CN102882570A

CN102882570A - Optimum transceiving combined processing method for communication among equipment in mobile communication network

Info

Publication number: CN102882570A
Application number: CN2012103704430A
Authority: CN
Inventors: 许威; 朱道华; 李朝峰; 雷鸣; 赵春明
Original assignee: Southeast University
Current assignee: NEC China Co Ltd; Southeast University
Priority date: 2012-09-28
Filing date: 2012-09-28
Publication date: 2013-01-16
Anticipated expiration: 2032-09-28
Also published as: CN102882570B

Abstract

The present invention discloses an optimal sending and receiving joint processing method for inter-device communication in a mobile communication network, which is carried out in the following steps: firstly, the base station in the time division duplex communication system can equivalently obtain its communication with UE3 through uplink channel estimation Downlink channel information and downlink interference channel information to D2D receiving users; UE1 can obtain its transmission channel information to UE2 and its interference channel information to UE3 by using a similar channel estimation method, and feed them back to the base station. Next, the base station calculates the precoding matrix and receiving matrix from the base station to UE3 and from UE1 to UE2 according to the criterion of minimizing the mean square error or maximizing the channel capacity according to all four types of channel information obtained. Finally, the base station notifies UE1, UE2 and UE3 of the calculated transmit precoding matrix and receive matrix through the control channel.

Description

Optimal Transceiver Joint Processing Method for Inter-Device Communication in Mobile Communication Network

技术领域 technical field

本发明涉及一种移动通信网络下设备间通信的最优收发联合处理方法，属于多天线干扰信道中的收发联合处理方法设计领域。The invention relates to an optimal transceiver joint processing method for communication between devices under a mobile communication network, and belongs to the design field of the transceiver joint processing method in multi-antenna interference channels.

背景技术 Background technique

近几年随着LTE-A的进一步演进发展，向现有移动网络融合例如异构网络、多点协同传输(CoMP)等新型移动通信技术引起了广泛的关注。Device-to-Device(D2D)通信是一种允许本地距离较近的终端之间直接进行通信的新型技术。在已有的移动通信系统的控制下，通过复用小区资源能够增加蜂窝通信系统频谱效率，降低终端发射功率，在一定程度上解决无线通信系统频谱资源匮乏的问题。相对于现有的无线本地局域网和蓝牙等技术，D2D通信设备工作在许可频段，不仅能够灵活的采用多种资源分配策略还能有效的抑制干扰从而保证通信的可靠性，提升用户的服务质量。尽管D2D通信技术拥有很多优点，但是由于在同频段上传输的D2D信号会对原小区信号产生干扰，甚至有可能降低了小区容量因而导致频谱利用率下降。In recent years, with the further evolution and development of LTE-A, the integration of new mobile communication technologies such as heterogeneous networks and coordinated multipoint transmission (CoMP) into existing mobile networks has attracted widespread attention. Device-to-Device (D2D) communication is a new type of technology that allows direct communication between terminals with relatively close local distances. Under the control of the existing mobile communication system, the spectrum efficiency of the cellular communication system can be increased by multiplexing cell resources, the terminal transmission power can be reduced, and the problem of lack of spectrum resources in the wireless communication system can be solved to a certain extent. Compared with existing technologies such as wireless local area network and Bluetooth, D2D communication equipment works in the licensed frequency band, which can not only flexibly adopt multiple resource allocation strategies, but also effectively suppress interference to ensure communication reliability and improve user service quality. Although the D2D communication technology has many advantages, because the D2D signal transmitted on the same frequency band will interfere with the signal of the original cell, it may even reduce the capacity of the cell, resulting in a decrease in spectrum utilization.

