WO2017118099A1 - 上行导频分配及接收波束赋形矢量联合优化方法、装置 - Google Patents

上行导频分配及接收波束赋形矢量联合优化方法、装置 Download PDF

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WO2017118099A1
WO2017118099A1 PCT/CN2016/099533 CN2016099533W WO2017118099A1 WO 2017118099 A1 WO2017118099 A1 WO 2017118099A1 CN 2016099533 W CN2016099533 W CN 2016099533W WO 2017118099 A1 WO2017118099 A1 WO 2017118099A1
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channel estimation
beamforming vector
pilot
iteration
estimation error
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PCT/CN2016/099533
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English (en)
French (fr)
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项飞
张国梅
王兵
秦洪峰
王绍鹏
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中兴通讯股份有限公司
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines

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  • the present invention relates to a three-dimensional (3D, 3 Dimension) large-scale multiple-input multiple-output (MIMO) uplink channel estimation technique in a mobile communication system, and more particularly to an uplink pilot allocation and reception beamforming vector joint optimization.
  • Method, device a three-dimensional (3D, 3 Dimension) large-scale multiple-input multiple-output (MIMO) uplink channel estimation technique in a mobile communication system, and more particularly to an uplink pilot allocation and reception beamforming vector joint optimization.
  • Massive MIMO technology refers to a base station equipped with hundreds of antennas serving dozens of users using the same time-frequency resources at the same time. Studies have shown that when the number of base station antennas is large, the original random variables tend to determine the value, so a simple signal processing method can effectively reduce inter-user interference; in addition, when the base station antenna is large, the equivalent signal-to-noise Compared with the number of base station antennas, the number of antennas increases linearly. This means that the more the number of antennas, the smaller the transmit power required to obtain the same equivalent signal-to-noise ratio. Therefore, the large-scale MIMO technology can greatly reduce the transmit power of the uplink and downlink. "Green communication" requirements. However, considering the limitation of the physical size of the antenna in the actual system, the 3D antenna array such as the area array becomes a practical antenna array suitable for the large-scale MIMO system, and 3D MIMO becomes a practical large-scale MIMO implementation.
  • pilot pollution suppression methods can be divided into the following aspects: high-precision channel estimation, robust precoding, pilot structure design, and pilot optimization allocation.
  • channel estimation algorithm based on feature decomposition, Bayesian channel estimation algorithm, etc. are proposed.
  • precoding algorithm including precoding algorithm through cooperation between base stations, based on minimum mean square error (MMSE) ,Minimum Mean Square Error) robust precoding algorithm, etc.
  • pilot structure design it is divided into time-shifted pilot structure and redundant pilot structure
  • pilot optimization allocation including minimizing channel
  • the pilot-optimized allocation scheme with the mean square error as the target and the pilot allocation scheme with the total system throughput as the utility function are estimated.
  • the above techniques only consider the antenna The influence of the horizontal dimension characteristics on the system performance does not consider the influence of the vertical dimension on the system performance after the introduction of the area array antenna.
  • a typical 3D MIMO technology is a vertical splitting technique of a cell, which increases the user's Signal to Interference plus Noise Ratio (SINR) by increasing the number of users simultaneously served by the base station or by precise beam adjustment. Improve the overall performance of the system.
  • the introduction of vertical sectorization will bring more serious interference to the system, including inter-cell interference and inter-sector interference.
  • various methods have been proposed, including downtilt optimization, power allocation, coordinated beamforming, and vertical beam optimization combined with joint transmission.
  • CSI channel state information
  • an embodiment of the present invention provides a method and an apparatus for jointly optimizing an uplink pilot allocation and a receiving beamforming vector.
  • the beamforming vector of the receiving antenna array of each vertical sector of each cell is fixed, and a pilot sequence is allocated to users in each vertical sector to minimize channel estimation error, and a pilot scheduling result is obtained;
  • the assigning the pilot sequence is iteratively performed and the receive beamforming vector is optimized until the asymptotic channel estimation error satisfies a predetermined condition.
  • the receiving antenna array beamforming vector of each vertical sector of each cell is fixed, including:
  • the receive antenna array beamforming vector for each vertical sector of each cell is fixed to an initialization value or an optimized value of the previous iteration.
  • the pilot sequence is allocated to users in each vertical sector to minimize channel estimation errors, including:
  • a user of one multiplexed pilot sequence is scheduled in each vertical sector by greedy search.
  • the receiving a beamforming vector is optimized according to the pilot scheduling result, so as to gradually Near channel estimation error and reduction, including:
  • the progressive channel estimation mean square error of all users in the sector is set as the evaluation function, and the evaluation function values of all particles of all variables from the initial iteration to the current iteration number are compared to obtain the local optimal solution, the global optimal solution and Evaluating the optimal value of the function;
  • the particle swarm iterative algorithm is stopped when the maximum number of iterations or the condition of the iterative error of the evaluation function is satisfied.
  • the up to the channel estimation error and the preset condition are met, including:
  • a pilot allocation unit configured to fix a receive antenna array beamforming vector of each vertical sector of each cell, and allocate a pilot sequence to users in each vertical sector to minimize channel estimation error and obtain a guide Frequency scheduling result;
  • a beamforming vector adjusting unit configured to optimize a receiving beamforming vector according to the pilot scheduling result, so as to make an asymptotic channel estimation error and decrease;
  • an iteration unit configured to iteratively execute the allocated pilot sequence and optimize a receive beamforming vector until the asymptotic channel estimation error satisfies a preset condition.
  • the pilot allocation unit includes:
  • a fixed subunit is configured to fix the receive antenna array beamforming vector of each vertical sector of each cell to an initialization value or an optimized value of the previous iteration.
  • the pilot allocation unit includes:
  • the beamforming vector adjusting unit includes:
  • the control subunit is configured to stop the particle swarm iterative algorithm when the maximum number of iterations or the condition of the iterative error of the evaluation function is satisfied.
  • the iterative unit is further configured to stop the iteration when the value of the channel estimation error evaluation function corresponding to the beamforming vector optimized by the iteration is greater than the result of the previous iteration.
  • a beamforming vector of a receiving antenna array of each vertical sector of each cell is fixed, and a pilot sequence is allocated to users in each vertical sector to minimize channel estimation error.
  • the pilot pollution level of the system when multiplexing The embodiments of the present invention extend the existing inter-cell cooperative pilot scheduling method in a general large-scale MIMO system for a vertical split scenario, and perform vertical inter-sector cooperative pilot scheduling.
  • the channel estimation accuracy is also related to the weighting vector of the receiving antenna array (ie, the receiving beamforming vector). Therefore, the pilot scheduling scheme is jointly optimized with the receiving antenna weight vector to estimate the mean square error of the channel estimation.
  • the cost function is implemented, and the particle swarm optimization algorithm with lower complexity is used to complete the optimal solution of the receive beamforming vector.
  • FIG. 1 is a schematic flowchart of a method for jointly optimizing an uplink pilot allocation and a receiving beamforming vector according to an embodiment of the present invention
  • FIG. 2 is a schematic structural diagram of an apparatus for jointly optimizing an uplink pilot allocation and a receiving beamforming vector according to an embodiment of the present invention
  • FIG. 3 is a schematic diagram of a model of a 3D massive MIMO system according to an embodiment of the present invention.
  • FIG. 4 is a schematic diagram of an LS channel estimation error in a seven-cell scenario in which the horizontal antenna spacing is 10 ⁇ and the vertical antenna spacing is 0.5 ⁇ in the twelve user scenarios of the embodiment of the present invention
  • FIG. 5 is a schematic diagram showing comparison of average spectral efficiency of a cell based on LS channel estimation, horizontal antenna spacing of 10 ⁇ , and vertical antenna spacing of 0.5 ⁇ according to different schemes of seven cells in each cell according to an embodiment of the present invention
  • 6 is a seven-cell, twelve users in each cell, with a horizontal antenna spacing of 10 ⁇ , The straight antenna spacing is in the 5 ⁇ scenario, and the LS channel estimation error is schematic;
  • FIG. 7 is a schematic diagram of comparison of average spectral efficiency of cells in different schemes based on LS channel estimation, in which seven cells, twelve users in each cell have a horizontal antenna spacing of 10 ⁇ and a vertical antenna spacing of 5 ⁇ .
  • the methods used in uplink transmission of traditional massive MIMO systems mainly include: channel estimation algorithm based on feature decomposition, Bayesian channel estimation algorithm and other more accurate channel estimation algorithms; time shifting The pilot structure design method such as the pilot structure and the redundant pilot structure; the pilot optimization allocation scheme aiming at minimizing the mean square error of the channel estimation; and the pilot allocation scheme using the total throughput of the system as the utility function.
  • the above solutions are all carried out in the scenario of a 2D conventional line antenna array.
  • the actual number of antennas in a large-scale MIMO system is not considered, and the space array is often limited.
  • the antenna array often adopts the actual situation of the area array, that is, the vertical is not considered.
  • the existing research on 3D MIMO vertical splitting technology mostly assumes that CSI is ideal, and does not consider the influence of channel estimation error that is definitely present in practice, and does not involve the need for inter-station users to reuse when there are many users. Pilot pollution problems caused by pilots.
