CN103957041A - 3D wave beam shaping method for large-scale MIMO TDD system - Google Patents

Abstract

The invention discloses a 3D wave beam shaping method for a large-scale MIMO TDD system. 3D wave beam shaping is a direct product of horizontal wave beam shaping and vertical wave beam shaping; wave beam shaping in a horizontal direction and wave beam shaping in a vertical dimension are double-layer precoding and are formed by DFT pre-wave beam shaping and SLNR precoding; according to channel sparsity, the DFT pre-wave beam shaping divides a cell into a plurality of annular areas in the vertical dimension and into a plurality of sub-sectors in the horizontal dimension; and according to counted CSIT, distributing users into groups by use of a separating user packet algorithm. The users disposed in the non-neighboring groups of the same sub-sector can multiplex a pilot frequency, the users disposed in the non-neighboring groups of the same annular areas can also multiplex the pilot frequency, and the SLNR precoding is obtained based on low-order instantaneous equivalent CSIT. The advantages are as follows: the calculating complexity is quite low, at the same time the pilot frequency cost is reduced, the reduced calculating complexity and the reduced pilot frequency cost are dependent on segmented group numbers, and when there are too many base station antennas, the method and rate provided by the invention are approximate to a non-packet system.

Description

3D beam-forming method towards extensive MIMO TDD system

Technical field

The present invention relates to a kind of low complex degree towards extensive MIMO(multiple-input and multiple-output) TDD(time division duplex) 3D(of system is three-dimensional) beam-forming method, belong to wireless communication technology.

Background technology

Extensive MIMO is the key technology that realizes next generation mobile communication.But the horizontal dimensions in base station configures too much antenna to be difficult for realizing, and proposes thus 3D MIMO.3D MIMO has overcome this restriction of base station, and antenna is placed in two-dimentional plane.The antenna for base station of vertical dimensions forms vertical beam, and the more degree of freedom can be provided.Vertical beam and horizontal beam have formed 3D wave beam jointly.3D wave beam can be identified the user with par angle.What in pertinent literature, propose at present is applicable to 3D MIMO FDD system based on the long-pending code book of Kronecker.Yet, in this system, can only call 2 users at every turn, do not give full play to the strong point of 3D mimo system.

In order effectively to realize 3D beam forming, CSIT accurately need to be known in base station.In FDD system, instantaneous CSIT obtains by user feedback to base station.And the needed descending training burden of FDD system is proportional to antenna for base station number.When antenna for base station number is a lot, utilize FDD to estimate that CSIT will become difficult.Therefore, extensive MIMO is more prone to obtain CSIT with tdd mode, and the pilot-frequency expense of tdd mode is proportional to number of users, and in extensive mimo system, this pilot-frequency expense is much smaller than fdd mode.Yet 3D mimo system is mainly benefited from multi-user diversity gain, the pilot-frequency expense that therefore reduces the extensive MIMO TDD of 3D system is also very necessary.

Summary of the invention

Goal of the invention: in order to overcome the deficiencies in the prior art, the invention provides a kind of 3D beam-forming method towards extensive MIMO TDD system of low complex degree, be the 3D wave beam formation scheme under a kind of a kind of extensive MIMO based on the sparse property design of channel, can effectively reduce computation complexity and pilot-frequency expense.

Technical scheme: for achieving the above object, the technical solution used in the present invention is:

Towards the 3D beam-forming method of extensive MIMO TDD system, the 3D wave beam carrying out under extensive MIMO based on the sparse property of channel forms, and comprises the steps:

Step 1, utilizing the sparse property of channel, DFT(discrete Fourier transform) switched-beam is divided into community in the horizontal direction some sub-sectors, is divided into some annular regions in the vertical direction;

Step 2,

Adopt separated user grouping algorithm to carry out three-dimensional grouping to user, first user is assigned in the annular region of vertical dimensions, the user in each annular region of vertical dimensions is assigned in the sub-sector of corresponding horizontal dimensions, the code name of user's place group consists of jointly the code name of annular region and the code name of sub-sector again; User in non-adjacent group in same sub-sector can multiplexed pilot, and the user in non-adjacent group in same annular region also can multiplexed pilot, and base station obtains CSIT;

Step 3, at every group with indoor, adopt SLNR(letter to leak the ratio of making an uproar) maximum tactful dispatched users;

Step 4, in conjunction with the DFT switched-beam of each group, first obtain the instantaneous equivalent CSIT of low order, then calculate SLNR precoding, when calculating SLNR precoding, consider in group and disturb between interference and adjacent set;

Step 5,3D beam forming scheme are the direct products of horizontal and vertical beam forming, and the beam forming of horizontal and vertical dimension is double-deck precoding, DFT switched-beam and SLNR precoding, consist of.