近年来，多天线技术（MIMO）已经受到研究人员的广泛关注，该技术能够利用发射端和接收端的多根天线提供的空间自由度对已有时间和频率资源进行空间上的复用，从而显著提高频谱资源的利用效率和系统的通信容量。另一方面，多天线技术可以利用空间自由度抑制同频干扰。In recent years, multi-antenna technology (MIMO) has received extensive attention from researchers. This technology can use the spatial freedom provided by multiple antennas at the transmitting end and receiving end to spatially multiplex existing time and frequency resources, thereby significantly Improve the utilization efficiency of spectrum resources and the communication capacity of the system. On the other hand, multi-antenna technology can suppress co-channel interference by using the spatial degree of freedom.

在传统多用户MIMO系统中，几种基于MIMO技术抑制多用户干扰的优化算法被广泛运用于点到点信道和两跳信道中。优化的系统指标包含有接收信号的均方误差(MSE)、系统容量(Sum-Rate)等。还有一些次优的算法例如迫零算法(ZF)和最大化信泄噪比(SLNR)算法，迫零算法将各个用户的信号通过预处理完全映射到干扰信道的零空间中，这样可以完全消除其它干扰用户对目标用户的干扰。SLNR则将传统地对信干噪比(SINR)指标的优化转变为对信泄露噪比的优化，将优化问题去耦合后可以求得闭式解，从而大大降低了优化问题的计算复杂度，SLNR的性能却没有很多损失。In traditional multi-user MIMO systems, several optimization algorithms based on MIMO technology to suppress multi-user interference are widely used in point-to-point channels and two-hop channels. The optimized system index includes the mean square error (MSE) of the received signal, the system capacity (Sum-Rate) and so on. There are also some suboptimal algorithms such as the zero-forcing algorithm (ZF) and the maximum signal-to-noise ratio (SLNR) algorithm. The zero-forcing algorithm completely maps the signals of each user to the null space of the interference channel through preprocessing, so that it can completely Eliminate the interference of other interfering users on the target user. SLNR transforms the traditional optimization of the signal-to-interference-noise ratio (SINR) index into the optimization of the signal leakage-to-noise ratio. After decoupling the optimization problem, a closed-form solution can be obtained, thereby greatly reducing the computational complexity of the optimization problem. SLNR performance without much loss.

对于包含D2D的通信场景很少有研究关注基于多天线技术的干扰抑制问题。最近，有研究人员设计了一种基于迫零思想的完全抑制基站对D2D接收用户干扰的方案。该方案的思想就是将基站发送给无线移动网络用户的信号映射到基站对D2D接收用户的干扰信道零空间中去。由于完全抑制了来自于无线移动网络链路的干扰，D2D链路可以最大功率发送数据。该方案存在不足就是没有考虑D2D发送用户对无线移动网络用户的干扰问题。本专利将利用所有的传输信道和干扰信道信息，综合地优化无线移动网络链路和D2D链路的发送预编码矩阵和接收矩阵，达到最小化小区接收信号均方误差以降低接收误比特率以及最大化两条链路和速率的目标。对实际系统的性能有很大的提高。For communication scenarios involving D2D, few studies focus on the problem of interference suppression based on multi-antenna technology. Recently, some researchers have designed a scheme based on the idea of zero-forcing to completely suppress the interference of base stations to D2D receiving users. The idea of this solution is to map the signal sent by the base station to the wireless mobile network user into the interference channel null space of the base station to the D2D receiving user. Since the interference from the wireless mobile network link is completely suppressed, the D2D link can transmit data with maximum power. The disadvantage of this solution is that it does not consider the interference problem of the D2D sending user to the wireless mobile network user. This patent will use all the transmission channel and interference channel information to comprehensively optimize the transmission precoding matrix and reception matrix of the wireless mobile network link and the D2D link, so as to minimize the mean square error of the received signal of the cell to reduce the received bit error rate and The goal of maximizing both links and rates. The performance of the actual system has been greatly improved.