  • the purpose of the embodiments of the present invention is to provide an uplink pilot allocation problem after the vertical sectorization technology is introduced in a 3D massive MIMO scenario, and the pilot pollution is more severe when the pilots are multiplexed between vertical sectors.
  • the receiver beamforming vector joint optimization method which can effectively reduce the pilot pollution level, improve the channel estimation accuracy of the 3D massive MIMO system, and thereby improve the system spectrum efficiency.
  • the core idea of the embodiment of the present invention is as follows: in the first step, the beamforming vector of the receiving antenna array of each vertical sector of each cell is fixed to minimize the channel estimation error, and each vertical fan is cooperatively used by the greedy search algorithm. The user in the zone assigns a pilot sequence. In the second step, based on the pilot scheduling result determined in the first step, the received beamforming vector is optimized with the asymptotic channel estimation error and the minimum as the target. The above two steps perform alternate iterations, and if the asymptotic channel estimation error does not further decrease, the joint optimization ends.
  • FIG. 1 is a schematic flowchart of a method for jointly optimizing an uplink pilot allocation and a receiving beamforming vector according to an embodiment of the present invention. As shown in FIG. 1 , the uplink pilot allocation and receiving beamforming vector joint optimization method includes the following steps:
  • Step 101 Fix the receive antenna array beamforming vector of each vertical sector of each cell, and allocate a pilot sequence to users in each vertical sector to minimize channel estimation error, and obtain a pilot scheduling result.
  • Vertical sectorization is a 3D MIMO cell vertical splitting technique.
  • the vertical beam of the base station antenna array is used to divide each cell into multiple sectors in the vertical direction, and each sector is served by a vertical beam.
  • the multiplexing of time-frequency resources can be realized between vertical sectors, thereby improving the system capacity of the entire network.
  • 3D beamforming can dynamically change the base station antenna array downtilt or weight vector, optimize the vertical coverage of the base station, and enable edge users to effectively receive signals.
  • the use of the 3D beamforming technology can simultaneously implement cell splitting in the horizontal and vertical dimensions, so that the number of users of the served cell at the same time is doubled, thereby increasing the average throughput of the system.
  • the receive antenna array beamforming vector of each vertical sector of each cell is fixed as an initialization value or an optimized value of the previous iteration.
  • a user of one multiplexed pilot sequence is scheduled in each vertical sector by greedy search.
  • L horizontal sectors constitute a cooperative cluster, and each cell simultaneously serves K (K ⁇ M) single antenna users, and the users are evenly distributed.
  • Each cell divides two vertical sectors, and ⁇ l0 and ⁇ l1 respectively represent the antenna downtilt angles of serving the near and far sectors in the first cell.
  • Each vertical sector is served by an array antenna having an array of M (the vertical direction N v roots and the horizontal direction N t roots), wherein the number of Ms can be several hundred.
  • the total power of the base station is P
  • the near-sector power in the first cell is P l0
  • the far-sector power is P l1
  • the uplink transmission power of the user is p r .
  • the M ⁇ 1D uplink channel vector of the user k to the target base station j in the cell 1 can be expressed as
  • ⁇ jlk represents the large-scale fading coefficient, including path loss and shadow fading.
  • R jlk represents the M x M-dimensional channel reception correlation matrix of the user k and the base station j in the first cell.
  • the channel coefficient of the mth antenna of the user and the base station is
  • g u —random variable, g u ⁇ CN(0,1);
  • the total gain of the antenna array is A( ⁇ , ⁇ ), with Representing the beam gains of the near and far sectors from the base station 1 corresponding to the user i 0 in the cell j, where ⁇ D,l is the downtilt angle (pitch angle) of the antenna array of the base station 1 and ⁇ llk is in the cell l The user k looks at the elevation angle of the base station 1.
  • Array antenna gain is defined as Where: w l0 ⁇ W M ⁇ 1 is the weight vector of the antenna array.
  • B r ( ⁇ , ⁇ D, l0 ) ⁇ B r M ⁇ 1 is a pattern of the antenna array of the base station, where the (n-1)N V +m elements are B r,m,n ( ⁇ , ⁇ D ) , representing the gain of the mth row of the nth column antenna element, the expression of which is among them
  • ⁇ 3dB represents the half power angle
  • d y and d z represent the horizontal and vertical spacing of the antenna.
  • the antenna array weight vector of each sector vertical beam is fixed (the initialization result or the optimization result of the previous iteration).
  • a user multiplexing the kth pilot sequence is scheduled in each vertical sector by greedy search.
  • represents the user set size using the same pilot sequence ⁇ k .
  • the progressive estimation error of user k i in cell j is
  • A( ⁇ ) u denotes an uplink beam pattern.
  • the greedy search process includes:
  • K represents the number of users per sector
  • ⁇ i represents sector i of the selected user set.
  • a first set from a user in a sector ⁇ randomly select a user designated j 1, k, ⁇ k while allocating pilot for it. The user is then removed from the user set ⁇ 1 , ie ⁇ 1 ⁇ 1 /j 1,k .
  • Step 102 Optimize the received beamforming vector according to the pilot scheduling result to make the asymptotic channel estimation error and decrease.
  • the position and update speed of each particle are initialized; the position and update speed of each particle are iteratively updated according to the particle swarm algorithm; and the mean square error of the progressive channel estimation of all users in the sector is set as an evaluation function. , compares the evaluation function values of all particles of all variables from the initial iteration to the current iteration number, and obtains the local optimal solution, the global optimal solution, and the optimal value of the evaluation function; when the maximum number of iterations or the iteration satisfying the evaluation function is satisfied When the condition of the error is met, the particle swarm iteration algorithm is stopped.
  • the vertical beam antenna array weight vector of each sector is optimized with the goal of minimizing the sum of the progressive channel estimation errors of all users in the cooperative cluster, and the optimization target is Considering the complexity problem, the particle swarm algorithm with relatively low complexity is used in the solution.
  • r 1 and r 1 represent random numbers uniformly distributed in [0, 1]
  • c 1 and c 2 represent learning factors, generally take the value 2
  • p j represents a local optimum position
  • p g for all particles in the global best represents all particles.
  • ⁇ represents an iterative index.
  • Step 103 Iteratively execute the allocated pilot sequence and optimize the receive beamforming vector until the asymptotic channel estimation error satisfies a preset condition.
  • the iteration is stopped. Specifically, if the channel estimation error evaluation function value f m corresponding to the antenna array weight vector optimized by the iteration is greater than the result of the previous iteration, the iteration stops.
  • the channel estimation criterion considered is least squares (LS, Least Square) It is estimated that the cost function in the optimization is also calculated based on the LS estimation error.
  • the specific channel estimation criterion can replace other criteria, such as MMSE, Maximum A Posteriori (MAP), etc., and the cost function only needs to be modified accordingly.
  • the receive beamforming vector design of the embodiment of the present invention is to obtain a suboptimal solution by using a particle swarm algorithm with low complexity, and may also be solved by other methods, such as a semi-positive definite relaxation method.
  • the technical solution of the embodiment of the present invention is mainly for solving the uplink channel estimation problem after the 3D vertical sectorization technology is introduced in the large-scale MIMO system, in particular, to suppress the more serious pilot caused by the inter-vertical inter-channel multiplexing pilot. Pollution.
  • the pilot scheduling between vertical sectors and the receiving shape vector design of each vertical beam By optimizing the pilot scheduling between vertical sectors and the receiving shape vector design of each vertical beam, the channel estimation accuracy is improved and the influence of pilot pollution is reduced.
  • the LS normalized channel estimation error is reduced, the channel estimation accuracy is improved by about 4.5 dB, and the average spectrum efficiency of the cell is also As a result, when the number of antennas is 100, the average cell spectrum efficiency is increased by about 5 bps/Hz.
  • the channel estimation accuracy using the MMSE channel estimation criterion is related to the half-power beam bandwidth.
  • the channel estimation accuracy is also improved, and the average spectrum efficiency of the cell is also improved accordingly.
  • the embodiment of the present invention provides an uplink pilot allocation and receiving beamforming vector joint optimization apparatus, as shown in FIG. 2, the uplink guiding
  • the frequency allocation and receiving beamforming vector joint optimization device comprises:
  • a pilot allocation unit 21 configured to fix a receive antenna array beamforming vector of each vertical sector of each cell, and allocate a pilot sequence to users in each vertical sector to minimize channel estimation error, and obtain Pilot scheduling result;
  • a beamforming vector adjustment unit 22 configured to optimize a received beamforming vector according to the pilot scheduling result, so as to make an asymptotic channel estimation error and decrease;
  • An iteration unit 23 is configured to iteratively execute the allocated pilot sequence and optimize the receive beamforming vector until the asymptotic channel estimation error satisfies a preset condition.
  • the pilot allocation unit 21 includes:
  • the fixed subunit 211 is configured to fix the receive antenna array beamforming vector of each vertical sector of each cell to an initialization value or an optimized value of the previous iteration.
