CN109039410B - Beam forming method of heterogeneous cloud wireless access network and communication network - Google Patents
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- H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
- H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
- H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
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- H04W52/244—Interferences in heterogeneous networks, e.g. among macro and femto or pico cells or other sector / system interference [OSI]
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- H04W52/26—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service]
- H04W52/267—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service] taking into account the information rate
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Abstract
The invention provides a beam forming method of a heterogeneous cloud wireless access network and a communication network using the same, wherein the method comprises the steps of calculating the data transmission rate of a macro cell user (MU); calculating the data transmission rate of a wireless remote radio unit user (RU) and calculating the total energy efficiency of the heterogeneous cloud wireless access network; determining a beamforming vector joint optimization problem of MBS and RRHs; and a final step of solving the beamforming problem. The communication network using the method comprises a baseband processing unit pool, a macro base station MBS, a plurality of wireless remote radio frequency units RRHs, a plurality of macro cellular users and a plurality of RRH users. Aiming at the problem that the existing beam forming technical scheme is not suitable for a large-scale MIMO-assisted heterogeneous cloud wireless access network, the beam forming method of the large-scale MIMO-assisted heterogeneous cloud wireless access network takes the total energy efficiency of the maximized network as an optimization target, improves the energy efficiency of the heterogeneous cloud wireless access network, and reduces the total power consumption of MBS and RRHs.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to a beam forming method of a large-scale MIMO (multiple input multiple output) assisted heterogeneous cloud wireless access network and a communication network for realizing the method.
Background
With the rapid development of the internet of things and the mobile internet, the existing wireless communication system faces the needs of spectrum resource shortage, excessive energy consumption and difficulty in meeting the future communication development of the transmission rate. Therefore, how to utilize limited resources to greatly increase the transmission rate and reduce the energy consumption becomes a problem to be considered in the next generation wireless communication system.
As a new network architecture, a heterogeneous cloud wireless access network is considered as one of the most promising solutions to meet the requirements of future wireless communication networks. The heterogeneous cloud wireless access network uses the advantages of the control and service plane separation technology realized by high-power nodes in the heterogeneous network and the efficient local service supporting of the wireless remote radio frequency unit in the cloud wireless access network for reference, and can remarkably improve the performance of the wireless network by utilizing the advanced cooperative signal processing technology. In addition, the heterogeneous cloud wireless access network has the characteristics of low cost, flexible network deployment, high resource utilization rate and the like. Meanwhile, the newly emerging large-scale multiple-input multiple-output (MIMO) technology can improve the capacity and the spectrum efficiency of a communication system by multiples and obtain higher space diversity capability by configuring dozens or even hundreds of antennas at the base station side. Therefore, the heterogeneous cloud wireless access network is combined with the large-scale MIMO technology, the respective technical advantages are exerted, and the requirement of high-performance communication in the next generation wireless network can be met.
In view of the above, a new method for beam forming in a heterogeneous cloud radio access network is needed to solve the above problem.
Disclosure of Invention
The invention aims to provide a large-scale MIMO-assisted heterogeneous cloud wireless access network beam forming method, namely, a large-scale MIMO technology is introduced into a heterogeneous cloud wireless access network, the total energy efficiency of the network is maximized as an optimization target, and under the condition that power constraint is considered, the beam forming vectors of a macro base station and a wireless remote radio frequency unit are jointly optimized.
In order to achieve the above object, the present invention provides a beam forming method for a heterogeneous cloud radio access network, including the following steps:
S3, calculating the total energy efficiency xi of the heterogeneous cloud wireless access network:
wherein,
R(wj,vk) Is the total data transmission rate of RUs and MUs in a heterogeneous cloud wireless access network, the RUs representing a number of RUs, the MUs representing a number of MUs,
P(wj,vk) The total power consumption of a radio remote radio unit RRH and a macro base station MBS in the heterogeneous cloud wireless access network is calculated;
s4, determining the beamforming vector joint optimization problem of MBS and RRHs, wherein the optimization problem is expressed as:
wherein, PRRHAnd PMBSRespectively the maximum transmitting power threshold values of all RRHs and MBS, the RRHsRepresents a number of RRHs;
s5, solving the beam forming problem, and equating the optimization problem in the step S4 as the following convex optimization problem:
constraint (1), constraint (2) and
wherein j represents the index number of the RU; k andkall represent index numbers of the MU, where k ≠k(ii) a J represents the total number of RUs; k represents the total number of MU; w is ajRepresenting a beam formed by N beamforming vectors wn,jThe formed cumulative beamforming vectors are then used to form,a beamforming vector for RRH n versus RU j, where n represents the index number of the RRH;beamforming vector, v, for MBS to MU k k For MBS to MUkA beamforming vector;is interference channel vector between MBS and RU j;is the channel vector between MBS and MU k;representing a channel consisting of N channel vectors hn,jThe accumulated channel vector of the component(s),is the channel vector between RRH n and RU j;representing a channel consisting of N channel vectors gn,kThe accumulated channel vector of the component(s),is an interference channel vector between RRH n and MU k;represents TMA column vector of dimensions;represents TRA row vector of dimensions, where TMIndicating the number of antennas, T, with which the MBS is equippedRRepresenting the number of antennas provided for the RRH; the number of the lambda-beams is increased,to introduce an auxiliary variable.