发明内容 Contents of the invention

本发明针对现有技术的不足，提供一种移动通信网络下设备间通信的最优收发联合处理方法，联合优化的具体实现可以通过使无线移动网络链路和D2D链路的接收信号的均方误差最小化从而降低误比特率，或者优化整个系统的和速率使之最大化。Aiming at the deficiencies of the prior art, the present invention provides an optimal sending and receiving joint processing method for inter-device communication in a mobile communication network. The specific realization of the joint optimization can be achieved by making the mean square of the received signals of the wireless mobile network link and the D2D link Minimize the error to reduce the bit error rate, or optimize the sum rate of the entire system to maximize it.

本发明采用的技术方案为：一种移动通信网络下设备间通信的最优收发联合处理方法，该方法按以下步骤进行：The technical scheme adopted by the present invention is: a method for optimally transmitting and receiving joint processing of communication between devices under a mobile communication network, the method is carried out according to the following steps:

1）D2D(Device-to-Device)发送用户UE1(User Equipment)和D2D接收用户UE2分别拥有N₁根发送天线和M₁根接收天线，基站拥有N₂根发送天线，网络用户UE3拥有M₂根接收天线。D2D发送用户和基站的发送功率分别为P₁、P₂，它们分别发送多流数据x₁和x₂(维度分别为S₁×1和S₂×1)给各自的接收机。S₁和S₂分别表示D2D链路和无线移动网络链路传输的数据流数。1) D2D (Device-to-Device) sending user UE1 (User Equipment) and D2D receiving user UE2 have N ₁ sending antennas and M ₁ receiving antennas respectively, the base station has N ₂ sending antennas, and network user UE3 has M ₂ Root receiving antenna. The transmission powers of the D2D transmitting user and the base station are respectively P ₁ and P ₂ , and they respectively transmit multi-stream data x ₁ and x ₂ (dimensions are S ₁ ×1 and S ₂ ×1 respectively) to their respective receivers. S ₁ and S ₂ represent the number of data streams transmitted by the D2D link and the wireless mobile network link, respectively.

2）D2D链路与无线移动网络链路采用非正交方式共享时频资源，即D2D设备对之间通信的物理信道与某个无线移动网络用户的物理资源重叠。在TDD(TimeDivision Duplexing)系统中，为了使基站获得完整的信道信息来实现最优收发设计，基站可以通过上行信道估计获得基站至UE3的下行信道H₂以及基站至UE2的下行干扰信道H_2，1；通过上述相同的信道估计方式，UE1同样可以获得它至UE2的传输信道H₁和它与UE3之间的干扰信道H_1，2。最后UE1将H₁和H_1，2反馈给基站。2) D2D links and wireless mobile network links share time-frequency resources in a non-orthogonal manner, that is, the physical channel for communication between D2D device pairs overlaps with the physical resources of a certain wireless mobile network user. In a TDD (Time Division Duplexing) system, in order for the base station to obtain complete channel information to achieve optimal transceiver design, the base station can obtain the downlink channel _H2 from the base station to UE3 and the downlink interference channel H2 from the base station to UE2 through uplink channel estimation _{, 1} ; UE1 can also obtain the transmission channel H ₁ from it to UE2 and the interference channel H _1,2 between it and UE3 through the same channel estimation method as above. Finally, UE1 feeds back H ₁ and H _{1, 2} to the base station.

3）基站根据获得的完整的四类信道信息通过最小化接收信号均方误差降低误比特率或者最大化信道信道容量准则优化基站、UE1的预编码矩阵和UE2、UE3的接收矩阵。由于上述最优化问题是非凸问题且不存在闭式解，所以本专利提供了两种可选的迭代算法。为方便叙述，将最小化接收信号均方误差迭代算法称作算法一，而将最大化信道容量算法称作算法二。算法一的迭代计算方法如下：3) The base station optimizes the precoding matrix of the base station and UE1 and the receiving matrices of UE2 and UE3 by minimizing the mean square error of the received signal to reduce the bit error rate or maximizing the channel capacity criterion according to the obtained complete four types of channel information. Since the above optimization problem is a non-convex problem and there is no closed-form solution, this patent provides two optional iterative algorithms. For the convenience of description, the iterative algorithm for minimizing the received signal mean square error is called Algorithm 1, and the algorithm for maximizing channel capacity is called Algorithm 2. The iterative calculation method of Algorithm 1 is as follows:

步骤1：令迭代次数变量n=0，按照传输信道H₁和H₂奇异值分解结果初始化发送预编码矩阵

u＝1,2；下标u是标识链路的变量，其中u=1和u=2分别表示D2D链路(UE1和UE2之间的通信链路)和移动网络下行链路(基站到UE3的通信链路)。Step 1: Let the number of iterations variable n=0, initialize the transmission precoding matrix according to the singular value decomposition results of the transmission channels H ₁ and H ₂

u=1,2; the subscript u is a variable that identifies the link, where u=1 and u=2 represent the D2D link (communication link between UE1 and UE2) and mobile network downlink (base station to UE3 communication link).

步骤2：n=n+1，然后根据下面式(2)计算第n+1次迭代过程中链路u的接收矩阵 $F_{u}^{(n + 1)};$ Step 2: n=n+1, and then calculate the receiving matrix of link u in the n+1th iteration according to the following formula (2): $f_{u}^{(no + 1)};$

步骤3：将步骤2计算得到的

代入功率约束条件下面式(3)求得参数

其中

是保证功率约束条件成立而引入的拉格朗日乘数；Step 3: Calculated in step 2

Substituting the power constraints into the following equation (3) to obtain the parameters

in

is the Lagrangian multiplier introduced to ensure the establishment of power constraints;

步骤4：将步骤2和步骤3中计算得到的

和

代入下面式(1)可以计算得到第n+1次迭代过程中的发送预编码矩阵

Step 4: Calculated in Step 2 and Step 3

and

Substituting the following formula (1) can be calculated to obtain the transmission precoding matrix in the n+1th iteration process

步骤5：重复步骤2到步骤4直到和收敛。Step 5: Repeat Step 2 to Step 4 until and convergence.

${W W}_{u u}^{((n no + + 11))} = = {(({H h}_{u u}^{H h} {F f}_{u u}^{((n no + + 11))} {F f}_{u u}^{H h,, ((n no + + 11))} {H h}_{u u} + + {H h}_{u u,, m m}^{H h} {F f}_{m m}^{((n no + + 11))} {F f}_{m m}^{H h,, ((n no + + 11))} {H h}_{u u,, m m} + + {μ μ}_{u u}^{((n no + + 11))} I I))}^{- - 11} {H h}_{u u}^{H h} {F f}_{u u}^{((n no + + 11))} - - - - - - ((11))$

${F f}_{u u}^{((n no + + 11))} = = {(({H h}_{u u} {W W}_{u u}^{((n no))} {W W}_{u u}^{H h,, ((n no))} {H h}_{u u}^{H h} + + {H h}_{m m,, u u}^{H h} {W W}_{m m}^{((n no))} {W W}_{m m}^{H h,, ((n no))} {H h}_{m m,, u u} + + {σ σ}_{u u}^{22} I I))}^{- - 11} {H h}_{u u} {W W}_{u u}^{((n no))} - - - - - - ((22))$

${Σ Σ}_{i i = = 11}^{{N N}_{u u}} \frac{{G G}_{u u}^{[[tt tt]],, ((n no + + 11))}}{[[{μ μ}_{u u}^{((n no + + 11))} + + {Δ Δ}_{u u}^{[[tt tt]],, ((n no + + 11))}]]} = = Pu Pu - - - - - - ((33))$