  • the pilot allocation unit 21 yuan includes:
  • the greedy search sub-unit 212 is configured to arbitrarily search for users of each of the multiplexed pilot sequences in each vertical sector by greedy search to minimize the progressive channel estimation error and to optimize the target.
  • the beamforming vector adjustment unit 22 includes:
  • Initializing subunit 221 for initializing the position and update speed of each particle
  • An iteration sub-unit 222 configured to iteratively update the position and update speed of each particle according to the particle swarm algorithm
  • the comparison sub-unit 223 is configured to set the mean square error of the progressive channel estimation of all users in the sector as an evaluation function, and compare the evaluation function values of all the particles of all variables from the initial iteration to the current number of iterations to obtain a local optimum. Solution, global optimal solution, and optimal value of the evaluation function;
  • the control sub-unit 224 is configured to stop the particle swarm iterative algorithm when the maximum number of iterations or the condition of the iterative error of the evaluation function is satisfied.
  • the iterative unit 23 is further configured to stop the iteration when the value of the channel estimation error evaluation function corresponding to the beamforming vector optimized by the iteration is greater than the result of the previous iteration.
  • each unit in the uplink pilot allocation and reception beamforming vector joint optimization apparatus shown in FIG. 2 may refer to the foregoing uplink pilot allocation and reception beamforming vector joint optimization method. Describe and understand.
  • the functions of each unit in the uplink pilot allocation and reception beamforming vector joint optimization apparatus shown in FIG. 2 can be implemented by a program running on a processor, or can be implemented by a specific logic circuit.
  • L horizontal sectors constitute a cooperative cluster, and each cell simultaneously serves K (K ⁇ M) single antenna users, and the users are evenly distributed.
  • Each cell divides two vertical sectors, and ⁇ l0 and ⁇ l1 respectively represent the antenna downtilt angles of serving the near and far sectors in the first cell.
  • Each vertical sector is served by an array antenna having an array of M (the vertical direction N v roots and the horizontal direction N t roots), wherein the number of Ms can be several hundred.
  • the total power of the base station is P
  • the near-sector power in the first cell is P l0
  • the far-sector power is P l1
  • the uplink transmission power of the user is p r .
  • the M ⁇ 1D uplink channel vector of the user k to the target base station j in the cell 1 can be expressed as
  • ⁇ jlk represents the large-scale fading coefficient, including path loss and shadow fading.
  • R jlk represents the M x M-dimensional channel reception correlation matrix of the user k and the base station j in the first cell.
  • the channel coefficient of the mth antenna of the user and the base station is
  • the total gain of the antenna array is A( ⁇ , ⁇ ), with Representing the beam gains of the near and far sectors from the base station 1 corresponding to the user i 0 in the cell j, where ⁇ D,l is the downtilt angle (pitch angle) of the antenna array of the base station 1 and ⁇ llk is in the cell l The user k looks at the elevation angle of the base station 1.
  • Array antenna gain is defined as Where: w l0 ⁇ W M ⁇ 1 is the weight vector of the antenna array.
  • B r ( ⁇ , ⁇ D, l0 ) ⁇ B r M ⁇ 1 is a pattern of the antenna array of the base station, where the (n-1)N V +m elements are B r,m,n ( ⁇ , ⁇ D ) , representing the gain of the mth row of the nth column antenna element, the expression of which is among them
  • ⁇ 3dB represents the half power angle
  • d y and d z represent the horizontal and vertical spacing of the antenna.
  • p r represents the average transmit power of each user
  • A( ⁇ ) u represents the uplink beam pattern
  • ⁇ D,j0 represents the downtilt angle of the near-sector user antenna array of the serving cell j
  • z j represents the additional Gaussian white noise.
  • each sector still uses a downlink beam for service.
  • use Indicates the symbol that the base station sends to the cell j near sector user i 0 .
  • Hypothesis Obey an independent and identical distribution with a mean of 0 and a variance of 1. If maximum ratio transmission is used, the beamforming vector can be expressed as among them Then, the data symbol received by the cell j near sector user i0 can be expressed as
  • A( ⁇ ) d represents the downlink beam pattern of the base station
  • P l0 and P l1 respectively serve the transmission power of the near-sector user and the far-sector user, Indicates the white Gaussian noise received by the user.
  • the first term represents the desired signal
  • the other terms are noise terms, including intra-sector interference, inter-sector interference, inter-cell interference, and noise.
  • (T- ⁇ )/T represents the pilot overhead of the system.
  • the first step pilot allocation.
  • the definition U k is the set of users transmitting the pilot sequence ⁇ k ,
  • 2L, the set S k represents the set of sectors corresponding to the pilot sequence, the user of the sector i
  • the collection is ⁇ i .
  • the utility function of the pilot allocation strategy can be defined as
  • the second step vertical beam receiving beamforming vector optimization.
  • the subsequent vertical beam receiving beamforming vector optimization will further improve the channel estimation accuracy.
  • the optimization goal can be expressed as
  • r 1 and r 1 represent random numbers uniformly distributed in [0,1]
  • c 1 and c 2 represent learning factors, generally take the value 2
  • the empirical value is 0.8.
  • p j represents the local optimal position of all particles
  • p g represents the global optimal position of all particles.
  • ⁇ represents the iteration number.
  • a particle swarm algorithm with lower complexity In the process of solving the optimization, a particle swarm algorithm with lower complexity is used. In each iteration, the complexity of calculating the antenna gain of all users is o(2LK' ⁇ 2L ⁇ M 2 ), and the complexity of calculating the channel estimation mean square error of all users is o(2LK'), so all particles are calculated.
  • the complexity of the channel estimation mean square error is o(S[(2L) 2 K'M 2 +2LK']).
  • the complexity required to iteratively update the position and update speed of all particles is o(2LMS). Therefore, the computational complexity required by our proposed scheme is o(2LMS)+o(S[(2L) 2 K'M 2 +2LK']).
  • the number of antennas is much larger than the number of cells L and the number of users K, and the computational complexity increases with the square of the number of antennas.
  • the number of antennas per user is 1.
  • the channel uses a block fading model as described above. Since the channel separation between the antennas is assumed to be independent, the number of columns of the planar antenna array in the simulation is set to be unchanged in four columns, and the number of row antennas increases as the total number of antennas increases.
  • distance between base stations is 1000m
  • base station height is 32m
  • user height is 1.5m
  • horizontal antenna spacing is 10 ⁇
  • vertical antenna spacing is 0.5 ⁇
  • path loss coefficient is 3
  • vertical half power bandwidth is 6.5°
  • the pilot signal-to-noise ratio is 5 dB
  • the downlink data signal-to-noise ratio is 20 dB
  • the coherence time length is assumed to be 18 OFDM symbols.
  • the scheme of the embodiment of the present invention is only performed in the LS estimation scheme in the traditional 2D massive MIMO system (inter-cell multiplexed pilot), the traditional LS estimation scheme (no optimization) in the 3D massive MIMO system, and the 3D massive MIMO system.
  • the pilot allocation scheme (without vertical beam adjustment) is compared.
  • Figure 4 compares the normalized channel estimation errors for each scheme. Since the 2D massive MIMO system is inter-cell multiplexed pilot, the pilot pollution is light, so the channel estimation error is the smallest. In the 3D massive MIMO system, the same set of pilots are multiplexed between sectors, and the pilot pollution is serious. Therefore, the channel estimation error is large, and the proposed scheme can reduce the pilot pollution to a certain extent and improve the channel estimation accuracy.
  • Figure 5 compares the average spectral efficiency of the traditional massive MIMO scheme and the 3D massive MIMO scheme. Although the channel estimation error of the 3D massive MIMO system is large, the pilot overhead is small and the performance is still good in the 3D massive MIMO system.
  • the proposed scheme can reduce the pilot pollution and improve the channel estimation accuracy through joint scheduling of pilot scheduling and receiving weight vector, thus effectively improving the average spectral efficiency of the system.
  • the LS channel estimation algorithm is used for channel estimation.
  • the horizontal antenna spacing is 10 ⁇ and the vertical antenna spacing is 5 ⁇ .
  • the rest of the scene configuration is the same as in Embodiment 1.
  • FIG. 6 when the vertical antenna spacing is 5 ⁇ and the horizontal antenna spacing is 10 ⁇ , the correlation between the antennas is reduced, so the performance gain brought by the beamforming technique is degraded. It can be seen from Fig. 7 that the average spectral efficiency of the cell is lower than that of the vertical antenna spacing of 0.5 ⁇ , but the proposed scheme can still improve the channel estimation accuracy to some extent.
  • Figure 4-7 shows a comparison of the average spectrum efficiency of the corresponding cells. It can be seen from the figure that the MRT precoding based on the estimated channel is designed because the correlation between the antennas is reduced. The performance gains from the solution will increase, so the performance of each solution is improved compared to the scenario where the vertical antenna spacing is 0.5 ⁇ . However, after designing the precoding matrix using the improved channel estimation information in the large-scale MIMO vertical splitting scenario, the performance of the system is further improved.
  • the disclosed method and smart device may be implemented in other manners.
  • the device embodiments described above are merely illustrative.