As a further improvement of the present invention, the optimization problem (a) is solved in step S5 by transforming into the following optimization problem:
as a further improvement of the present invention, the optimization problem (C) is further equivalent to the optimization problem (B), wherein the constraint conditions are:
constraint (1) and constraint (2).
As a further improvement of the invention, the non-convex constraint (7) and the non-convex constraint (8) are respectively approximated to the following convex constraints:
as a further improvement of the present invention, the step of solving the optimization problem (B) is as follows:
Step 6, repeating the steps 2-5 until the lambda is converged to obtain an optimal solutionAndrespectively represents wjAnd vkAnd (4) corresponding optimal solution.
In order to achieve the above object, the present invention further provides a heterogeneous cloud wireless access network beam forming method based on massive MIMO assistance, comprising the following steps:
s1, calculating a data transmission rate of the MU, where the data transmission rate of the MU k is:
wherein j denotes the RU index number(ii) a k andkall represent index numbers of the MU, where k ≠k,Beamforming vector, v, for MBS to MU kkFor MBS to MUkThe number of the beamforming vectors is determined,for the channel vector between MBS and MU k, is an interference channel vector between RRH n and MU k, where n represents the index number of the RRH, beamforming vector for RRH N to RU J, J denotes the total number of RUs, K denotes the total number of MUs, N denotes the total number of RRHs, MU K, where K ∈ K, C denotes the complex field, (g)TRepresenting a transpose; the MU is a macro cellular user, the MUs are a plurality of macro cellular users, the MBS is a macro base station, and the RRH is a wireless remote radio frequency unit;
s2, calculating a data transmission rate of RU, the data transmission rate of RU j being:
wherein,is the channel vector between RRH n and RU j,for interference signals between MBS and RU jA lane vector, RU J, where J ∈ J; the RUs are wireless remote radio frequency unit users, and the RUs are a plurality of wireless remote radio frequency unit users;
s3, calculating the total energy efficiency of the heterogeneous cloud wireless access network, wherein the total data transmission rate of the RUs and MUs in the heterogeneous cloud wireless access network is as follows:
the total power consumption of the RRH and the MBS in the heterogeneous cloud wireless access network is as follows:
then, the total energy efficiency of the heterogeneous cloud wireless access network is:
s4, determining a beamforming vector joint optimization problem of MBS and RRHs, wherein the large-scale MIMO-assisted heterogeneous cloud wireless access network beamforming vector joint optimization problem can be expressed as:
wherein, PRRHAnd PMBSRespectively the maximum transmitting power threshold values of all RRHs and MBS; the RRHs are a plurality of RRHs;
s5, solving the beam forming problem, converting the solving of the non-convex and non-linear optimization problem (6) into the solving of the following optimization problem (7)
s.t.(6b),(6c) (7b)
The objective function in the optimization problem (7) is non-convex, and auxiliary variables are introduced for solving conveniently The optimization problem (7) can be equivalent to the following optimization problem:
s.t.(6b),(6c) (8f)
in the optimization problem (8), the objective function and constraints (6b), (6c), (8c), (8e) are all convex, while constraints (8b) and (8d) are non-convex, and constraints (8b) and (8d) can be approximated as convex constraints as follows:
from the above analysis, the optimization problem (7) can be finally equivalent to the following convex optimization problem, namely:
s.t.(6b),(6c),(8c),(8e),(9),(10) (11b)
wherein,kindex number, w, representing MUjRepresenting a beam formed by N beamforming vectors wn,jForming a cumulative beamforming vector;beamforming vectors for MBS to MU k; MBS with large scale antenna array, TMIndicating the number of antennas, T, with which the MBS is equippedRRepresenting the number of antennas provided for the RRH;is interference channel vector between MBS and RU j;is the channel vector between MBS and MU k;representing a channel consisting of N channel vectors hn,jA constituent accumulated channel vector; the number of the lambda-beams is increased,to introduce an auxiliary variable.