Further, described base station is positioned at the center of community, serves a plurality of users, and described user is single antenna user; The projecting depth of building in position of base station, user is positioned in the plane of street; Antenna for base station is the Rectangular array antenna of N * M dimension, every row configuration M root antenna element, and total N is capable;

In described step 1, community is divided in the horizontal direction to G sub-sector, is divided into L annular region in the vertical direction, the DFT switched-beam of the horizontal dimensions of each group is B _g,H=F _h[:, (g-1) r _h+ (1:r _h)], (g=1 ..., G), F wherein _h∈ for DFT matrix, the DFT switched-beam of the vertical dimensions of each group is B _l,V=F _v[:, (l-1) r _v+ (1:r _v)], (l=1 ..., L), F wherein _v∈ for DFT matrix,

Further, in described step 2, in note community, all numbers of users are K, and separated user grouping algorithm carries out three-dimensional grouping to user, specifically describes to be:

Step2-1: for l=1 ..., L, is positioned at all users' of annular region l set for l=1 ..., L, g=1 ..., G, all users that are positioned at annular region l, sub-sector g are designated as (l, g) group user, (l, g) group user's set

Step2-2: for user k=1 ..., K, to horizontal and vertical channel covariance matrix R _k,Hand R _k,Vcarry out Eigenvalues Decomposition, obtain U _k,Hand U _k,V;

Step2-3: for user k=1 ..., K, find annular region l, $l=\arg \underset{l^{\′}}{\min }{\left|\right|U_{k,V}{U}_{k,V}^H-B_{l^{\′},V}{B}_{l^{\′},V}^H\left|\right|}_F^2,$ User k is joined to set in,

Step2-4: l=1 ..., L, finds sub-sector g, $g=\arg \underset{g^{\′}}{\min }{\left|\right|U_{t,H}{U}_{t,H}^H-B_{g^{\′},H}{B}_{g^{\′},H}^H\left|\right|}_F^2,$ User t is joined to set in,

Further, in described step 3, while adopting the maximum tactful dispatched users of SLNR, making the dispatched users number of (l, g) group is S _l,g=min (r _h, r _v), if scheduled user's number of (l, g) group is K _l,g≤ min (r _h, r _v), all scheduling; This dispatching algorithm specifically describes:

Step3-1: initialization if K _l,g≤ min (r _h, r _v), exit dispatching algorithm simultaneously, otherwise carry out Step3-2;

Step3-2: each user in searching loop (l, g) group, solve the SLNR of each user to other users, for user k=1 ..., K _l,g, ${\tilde{h}}_{{(l,g)}_k}=[h_{{(l,g)}_1},...,h_{{(l,g)}_{k-1}},h_{{(l,g)}_{k+1}},...,h_{{(l,g)}_{K_{l,g}}}]$ Expression except extended channel matrices, calculate ${\mathrm{SLNR}}_{{(l,g)}_k}={\mathrm{\λ}}_{\max }\left[{(\frac{1}{\mathrm{\ρ}}I+{\tilde{h}}_{{(l,g)}_k}{\tilde{h}}_{{(l,g)}_k}^H)}^{-1}{h}_{{(l,g)}_k}{h}_{{(l,g)}_k}^H\right],$ Wherein ρ is the tolerance of signal to noise ratio snr, is proportional to transmitted power divided by noise variance, λ _max(A) eigenvalue of maximum of representing matrix A; Carry out Step3-3;

Step3-3: all users' to be selected SLNR is carried out to descending, export all users' to be selected descending situation:

[SLNR_value, Index]=sort (SLNR, ' descend'), wherein SLNR_value represents the array forming by the SLNR value of descending, and Index represents corresponding User ID, and sort is sorting operation, ' descend' represents descending; Carry out Step3-4;

Step3-4: the front S that selects SLNR maximum _l,gindividual user, as service object, obtains final user's collection of selecting