  • the division of the unit is only a logical function division.
  • there may be another division manner such as: multiple units or components may be combined, or Can be integrated into another system, or some features can be ignored or not executed.
  • the coupling, or direct coupling, or communication connection of the components shown or discussed may be indirect coupling or communication connection through some interfaces, devices or units, and may be electrical, mechanical or other forms. of.
  • the units described above as separate components may or may not be physically separated, and the components displayed as the unit may or may not be physical units, that is, may be located in one place or distributed to multiple network units; Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • each functional unit in each embodiment of the present invention may be integrated into one second processing unit, or each unit may be separately used as one unit, or two or more units may be integrated into one unit;
  • the above integrated unit can be implemented in the form of hardware or in the form of hardware plus software functional units.
  • the modules or units in the uplink pilot allocation and reception beamforming vector joint optimization apparatus may pass through one or more digital signal processors (DSPs), application specific integrated circuits (ASICs), processors, and micros.
  • DSPs digital signal processors
  • ASICs application specific integrated circuits
  • processors and micros.
  • DSPs digital signal processors
  • ASICs application specific integrated circuits
  • micros Implemented by a processor, controller, microcontroller, field programmable array (FPGA), programmable logic device, or other electronic unit, or any combination thereof.
  • FPGA field programmable array
  • Some of the functions or processes described in this application embodiment may also be implemented by software executing on a processor.
  • the embodiment of the present invention further provides an uplink pilot allocation and reception beamforming vector joint optimization apparatus, for example, the apparatus can be applied to a base station, including:
  • a memory for storing processor executable instructions
  • processor is configured to:
  • the beamforming vector of the receiving antenna array of each vertical sector of each cell is fixed, and a pilot sequence is allocated to users in each vertical sector to minimize channel estimation error, and a pilot scheduling result is obtained;
  • the assigning the pilot sequence is iteratively performed and the receive beamforming vector is optimized until the asymptotic channel estimation error satisfies a predetermined condition.
  • the method and apparatus of the present application are applicable to the field of communications, and are mainly applicable to base station side uplink pilot allocation and reception beamforming vector joint optimization.
  • the embodiments of the present invention extend the existing inter-cell cooperative pilot scheduling method in a general large-scale MIMO system for a vertical split scenario, and perform vertical inter-sector cooperative pilot scheduling.
  • the channel estimation accuracy is also related to the weighting vector of the receiving antenna array (ie, the receiving beamforming vector). Therefore, the pilot scheduling scheme is jointly optimized with the receiving antenna weight vector to estimate the mean square error of the channel estimation.
  • the cost function is implemented, and the particle swarm optimization algorithm with lower complexity is used to complete the optimal solution of the receive beamforming vector.

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Abstract

本发明公开了一种上行导频分配及接收波束赋形矢量联合优化方法、装置，包括：将每个小区各垂直扇区的接收天线阵列波束赋形矢量固定，并为每个垂直扇区内的用户分配导频序列，以使信道估计误差最小化，得到导频调度结果；根据所述导频调度结果，优化接收波束赋形矢量，以使渐近信道估计误差和减小；迭代执行所述分配导频序列以及优化接收波束赋形矢量，直至所述渐近信道估计误差和满足预设条件。

Description

上行导频分配及接收波束赋形矢量联合优化方法、装置
本申请基于申请号为CN201610006785.2、申请日为2016年1月4日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本申请作为参考。
技术领域
本发明涉及移动通信系统中三维(3D，3Dimension)大规模多输入多输出(MIMO，Multiple-Input Multiple-Output)上行信道估计技术，尤其涉及一种上行导频分配及接收波束赋形矢量联合优化方法、装置。
背景技术