As a further improvement of the invention, the steps for solving the original optimization problem (6) are as follows:
Step 6, repeating the steps 2-5 until the lambda is converged to obtain an optimal solutionAndrespectively represents wjAnd vkAnd (4) corresponding optimal solution.
In order to achieve the above object, the present invention further provides a communication network, where the communication network includes a baseband processing unit pool, a macro base station MBS, a radio remote unit RRH, a plurality of macro cell users, and a plurality of RRH users, where the MBS provides wide area radio signal coverage, the RRH provides radio signal coverage in a hot spot area or a fringe area, the MBS is equipped with a large-scale antenna array, and the macro cell users and the RRH users are equipped with at least one antenna, and the communication network may implement the beam forming method according to any one of the foregoing embodiments.
As a further improvement of the invention, the MBS is provided with a large-scale antenna array, the number of the antennas is TM, the RRH is provided with TR antennas, wherein TM>>TR。
As a further improvement of the invention, the number of antennas TMNot less than one hundred.
The invention has the beneficial effects that: aiming at the problem that the existing beam forming technical scheme is not suitable for a large-scale MIMO-assisted heterogeneous cloud wireless access network, the beam forming method of the large-scale MIMO-assisted heterogeneous cloud wireless access network is provided, the method takes the total energy efficiency of the network as an optimization target, and inhibits the interference in the heterogeneous cloud wireless access network by performing combined optimization on the beam forming vectors of MBS and RRHs, so that the energy efficiency of the heterogeneous cloud wireless access network is improved, and the total power consumption of MBS and RRHs is reduced.
Drawings
Fig. 1 is a schematic diagram of a communication network implementing the method of the present invention.
Fig. 2 is a flowchart of a massive MIMO-assisted heterogeneous cloud wireless access network beamforming method.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
It should be emphasized that in describing the present invention, various formulas and constraints are identified with consistent labels, but the use of different labels to identify the same formula and/or constraint is not precluded and is provided for the purpose of more clearly illustrating the features of the present invention.
As shown in fig. 1 and fig. 2, the present invention provides a heterogeneous cloud radio access network based on massive MIMO assistance. The communication network comprises a baseband processing unit pool, aMacro Base Station (Macro Base Station MBS), N Radio Remote Heads (RRHs), K Macro cellular users (Macro cellular users are denoted by MU), and J RRH users (RRH users are denoted by RU). MBS provides wide-area wireless signal coverage, while RRHs are primarily responsible for wireless signal coverage in hot spot areas or edge areas. MBS and RRHs provide communication services for MUs and RUs, respectively. MUs and RUs denote MU and RU, respectively. Suppose that the MBS is provided with a large-scale antenna array with the number of antennas TM(TM can take on hundreds of values) and RRH is provided with TRRoot antenna (T)M>>TR) Wherein T isMIndicating the number of antennas, T, with which the MBS is equippedRIndicating the number of antennas provided for the RRH, and one antenna for all users, as shown in fig. 1. Let N ═ {1,2, …, N } denote the set of all RRHs, K ═ {1,2, …, K } denotes the set of all MUs, J ═ {1,2, …, J } denotes the set of all RUs, and RRHs denote several RRHs.