Further, in described step 4:

S the user's of (l, g) group horizontal SLNR precoding is:

$p_{{(l,g)}_s,H}={\mathrm{\δ}}_{{(l,g)}_s,H}{(\frac{1}{\mathrm{\ρ}}I+B_{g,H}^H{\tilde{h}}_{{(l,g)}_s,H}{\tilde{h}}_{{(l.g)}_s,H}^H{B}_{g,H}+B_{g,H}^H{\tilde{H}}_{(l,g).H}{\tilde{H}}_{(l,g),H}^H{B}_{g,H})}^{-1}{B}_{g,H}^H{h}_{{(l.g)}_s}H$

Wherein: ${\tilde{h}}_{{(l,g)}_s,H}=[h_{{(l,g)}_1,H},...,h_{{(l,g)}_{s-1},H},h_{{(l,g)}_{s+1},H},...,h_{{(l,g)}_{S_{l,g}},H}]$ Be except expansion horizontal channel matrix; it is the expansion horizontal channel matrix of adjacent sub-sector; be the power normalization factor, meet

S the user's of (l, g) group vertical SLNR precoding is:

$p_{{(l,g)}_s,V}={\mathrm{\δ}}_{{(l,g)}_s,V}{(\frac{1}{\mathrm{\ρ}}I+B_{g,V}^H{\tilde{h}}_{{(l,g)}_s,V}{\tilde{h}}_{{(l,g)}_s,V}^H+B_{g,V}+B_{g,V}^H{\tilde{H}}_{(l,g),V}{\tilde{H}}_{(l,g),V}^H{B}_{g,V})}^{-1}{B}_{g,V}^H{h}_{{(l,g)}_s,V}$

Wherein: ${\tilde{h}}_{{(l,g)}_s,V}=[h_{{(l,g)}_1,V}{,...,h}_{{(l,g)}_{s-1},V},h_{{(l,g)}_{s+1},V}{,...,h}_{{(l,g)}_{S_{l,g}},V}]$ Be except expansion vertical channel matrix, it is the expansion vertical channel matrix in adjacent annular region; be the power normalization factor, meet

Further, in described step 5: the 3D beam forming of base station transmitting is $x=\underset{l=1}{\overset{L}{\mathrm{\Σ}}}\underset{g=1}{\overset{G}{\mathrm{\Σ}}}\underset{s=1}{\overset{S_{l,g}}{\mathrm{\Σ}}}[\left(B_{g,H}{p}_{{(l.g)}_s,H}\right)\⊗\left(B_{l,V}{p}_{{(l,g)}_s,V}\right)]d_{{(l,g)}_s,}$ Wherein for sending to s the user's of (l, g) group data.

Beneficial effect: the 3D beam-forming method towards extensive MIMO TDD system provided by the invention, can reduce the complexity of the calculating precoding of extensive MIMO, can save pilot-frequency expense simultaneously.

SLNR precoding is that the instantaneous equivalent channel based on reducing obtains, in calculated level SLNR precoding time, the complexity of matrix inversion is when calculating vertical SLNR precoding the complexity of matrix inversion is the leakage of adjacent group is only considered in SLNR precoding here, and this is because if consider the leakage of all groups, can increase multiplicative complexity; In the system of not dividing into groups, the matrix inversion complexity of horizontal SLNR precoding is O (M ³), the matrix inversion complexity of vertical SLNR precoding is O (N ³); Therefore, the matrix inversion reduced complexity of SLNR precoding of the present invention, the complexity of reduction depends on annular region number and sub-sector number.

S user's of (l, g) group channel covariance matrix is wherein and R respectively user's horizontal and vertical channel covariance matrix, order (l' ≠ l or g' ≠ g) represents s user of (l', g') group, have $R_{{(l,g)}_s}{R}_{{(l^{\text{'}},g^{\text{'}})}_s}=(R_{{(l,g)}_s}\⊗R_{{(l,g)}_s})(R_{{(l^{\text{'}},g^{\text{'}})}_s}\⊗R_{{(l^{\text{'}},g^{\text{'}})}_s})=\left(R_{{(l,g)}_s}{R}_{{(l^{\text{'}},g^{\text{'}})}_s}\right)\⊗\left(R_{{(l,g)}_{s,}V}{R}_{{(l^{\text{'}},g^{\text{'}})}_{s,}V}\right)$ ; Therefore as long as the channel covariance matrix product of arbitrary dimension is 0,3D channel covariance matrix is just 0 so, can multiplexed pilot according to known these two users of pertinent literature; As long as the angular range of the arbitrary dimension of user is not overlapping, the eigenmatrix quadrature of their channel covariance matrix so, the product of 3D channel covariance matrix is 0, means that these two users can multiplexed pilot; And user in same sub-sector non-adjacent group, and the angular range of the user in same annular region non-adjacent group is not overlapping, thereby can multiplexed pilot, reduces pilot-frequency expense.