大规模MIMO技术是指配备数百根天线的基站同时为数十个使用相同时频资源的用户服务。研究表明，当基站天线数很大时，原本随机的变量趋向于确定值，因此简单的信号处理方法就可有效地减小用户间干扰；另外，当基站天线规模很大时，等效信噪比随着基站天线数线性增长，这意味着天线数越多要获得相同的等效信噪比所需发射功率可以越小，因此大规模MIMO技术可以大大降低上下行链路的发射功率，符合“绿色通信”的要求。然而，在实际系统中考虑到天线物理尺寸的限制，面阵等3D天线阵列成为适用于大规模MIMO系统的实用天线阵列，进而3D MIMO成为了一种实用的大规模MIMO实现方案。
已有研究表明，当基站天线数趋于无穷大时，不相关的干扰和噪声都可以被平均掉，限制系统性能的决定性因素仅剩下导频污染。所以，如何有效地抑制导频污染、减小由导频污染造成的性能损失，成为大规模MIMO系统设计要解决的一个关键问题。已有的导频污染抑制方法可以划分为以下几个方面：高精度的信道估计、鲁棒的预编码、导频结构设计和导频优化分配。在信道估计方面，人们提出了基于特征分解的信道估计算法、贝叶斯信道估计算法等；在鲁棒的预编码算法方面，包括通过基站间协作的预编码算法、基于最小均方误差(MMSE，Minimum Mean Square Error)准则的鲁棒预编码算法等；在导频结构设计方面，分为时移的导频结构和冗余的导频结构；在导频优化分配方面，包括以最小化信道估计均方误差为目标的导频优化分配方案和以系统总吞吐量为效用函数的导频分配方案等。但是，以上技术都仅考虑了天线 水平维度特性对系统性能的影响，并没有考虑引入面阵天线后垂直维度对系统性能的影响。
一种典型的3D MIMO技术是小区的垂直分裂技术，它是通过增加基站同时服务的用户数量或通过精确的波束调整提高用户的接收信干噪比(SINR，Signal to Interference plus Noise Ratio)，来提升系统总体性能的。垂直扇区化的引入会给系统带来更严重的干扰，包括小区间干扰及扇区间干扰。为了抑制这些干扰，人们提出了各种方法，包括下倾角优化、功率分配、协调波束赋形以及与联合传输结合的垂直波束优化等处理。然而，目前的研究大多在理想信道状态信息(CSI，Channel State Information)的假设下进行，并未涉及当天线数量较大、服务用户数较多时系统性能会受到导频污染限制的问题，尤其是在当每个垂直扇区内服务用户数较多时可能需要在扇区间复用导频的情况下，导频污染问题变得更加严峻。
发明内容
为解决上述技术问题，本发明实施例提供了一种上行导频分配及接收波束赋形矢量联合优化方法、装置。
本发明实施例提供的上行导频分配及接收波束赋形矢量联合优化方法，包括：
将每个小区各垂直扇区的接收天线阵列波束赋形矢量固定，并为每个垂直扇区内的用户分配导频序列，以使信道估计误差最小化，得到导频调度结果；
根据所述导频调度结果，优化接收波束赋形矢量，以使渐近信道估计误差和减小；
迭代执行所述分配导频序列以及优化接收波束赋形矢量，直至所述渐近信道估计误差和满足预设条件。
本发明实施例中，所述将每个小区各垂直扇区的接收天线阵列波束赋形矢量固定，包括：
将每个小区各垂直扇区的接收天线阵列波束赋形矢量固定为初始化值或者前次迭代的优化值。
本发明实施例中，所述为每个垂直扇区内的用户分配导频序列，以使信道估计误差最小化，包括：
以最小化渐进信道估计误差和为优化目标，通过贪婪搜索在各垂直扇区内各调度一个复用导频序列的用户。
本发明实施例中，所述根据所述导频调度结果，优化接收波束赋形矢量，以使渐 近信道估计误差和减小，包括：
初始化各个粒子的位置及更新速度；
根据粒子群算法对每个粒子的位置及更新速度进行迭代更新；
将扇区中所有用户的渐进信道估计均方误差和设置为评价函数，比较所有变量的所有粒子从初始迭代到当前迭代次数范围内的评价函数值，得到局部最优解、全局最优解以及评价函数的最优值；
当满足最大迭代次数或满足评价函数的迭代误差的条件时，停止粒子群迭代算法。
本发明实施例中，所述直至所述渐近信道估计误差和满足预设条件，包括：
当次迭代优化出的波束赋形矢量所对应的信道估计误差评价函数值大于前次迭代的结果时，停止迭代。
本发明实施例提供的上行导频分配及接收波束赋形矢量联合优化装置，包括：
导频分配单元，用于将每个小区各垂直扇区的接收天线阵列波束赋形矢量固定，并为每个垂直扇区内的用户分配导频序列，以使信道估计误差最小化，得到导频调度结果；
波束赋形矢量调整单元，用于根据所述导频调度结果，优化接收波束赋形矢量，以使渐近信道估计误差和减小；
迭代单元，用于迭代执行所述分配导频序列以及优化接收波束赋形矢量，直至所述渐近信道估计误差和满足预设条件。
本发明实施例中，所述导频分配单元包括：
固定子单元，用于将每个小区各垂直扇区的接收天线阵列波束赋形矢量固定为初始化值或者前次迭代的优化值。
本发明实施例中，，所述导频分配单元包括：
贪婪搜索子单元，用于以最小化渐进信道估计误差和为优化目标，通过贪婪搜索在各垂直扇区内各调度一个复用导频序列的用户。
本发明实施例中，所述波束赋形矢量调整单元，包括：
初始化子单元，用于初始化各个粒子的位置及更新速度；
迭代子单元，用于根据粒子群算法对每个粒子的位置及更新速度进行迭代更新；
比较子单元，用于将扇区中所有用户的渐进信道估计均方误差和设置为评价函数，比较所有变量的所有粒子从初始迭代到当前迭代次数范围内的评价函数值，得到 局部最优解、全局最优解以及评价函数的最优值；
控制子单元，用于当满足最大迭代次数或满足评价函数的迭代误差的条件时，停止粒子群迭代算法。
本发明实施例中，所述迭代单元，还用于当次迭代优化出的波束赋形矢量所对应的信道估计误差评价函数值大于前次迭代的结果时，停止迭代。
本发明实施例的技术方案中，将每个小区各垂直扇区的接收天线阵列波束赋形矢量固定，并为每个垂直扇区内的用户分配导频序列，以使信道估计误差最小化，得到导频调度结果；根据所述导频调度结果，优化接收波束赋形矢量，以使渐近信道估计误差和减小；迭代执行所述分配导频序列以及优化接收波束赋形矢量，直至所述渐近信道估计误差和满足预设条件。可见，针对大规模MIMO垂直分裂场景下的上行信道估计问题，本发明实施例通过对用户导频分配和基站端接收波束赋形的联合优化来提高上行信道估计精度，进而改善导频在扇区间进行复用时系统的导频污染水平。本发明实施例将一般大规模MIMO系统中已有的小区间协作导频调度方法扩展用于垂直分裂场景，进行垂直扇区间协作导频调度。另一方面，信道估计精度还与接收天线阵的加权向量(即接收波束赋形矢量)有关，因此将导频调度方案与接收天线加权向量一起进行联合优化设计，以信道估计均方误差和为代价函数，并采用复杂度较低的粒子群算法完成接收波束赋形矢量的优化求解。
附图说明
图1为本发明实施例的上行导频分配及接收波束赋形矢量联合优化方法的流程示意图；
图2为本发明实施例的上行导频分配及接收波束赋形矢量联合优化装置的结构组成示意图；
图3为本发明实施例的3D大规模MIMO系统模型示意图；
图4为本发明实施例的七小区，每个小区十二个用户场景下水平天线间距为10λ，垂直天线间距都为0.5λ，LS信道估计误差示意图；
图5为本发明实施例的七小区，每个小区十二个用户场景下基于LS信道估计，水平天线间距为10λ，垂直天线间距都为0.5λ不同方案的小区平均频谱效率对比示意图；
图6为本发明实施例的七小区，每个小区十二个用户，水平天线间距为10λ，垂 直天线间距都为5λ场景下，LS信道估计误差示意图；
图7为本发明实施例的七小区，每个小区十二个用户，水平天线间距为10λ，垂直天线间距都为5λ，基于LS信道估计的不同方案的小区平均频谱效率对比示意图。
具体实施方式
为了能够更加详尽地了解本发明的特点与技术内容，下面结合附图对本发明的实现进行详细阐述，所附附图仅供参考说明之用，并非用来限定本发明。
对于降低导频污染，提升信道估计精度，传统大规模MIMO系统上行传输中使用的方法主要包括：基于特征分解的信道估计算法、贝叶斯信道估计算法等精度更高的信道估计算法；时移的导频结构和冗余的导频结构等导频结构设计方法；以最小化信道估计均方误差为目标的导频优化分配方案和以系统总吞吐量为效用函数的导频分配方案等。但是以上方案都是在2D传统线天线阵的场景中进行的，并没有考虑实际大规模MIMO系统中天线数较多，受到空间的限制，天线阵列往往采用面阵的实际情况，即没有考虑垂直维度对系统性能的影响。另一方面，现有关于3D MIMO垂直分裂技术的研究，大多假设CSI是理想的，并未考虑实际中肯定存在的信道估计误差的影响，更未涉及到用户数较多时站间用户需要复用导频而带来的导频污染问题。针对以上研究现状，本发明实施例的目的就是针对3D大规模MIMO场景下引入垂直扇区化技术后，垂直扇区间复用导频时导频污染更加严峻的问题，提供一种上行导频分配及接收波束赋形矢量联合优化方法，该方法能够有效降低导频污染水平，提升3D大规模MIMO系统的信道估计精度，从而提升系统频谱效率。
本发明实施例的核心思想：第一步，固定每个小区各垂直扇区的接收天线阵列波束赋形矢量，以最小化信道估计误差为目标，用贪婪搜索算法协作式地为每个垂直扇区内的用户分配导频序列。第二步，在第一步已确定的导频调度结果的基础上，以渐近信道估计误差和最小为目标，优化接收波束赋形矢量。以上两个步骤进行交替迭代，若渐近信道估计误差和不再进一步降低，则联合优化结束。
图1为本发明实施例的上行导频分配及接收波束赋形矢量联合优化方法的流程示意图，如图1所示，所述上行导频分配及接收波束赋形矢量联合优化方法包括以下步骤：
步骤101：将每个小区各垂直扇区的接收天线阵列波束赋形矢量固定，并为每个垂直扇区内的用户分配导频序列，以使信道估计误差最小化，得到导频调度结果。
其中，垂直扇区化是一种3D MIMO小区垂直分裂技术，利用基站天线阵列的垂直波束将每个小区在垂直方向上划分为多个扇区，每个扇区都通过一个垂直波束进行服务。垂直扇区间可以实现时频资源的复用，从而提升整个网络的系统容量。3D波束赋形能够动态改变基站天线阵列下倾角或加权向量，优化基站的垂直覆盖范围，使边缘用户能有效接收信号。同时，3D波束赋形技术的使用可以同时实现水平维和垂直维的小区分裂，使同一时刻被服务的小区的用户数目加倍，进而提高系统的平均吞吐量。
