CN103188002B - A kind of multi-antenna multi-user distributed system beamforming strategy - Google Patents

A kind of multi-antenna multi-user distributed system beamforming strategy Download PDF

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CN103188002B
CN103188002B CN201310025501.0A CN201310025501A CN103188002B CN 103188002 B CN103188002 B CN 103188002B CN 201310025501 A CN201310025501 A CN 201310025501A CN 103188002 B CN103188002 B CN 103188002B
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user
beamforming
edge customer
optimal
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CN103188002A (en
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赵睿
李菊芳
郭荣新
林志丕
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Weihai High Tech Park Operation Management Co ltd
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XIAMEN LANDI ELECTRONIC TECHNOLOGY Co Ltd
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Abstract

A kind of multi-antenna multi-user distributed collaboration beamforming strategy, first base station A selects maximum one to serve from the received signal to noise ratio of service area inward flange user feedback, namely selects optimal user i *; Then user i *from random code book, a best code book f is selected according to minimum distance criterion 1, opt, and its label is fed back to base station A, user i subsequently *estimate the channel condition information between itself and base station B, and determine the beamforming vectors f making the minimized base station B of inter-cell interference 2, optand fed back to base station A; Base station A is by optimal user i *the beamforming vectors information of the base station B sent sends to base station B; Last base station B selects a best user j from the Signal to Interference plus Noise Ratio of user feedback in service area *serve.Effectively can make minimum interference, the burden that between base station, control information is mutual can be alleviated simultaneously, obtain obvious performance gain with lower complexity.