Accompanying drawing explanation

Fig. 1 is module map of the present invention;

Fig. 2 is cell division figure;

Fig. 3 is transmitted power for a change, three kinds of systems with speed comparison diagram;

Fig. 4 is horizontal dimensions and vertical dimensions group number for a change, three kinds of systems with speed comparison diagram;

Fig. 5 is antenna for base station number for a change, three kinds of systems with speed comparison diagram.

Embodiment

Below in conjunction with accompanying drawing, the present invention is further described.

Towards a 3D beam-forming method for extensive MIMO TDD system, base station is positioned to the center of community when arranging, serve a plurality of users, described user is single antenna user; The projecting depth of building in position of base station, user is positioned in the plane of street; Antenna for base station is the Rectangular array antenna of N * M dimension, every row configuration M root antenna element, and total N is capable.

Utilize the sparse property of channel, DFT switched-beam is divided into G sub-sector by community in horizontal dimensions, in vertical dimensions, is divided into L annular region, like this: the DFT switched-beam of the horizontal dimensions of each group is B _g,H=F _h[:, (g-1) r _h+ (1:r _h)], (g=1 ..., G), F wherein _h∈ for DFT matrix, virtual-antenna number for each group of level; The DFT switched-beam of the vertical dimensions of each group is B _l,V=F _v[:, (l-1) r _v+ (1:r _v)], (l=1 ..., L), F wherein _v∈ for DFT matrix, virtual-antenna number for vertical each group.

In note community, all numbers of users are K, adopt separated user grouping algorithm to carry out three-dimensional grouping to user, first user is assigned in the annular region of vertical dimensions, the user in each annular region of vertical dimensions is assigned in the sub-sector of horizontal dimensions, the code name of user's place group consists of jointly the code name of annular region and the code name of sub-sector again; User in non-adjacent group in same sub-sector can multiplexed pilot, and the user in non-adjacent group in same annular region also can multiplexed pilot, and base station obtains CSIT; Specific descriptions are:

Step2-1: for l=1 ..., L, is positioned at all users' of annular region l set ; For l=1 ..., L, g=1 ..., G, all users that are positioned at annular region l, sub-sector g are designated as (l, g) group user, (l, g) group user's set

Step2-2: for user k=1 ..., K, to horizontal and vertical channel covariance matrix R _k,Hand R _k,Vcarry out Eigenvalues Decomposition, obtain U _k,Hand U _k,V;

Step2-3: for user k=1 ..., K, find annular region l, $l=\arg \underset{l^{\′}}{\min }{\left|\right|U_{k,V}{U}_{k,V}^H-B_{l^{\′},V}{B}_{l^{\′},V}^H\left|\right|}_F^2,$ User k is joined to set in,

Step2-4: l=1 ..., L, finds sub-sector g, $g=\arg \underset{g^{\′}}{\min }{\left|\right|U_{t,H}{U}_{t,H}^H-B_{g^{\′},H}{B}_{g^{\′},H}^H\left|\right|}_F^2,$ User t is joined to set in,

At every group, with indoor, adopt the maximum tactful dispatched users of SLNR, making the dispatched users number of (l, g) group is S _l,g=min (r _h, r _v), if scheduled user's number of (l, g) group is K _l,g≤ min (r _h, r _v), all scheduling; This dispatching algorithm specifically describes:

Step3-1: initialization if K _l,g≤ min (r _h, r _v), exit dispatching algorithm simultaneously, otherwise carry out Step3-2;