式(1)至式(3)中下标u和m都是标识链路的变量且u,m∈{1,2},u≠m，而n表示迭代次数变量，大写H表示矩阵的共轭转置操作，I表示单位阵。

和

分别代表了矩阵

和

主对角线上的第t个元素。其中，

G_{u}^{(n + 1)} = U_{u}^{H, (n + 1)} H_{u}^{H} F_{u}^{(n + 1)} F_{u}^{H, (n + 1)} H_{u}^{H} U_{u}^{(n + 1)},

和

分别是

特征分解的对角阵和酉矩阵。In formulas (1) to (3), the subscripts u and m are variables that identify links and u, m∈{1,2}, u≠m, while n represents the number of iterations variable, and capital H represents the total number of matrices. Yoke transpose operation, I means identity matrix.

and

respectively represent the matrix

and

The tth element on the main diagonal. in,

G_{u}^{(no + 1)} = u_{u}^{h, (no + 1)} h_{u}^{h} f_{u}^{(no + 1)} f_{u}^{h, (no + 1)} h_{u}^{h} u_{u}^{(no + 1)},

and

respectively

Diagonal and unitary matrices for eigendecomposition.

算法二的迭代计算方法如下：The iterative calculation method of Algorithm 2 is as follows:

步骤1：令迭代次数变量n=0，选择足够小的正数τ以及门限(一般为极小的正数，例如取0.001)，按照传输信道H₁和H₂奇异值分解结果初始化发送预编码矩阵

和接收矩阵

并将它们代入下面式(4)得到初始化信道容量并记为cap(0)；Step 1: Let the number of iterations variable n=0, select a small enough positive number τ and a threshold (generally a very small positive number, such as 0.001), and initialize the transmission precoding according to the singular value decomposition results of the transmission channels H ₁ and H ₂ matrix

and receiving matrix

And substitute them into the following formula (4) to get the initial channel capacity and record it as cap(0);

步骤2：n=n+1，然后根据(5)式二次规划计算出第n+1次迭代中更新向量z；z的表达式为Step 2: n=n+1, and then calculate the update vector z in the n+1th iteration according to the quadratic programming of formula (5); the expression of z is

其中，

和分别表示链路u预编码矩阵和接收矩阵在地n+1次迭代过程中的更新值；u=1、2分别表示D2D链路(UE1和UE2之间的通信链路)和移动网络下行链路(基站到UE3的通信链路)；表示拉直运算，T表示矩阵转置。 in,

and respectively represent the update values of the link u precoding matrix and receiving matrix in the n+1 iteration process; u=1 and 2 respectively represent the D2D link (the communication link between UE1 and UE2) and the mobile network downlink Road (communication link from base station to UE3); Represents a straightening operation, and T represents a matrix transpose.

步骤3：利用步骤2中获得的更新向量并引入线性搜索参数β，β在区间[0,1]中线性变化，同时记

和

并将它们代入式(4)计算得到含参的cap(n+1,β)，最终求得最大化cap(n+1,β)的β^*；Step 3: Use the update vector obtained in step 2 and introduce a linear search parameter β, β changes linearly in the interval [0,1], and record

and

And they are substituted into formula (4) to calculate cap(n+1, β) containing parameters, and finally obtain the β ^* of maximizing cap(n+1, β);

步骤4：更新

和

并得到在n+1迭代过程中最优的信道容量记为cap(n+1)＝cap(n+1,β^*)。Step 4: Update

and

And obtain the optimal channel capacity in the iterative process of n+1, which is recorded as cap(n+1)=cap(n+1,β ^* ).

