CN107493598B - Base station power control method based on motion model in heterogeneous communication coexisting network - Google Patents

Base station power control method based on motion model in heterogeneous communication coexisting network Download PDF

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CN107493598B
CN107493598B CN201710332879.3A CN201710332879A CN107493598B CN 107493598 B CN107493598 B CN 107493598B CN 201710332879 A CN201710332879 A CN 201710332879A CN 107493598 B CN107493598 B CN 107493598B
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user
base station
node
relay
communication
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CN107493598A (en
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李莉
裴仁超
沈一豪
叶鹏
陈虹豆
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Shanghai Normal University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
    • H04W52/242—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account path loss
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/28—TPC being performed according to specific parameters using user profile, e.g. mobile speed, priority or network state, e.g. standby, idle or non transmission
    • H04W52/282—TPC being performed according to specific parameters using user profile, e.g. mobile speed, priority or network state, e.g. standby, idle or non transmission taking into account the speed of the mobile
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/30—TPC using constraints in the total amount of available transmission power

Abstract

The invention discloses a base station power control method based on a motion model in a heterogeneous communication coexisting network, in particular to a relay based on the motion model and a base station power control method under a D2D heterogeneous communication coexisting network, and relates to the technical field of wireless communication signal processing. The method comprises a step a. nodes in the communication network scenario, comprising a cellular subscriber (CU), a Base Station (BS), a relay (R), an edge subscriber (EU) communicating with the Base Station (BS) via the relay (R), a subscriber (D) in D2D communication1) And user (D)2) (ii) a Consider Edge Users (EU) and users (D) in a communications network scenario1) The node is in a motion state, and other nodes are in a static state; b. user establishment (D)1) An Edge User (EU) motion velocity and path loss related motion model; c. and considering the transmission power constraints of a Base Station (BS) and a relay (R), and considering the communication quality of a user to realize the maximization of the network throughput.