Order toFor the beamforming vector of MBS to MU k,the signal for MU k is sent for MBS,is the channel vector between MBS and MU k. Then, the signal received by MU k is:whereinIs an interference channel vector between RRH n and MU k, where n represents the index number of the RRH,a beamforming vector for RRH N versus RU j, N representing the total number of RRHs,for signals sent by the RRHs to RU j,for received noise, CN (0,1) denotes a complex Gaussian distribution obeying a mean vector of 0 and a covariance of 1, C denotes a complex field, (g)TIndicating transposition. Wherein,represents TMA column vector of dimensions;represents TRA row vector of dimensions; the data transmission rate of MU k is:
the signal received by RU j is:wherein, is the channel vector between RRH n and RU j,for the interfering channel vector between MBS and RU j,is the received noise. Then, the data transmission rate for RU j is:
therefore, the total data transmission rate of RUs and MUs in the heterogeneous cloud wireless access network is:
the total power consumption of RRHs and MBS in the heterogeneous cloud wireless access network is as follows:
then, the total energy efficiency of the heterogeneous cloud wireless access network is:
the MBS and RRHs joint beamforming problem in the heterogeneous cloud wireless access network can be expressed as:
wherein P isRRHAnd PMBSRespectively the maximum transmitting power threshold values of all RRHs and MBS; j represents the index number of the RU; k andkall represent the index number of the MU, wherein k ≠k。
Before solving the optimization problem (6) or (a), the following differential optimization problem is introduced:
s.t.(6b),(6c) (7b)
order toAndto optimize the optimal solution of problem (6) or (A),according to the theory of non-linear programming, if and only if
When true, optimization problem (6) or (a) and optimization problem (7) or (C) have the same solution. Therefore, solving the optimization problem (6) or (a) can be converted into solving the optimization problem (7) or (C).
The objective function of the optimization problem (7) or (C) is non-convex, and auxiliary variables are introduced to facilitate solutionThe optimization problem (7) or (C) may be equivalent to the following optimization problem:
s.t.(6b),(6c) (8f)
in the optimization problem (8) or (B), other constraints and objective functions are convex except for the constraints (8B) and (8 d). Constraints (8b) and (8d) are processed below to define functions g (x, y) xy andwherein f (x, y) ≧ g (x, y). It is apparent that f (x, y) is a convex function whenWhen f (x, y) is g (x, y). Based on the above analysis, constraints (8b) and (8d) can be approximated as:
from the above analysis, the optimization problem (7) can be finally equivalent to the following optimization problem, namely:
s.t.(6b),(6c),(8c),(8e),(9),(10) (11b)
as shown in fig. 2, the specific steps of solving the original optimization problem (6) or (a) by the beam forming method provided by the present scheme are as follows:
Step 6, repeating the steps 2-5 until the convergence lambda is converged to obtain the optimal solutionAndrespectively represents wjAnd vkAnd (4) corresponding optimal solution.
Wherein the ratio of lambda to lambda is,to introduce an auxiliary variable.Is introduced for approximating the non-convex constraint (7) as a convex constraint (5);is to be measured byThe convex constraint (8) is introduced approximately for the convex constraint (6); λ is introduced to transform the objective function in the form of a fraction in the optimization problem a into the objective function in the form of a difference in the optimization problem C.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the present invention.
Claims (10)
1. A beam forming method of a heterogeneous cloud wireless access network is characterized by comprising the following steps:
S3, calculating the total energy efficiency xi of the heterogeneous cloud wireless access network:
wherein,
R(wj,vk) Is the total data transmission rate of RUs and MUs in a heterogeneous cloud wireless access network, the RUs representing a number of RUs, the MUs representing a number of MUs,
P(wj,vk) The total power consumption of a radio remote radio unit RRH and a macro base station MBS in the heterogeneous cloud wireless access network is calculated;
s4, determining the beamforming vector joint optimization problem of MBS and RRHs, wherein the optimization problem is expressed as:
wherein, PRRHAnd PMBSRespectively the maximum transmitting power threshold values of all RRHs and MBS, wherein the RRHs represent a plurality of RRHs;
s5, solving the beam forming problem, and equating the optimization problem in the step S4 as the following convex optimization problem:
constraint (1), constraint (2) and
wherein j represents the index number of the RU; k andkall represent index numbers of the MU, where k ≠k(ii) a J watchShowing the total number of RUs; k represents the total number of MU; w is ajRepresenting a beam formed by N beamforming vectors wn,jThe formed cumulative beamforming vectors are then used to form,a beamforming vector for RRH n versus RU j, where n represents the index number of the RRH;beamforming vector, v, for MBS to MU k k For MBS to MUkA beamforming vector;is interference channel vector between MBS and RU j;is the channel vector between MBS and MU k;representing a channel consisting of N channel vectors hn,jThe accumulated channel vector of the component(s),is the channel vector between RRH n and RU j;representing a channel consisting of N channel vectors gn,kThe accumulated channel vector of the component(s),is an interference channel vector between RRH n and MU k;represents TMA column vector of dimensions;represents TRA row vector of dimensions, where TMIndicating the number of antennas, T, with which the MBS is equippedRRepresenting the number of antennas provided for the RRH; the number of the lambda-beams is increased,to introduce an auxiliary variable.
