured antenna grouping operation according to another embodiment of the present invention.

Figure 3 is a graph of energy efficiency versus number of users.

Figure 4 is a graph of energy efficiency and coverage.

Specific embodiments will be described below to illustrate embodiments of the invention, but It is not intended to limit the scope of the invention as claimed.

Please refer to FIG. 1 , which is an iterative configuration of an antenna grouping operation according to the present invention. Multi-antenna network power distribution system 1. The system includes a distributed multi-antenna network power distribution device 11 and an antenna device 12. The distributed multi-antenna network power distribution device 11 includes a control interface 111 and a controller 112. The controller 112 is connected to the control interface 111, and is connected to the external antenna device 12 through the control interface 111 to perform the management on the antenna device 12. Power allocation is performed for the network services provided by each antenna device 12.

The foregoing control interface 111 is used for communication of the communication connection antenna device 12 Surface, such as optical fiber communication interface, network routing communication interface, copper shaft power communication interface. The aforementioned controller 112 is an electronic device having computing power, such as a computer device, a programmable digital circuit, or the like.

One of the application scenarios of the system is a cloud-based wireless access network architecture distributed for many days. The line system is below the line. If the distributed multi-antenna network power distribution system 1 has a total of M distributed antenna units 12 (Remote Radio Unit; code: RRU), K user devices 2 (User Equipment; code: UE), such as handheld mobile communication devices The computer has a network connection, and the system bandwidth is B, and the distributed antenna is divided into a plurality of clusters (c), for example, two clusters of C1 and C2. All of the distributed antenna devices 12 can be connected to the controller 112 via a wired network (e.g., fiber optic cable) and a control interface 111. Each of the distributed antenna devices 12 can be synchronized with each other, and its transmission delay can also be ignored. Each user device 2 uses an antenna device 12 and is only allowed to be assigned into a cluster.

When the distributed multi-antenna network power distribution system 1 is in operation, the user device 2 receives intra-cluster interference and inter-cluster interference. Inter-cluster interference multiple user devices 2 are simultaneously transmitted in the same cluster, and the inter-cluster interference is caused by the lack of interference suppression coordination between adjacent clusters. At this time, a linear precoding mechanism can be used in each cluster, for example, Zero-forcing beamforming and MRT precoding to effectively reduce intra-cluster interference. Therefore, the signal to interference plus noise ratio (SINR) for user device 2 is:

Where φ _c,m,k {0, 1} is used to indicate whether the mth antenna device 12 and the kth user device 2 are classified into the cluster c. h _m,k and w _m,k are the channel gain and precoding weights between the antenna device 12 and the user device 2 _, respectively. The channel gain can be obtained by the user feedback information or the base station measuring the uplink signal. p _k is the transmission symbol power of the user device 2, and N ₀ is the power spectral density of the additive white Gaussian noise. And I _k is the interference power, and its formula is as described in Eq (1.2):

According to Shannon Capacity, the kth user device 2 transmission rate can be expressed by Eq (1.3): R _k = B log ₂ (1 + Γ _k ) Eq (1.3)

The energy consumption of the entire network is P _t , P _t = αP _total + γ : Where P _t is the total transmission power, and α and γ are linear power consumption parameters, respectively.

In this case, resource allocation is made with the goal of improving energy efficiency. Energy efficiency is defined as the ratio of total system capacity to power consumption. The formula is as described in Eq(1.5):

Based on the above formula, the present invention maximizes network energy efficiency by combining the clustering and power allocation mechanisms in accordance with the requirements of the user device 2 and the transmission power limit of each antenna device 12. The goal of this optimization problem is to obtain the grouping strategy and power allocation strategy as Φ={ φ _{c , m , k} |1 respectively. c C , 1 m M , 1 k K ,c Z ⁺ ,m Z ⁺ ,k Z ⁺ } and P ={ p _k |1 k K , k Z ⁺ }. Therefore, the target and constraints of the joint clustering and power allocation optimization problem are as follows:

Eq (1.6b) and Eq (1.6c) respectively limit the power and signal power of each antenna device 12, and Eq (1.6d) guarantees the quality of service (QoS) of each user. . Eq (1.6e) is limited to each user device 2 can only be selected to be served by a cluster c, Eq (1.6f) is limited to each antenna device 12 can only be selected into a cluster c, Eq (1.6g) For each user device 2 must be assigned into a cluster and at least one antenna device 12 provides service, Eq (1.6h) is a binary indicator that limits φ _{c , m , k} .