Description

Base station power control method based on motion model in heterogeneous communication coexisting network
Technical Field
The invention relates to a signal processing method in the field of wireless communication, in particular to a base station power control method under a relay and D2D heterogeneous communication coexisting network based on a motion model.
Background
With the increasing functionality of mobile devices, the utilization efficiency of wireless resources is greatly improved due to advanced wireless access technologies and coded modulation schemes. Meanwhile, the demand of people for mobile communication and bandwidth wireless access services is continuously increasing, the shortage of wireless spectrum resources is more and more serious, but the problem of the shortage of wireless spectrum resources is not fundamentally solved. Therefore, Device-to-Device (D2D) technology has been developed, which improves spectrum utilization efficiency while reducing power consumption, reducing base station load, improving battery life, and so on, and provides possibility for solving the problem of spectrum resource scarcity.
The D2D technology enables both communication devices to communicate directly without going through the uplink and downlink of the base station, enabling the base station to serve more communication devices simultaneously. This improves the overall throughput of the cellular network while reducing the burden on the base station. If the D2D communication taking the movement of the user into consideration is adopted, the communication is closer to the actual communication situation. Further, the introduction of bidirectional relay can increase the throughput of cellular networks, and is also a hot spot of research in the communication field today.
Through the search of the existing documents, it is found that the problems of power distribution of a base station and a cellular user end are researched in 'resource allocation optimization and network Conference for Device-to-Device Communication Two-Way cellular network'. IEEE Wireless Communication and network Conference, 2013 (resource allocation of bidirectional cellular network Communication covered by D2D Communication, IEEE Wireless Communication and network Conference). The power distribution optimization objective function is converted into a quadratic function form by a relay end power distribution factor and is solved optimally, so that the total rate of a D2D network and a cellular network is increased, but the interference of path loss caused by the distance between the Device and the Device is not considered.
Also found by search L K.Saliya Jayasinghe, Pranetho Jayasinghe, Nandanaajava: 'MIMO Physical L a layer Network Coding based Underlay Device-to-Device Communication' IEEE 24thIn International Symposium on Personal, indoor Mobile Radio Communications, 2013(D2D communication based on MIMO physical layer network coding, International seminar of Personal, indoor and Mobile Radio Communications), physical layer network coding design is studied, and an article establishes an optimization function by designing a transmission coding matrix and an equalizer matrix when receiving of each communication device end, and finally utilizes a minimum mean square error to make a network error rate to be minimum. However, the disclosure is in a scenario that does not take into account the user's motion, and the entire network device is static.
From the prior art, the research on the relay based on the motion model and the base station power control method under the D2D coexisting network still has very important significance.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings in the prior art, provides a relational motion model based on the motion speed and the path loss of a user, and establishes an optimization problem about network throughput by using the motion model. In the optimization problem, the base station performs power control, then controls the transmission power of the relay, and finally realizes the maximization of the whole network throughput under the condition of ensuring the communication quality of the user.
The invention assumes that the channel state information in the whole cellular network is known and is a perfect channel information state; the transmit power of a base station may cause interference to non-target users in its coverage area. The power of the base station and the relay and the receiving signal-to-interference-and-noise ratio of the user are used as constraint conditions, the problem about network throughput optimization is established, and the influence of the movement speed of the user on path loss is considered.
The invention relates to a base station power control method under a relay and D2D coexisting network based on a motion model, which comprises the following steps:
1. considering a node in a communication network scenario, the node: base station BS, relay R and cellular user CU, edge user EU and user D in D2D communication1And user D2。
And only the edge users EU and the users D are considered in the communication scene1In a moving state. Cellular user CU, base station BS, Relay R, user D2In a static state;
2. establishing user D1The motion model related to the motion speed and the path loss of the edge user EU is as follows:
Figure GDA0002448938690000031
where i denotes a node in motion in the network, including edge users EU and users D1. j denotes nodes in a network in a quiescent state, which nodes comprise base stations BS, relays R, cellular users CU and users D2。
Pi jIndicating the transmission power, P, received by node j from node i in motioniThe transmission power of the node i in motion state. dijRepresenting the distance between nodes i and j.
αlg(|viL +1) indicates consideration of edge user EU and user D1The speed of motion of (a) brings about the influence of path loss, redefined path loss factor, which follows the EU and D users of the edge user1The instantaneous speed of movement varies.
For being in a static state, not subject to user D1Or the node influenced by the path loss brought by the movement speed of the edge user EU, and establishing a path loss model as follows:
Figure GDA0002448938690000032
Figure GDA0002448938690000033
representing the transmission power received by node j from node j'. djj′Represents the distance, P, between nodes j' and jj′For the transmission power of node j ', j' and j are both nodes in a quiescent state.
3. Considering the transmission power constraints of the base station and the relay, and considering the maximization of the network throughput under the condition of the communication quality of the user, specifically, the optimization problem is as follows:
Figure GDA0002448938690000041
wherein R isCU、
Figure GDA0002448938690000045
REURespectively cellular users CU and user D1User D2The transmission rate of the edge user EU.
In the constraint of the optimization problem PBSRepresenting the transmission power, P, of the base stationminAnd PmaxDenotes the minimum and maximum transmission power, lambda, of the base stationBSDenotes the transmission power of the relay controlled by the base station transmission power, λ ∈ (0,1),
Figure GDA0002448938690000042
and
Figure GDA0002448938690000043