Step3-2: each user in searching loop (l, g) group, solve the SLNR of each user to other users, for user k=1 ..., K _l,g, ${\tilde{h}}_{{(l,g)}_k}=[h_{{(l,g)}_1},...,h_{{(l,g)}_{k-1}},h_{{(l,g)}_{k+1}},...,h_{{(l,g)}_{K_{l,g}}}]$ Expression except extended channel matrices, calculate ${\mathrm{SLNR}}_{{(l,g)}_k}={\mathrm{\λ}}_{\max }\left[{(\frac{1}{\mathrm{\ρ}}I+{\tilde{h}}_{{(l,g)}_k}{\tilde{h}}_{{(l,g)}_k}^H)}^{-1}{h}_{{(l,g)}_k}{h}_{{(l,g)}_k}^H\right],$ Wherein ρ is the tolerance of signal to noise ratio snr, is proportional to transmitted power divided by noise variance, λ _max(A) eigenvalue of maximum of representing matrix A; Carry out Step3-3;

Step3-3: all users' to be selected SLNR is carried out to descending, export all users' to be selected descending situation:

[SLNR_value, Index]=sort (SLNR, ' descend'), wherein SLNR_value represents the array forming by the SLNR value of descending, and Index represents corresponding User ID, and sort is sorting operation, ' descend' represents descending; Carry out Step3-4;

Step3-4: the front S that selects SLNR maximum _l,gindividual user, as service object, obtains final user's collection of selecting

In conjunction with the DFT switched-beam of each group, first obtain the instantaneous equivalent CSIT of low order, then calculate SLNR precoding, when calculating SLNR precoding, consider in group and disturb between interference and adjacent set; Wherein s the user's of (l, g) group horizontal SLNR precoding is:

$p_{{(l,g)}_s,H}={\mathrm{\δ}}_{{(l,g)}_s,H}{(\frac{1}{\mathrm{\ρ}}I+B_{g,H}^H{\tilde{h}}_{{(l,g)}_s,H}{\tilde{h}}_{{(l.g)}_s,H}^H{B}_{g,H}+B_{g,H}^H{\tilde{H}}_{(l,g).H}{\tilde{H}}_{(l,g),H}^H{B}_{g,H})}^{-1}{B}_{g,H}^H{h}_{{(l.g)}_s}H$

Wherein: ${\tilde{h}}_{{(l,g)}_k}=[h_{{(l,g)}_1},...,h_{{(l,g)}_{k-1}},h_{{(l,g)}_{k+1}},...,h_{{(l,g)}_{K_{l,g}}}]$ Be except expansion horizontal channel matrix; it is the expansion horizontal channel matrix of adjacent sub-sector; be the power normalization factor, meet

S the user's of (l, g) group vertical SLNR precoding is:

$p_{{(l,g)}_s,V}={\mathrm{\δ}}_{{(l,g)}_s,V}{(\frac{1}{\mathrm{\ρ}}I+B_{g,V}^H{\tilde{h}}_{{(l,g)}_s,V}{\tilde{h}}_{{(l,g)}_s,V}^H+B_{g,V}+B_{g,V}^H{\tilde{H}}_{(l,g),V}{\tilde{H}}_{(l,g),V}^H{B}_{g,V})}^{-1}{B}_{g,V}^H{h}_{{(l,g)}_s,V}$

Wherein: ${\tilde{h}}_{{(l,g)}_s,V}=[h_{{(l,g)}_1,V}{,...,h}_{{(l,g)}_{s-1},V},h_{{(l,g)}_{s+1},V}{,...,h}_{{(l,g)}_{S_{l,g}},V}]$ Be except expansion vertical channel matrix, it is the expansion vertical channel matrix in adjacent annular region; be the power normalization factor, meet

3D beam forming scheme is the direct product of horizontal and vertical beam forming, and the beam forming of horizontal and vertical dimension is double-deck precoding, DFT switched-beam and SLNR precoding, consists of; The 3D beam forming of base station transmitting is $x=\underset{l=1}{\overset{L}{\mathrm{\Σ}}}\underset{g=1}{\overset{G}{\mathrm{\Σ}}}\underset{s=1}{\overset{S_{l,g}}{\mathrm{\Σ}}}[\left(B_{g,H}{p}_{{(l.g)}_s,H}\right)\⊗\left(B_{l,V}{p}_{{(l,g)}_s,V}\right)]d_{{(l,g)}_s,}$ Wherein for sending to s the user's of (l, g) group data.