Description

A kind of multi-antenna multi-user distributed system beamforming strategy
Technical field
The present invention relates to mobile cellular communication system regions, particularly a kind of multi-antenna multi-user distributed collaboration beamforming strategy.
Background technology
In the transmission of traditional network MIMO, multiple collaborative base station is mutually shared data message and is formed a super base station, now interference channel just changes MIMO broadcast channel into, can eliminate inter-cell interference completely, and this technology becomes " Combined Treatment (JP) ".Between cooperative base station, use dirty paper code (DPC) to carry out JP, the theoretical maximum capacity of many honeycombs MIMO down link can be obtained; Except DPC, the game theory optimisation technique that close-to zero beam is shaped and dispatches based on time-frequency, also can be used for eliminating ICI in many cellular transmission system; Linear block diagonalization eliminates ICI by utilizing linear zero-forcing technique to construct base station to the block diagonal effective channel of user.But above JP strategy, need reliable high-speed backbone to connect for shared data message and channel condition information (CSI), computation complexity is higher, is difficult in systems in practice realize.
In addition, MIMO technology also brings serious presence of intercell interference while raising spectrum efficiency, because user in cellular cell is by shadow fading in wireless channel, multipath fading, the difference of the factor influence degrees such as interference, in community, user communication quality can be different, generally speaking, in cellular cell away from the user communication quality of base station not as good as the user near base station, the signal being particularly in cell edge nearby users also can be subject to the interference from its neighbor cell signal, its serious interference have impact on user communication quality, even degrading communication environment, so for edge customer, how to reduce presence of intercell interference, improve the focus that its speed becomes research.
Summary of the invention
Main purpose of the present invention is to overcome edge customer communication quality in prior art cellular cell not as good as the user near base station, and also can be subject to the shortcoming of the interference from its neighbor cell signal, propose a kind of communication quality improving edge cell user, reduce the multi-antenna multi-user distributed collaboration beamforming strategy of neighbor cell signal interference.
The present invention adopts following technical scheme:
A kind of multi-antenna multi-user distributed collaboration beamforming strategy, for in adjacent two cellular cells of multi-user's multiple-input-multiple-output communication system, these adjacent two honeycombs are equipped with a multi-antenna base station and multiple edge customer, set this two base station and be respectively base station A and base station B, be in the user of this base station A and base station B coverage edge for edge customer, it is characterized in that: each base station all configures N troot antenna, each use configure N per family rroot antenna, edge customer, base station A and base station B all use identical random code book, and the code word of code book is beamforming vectors, specifically comprises the steps:
1) base station A sends any one beamforming vectors, and pilot signal transmitted; Edge customer Received signal strength also feeds back himself information, and base station A selects optimal user i according to edge customer feedack *serve;
2) optimal user i *from its random code book, select optimal codes determine the beamforming vectors information of base station A and feed back to base station A; Then, optimal user i *estimate channel status between itself and base station B and determine the beamforming vectors information of the base station B making two inter-cell interference minimum and fed back to base station A;
3) base station A is by optimal user i *the beamforming vectors information of the base station B sent sends to base station B;
4) base station B sends beamforming vectors, and pilot signal transmitted; Edge customer Received signal strength also feeds back himself information, and the information of base station B estimated edge user feedback selects optimal user j *serve.
Further, in step 1) in, edge customer Received signal strength also estimates its channel matrix H iand signal to noise ratio, adopt MRC to merge and maximize its received signal to noise ratio, the signal to noise ratio of self is fed back to base station A.
Further, in step 1) in, base station A chooses the maximum user of signal to noise ratio of edge customer feedback as optimal user i *serve.
Further, edge customer, base station A and base station B all use identical random code book, and this random code book is that random vector quantizes code book, is the N of 1 by N number of mould tdimension unit vector composition, N is code word number, and the code word in code book is beamforming vectors n ∈ [1, N].
Further, in step 2) in, this optimal user i *from its random code book, a code word and beamforming vectors f is selected according to minimal distance principle 1, opt, make this beamforming vectors f 1, optwith channel matrix vector H ithe modulus value of product is maximum, and this code word is optimal codes.
Further, edge customer, base station A and base station B all carry out Unified number to the code word of random code book, code fetch in this every column vector label be the numbering of numbering also i.e. this code word of this column vector, in step 2) edge customer i *numbering corresponding for described optimal codes is sent to base station A.
Further, in step 2) middle optimal user i *estimate the channel matrix of itself and base station B and according to this channel matrix with optimal user i in the A of base station *mRC merge vector determine the beamforming vectors f of the base station B making interference minimum 2, opt, specific formula for calculation is and optimal user i *also by this beamforming vectors f 2, optthe numbering of corresponding code word sends to base station A.
Further, in step 3) in base station A by the beamforming vectors f of base station B 2, optthe numbering of corresponding code word sends to base station B.
Further, in step 4) middle base station B transmission beamforming vectors f 2, optand pilot signal, edge customer Received signal strength also estimates its channel matrix H jand signal to noise ratio, adopt MRC to merge and maximize its received signal to noise ratio, the signal to noise ratio of self is fed back to base station B, base station B chooses the maximum user of signal to noise ratio of edge customer feedback as optimal user j *serve
From the above-mentioned description of this invention, compared with prior art, the present invention has following beneficial effect:
The invention provides a kind of low complex degree distributed collaboration beamforming strategy, effectively can make minimum interference, the burden that between base station, control information is mutual can be alleviated simultaneously, obtain obvious performance gain with lower complexity.The rate capability of transmission policy of the present invention is better than the known transmission policy with disturbing unknown beam forming of existing interference, works as N t=N r, when number of users is 20 in number of users and honeycomb 2 in honeycomb 1, when number of bits of feedback is 5, there is the rate capability gain of about 2bps=2, δ=0.5 compared with the known beamforming strategy of interference.Thus can find out, transmission policy of the present invention obtains obvious performance gain with lower complexity.When fixing number of bits of feedback is 4, when in honeycomb 1, in number of users and honeycomb 2, number of users is 20, δ=0.5, the speed of transmission policy of the present invention is along with N t, N rincreasing and increase, more obvious when when concurrent present antenna number is less, the increase of antenna number is more more than antenna number to the lifting of performance.Work as fix N t=N rduring=2, δ=0.5, its speed increases along with increasing of number of users, and multi-user diversity gain is obvious, and speed increases along with the increase of number of bits of feedback.
Accompanying drawing explanation
Fig. 1 is the schematic diagram of adjacent two honeycombs of the present invention;
Fig. 2 is fundamental diagram of the present invention;
Fig. 3 is the experiment effect figure that transmission policy of the present invention and other transmission policys contrast;
Fig. 4 is that the present invention gets different N t, N rthe experiment effect figure of value;
Fig. 5 is transmission policy of the present invention and other transmission policys experiment effect figure under different user number.
Embodiment
Below by way of embodiment, the invention will be further described.
With reference to Fig. 1, Fig. 2, a kind of multi-antenna multi-user distributed collaboration beamforming strategy, for in adjacent two cellular cells of multi-user's multiple-input-multiple-output communication system, these adjacent two honeycombs are honeycomb 1 and honeycomb 2, this two honeycomb is equipped with a multi-antenna base station and multiple edge customer, each base station at one time/frequency resource in service a user.Set this two base station and be respectively base station A and base station B, be in the user of this base station A and base station B coverage edge for edge customer, each base station all configures N troot antenna, each use configure N per family rroot antenna, edge customer, base station A and base station B all use identical random code book, and this random code book is that random vector quantizes code book, and the code word of code book is beamforming vectors be the N of 1 by N number of mould tdimension unit vector composition, N is code word number, n ∈ [1, N].Edge customer, base station A and base station B all carry out Unified number to the code word of random code book, code fetch in this every column vector label be the numbering of numbering also i.e. this code word of this column vector.
Specifically comprise the steps:
1) base station A sends any one beamforming vectors, and pilot signal transmitted; Edge customer Received signal strength also estimates its channel matrix H iand signal to noise ratio, adopt MRC to merge and maximize its received signal to noise ratio, the signal to noise ratio of self is fed back to base station A, choose the maximum user of signal to noise ratio of edge customer feedback as optimal user i *serve.
2) this optimal user i *from its random code book, a code word and beamforming vectors f is selected according to minimal distance principle 1, opt, make this beamforming vectors f 1, optwith channel matrix vector H ithe modulus value of product is maximum, and this code word is optimal codes, and concrete formula is edge customer i *numbering corresponding for this optimal codes is sent to base station A.Also there is additive method and select code word, but the present invention adopts minimum distance criterion mode.
Then, optimal user i *estimate the channel matrix of itself and base station B and according to this channel matrix with optimal user i in the A of base station *mRC merge vector determine the beamforming vectors f of the base station B making interference minimum 2, opt, specific formula for calculation is and optimal user i *also by this beamforming vectors f 2, optthe numbering of corresponding code word sends to base station A.
Concrete, f 2 , opt = arg min f ∈ { f ‾ n } n = 1 N | W i * H G i * f | 2 = arg min f ∈ { f ‾ n } n = 1 N | u i * 1 H G i * f | 2 , Wherein optimal service user i in the A of base station *mRC merge vector, we are to user i *channel matrix carry out SVD decomposition: wherein for corresponding to the left singular vector of eigenvalue of maximum, so W i * = u i * 1 , Corresponding equivalent channel is h i * H = W i * H H i * , h i * ∈ C N t × 1 .
3) base station A is by the beamforming vectors f of base station B 2, optthe numbering of corresponding code word sends to base station B.
4) base station B sends beamforming vectors f 2, optand pilot signal, edge customer Received signal strength also estimates its channel matrix H jand signal to noise ratio, adopt MRC to merge and maximize its received signal to noise ratio, the signal to noise ratio of self is fed back to base station B, base station B chooses the maximum user of signal to noise ratio of edge customer feedback as optimal user j *serve.
In like manner, optimal user j *estimate the channel matrix of itself and base station A and according to this channel matrix with optimal user j in the B of base station *mRC merge vector determine the beamforming vectors of the base station A making interference minimum, and codeword number corresponding for this beamforming vectors is sent to base station B, this codeword number is sent to base station A by base station B.
The rate capability of transmission policy of the present invention is better than the known transmission policy with disturbing unknown beam forming of existing interference, with reference to Fig. 3, works as N t=N r, when number of users is 20 in number of users and base station B in the A of base station, when number of bits of feedback is 5, there is the rate capability gain of about 2bps=2, δ=0.5 compared with the known beamforming strategy of interference.Thus can find out, transmission policy of the present invention obtains obvious performance gain with lower complexity.With reference to Fig. 4, when fixing number of bits of feedback is 4, when in the A of base station, in number of users and base station B, number of users is 20, δ=0.5, the speed of transmission policy of the present invention is along with N t, N rincreasing and increase, more obvious when when concurrent present antenna number is less, the increase of antenna number is more more than antenna number to the lifting of performance.With reference to Fig. 5, honeycomb 1 i.e. base station A institute coverage, and honeycomb 2 i.e. base station B institute coverage, works as fix N t=N rduring=2, δ=0.5, its speed increases along with increasing of number of users, and multi-user diversity gain is obvious, and speed increases along with the increase of number of bits of feedback.
Above are only a specific embodiment of the present invention, but design concept of the present invention is not limited thereto, all changes utilizing this design the present invention to be carried out to unsubstantiality, all should belong to the behavior of invading scope.