representing the minimum and maximum transmission power of the relay R. I isiRepresenting the signal to interference plus noise ratio of node i,
Figure GDA0002448938690000044
and indicating the signal-to-interference-and-noise ratio threshold value for ensuring the communication quality of the node i.
According to the Shannon formula, the signal-to-interference-and-noise ratio I of each nodeiThe transmission rate R of each node in the formula (3) can be obtainediAccording to the constraint conditions of the transmitting power of the base station BS and the relay R and the signal-to-interference-and-noise ratio I of each nodeiUsing a nonlinear convex optimization (SVD) algorithm to obtain the base station power P under the condition of maximizing the network throughputBS。
The whole network realizes the maximization of network throughput under the condition of ensuring the communication quality of users by controlling the transmitting power of the base station.
Drawings
Fig. 1 is a schematic block diagram of a system for controlling power of a base station based on a motion model in a heterogeneous communication coexistence network according to the present invention.
Detailed Description
The invention is further described in the following with reference to the figures and examples
A base station power control method (shown in figure 1) under a relay R and D2D communication coexistence network based on a motion model is used for establishing a path loss model related to the motion speed of a user by considering path loss. In order to ensure the communication quality of users and simultaneously maximize the network throughput, an optimization problem model is established by taking power and the user receiving signal-to-interference-and-noise ratio as constraint conditions.
Under the condition that the channel state information in the whole cellular network is known, the method specifically comprises the following steps:
a) consider a communication network scenario in which a node has a cellular subscriber CU, a base station BS, a relay R, an edge subscriber EU communicating with the base station BS via the relay R, a subscriber D in D2D communication1And user D2。
Consider only edge users EU and D in this communication scenario1Is in motion. Cellular user CU, base station BS, Relay R, user D2Are all in a static state;
b) in the system network, user D is established while considering interference1And the movement model related to the EU movement speed and the path loss of the edge user is as shown in the formula (1):
Figure GDA0002448938690000051
Pi jindicating the transmission power, P, received by node j from node i in motioniThe transmission power of the node i in motion state. dijRepresenting the distance between nodes i and j α being the path loss factor, viRepresenting the instantaneous speed of motion of node i.
For being in a static state, not subject to user D1Or the node influenced by the path loss brought by the movement speed of the edge user EU, and establishing a path loss model as follows:
Figure GDA0002448938690000052
Figure GDA0002448938690000053
representing the transmission power received by node j from node j'. djj′Representing the distance between nodes j' and j. Pj′For the transmission power of node j ', j' and j are both nodes in a quiescent state.
c) A base station power control under a relay R and D2D communication coexistence network based on a motion model relates to an optimization problem of network throughput. And the network throughput is maximized on the premise that the communication quality of each user is ensured.
The optimization problem about the network throughput is established as follows:
Figure GDA0002448938690000061
wherein R isCU、
Figure GDA00024489386900000613
REURespectively cellular users CU and user D1User D2Transmission rate of edge user EU, PBSRepresenting the transmission power, P, of the base station BSminAnd PmaxRepresenting the minimum and maximum transmit powers of the base station BS,
Figure GDA0002448938690000062
and
Figure GDA0002448938690000063
denotes the minimum and maximum transmission power, λ, of the relay RBSDenotes the transmission power of the relay R controlled by the base station BS transmission power, λ ∈ (0, 1).
IiRepresenting the signal to interference plus noise ratio of node i,
Figure GDA0002448938690000064
and indicating the signal-to-interference-and-noise ratio threshold value for ensuring the communication quality of the node i.
For the node base station BS and the cellular user CU in the stationary state, the communication rate of the cellular user CU is obtained by establishing a communication rate model according to formula (2) according to the shannon formula, as follows:
Figure GDA0002448938690000065
Figure GDA0002448938690000066
representing the reception noise at the base station side.
User D in D2D communication1In motion state, it is associated with user D1The path loss of the communication link concerned is modeled by equation (1). User D in D2D communication can be obtained according to Shannon formula1Rate of communication
Figure GDA0002448938690000067
As shown in formula (5):
Figure GDA0002448938690000068
wherein the content of the first and second substances,
Figure GDA0002448938690000069
representing user D1The reception noise of the terminal.
User D in stationary state in D2D communication2And by velocity
Figure GDA00024489386900000614
User D of sports1The loss of the communication link between the users is established by the formula (1), and the user D in the D2D communication is established according to the Shannon formula2Rate of communication
Figure GDA00024489386900000610
As shown in formula (6):
Figure GDA00024489386900000611
wherein the content of the first and second substances,
Figure GDA00024489386900000612
representing user D2The reception noise of the terminal.
When the edge user EU is in motion, the path loss of the communication link related to the user EU is modeled by equation (1). And considering edge users EU and users D1All are in motion state, and the comprehensive influence of the motion speeds of the two is considered
Figure GDA0002448938690000071
Therefore, when the communication rate of the EU of the edge user is calculated, a path loss model is established
Figure GDA0002448938690000072
And obtaining the communication rate of the edge user EU according to a Shannon formula:
Figure GDA0002448938690000073
wherein the content of the first and second substances,
Figure GDA0002448938690000074
indicating the received noise at the EU side of the edge user.
According to the above formula, cellular user CU and user D are not difficult to obtain1User D2And the signal-to-interference-and-noise ratio of the edge user EU is as follows:
Figure GDA0002448938690000075
Figure GDA0002448938690000076
Figure GDA0002448938690000077
Figure GDA0002448938690000078
in summary, the present invention solves the optimization problem and realizes the maximization of the throughput of the network by controlling the transmission power of the base station through the constraint conditions of the powers of the base station BS and the relay R and the threshold constraint conditions of the received sir of each user under the condition of ensuring the communication quality of the user.