Below the performance comparison of the inventive method and additive method is explained:

That Fig. 3 has compared three kinds of extensive mimo systems of 3D to Fig. 5 and speed.What the sign in analogous diagram " traditional ZF precoding " referred to that the multi-user pre-coding of system uses is to only need the ZF precoding of disturbing except in group; Indicate " SLNR precoding of the present invention " and refer to that the multi-user pre-coding of use is in consideration group and the SLNR precoding of disturbing between adjacent set.Indicate " the not SLNR precoding of grouping system " and refer to that system Bu Dui community divides into groups, each user uses SLNR precoding.Fig. 3 arranges as follows to the simulating scenes of Fig. 5: base station height is 50m.User side antenna is that single antenna receives.User is that 30m is to 519.6m to the distance of base station.User is evenly distributed in the sector of 120 °, has 10 users and waits for scheduling.

Fig. 3 is transmitted power for a change, three kinds of systems with speed comparison diagram.Concrete simulating scenes arranges as follows: antenna for base station is 64 * 64 squaerial, and community is divided into 8 sub-sectors in horizontal dimensions, and vertical dimensions is divided into 8 annular regions, changes transmitted power from 0dB to 20dB.By simulation result, can be found out, in whole transmit power range, the more approaching not grouping system of the performance of the ZF precoding that Performance Ratio of the present invention is traditional, this is because the present invention, except disturbing in consideration group, has also considered to disturb between adjacent set.The computation complexity of the matrix inversion of horizontal and vertical SLNR precoding of the present invention is simultaneously O (8 ³), the computation complexity of grouping system is not O (64 ³), the computation complexity of grouping system is well below the computation complexity of grouping system not.In grouping system, user only need to use 4 groups of pilot tones, and grouping system need not used whole 64 groups of pilot tones.Therefore, pilot-frequency expense also greatly reduces.

Fig. 4 is horizontal dimensions and vertical dimensions group number for a change, three kinds of systems with speed comparison diagram.Concrete simulating scenes arranges as follows: antenna for base station is 64 * 64 squaerial, and the transmitted power of fixed base stations is 10dB, changes annular region number and sub-sector number simultaneously, and it is 4 groups, 8 groups, 12 groups and 16 groups that horizontal and vertical group is counted value.From simulation result, can find out, group number is more, and speed is lower, and this is because keep antenna for base station number constant, increase group number, and the virtual-antenna number of every group reduces, and diversity gain reduces like this, and the every group of number of users that can serve also reduces.But then, increase group number, the computation complexity of multi-user pre-coding reduces greatly.Therefore organize number and be balance computation complexity with and the key factor of speed.

Fig. 5 is antenna for base station number for a change, three kinds of systems with speed comparison diagram.Except the performance of grouping system not, two suite lines have been drawn.Simulating scenes arranges as follows: the transmitted power of fixed base stations is 10dB, changes horizontal antenna number and the vertical antenna number of base station, and value is 32 * 32,64 * 64,128 * 128 and 256 * 256.Group number is fixedly cut apart in first group of emulation, and community is divided into 8 sub-sectors in horizontal dimensions, and vertical dimensions is divided into 8 annular regions.From simulation result, can find out, antenna number is more, and speed is higher, and along with antenna number increases, of the present invention and speed is approached not grouping system gradually.It is 8 that second group of emulation keeps the virtual-antenna number of each group, changes thus annular region number and sub-sector number, and it is 4 groups, 8 groups, 16 groups and 32 groups that horizontal and vertical group is counted value.From simulation result, can find out, the performance of ZF precoding is approached the performance of SLNR precoding gradually.This is because keep the virtual-antenna number of every group constant, the interior interference of group of ZF precoding and SLNR precoding of the present invention is identical, but changes the main aerial number of base station, and the wave beam of DFT narrows down, the outer interference of group diminishes, so the performance of ZF precoding is approached the performance of SLNR precoding gradually.Along with the continuous increase of antenna for base station number, in second group of emulation experiment, the diversity gain of every group remains unchanged, and every group of virtual-antenna number of first group of situation increases along with the increase of antenna for base station number.And every group of selectable number of users is less than first group of experiment in second group of experiment, so the lifting with speed of the second suite line is not so good as first group.