Claims (9)

1. a multi-antenna multi-user distributed collaboration beamforming strategy, for in adjacent two cellular cells of multi-user's multiple-input-multiple-output communication system, these adjacent two honeycombs are equipped with a multi-antenna base station and multiple edge customer, set this two base station and be respectively base station A and base station B, be in the user of this base station A and base station B coverage edge for edge customer, it is characterized in that: each base station all configures N troot antenna, each use configure N per family rroot antenna, edge customer, base station A and base station B all use identical random code book, and the code word of code book is beamforming vectors, specifically comprises the steps:
1) base station A sends any one beamforming vectors, and pilot signal transmitted; Edge customer Received signal strength also feeds back himself information, and base station A selects optimal user i according to edge customer feedack *serve;
2) optimal user i *from its random code book, select optimal codes determine the beamforming vectors information of base station A and feed back to base station A; Then, optimal user i *estimate channel status between itself and base station B and determine the beamforming vectors information of the base station B making two inter-cell interference minimum and fed back to base station A;
3) base station A is by optimal user i *the beamforming vectors information of the base station B sent sends to base station B;
4) base station B sends beamforming vectors, and pilot signal transmitted; Edge customer Received signal strength also feeds back himself information, and the information of base station B estimated edge user feedback selects optimal user j *serve.
2. a kind of multi-antenna multi-user distributed collaboration beamforming strategy as claimed in claim 1, is characterized in that: in step 1) in, edge customer Received signal strength also estimates its channel matrix H iand signal to noise ratio, adopt MRC to merge and maximize its received signal to noise ratio, the signal to noise ratio of self is fed back to base station A.
3. a kind of multi-antenna multi-user distributed collaboration beamforming strategy as claimed in claim 2, is characterized in that: in step 1) in, base station A chooses the maximum user of signal to noise ratio of edge customer feedback as optimal user i *serve.
4. a kind of multi-antenna multi-user distributed collaboration beamforming strategy as claimed in claim 3, is characterized in that: edge customer, base station A and base station B all use identical random code book, and this random code book is that random vector quantizes code book, is the N of 1 by N number of mould tdimension unit vector composition, N is code word number, and the code word in code book is beamforming vectors n ∈ [1, N].
5. a kind of multi-antenna multi-user distributed collaboration beamforming strategy as claimed in claim 4, is characterized in that: in step 2) in, this optimal user i *from its random code book, a code word and beamforming vectors f is selected according to minimal distance principle 1, opt, make this beamforming vectors f 1, optwith channel matrix vector H ithe modulus value of product is maximum, and this code word is optimal codes.
6. a kind of multi-antenna multi-user distributed collaboration beamforming strategy as claimed in claim 5, it is characterized in that: edge customer, base station A and base station B all carry out Unified number to the code word of random code book, code fetch in this every column vector label be the numbering of numbering also i.e. this code word of this column vector, in step 2) edge customer i *numbering corresponding for described optimal codes is sent to base station A.
7. a kind of multi-antenna multi-user distributed collaboration beamforming strategy as claimed in claim 6, is characterized in that: in step 2) middle optimal user i *estimate the channel matrix G of itself and base station B i*, and according to this channel matrix G i*with optimal user i in the A of base station *mRC merge vector determine the beamforming vectors f of the base station B making interference minimum 2, opt, specific formula for calculation is and optimal user i *also by this beamforming vectors f 2, optthe numbering of corresponding code word sends to base station A.
8. a kind of multi-antenna multi-user distributed collaboration beamforming strategy as claimed in claim 7, is characterized in that: in step 3) in base station A by the beamforming vectors f of base station B 2, optthe numbering of corresponding code word sends to base station B.
9. a kind of multi-antenna multi-user distributed collaboration beamforming strategy as claimed in claim 8, is characterized in that: in step 4) middle base station B transmission beamforming vectors f 2, optand pilot signal, edge customer Received signal strength also estimates its channel matrix H jand signal to noise ratio, adopt MRC to merge and maximize its received signal to noise ratio, the signal to noise ratio of self is fed back to base station B, base station B chooses the maximum user of signal to noise ratio of edge customer feedback as optimal user j *serve.
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CN102545987A (en) * 2012-01-16 2012-07-04 东南大学 Multicell self-adaption cooperative transmission method on basis of delayed feedback

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CN102075959A (en) * 2011-01-07 2011-05-25 西安电子科技大学 Coordinated beamforming method under CoMP in LTE-A system
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