本发明实施例中，将每个小区各垂直扇区的接收天线阵列波束赋形矢量固定为初始化值或者前次迭代的优化值。以最小化渐进信道估计误差和为优化目标，通过贪婪搜索在各垂直扇区内各调度一个复用导频序列的用户。
考虑一个3D大规模MIMO系统，L个水平扇区(统称为“小区”)构成一个协作簇，每个小区同时服务K(K<M)个单天线用户，用户均匀分布。每个小区划分两个垂直扇区，用θl0和θl1分别表示第l小区中服务近扇区和远扇区的天线下倾角。每个垂直扇区由一个阵子数为M(垂直方向Nv根，水平方向Nt根)的面阵天线服务，其中M的个数可达数百根。基站的总功率为P，第l个小区中近扇区功率为Pl0，远扇区功率为Pl1，用户的上行传输功率为pr。
采用块衰落信道模型，信道系数在T个OFDM符号(即相干时间)内保持不变。小区l中的用户k到目标基站j的M×1维上行信道矢量可以表示为
Figure PCTCN2016099533-appb-000001
其中βjlk表示大尺度衰落系数，包括路径损耗和阴影衰落。Rjlk表示第l小区中的用户k与基站j的M×M维信道接收相关矩阵。假设用户到基站有U条独立路径，每条路径的垂直到达角为θ且
Figure PCTCN2016099533-appb-000002
水平到达角为φ且
Figure PCTCN2016099533-appb-000003
进而，该用户与基站的第m根天线的信道系数为
Figure PCTCN2016099533-appb-000004
式中：
θu——第u条径的垂直到达角；
φu——第u条径的水平到达角；
Figure PCTCN2016099533-appb-000005
——第u条径的相位，
Figure PCTCN2016099533-appb-000006
gu——随机变量，gu～CN(0,1)；
D——相邻天线元素间距；
um——第m根天线的位置。
则第m根天线和第p根天线之间的相关度为
Figure PCTCN2016099533-appb-000007
天线阵列的总增益为A(θ,θ)，
Figure PCTCN2016099533-appb-000008
和
Figure PCTCN2016099533-appb-000009
分别表示小区j内用户i0处对应的来自基站l的近扇区和远扇区的波束增益，其中θD,l为基站l天线阵的下倾角(俯仰角)，θllk为小区l中用户k看向基站l的仰角。
阵列天线增益定义为
Figure PCTCN2016099533-appb-000010
其中：wl0∈WM×1是天线阵列的加权矢量。Br(θ,θD,l0)∈Br M×1是基站天线阵列的方向图，其第(n-1)NV+m个元素为Br,m,n(θ,θD)，表示第m行第n列天线阵元的增益，其表达式为
Figure PCTCN2016099533-appb-000011
其中
Figure PCTCN2016099533-appb-000012
为单个天线阵元的垂直方向图，θ3dB表示半功率角，dy和dz表示天线水平间距和垂直间距。
基于上述场景，固定各扇区垂直波束的天线阵列加权矢量(初始化结果或前次迭代的优化结果)。以最小化渐进信道估计误差和
Figure PCTCN2016099533-appb-000013
为优化目标，通过贪婪搜索在各垂直扇区内各调度一个复用第k个导频序列的用户。其中|Uk|表示使用同一 导频序列Ψk的用户集大小。小区j中用户ki的渐进估计误差为
Figure PCTCN2016099533-appb-000014
其中A(·)u表示上行波束方向图。
贪婪搜索过程包括：
1)初始化
Figure PCTCN2016099533-appb-000015
其中K’表示每扇区中的用户数，Ξi表示扇区i所选的用户集合。
2)For k＝1,...,K' do:
从第一个扇区的用户集合Ξ1中随机选择一个用户标号为j1,k，同时为它分配导频Ψk。然后将该用户从用户集合Ξ1中去除，即Ξ1＝Ξ1/j1,k。
For l＝2,...,2L do:
Figure PCTCN2016099533-appb-000016
End
End
上述过程中，for表示循环函数，循环的条件为for后面的参数；do表示执行以下指令，End表示结束循环结束。
步骤102：根据所述导频调度结果，优化接收波束赋形矢量，以使渐近信道估计误差和减小。
本发明实施例中，初始化各个粒子的位置及更新速度；根据粒子群算法对每个粒子的位置及更新速度进行迭代更新；将扇区中所有用户的渐进信道估计均方误差和设置为评价函数，比较所有变量的所有粒子从初始迭代到当前迭代次数范围内的评价函数值，得到局部最优解、全局最优解以及评价函数的最优值；当满足最大迭代次数或满足评价函数的迭代误差的条件时，停止粒子群迭代算法。
基于步骤102的结果，以最小化协作簇中所有用户的渐进信道估计误差之和为目标，优化各扇区的垂直波束天线阵列加权矢量，优化目标为
Figure PCTCN2016099533-appb-000017
考虑到复杂度的问题，求解时采用复杂度相对较低的粒子群算法。
粒子群算法的步骤如下所示：
1)初始化各个粒子的位置及更新速度。
初始化wl0和wl1的第[(n-1)Nv+m]个天线阵列元素第j个粒子的位置和更新速度：
Figure PCTCN2016099533-appb-000018
Figure PCTCN2016099533-appb-000019
其中，随机数ε∈U(0,1)，j＝1,2,...,S表示粒子的序号，S表示总的粒子数。
2)迭代：
(a)根据粒子群算法的迭代公式对每个粒子的位置及更新速度进行迭代更新。
Figure PCTCN2016099533-appb-000020
其中，r1和r1表示在[0,1]内均匀分布的随机数，c1和c2表示学习因子，一般取值为2，a为惯性权重，在仿真中取为a＝1.2-0.4×迭代索引/总迭代数。pj表示所有粒子的局部最优位置，pg表示所有粒子的全局最优位置。xj和
Figure PCTCN2016099533-appb-000021
分别表示变量第j个粒子的位置和更新速度。τ表示迭代索引。
(b)将协作簇中所有用户的渐进信道估计均方误差和设置为评价函数f，然后比较所有变量的所有粒子从初始迭代到当前迭代次数τ范围内的评价函数值，找出局部最优解pj(τ)，全局最优解pg(τ)以及评价函数的最优值
Figure PCTCN2016099533-appb-000022
(c)若果满足最大迭代次数或满足评价函数的迭代误差
Figure PCTCN2016099533-appb-000023
的条件，其中δ表示一个比较小的常数，粒子群迭代算法将停止。否则，令τ＝τ+1，转到步骤(b)。
步骤103：迭代执行所述分配导频序列以及优化接收波束赋形矢量，直至所述渐近信道估计误差和满足预设条件。
当次迭代优化出的波束赋形矢量所对应的信道估计误差评价函数值大于前次迭代的结果时，停止迭代。具体地，若当次迭代优化出的天线阵列加权矢量所对应的信道估计误差评价函数值fm大于前次迭代的结果，则迭代停止。
本发明实施例的技术方案，1)优化面阵天线阵列的加权矢量，而非只优化下倾角；2)垂直扇区间协作式导频分配与天线阵列加权矢量交替迭代优化。
本发明实施例的上述方案中，考虑的信道估计准则是最小二乘(LS，Least Square) 估计，优化中的代价函数也是基于LS估计误差计算得到的。具体的信道估计准则可以替换其他准则，如MMSE、最大后验(MAP，Maximum A Posteriori)等，代价函数仅需做对应的修改即可。此外，本发明实施例的接收波束赋形矢量设计是用复杂度较低的粒子群算法进行求次优解的，也可以使用其他方法来求解，例如半正定松弛法。
本发明实施例的技术方案，主要是为了解决大规模MIMO系统引入3D垂直扇区化技术后的上行信道估计问题，特别是为了抑制垂直扇区间复用导频所带来的更严重的导频污染。通过将垂直扇区间的导频调度与各垂直波束的接收赋形矢量设计联合优化，提高了信道估计精度，降低了导频污染的影响。具体从仿真结果可以看到：在3D大规模MIMO系统中，使用我们所提出的上行信道估计方案，LS归一化信道估计误差有所降低，信道估计精度改善4.5dB左右，小区平均频谱效率也有所提升，当天线数为100时，平均小区频谱效率提升5bps/Hz左右。而使用MMSE信道估计准则的信道估计精度与半功率波束带宽有关，但是用我们所提出的方案，信道估计精度同样有所提升，小区平均频谱效率也得到了相应的改善。
为了实现上述上行导频分配及接收波束赋形矢量联合优化方法，本发明实施例对应提供了一种上行导频分配及接收波束赋形矢量联合优化装置，如图2所示，所述上行导频分配及接收波束赋形矢量联合优化装置包括：
导频分配单元21，用于将每个小区各垂直扇区的接收天线阵列波束赋形矢量固定，并为每个垂直扇区内的用户分配导频序列，以使信道估计误差最小化，得到导频调度结果；
波束赋形矢量调整单元22，用于根据所述导频调度结果，优化接收波束赋形矢量，以使渐近信道估计误差和减小；
迭代单元23，用于迭代执行所述分配导频序列以及优化接收波束赋形矢量，直至所述渐近信道估计误差和满足预设条件。
所述导频分配单元21包括：
固定子单元211，用于将每个小区各垂直扇区的接收天线阵列波束赋形矢量固定为初始化值或者前次迭代的优化值。
所述导频分配单21元包括：
贪婪搜索子单元212，用于以最小化渐进信道估计误差和为优化目标，通过贪婪搜索在各垂直扇区内各调度一个复用导频序列的用户。
所述波束赋形矢量调整单元22，包括：
初始化子单元221，用于初始化各个粒子的位置及更新速度；
迭代子单元222，用于根据粒子群算法对每个粒子的位置及更新速度进行迭代更新；
比较子单元223，用于将扇区中所有用户的渐进信道估计均方误差和设置为评价函数，比较所有变量的所有粒子从初始迭代到当前迭代次数范围内的评价函数值，得到局部最优解、全局最优解以及评价函数的最优值；
控制子单元224，用于当满足最大迭代次数或满足评价函数的迭代误差的条件时，停止粒子群迭代算法。
所述迭代单元23，还用于当次迭代优化出的波束赋形矢量所对应的信道估计误差评价函数值大于前次迭代的结果时，停止迭代。