The above is only the preferred embodiment of the present invention; be noted that for those skilled in the art; under the premise without departing from the principles of the invention, can also make some improvements and modifications, these improvements and modifications also should be considered as protection scope of the present invention.

Claims (6)

1. towards the 3D beam-forming method of extensive MIMO TDD system, it is characterized in that: the 3D wave beam carrying out under extensive MIMO based on the sparse property of channel forms, and comprises the steps:

Step 1, utilize the sparse property of channel, DFT switched-beam is divided into community in the horizontal direction some sub-sectors, is divided into some annular regions in the vertical direction;

Step 2, adopt separated user grouping algorithm to carry out three-dimensional grouping to user, first user is assigned in the annular region of vertical dimensions, the user in each annular region of vertical dimensions is assigned in the sub-sector of corresponding horizontal dimensions, the code name of user's place group consists of jointly the code name of annular region and the code name of sub-sector again; User in non-adjacent group in same sub-sector can multiplexed pilot, and the user in non-adjacent group in same annular region also can multiplexed pilot, and base station obtains CSIT;

Step 3, at every group with indoor, adopt the maximum tactful dispatched users of SLNR;

Step 4, in conjunction with the DFT switched-beam of each group, first obtain the instantaneous equivalent CSIT of low order, then calculate SLNR precoding, when calculating SLNR precoding, consider in group and disturb between interference and adjacent set;

Step 5,3D beam forming scheme are the direct products of horizontal and vertical beam forming, and the beam forming of horizontal and vertical dimension is double-deck precoding, DFT switched-beam and SLNR precoding, consist of.

2. the 3D beam-forming method towards extensive MIMO TDD system according to claim 1, is characterized in that: described base station is positioned at the center of community, serves a plurality of users, and described user is single antenna user; The projecting depth of building in position of base station, user is positioned in the plane of street; Antenna for base station is the Rectangular array antenna of N * M dimension, every row configuration M root antenna element, and total N is capable;

In described step 1, community is divided in the horizontal direction to G sub-sector, is divided into L annular region in the vertical direction, the DFT switched-beam of the horizontal dimensions of each group is B _g,H=F _h[:, (g-1) r _h+ (1:r _h)], (g=1 ..., G), F wherein _h∈ for DFT matrix, the DFT switched-beam of the vertical dimensions of each group is B _l,V=F _v[:, (l-1) r _v+ (1:r _v)], (l=1 ..., L), F wherein _v∈ for DFT matrix,

3. the 3D beam-forming method towards extensive MIMO TDD system according to claim 2, it is characterized in that: in described step 2, in note community, all numbers of users are K, use separated user grouping algorithm to carry out three-dimensional grouping to user, specifically describe to be:

Step2-1: for l=1 ..., L, is positioned at all users' of annular region l set for l=1 ..., L, g=1 ..., G, all users that are positioned at annular region l, sub-sector g are designated as (l, g) group user, (l, g) group user's set

Step2-2: for user k=1 ..., K, to horizontal and vertical channel covariance matrix R _k,Hand R _k,Vcarry out Eigenvalues Decomposition, obtain U _k,Hand U _k,V;

Step2-3: for user k=1 ..., K, find annular region l, $l=\arg \underset{l^{\′}}{\min }{\left|\right|U_{k,V}{U}_{k,V}^H-B_{l^{\′},V}{B}_{l^{\′},V}^H\left|\right|}_F^2,$ User k is joined to set in,

Step2-4: l=1 ..., L, finds sub-sector g, $g=\arg \underset{g^{\′}}{\min }{\left|\right|U_{t,H}{U}_{t,H}^H-B_{g^{\′},H}{B}_{g^{\′},H}^H\left|\right|}_F^2,$ User t is joined to set in,

4. the 3D beam-forming method towards extensive MIMO TDD system according to claim 3, is characterized in that: in described step 3, while adopting the maximum tactful dispatched users of SLNR, making the dispatched users number of (l, g) group is S _l,g=min (r _h, r _v), if scheduled user's number of (l, g) group is K _l,g≤ min (r _h, r _v), all scheduling; This dispatching algorithm specifically describes:

Step3-1: initialization if K _l,g≤ min (r _h, r _v), D _l,g=T _l,g, exit dispatching algorithm simultaneously, otherwise carry out Step3-2;