步骤5：重复步骤2到步骤4直到cap(n+1)-cap(n)≤门限；Step 5: Repeat steps 2 to 4 until cap(n+1)-cap(n)≤threshold;

步骤6：最后，根据功率约束条件对W_u线性加权；Step 6: Finally, linearly weight W _u according to the power constraints;

最大化信道容量准则的计算公式如下面式(4)所示，下面式(5)是式(4)的近似式且为二次规划问题；The formula for maximizing the channel capacity criterion is shown in the following formula (4), and the following formula (5) is an approximate formula of formula (4) and is a quadratic programming problem;

$\underset{Δ_{W, u}^{(n + 1)}, Δ_{F, u}^{(n + 1)}}{\max imize} Σ_{u = 1}^{2} Σ_{k = 1}^{S_{u}} \log_{2} (1 + γ_{u, k}^{(n + 1)})$ (4) $\underset{Δ_{W, u}^{(no + 1)}, Δ_{f, u}^{(no + 1)}}{\max imize} Σ_{u = 1}^{2} Σ_{k = 1}^{S_{u}} \log_{2} (1 + γ_{u, k}^{(no + 1)})$ (4)

$s the s . . t t . . {γ γ}_{u u,, k k}^{((n no + + 11))} = = \frac{{f f}_{u u,, k k}^{H h,, ((n no + + 11))} {H h}_{u u} {w w}_{u u,, k k}^{((n no + + 11))} {w w}_{u u,, k k}^{H h,, ((n no + + 11))} {H h}_{u u}^{H h} {f f}_{u u,, k k}^{((n no + + 11))}}{{f f}_{u u,, k k}^{H h,, ((n no + + 11))} (({H h}_{u u} {Σ Σ}_{i i &NotEqual; &NotEqual; k k}^{{S S}_{u u}} {w w}_{u u,, i i}^{((n no + + 11))} {w w}_{u u,, i i}^{H h,, ((n no + + 11))} {H h}_{u u}^{H h} + + {Σ Σ}_{m m &NotEqual; &NotEqual; u u}^{22} \frac{{ρ ρ}_{m m}}{{ρ ρ}_{u u}} {H h}_{m m,, u u} {W W}_{m m}^{((n no + + 11))} {W W}_{m m}^{H h,, ((n no + + 11))} {H h}_{m m,, u u}^{H h} + + \frac{{σ σ}_{u u}^{22}}{{ρ ρ}_{u u}} I I)) {f f}_{u u,, k k}^{((n no + + 11))}}$

$tr tr (({W W}_{m m}^{((n no + + 11))} {W W}_{m m}^{H h,, ((n no + + 11))})) = = \frac{{P P}_{u u}}{{ρ ρ}_{u u}};; u u = = 1,2 1,2;; k k = = 11,, . . . . . .,, {S S}_{u u}$

其中，式(4)中下标u和m都是标识链路的变量且u,m∈{1,2},u≠m，大写H表示矩阵的共轭转置操作，I表示单位阵。w_u，k和f_u,k分别是发送预编码矩阵W_u和接收矩阵F_u的第k列向量；ρ_u是链路u的发送功率约束因子，保证功率恒为P_u。

为链路u的第k个数据流的接收信干噪比，将它泰勒展开后用一阶多项式近似忽略高阶项后可以将(4)式的最优化问题等价的转化为如下的凸优化问题：Among them, the subscripts u and m in formula (4) are variables that identify the link and u,m∈{1,2},u≠m, the uppercase H represents the conjugate transpose operation of the matrix, and I represents the identity matrix. w _{u, k} and f _{u, k} are the k-th column vectors of the transmit precoding matrix _Wu and receive matrix _Fu respectively; ρ _u is the transmit power constraint factor of link u, and the guaranteed power is always P _u .

is the receiving signal-to-interference-noise ratio of the kth data stream of link u, after Taylor expansion, the optimization problem of equation (4) can be equivalently transformed into the following convex Optimization:

$\underset{z z}{min min imize imize} {z z}^{T T} (({Σ Σ}_{u u = = 11}^{22} {Σ Σ}_{k k = = 11}^{{S S}_{u u}} \frac{11}{22 {((11 + + {γ γ}_{u u,, k k}^{((n no + + 11))}))}^{22}} {p p}_{u u,, k k} {p p}_{u u,, k k}^{T T})) z z - - (({Σ Σ}_{u u = = 11}^{22} {Σ Σ}_{k k = = 11}^{{S S}_{u u}} \frac{11}{11 + + {γ γ}_{u u,, k k}^{((n no + + 11))}} {p p}_{u u,, k k}^{T T})) z z$

s.t. Qz＝e (5)s.t. Qz＝e (5)