本领域技术人员应当理解，图2所示的上行导频分配及接收波束赋形矢量联合优化装置中的各单元的实现功能可参照前述上行导频分配及接收波束赋形矢量联合优化方法的相关描述而理解。图2所示的上行导频分配及接收波束赋形矢量联合优化装置中的各单元的功能可通过运行于处理器上的程序而实现，也可通过具体的逻辑电路而实现。
考虑如图3所示的3D大规模MIMO系统，L个水平扇区(统称为“小区”)构成一个协作簇，每个小区同时服务K(K<M)个单天线用户，用户均匀分布。每个小区划分两个垂直扇区，用θl0和θl1分别表示第l小区中服务近扇区和远扇区的天线下倾角。每个垂直扇区由一个阵子数为M(垂直方向Nv根，水平方向Nt根)的面阵天线服务，其中M的个数可达数百根。基站的总功率为P，第l个小区中近扇区功率为Pl0，远扇区功率为Pl1，用户的上行传输功率为pr。
采用块衰落信道模型，信道系数在T个OFDM符号(即相干时间)内保持不变。小区l中的用户k到目标基站j的M×1维上行信道矢量可以表示为
Figure PCTCN2016099533-appb-000024
其中βjlk表示大尺度衰落系数，包括路径损耗和阴影衰落。Rjlk表示第l小区中的用户k与基站j的M×M维信道接收相关矩阵。假设用户到基站有U条独立路径，每条路径的垂直到达角为θ且
Figure PCTCN2016099533-appb-000025
水平到达角为φ且
Figure PCTCN2016099533-appb-000026
进而，该用户与基站的第m根天线的信道系数为
Figure PCTCN2016099533-appb-000027
式中：
θu——第u条径的垂直到达角；
φu——第u条径的水平到达角；
Figure PCTCN2016099533-appb-000028
——第u条径的相位，
Figure PCTCN2016099533-appb-000029
gu——随机变量，gu～CN(0,1)；
D——相邻天线元素间距；
um——第m根天线的位置。
则第m根天线和第p根天线之间的相关度为
Figure PCTCN2016099533-appb-000030
天线阵列的总增益为A(θ,θ)，
Figure PCTCN2016099533-appb-000031
和
Figure PCTCN2016099533-appb-000032
分别表示小区j内用户i0处对应的来自基站l的近扇区和远扇区的波束增益，其中θD,l为基站l天线阵的下倾角(俯仰角)，θllk为小区l中用户k看向基站l的仰角。阵列天线增益定义为
Figure PCTCN2016099533-appb-000033
其中：wl0∈WM×1是天线阵列的加权矢量。Br(θ,θD,l0)∈Br M×1是基站天线阵列的方向图，其第(n-1)NV+m个元素为Br,m,n(θ,θD)，表示第m行第n列天线阵元的增益，其表达式为
Figure PCTCN2016099533-appb-000034
其中
Figure PCTCN2016099533-appb-000035
为单个天线阵元的垂直方向图，θ3dB表示半功率角，dy和 dz表示天线水平间距和垂直间距。
在上行信道估计阶段，根据用户的地理位置将用户分为两个扇区，用每个扇区用户的平均俯仰角作为服务每个扇区天线阵列的下倾角。假设每个小区近扇区的用户数为K0，远扇区用户数为K1，且K0+K1＝K。用于上行信道估计的导频长度τ(τ≥max(K0,K1))。另外，我们将导频长度与每个小区总用户数的比值定义为导频复用因子，即：r＝τ/K。对于导频污染最严重的情况，即K0＝K1＝K'＝K/2，r＝1/2时，且各小区的近扇区用户k0和远扇区用户k1使用相同的导频序列，记为Ψk(1×τ的行向量)，则基站l接收到的信号为
Figure PCTCN2016099533-appb-000036
其中pr表示每个用户的平均发送功率，A(·)u表示上行波束方向图，θD,j0表示服务小区j近扇区用户天线阵列的下倾角，
Figure PCTCN2016099533-appb-000037
表示小区l中的用户ki到基站j的仰角，zj表示附加高斯白噪声。
通过LS信道估计，则小区j中的用户ki(i＝0,1)的信道估计结果可以表示为：
Figure PCTCN2016099533-appb-000038
当M→∞时，归一化信道估计均方误差可以表示为
Figure PCTCN2016099533-appb-000039
从上式中我们可以看到LS估计器的渐进MSE结果只跟大尺度衰落系数以及天线增益有关，合理的优化这些参量信道估计精确度将得到进一步提升。
在下行数据传输阶段，对于r＝1/2的情况，每个扇区仍然使用一个下行波束进行服务。用
Figure PCTCN2016099533-appb-000040
表示基站发送给小区j近扇区用户i0的符号。假设
Figure PCTCN2016099533-appb-000041
服从均值为0，方差为1的独立同分布。若采用最大比发送，则波束赋形矢量可以表示为
Figure PCTCN2016099533-appb-000042
其中
Figure PCTCN2016099533-appb-000043
那么，小区j近扇区用户i0收到的数据符号可以表示为
Figure PCTCN2016099533-appb-000044
其中A(·)d表示基站的下行波束方向图，Pl0和Pl1分别基站服务近扇区用户和远扇区用户的发送功率，
Figure PCTCN2016099533-appb-000045
表示用户接收到的高斯白噪声。
令
Figure PCTCN2016099533-appb-000046
则式(4)可以重新表示为
Figure PCTCN2016099533-appb-000047
上式中，第一项表示期望信号，其它项为噪声项，包括扇区内干扰，扇区间干扰，小区间干扰和噪声。进一步，我们可以得到小区j近扇区用户i0的SINR以及和速率的表达式：
Figure PCTCN2016099533-appb-000048
Figure PCTCN2016099533-appb-000049
上式中，(T-τ)/T表示系统的导频开销。
本发明实施例提出的上行导频分配及接收波束赋形矢量联合优化方法如下(针对r＝1/2的场景)：
第一步：导频分配。
定义Uk为发送导频序列ψk的用户集合，|Uk|表示用户集合的大小且|Uk|＝2L，集合Sk表示与导频序列对应的扇区集合，扇区i的用户集合为Ξi。对于给定的集合Uk，若使用式(3)中的渐进信道估计误差作为优化目标，则导频分配策略的效用函数可以定义为
Figure PCTCN2016099533-appb-000050
其中，
Figure PCTCN2016099533-appb-000051
表示集合Uk中的用户j的渐进信道估计误差，该用户位于集合Sk中的第j个垂直扇区中。
固定服务各扇区垂直波束的天线阵列加权矢量，以最小化渐进信道估计误差
Figure PCTCN2016099533-appb-000052
为优化目标，用贪婪搜索算法为给用户分配导频。具体步骤为：
1)初始化
Figure PCTCN2016099533-appb-000053
2)For k＝1,...,K' do:
从第一个扇区的用户集合Ξ1随机选择选择一个用户j1,k，同时为它分配导频Ψk，同时将该用户从用户集合中去除，Ξ1＝Ξ1/j1,k.
For l＝2,...,2L do:
Figure PCTCN2016099533-appb-000054
End
End
第二步：垂直波束接收波束赋形矢量优化。
在第一步得到的用户导频分配基础上，接下来的垂直波束接收波束赋形矢量优化将进一步提升信道估计精度。这里优化目标可以表示为
Figure PCTCN2016099533-appb-000055
考虑到优化问题求解复杂度的问题，这里考虑使用一种复杂度较低的次优求解算法粒子群算法。对于本优化问题，粒子群算法的步骤如下所示：
1)初始化各个粒子的位置及更新速度。
初始化wl0和wl1的第[(n-1)Nv+m]个天线阵列元素第j个粒子的位置和更新速度
Figure PCTCN2016099533-appb-000056
Figure PCTCN2016099533-appb-000057
其中，随机数ε∈U(0,1)，j＝1,2,...,S表示粒子的序号，S表示表示总的粒子数目。
2)迭代：
(a)根据粒子群算法的迭代公式对每个粒子的位置及更新速度进行迭代更新。
Figure PCTCN2016099533-appb-000058
其中，r1和r1表示在[0,1]内均匀分布的随机数，c1和c2表示学习因子，一般取值为2，a表示惯性因子(仿真中我们取a＝1.2-0.4×Iterationindex/MaximumIterations)，经验值为0.8。pj表示所有粒子的局部最优位置，pg表示所有粒子的全局最优位置。xj和
Figure PCTCN2016099533-appb-000059
分别表示变量第j个粒子的位置和更新速度。τ表示迭代序号。
(b)将系统中所有用户的信道估计均方误差设置为评价函数f，然后比较所有变量的所有粒子从初始迭代到当前迭代次数τ范围内的评价函数函数值，找出局部最优解pj(τ)，全局最优解pg(τ)以及评价函数的最优值
Figure PCTCN2016099533-appb-000060
(c)若果满足最大迭代次数或满足评价函数的迭代误差
Figure PCTCN2016099533-appb-000061
的条件，其中δ表示一个比较小的常数，粒子群迭代算法将停止。否则，令τ＝τ+1，转到步骤(b)。
重复第一步和第二步，若当前迭代下优化的天线阵列加权矢量对应的系统信道估计误差大于上次迭代优化的天线阵列加权矢量对应的系统信道估计误差，则迭代停止。