Joint grouping and power allocation algorithm

Because Eq(1.6) is a non-convex optimization problem, this case converts Eq(1.6) into a convex optimization problem and performs grouping mechanism and power allocation through the following convex optimization conversion mode. According to Fractional programing, the original target function can be converted into a fractional pattern, such as: . The best grouping and power allocation strategies are defined as Φ ^* and P ^* , and the best energy benefit η ^* can be expressed as:

Given η , the original target Eq(1.6) can be rewritten as: Thereafter, the objective function is equivalent to the formula Eq(1.8).

In order to deal with the non-convex optimization problem caused by intra-cluster interference and inter-cluster interference, it is assumed that each user device 2 receives the worst case interference, and the maximum interference energy can be represented from the antenna device 12 to the user device 2. for:

Furthermore, the discrete variables included in the limit Eq (1.6h) are relaxed into continuous variables, so the original φ _{c , m , r} {0,1} is replaced by φ _{c , m , r} [0,1].

Using the Lagrange multiplier method to perform the best solution for grouping and transmission power allocation, Eq (1.8) and Eq (1.9) are replaced into Eq (1.6), and the original optimization problem can be rewritten. for: among them ,and

Therefore, the original non-convex optimization problem is transformed into a convex optimization problem and can be solved by the Lagrangian method. The Lagrangian function is as follows:

Where ε _m , θ _k , μ _{c , m} , ψ _{c , k} , β _c and σ _{c , m , k} are the Lagrangian doublings of the limit Eq(1.6b)-(1.6h), respectively. For convenience, the Lagrangian doubling set is defined as ε , θ , μ , ψ , β , and σ . This function and the Karush-Kuhn-Tucker (KKT) condition are used to solve the clustering and power allocation.

The grouping and power allocation are solved by the Lagrangian method and the Karush-Kuhn-Tucker (KKT) condition, and the obtained clustering index of the user device 2 and the antenna device 12 in the cluster c is:

The power allocation indicator of the user device 2 obtained is:

The Lagrangian doubling is updated as follows: Where n 0 is the number of iterations, s is the step size, and [ x ] ⁺ = max{0, x }.

In order to obtain the best discrete clustering allocation indicator ( ), the maximum value obtained in Eq (1.12) is selected as 1, which can effectively improve the system performance, which is expressed as:

In order to optimize the power distribution efficiency of the antenna device 12, it can be optimally configured with each other by Eq (1.12) and Eq (1.13). Further, the controller 112 iteratively determines whether the energy benefit η converges (for example: |η ^{(f+1) -} η ^(f) | < 0.1), or whether the number of iterations f exceeds the upper limit value Whether to reconfigure the grouping job. In another embodiment, the controller 112 determines whether the energy benefit η converges or the number of iterations exceeds an upper limit value to conditionally stop the iterative operation immediately.

Please refer to FIG. 2 , which is a decentralized multi-antenna network power allocation method with an iteratively configured antenna grouping operation according to another embodiment of the present invention. The method is applied to have M antenna devices 12, K user devices 2, bandwidth B, cluster number C, and h _{m , k} is the large-scale attenuation of the kth user device 2 of the mth antenna The channel information, w _{m , k} is the operating environment of the precoding weight of the kth user device 2 of the mth antenna. The steps of the foregoing method are described as follows: S101: Initialize η , Φ, P, ε , θ , μ , ψ , β , σ given an arbitrary value, and set the number of Lagrangian iterations n=0, the number of fractional plan iterations f= 0, with the maximum number of iterations L.

S102: The number of fractional plan iterations f=f+1.

S103: Using the Lagrangian method, find the following clustering allocation indicators:

S104: Using the Lagrangian method, find the following power allocation indicators:

S105: Update the following Lagrangian constants:

S106: Determine whether the cluster allocation indicator and the power allocation indicator converge?

(E.g: with )

If it does not converge, it goes to S107; if it converges, it goes to S108.

S107: The number of progressive Lagrangian iterations n=n+1, and returns to S103.

S108: Update power benefit η :

S109: Determine whether η converges? (Example: | η ^(f+1) - η ^(f) | <0.1), or whether the number of fractional plan iterations reaches the upper limit (f = L). If one of the conditions is to complete the grouping and power allocation immediately; otherwise, to S102.

In another embodiment, the present invention further provides a decentralized multi-antenna network power distribution computer program product with an iteratively configured antenna grouping operation, when the computer device loads and executes the computer The program product can complete the steps described in the foregoing method.

The aforementioned computer program products can be obtained by ASP, C/C++/C#, JAVA, It is implemented in Python, PHP, Perl, etc., but the category of the programming language is not limited to this.