Step3-2: each user in searching loop (l, g) group, solve the SLNR of each user to other users, for user k=1 ...., K _{l, g} ${\tilde{h}}_{{(l,g)}_k}=[h_{{(l,g)}_1},...,h_{{(l,g)}_{k-1}},h_{{(l,g)}_{k+1}},...,h_{{(l,g)}_{K_{l,g}}}]$ Expression except extended channel matrices, calculate ${\mathrm{SLNR}}_{{(l,g)}_k}={\mathrm{\λ}}_{\max }\left[{(\frac{1}{\mathrm{\ρ}}I+{\tilde{h}}_{{(l,g)}_k}{\tilde{h}}_{{(l,g)}_k}^H)}^{-1}{h}_{{(l,g)}_k}{h}_{{(l,g)}_k}^H\right],$ Wherein ρ is the tolerance of signal to noise ratio snr, is proportional to transmitted power divided by noise variance, λ _max(A) eigenvalue of maximum of representing matrix A; Carry out Step3-3;

Step3-3: all users' to be selected SLNR is carried out to descending, export all users' to be selected descending situation: [SLNR_value, Index]=sort (SLNR, ' descend'), wherein SLNR_value represents the array forming by the SLNR value of descending, Index represents corresponding User ID, and sort is sorting operation, ' descend' represents descending; Carry out Step3-4;

Step3-4: the front S that selects SLNR maximum _l,gindividual user, as service object, obtains final user's collection of selecting

5. the 3D beam-forming method towards extensive MIMO TDD system according to claim 4, is characterized in that: in described step 4:

S the user's of (l, g) group horizontal SLNR precoding is:

$p_{{(l,g)}_s,H}={\mathrm{\δ}}_{{(l,g)}_s,H}{(\frac{1}{\mathrm{\ρ}}I+B_{g,H}^H{\tilde{h}}_{{(l,g)}_s,H}{\tilde{h}}_{{(l.g)}_s,H}^H{B}_{g,H}+B_{g,H}^H{\tilde{H}}_{(l,g).H}{\tilde{H}}_{(l,g),H}^H{B}_{g,H})}^{-1}{B}_{g,H}^H{h}_{{(l.g)}_s}H$

Wherein: ${\tilde{h}}_{{(l,g)}_s,H}=[h_{{(l,g)}_1,H},...,h_{{(l,g)}_{s-1},H},h_{{(l,g)}_{s+1},H},...,h_{{(l,g)}_{S_{l,g}},H}]$ Be except expansion horizontal channel matrix; it is the expansion horizontal channel matrix of adjacent sub-sector; be the power normalization factor, meet

S the user's of (l, g) group vertical SLNR precoding is:

$p_{{(l,g)}_s,V}={\mathrm{\δ}}_{{(l,g)}_s,V}{(\frac{1}{\mathrm{\ρ}}I+B_{g,V}^H{\tilde{h}}_{{(l,g)}_s,V}{\tilde{h}}_{{(l,g)}_s,V}^H+B_{g,V}+B_{g,V}^H{\tilde{H}}_{(l,g),V}{\tilde{H}}_{(l,g),V}^H{B}_{g,V})}^{-1}{B}_{g,V}^H{h}_{{(l,g)}_s,V}$

Wherein: ${\tilde{h}}_{{(l,g)}_s,V}=[h_{{(l,g)}_1,V}{,...,h}_{{(l,g)}_{s-1},V},h_{{(l,g)}_{s+1},V}{,...,h}_{{(l,g)}_{S_{l,g}},V}]$ Be except expansion vertical channel matrix, it is the expansion vertical channel matrix in adjacent annular region; be the power normalization factor, meet

6. the 3D beam-forming method towards extensive MIMO TDD system according to claim 5, is characterized in that: in described step 5: the 3D beam forming of base station transmitting is $x=\underset{l=1}{\overset{L}{\mathrm{\Σ}}}\underset{g=1}{\overset{G}{\mathrm{\Σ}}}\underset{s=1}{\overset{S_{l,g}}{\mathrm{\Σ}}}[\left(B_{g,H}{p}_{{(l.g)}_s,H}\right)\⊗\left(B_{l,V}{p}_{{(l,g)}_s,V}\right)]d_{{(l,g)}_s,}$ Wherein for sending to s the user's of (l, g) group data.