Claims (1)

1. A method for controlling power of a base station based on a motion model in a heterogeneous communication coexisting network is characterized by comprising the following steps:
a. a node in the context of a communication network, comprising a cellular subscriber CU, a base station BS, a relay R, an edge subscriber EU communicating with the base station BS via the relay R, a subscriber D in D2D communication1And user D2；
Consider edge user EU and user D in a communications network scenario1Is the motion state, cellular user CU, base station BS, Relay R, user D2Are all in a static state;
b. establishing a moving user D1The movement model of the EU movement speed and path loss of the edge user is as follows:
Figure FDA0002490564450000011
Pi jtransmission power, P, received for node j from node i in motioniThe transmission power of the node i in the motion state; dijDistance between nodes i and j, α is the path loss factor, viIs the instantaneous velocity of motion of node i;
for being in a static state, not subject to user D1Or the node influenced by the path loss brought by the movement speed of the edge user EU, and establishing a path loss model as follows:
Figure FDA0002490564450000012
Figure FDA0002490564450000013
a transmission power received for node j from node j'; djj′Is the distance between nodes j' and j, Pj′J 'and j are nodes in a static state, which is the transmission power of the node j';
c. considering the constraints of the base station BS and the relay R transmitting power, and considering the user communication quality condition, the problem of establishing the maximum optimization of the network throughput is as shown in formula (3):
Figure FDA0002490564450000014
wherein R isCU、
Figure FDA0002490564450000021
REURespectively cellular users CU and user D1User D2Transmission rate of edge user EU, PBSRepresenting the transmission power, P, of the base station BSminAnd PmaxRepresenting the minimum and maximum transmit powers of the base station BS,
Figure FDA0002490564450000022
and
Figure FDA0002490564450000023
denotes the minimum and maximum transmission power of the relay R, λ PBSDenotes the transmission power of the relay R controlled by the base station BS transmission power, lambda ∈ (0, 1); IiRepresenting the signal to interference plus noise ratio of node i,
Figure FDA0002490564450000024
representing a signal-to-interference-and-noise ratio threshold value for ensuring the communication quality of the node i;
according to the shannon formula, the corresponding transmission rate model is as follows:
Figure FDA0002490564450000025
Figure FDA0002490564450000026
Figure FDA0002490564450000027
Figure FDA0002490564450000028
wherein the content of the first and second substances,
Figure FDA0002490564450000029
respectively represent a base station end and a user D1End, user D2Receiving noise at an end and an edge user EU end;
cellular subscriber CU, subscriber D1User D2And the signal-to-interference-and-noise ratio of the edge user EU is as follows:
Figure FDA00024905644500000210
Figure FDA00024905644500000211
Figure FDA00024905644500000212
Figure FDA00024905644500000213
according to the signal-to-interference-and-noise ratio I of each nodeiAnd the transmission power constraint conditions of the base station BS and the relay R, and the base station power P under the condition of maximizing the network throughput is obtained by utilizing a nonlinear convex optimization algorithm SVDBSThe whole network realizes the maximization of network throughput under the condition of ensuring the communication quality of users by controlling the transmitting power of the base station.
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