在求解优化的过程中，使用了复杂度较低的粒子群算法。在每次迭代中，计算所有用户的天线增益的复杂度是o(2LK'·2L·M2)，计算所有用户的信道估计均方误差的复杂度是o(2LK')，所以计算所有粒子的信道估计均方误差需要的复杂度是o(S[(2L)2K'M2+2LK'])。在我们的方案中需要优化2LM个变量，迭代更新所有粒子的位置及更新速度需要的复杂度是o(2LMS)。所以我们所提出的方案需要的计算复杂度为o(2LMS)+o(S[(2L)2K'M2+2LK'])。在3D大规模MIMO系统中，天线数远远大于小区数L和用户数K，计算复杂度随着天线数的平方增长。
下面结合具体应用场景对本发明实施例的技术方案做进一步详细描述。
实施例1：
考虑一个包含七小区的协作簇，每个小区十二个用户的3D大规模MIMO场景， 每用户天线数为1。信道采用块衰落模型，如上所述。由于假设天线间信道独立，仿真中的面天线阵的列数设为4列不变，行天线数随着天线总数的增加而增加。其他参数设置如下：基站间距离为1000m，基站高度为32m，用户高度为1.5m，水平天线间距为10λ，垂直天线间距都为0.5λ，路径损耗系数为3，垂直半功率带宽为6.5°，导频信噪比为5dB，下行数据信噪比为20dB，相干时间长度假设为18个OFDM符号。
将本发明实施例的方案与传统2D大规模MIMO系统(小区间复用导频)中LS估计方案，3D大规模MIMO系统中传统LS估计方案(无优化)以及3D大规模MIMO系统中只进行导频分配的方案(无垂直波束调整)进行比较。图4对比了各方案的归一化信道估计误差。2D大规模MIMO系统由于是小区间复用导频，导频污染较轻，因此信道估计误差最小。而在3D大规模MIMO系统中扇区间复用同一组导频，导频污染较为严重，因此信道估计误差较大，而所提方案在一定程度上能够降低导频污染，提升信道估计精度，但是性能仍然不如小区间复用相同导频的传统大规模MIMO系统信道估计误差，性能差距在1dB左右。图5对比了传统大规模MIMO方案和3D大规模MIMO方案的小区平均频谱效率，虽然3D大规模MIMO系统信道估计误差较大，但是3D大规模MIMO系统中导频开销小，性能仍然较好。而所提方案能够通过导频调度以及接收加权矢量的联合优化降低导频污染，提升信道估计精度，因此有效提升了系统的平均频谱效率。另外在传统的2D大规模MIMO方案中不仅导频开销较高，同时由于垂直天线间的相关性较高，因此在下行传输时，根据估计信道所设计的MRT预编码方案性能也较差，这也会降低传统2D大规模MIMO系统的性能。以上两方面原因会导致传统的2D大规模MIMO系统性能较差。
实施例2：
考虑七小区十二用户的3D大规模MIMO场景，采用LS信道估计算法进行信道估计。水平天线间距为10λ，垂直天线间距都为5λ。其余场景配置与实施例1相同。
从图6中可以看出，当垂直天线间距为5λ，水平天线间距为10λ时,天线间的相关性有所降低，因此波束赋形技术的所带来的性能增益有所下降。从图7中可以看出，小区平均频谱效率较垂直天线间距为0.5λ时有所降低，但是所提方案在一定程度上仍然可以提升信道估计精度。图4-7给出了对应的小区平均频谱效率对比图，从图中可以看出，由于天线间的相关性有所降低，因此根据估计信道所设计的MRT预编码 方案所带来的性能增益将有所提升，因此相对于垂直天线间距为0.5λ的场景来说，各个方案的性能都有所提升。而在大规模MIMO垂直分裂场景下使用改善的信道估计信息设计预编码矩阵后，系能性能得到了进一步提升。
本发明实施例所记载的技术方案之间，在不冲突的情况下，可以任意组合。
在本发明所提供的几个实施例中，应该理解到，所揭露的方法和智能设备，可以通过其它的方式实现。以上所描述的设备实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，如：多个单元或组件可以结合，或可以集成到另一个系统，或一些特征可以忽略，或不执行。另外，所显示或讨论的各组成部分相互之间的耦合、或直接耦合、或通信连接可以是通过一些接口，设备或单元的间接耦合或通信连接，可以是电性的、机械的或其它形式的。
上述作为分离部件说明的单元可以是、或也可以不是物理上分开的，作为单元显示的部件可以是、或也可以不是物理单元，即可以位于一个地方，也可以分布到多个网络单元上；可以根据实际的需要选择其中的部分或全部单元来实现本实施例方案的目的。
另外，在本发明各实施例中的各功能单元可以全部集成在一个第二处理单元中，也可以是各单元分别单独作为一个单元，也可以两个或两个以上单元集成在一个单元中；上述集成的单元既可以采用硬件的形式实现，也可以采用硬件加软件功能单元的形式实现。
本申请实施例提供的上行导频分配及接收波束赋形矢量联合优化装置中的各个模块或单元可以通过一个或多个数字信号处理器(DSP)、专用集成电路(ASIC)、处理器、微处理器、控制器、微控制器、现场可编程阵列(FPGA)、可编程逻辑器件或其他电子单元或其任意组合来实现。在本申请实施例中描述的一些功能或处理也可以通过在处理器上执行的软件来实现。
例如，本发明的实施例还提供了一种上行导频分配及接收波束赋形矢量联合优化装置，例如该装置可以应用于一基站，包括：
处理器；
用于存储处理器可执行指令的存储器；
其中，所述处理器被配置为：
将每个小区各垂直扇区的接收天线阵列波束赋形矢量固定，并为每个垂直扇区内的用户分配导频序列，以使信道估计误差最小化，得到导频调度结果；
根据所述导频调度结果，优化接收波束赋形矢量，以使渐近信道估计误差和减小；
迭代执行所述分配导频序列以及优化接收波束赋形矢量，直至所述渐近信道估计误差和满足预设条件。
工业实用性
本申请的方法和装置可应用于通信领域中，主要可应用于基站侧上行导频分配及接收波束赋形矢量联合优化。本发明实施例将一般大规模MIMO系统中已有的小区间协作导频调度方法扩展用于垂直分裂场景，进行垂直扇区间协作导频调度。另一方面，信道估计精度还与接收天线阵的加权向量(即接收波束赋形矢量)有关，因此将导频调度方案与接收天线加权向量一起进行联合优化设计，以信道估计均方误差和为代价函数，并采用复杂度较低的粒子群算法完成接收波束赋形矢量的优化求解。
以上所述，仅为本发明的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本发明的保护范围之内。

Claims (10)

  1. 一种上行导频分配及接收波束赋形矢量联合优化方法，其中，所述方法包括：
    将每个小区各垂直扇区的接收天线阵列波束赋形矢量固定，并为每个垂直扇区内的用户分配导频序列，以使信道估计误差最小化，得到导频调度结果；
    根据所述导频调度结果，优化接收波束赋形矢量，以使渐近信道估计误差和减小；
    迭代执行所述分配导频序列以及优化接收波束赋形矢量，直至所述渐近信道估计误差和满足预设条件。
  2. 根据权利要求1所述的上行导频分配及接收波束赋形矢量联合优化方法，其中，所述将每个小区各垂直扇区的接收天线阵列波束赋形矢量固定，包括：
    将每个小区各垂直扇区的接收天线阵列波束赋形矢量固定为初始化值或者前次迭代的优化值。
  3. 根据权利要求1所述的上行导频分配及接收波束赋形矢量联合优化方法，其中，所述为每个垂直扇区内的用户分配导频序列，以使信道估计误差最小化，包括：
    以最小化渐进信道估计误差和为优化目标，通过贪婪搜索在各垂直扇区内各调度一个复用导频序列的用户。
  4. 根据权利要求1所述的上行导频分配及接收波束赋形矢量联合优化方法，其中，所述根据所述导频调度结果，优化接收波束赋形矢量，以使渐近信道估计误差和减小，包括：
    初始化各个粒子的位置及更新速度；
    根据粒子群算法对每个粒子的位置及更新速度进行迭代更新；
    将扇区中所有用户的渐进信道估计均方误差和设置为评价函数，比较所有变量的所有粒子从初始迭代到当前迭代次数范围内的评价函数值，得到局部最优解、全局最优解以及评价函数的最优值；
    当满足最大迭代次数或满足评价函数的迭代误差的条件时，停止粒子群迭代算法。
  5. 根据权利要求4所述的上行导频分配及接收波束赋形矢量联合优化方法，其中，所述直至所述渐近信道估计误差和满足预设条件，包括：
    当次迭代优化出的波束赋形矢量所对应的信道估计误差评价函数值大于前次迭代的结果时，停止迭代。
  6. 一种上行导频分配及接收波束赋形矢量联合优化装置，其中，所述装置包括：
    导频分配单元，设置为将每个小区各垂直扇区的接收天线阵列波束赋形矢量固定，并为每个垂直扇区内的用户分配导频序列，以使信道估计误差最小化，得到导频调度结果；
    波束赋形矢量调整单元，设置为根据所述导频调度结果，优化接收波束赋形矢量，以使渐近信道估计误差和减小；
    迭代单元，设置为迭代执行所述分配导频序列以及优化接收波束赋形矢量，直至所述渐近信道估计误差和满足预设条件。
  7. 根据权利要求6所述的上行导频分配及接收波束赋形矢量联合优化装置，其中，所述导频分配单元包括：
    固定子单元，设置为将每个小区各垂直扇区的接收天线阵列波束赋形矢量固定为初始化值或者前次迭代的优化值。
  8. 根据权利要求6所述的上行导频分配及接收波束赋形矢量联合优化装置，其中，所述导频分配单元包括：
    贪婪搜索子单元，设置为以最小化渐进信道估计误差和为优化目标，通过贪婪搜索在各垂直扇区内各调度一个复用导频序列的用户。
  9. 根据权利要求6所述的上行导频分配及接收波束赋形矢量联合优化装置，其中，所述波束赋形矢量调整单元，包括：
    初始化子单元，设置为初始化各个粒子的位置及更新速度；
    迭代子单元，设置为根据粒子群算法对每个粒子的位置及更新速度进行迭代更新；
    比较子单元，设置为将扇区中所有用户的渐进信道估计均方误差和设置为评价函数，比较所有变量的所有粒子从初始迭代到当前迭代次数范围内的评价函数值，得到局部最优解、全局最优解以及评价函数的最优值；
    控制子单元，设置为当满足最大迭代次数或满足评价函数的迭代误差的条件时，停止粒子群迭代算法。
  10. 根据权利要求9所述的上行导频分配及接收波束赋形矢量联合优化装置，其中，所述迭代单元，还设置为当次迭代优化出的波束赋形矢量所对应的信道估计误差评价函数值大于前次迭代的结果时，停止迭代。
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