5. the beamforming method according to any of claims 1-4, wherein: the steps for solving the optimization problem (B) are as follows:
6. A large-scale MIMO-assisted heterogeneous cloud wireless access network beam forming method is characterized by comprising the following steps:
s1, calculating a data transmission rate of the MU, where the data transmission rate of the MU k is:
wherein j represents the index number of the RU; k andkall represent index numbers of the MU, where k ≠k,Beamforming vector, v, for MBS to MU k k For MBS to MUkThe number of the beamforming vectors is determined,for the channel vector between MBS and MU k, is an interference channel vector between RRH n and MU k, where n represents the index number of the RRH, beamforming vector for RRH N to RU J, J denotes the total number of RUs, K denotes the total number of MUs, N denotes the total number of RRHs, MU K, where K ∈ K, C denotes the complex field, (g)TRepresenting a transpose; the MU is a macro cellular user, the MUs are a plurality of macro cellular users, the MBS is a macro base station, and the RRH is a wireless remote radio frequency unit;
s2, calculating a data transmission rate of RU, the data transmission rate of RU j being:
wherein, is the channel vector between RRH n and RU j,is an interference channel vector between MBS and RU J, wherein J belongs to J; the RUs are wireless remote radio frequency unit users, and the RUs are a plurality of wireless remote radio frequency unit users;
s3, calculating the total energy efficiency of the heterogeneous cloud wireless access network, wherein the total data transmission rate of the RUs and MUs in the heterogeneous cloud wireless access network is as follows:
the total power consumption of the RRH and the MBS in the heterogeneous cloud wireless access network is as follows:
then, the total energy efficiency of the heterogeneous cloud wireless access network is:
s4, determining a beamforming vector joint optimization problem of MBS and RRHs, wherein the large-scale MIMO-assisted heterogeneous cloud wireless access network beamforming vector joint optimization problem can be expressed as:
wherein, PRRHAnd PMBSRespectively the maximum transmitting power threshold values of all RRHs and MBS; the RRHs are a plurality of RRHs;
s5, solving the beam forming problem, converting the solving of the non-convex and non-linear optimization problem (6) into the solving of the following optimization problem (7)
s.t.(6b),(6c) (7b)
The objective function in the optimization problem (7) is non-convex, and auxiliary variables are introduced for solving conveniently The optimization problem (7) can be equivalent to the following optimization problem:
s.t.(6b),(6c) (8f)
in the optimization problem (8), the objective function and constraints (6b), (6c), (8c), (8e) are all convex, while constraints (8b) and (8d) are non-convex, and constraints (8b) and (8d) can be approximated as convex constraints as follows:
from the above analysis, the optimization problem (7) can be finally equivalent to the following convex optimization problem, namely:
s.t.(6b),(6c),(8c),(8e),(9),(10)(11b)
wherein,kindex number, w, representing MUjRepresenting a beam formed by N beamforming vectors wn,jForming a cumulative beamforming vector;beamforming vectors for MBS to MU k; MBS with large scale antenna array, TMIndicating the number of antennas, T, with which the MBS is equippedRRepresenting the number of antennas provided for the RRH;is interference channel vector between MBS and RU j;is the channel vector between MBS and MU k;representing a channel consisting of N channel vectors hn,jA constituent accumulated channel vector; the number of the lambda-beams is increased,to introduce an auxiliary variable.
7. The beamforming method according to claim 6, wherein: the steps for solving the original optimization problem (6) are as follows:
8. A communication network comprising a pool of baseband processing units, a macro base station MBS, a radio remote unit RRH, a plurality of macro-cellular users and a plurality of RRH users, the MBS providing wide area radio signal coverage, the RRH providing radio signal coverage in hot spot or edge areas, the MBS being equipped with a large scale antenna array, the macro-cellular users and the RRH users being equipped with at least one antenna, characterized in that: the communication network may implement the beamforming method of any of claims 1-4 or claims 6-7.
9. The communication network of claim 8, wherein: MBS is provided with large-scale antenna array with the number of antennas being TMRRH is provided with TRA root antenna, wherein TM>>TR。
10. The communication network of claim 9, wherein: the number of antennas TMNot less than one hundred.
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