Effectiveness analysis

The wireless transmission range formed by the analog antenna device 12 of the present invention is a square-shaped region, and the antenna device 12 has a lattice distribution, and the user device 2 is evenly distributed in the transmission range, and uses zero-forcing beamforming (Zero-forcing beamforming). ) to reduce interference within the cluster. The simulation results are shown in Table 1 for the relevant simulation parameters as shown in Table 1:

Please refer to Figure 3, which is a graph of energy efficiency and number of users. In this In the figure, the vertical axis is energy efficiency, the horizontal axis is the number of users, the line segment L1 is the simulation result of the technical means of the present invention, and the line segment L2 is the simulation result of the conventional technique (grouping and power allocation without iteration). It can be seen from Fig. 3 that the technical means of the present invention has a better performance in energy efficiency.

Please refer to Figure 4 for a diagram of energy efficiency and coverage. The vertical axis of the figure is energy efficiency, and the horizontal axis is the length of the coverage area. The line segment L3 is a simulation result using the technical means of the present invention, and the line segment L4 is a simulation result of a conventional technique. It can be seen from Figure 4 that the energy efficiency of this case is about 1.2 times higher than that of the conventional technology.

The detailed description above is for the specific description of a possible embodiment of the present invention, but the embodiment is not intended to limit the scope of the patent in this case. Any equivalent implementation or modification that is not departing from the spirit of the present invention shall be included in the patent of the present case. In the scope.

1‧‧‧Distributed multi-antenna network power distribution system

11‧‧‧Distributed multi-antenna network power distribution device

12‧‧‧Antenna device

2‧‧‧User device

C1, C2‧‧‧ cluster

Claims (12)

A decentralized multi-antenna network power distribution device with an iteratively configured antenna grouping operation, comprising: a control interface for externally distributing a plurality of distributed antenna devices, wherein the antenna devices are used to provide wireless network services a plurality of external user devices; and a controller connected to the control interface, wherein the controller performs grouping operations on the antenna devices, and performs power allocation on the wireless network service areas provided by the group of the antenna devices. Wherein the controller more iteratively evaluates the energy benefit of the power allocation of the antenna devices to determine whether to reconfigure the grouping operation. The decentralized multi-antenna network power distribution device of claim 1, wherein the controller configures each of the antenna devices to select only one cluster, configure each user device to be allocated into the cluster, and configure at least one of the The antenna device provides wireless network services. The distributed multi-antenna network power distribution device of claim 2, wherein the controller performs group conversion according to the convex optimized conversion mode. The distributed multi-antenna network power distribution device of claim 3, wherein the controller performs grouping and power allocation according to the Lagrangian processing mode under the group switching. The decentralized multi-antenna network power distribution device of claim 1, wherein the controller is configured to determine whether the benefit or the number of iterations of the power allocation of the antenna devices reaches an upper limit to determine whether to reconfigure the grouping operation. . A decentralized multi-antenna network power distribution system with an iteratively configured antenna grouping operation, comprising: a plurality of decentralized antenna devices, wherein the antenna devices are used to provide wireless network services to a plurality of external user devices; And a distributed multi-antenna network power distribution device according to any one of claims 1 to 4, wherein the The distributed multi-antenna network power distribution device performs grouping on the antenna devices and performs power allocation on the wireless network service areas provided by the group of antenna devices, wherein the distributed multi-antenna network power distribution device further Iteratively evaluates the energy benefits of the power distribution of the antenna devices to determine whether to reconfigure the clustering operation. A decentralized multi-antenna network power allocation method with an iteratively configured antenna grouping operation, comprising: a plurality of external distributed antenna devices, wherein the antenna devices are used to provide wireless network services to multiple external uses And arranging the antenna devices, and performing power allocation on the wireless network service areas provided by the antenna devices that complete the grouping; iteratively evaluating the energy efficiency of the power distribution of the antenna devices to determine Whether to reconfigure the grouping job. The decentralized multi-antenna network power allocation method according to claim 7, wherein each of the antenna devices is configured to select only one cluster, each user device is configured to be allocated into the cluster, and at least one of the antenna devices is configured to provide Wireless internet service. The distributed multi-antenna network power allocation method according to claim 8 further performs group switching according to the convex optimized conversion mode. The distributed multi-antenna network power allocation method according to claim 9 further performs grouping and power allocation according to the Lagrangian processing mode under the group switching. The decentralized multi-antenna network power allocation method according to claim 7, further selecting whether the energy benefit or the number of iterations of the power allocation of the antenna devices reaches an upper limit to determine whether to reconfigure the grouping operation. Decentralized multi-antenna network power distribution computer program with iterative configuration antenna grouping operation The method of any one of claims 7 to 11 can be completed when the computer device loads and executes the computer program product.