-τ1≤z≤τ1-τ1≤z≤τ1

为描述方便，式(5)中已经将某些项合并为如下所列中间变量：For the convenience of description, some items in formula (5) have been combined into the following intermediate variables:

${p p}_{u u,, k k} = = ((\frac{11}{{y the y}_{u u,, k k}^{((n no))}} {g g}_{x x,, u u,, k k} - - \frac{{x x}_{u u,, k k}^{((n no))}}{{y the y}_{u u,, k k}^{22,, ((n no))}} {g g}_{y the y,, u u,, k k})),,$

$Q Q = = [\begin{matrix} {q q}_{11}^{T T} \\ {q q}_{22}^{T T} \end{matrix}],, - - - - - - ((66))$

e=[P₁/ρ₁ P₂/ρ₂]^T e=[P ₁ /ρ ₁ P ₂ /ρ ₂ ] ^T

p_u，k表达式中的

g_x，u,k和g_y，u，k如下所示，p _{u, k} in the expression

g _{x, u, k} and g _{y, u, k} are as follows,

${x x}_{u u,, k k}^{((n no))} = = {f f}_{u u,, k k}^{H h,, ((n no))} {H h}_{u u} {w w}_{u u,, k k}^{((n no))} {w w}_{u u,, k k}^{H h,, ((n no))} {H h}_{u u}^{H h} {f f}_{u u,, k k}^{((n no))}$

${y the y}_{u u,, k k}^{((n no))} = = {f f}_{u u,, k k}^{H h,, ((n no))} (({H h}_{u u} {Σ Σ}_{i i &NotEqual; &NotEqual; k k}^{{S S}_{u u}} {w w}_{u u,, i i}^{((n no))} {w w}_{u u,, i i}^{H h,, ((n no))} {H h}_{u u}^{H h} + + {Σ Σ}_{m m &NotEqual; &NotEqual; u u}^{22} \frac{{ρ ρ}_{m m}}{{ρ ρ}_{u u}} {H h}_{m m,, u u} {W W}_{m m}^{((n no))} {W W}_{m m}^{H h,, ((n no))} {H h}_{m m,, u u}^{H h} + + \frac{{σ σ}_{u u}^{22}}{{ρ ρ}_{u u}} I I)) {f f}_{u u,, k k}^{((n no))} - - - - - - ((77))$

式(7)中

和

分别为

的分子和分母，变量a_u，i，b_u,i，c_m和d_u，k定义如下In formula (7)

and

respectively

The numerator and denominator of , the variables a _u,i , b _u,i , c _m and d _u,k are defined as follows

${a a}_{u u,, i i} = = vec vec (({H h}_{u u}^{H h} {f f}_{u u,, k k}^{((n no))} {f f}_{u u,, k k}^{H h,, ((n no))} {H h}_{u u} {w w}_{u u,, i i}^{((n no))})),, i i = = 11,, . . . . . .,, {S S}_{u u};;$

${b b}_{u u,, i i} = = vec vec (({H h}_{u u} {w w}_{u u,, k k}^{((n no))} {w w}_{u u,, k k}^{H h,, ((n no))} {H h}_{u u}^{H h} {f f}_{u u,, k k}^{((n no))})),, i i = = 11,, . . . . . .,, {S S}_{u u};;$

${c c}_{m m} = = vec vec ((\frac{{ρ ρ}_{m m}}{{ρ ρ}_{u u}} {H h}_{m m,, u u}^{H h} {f f}_{u u,, k k}^{((n no))} {f f}_{u u,, k k}^{H h,, ((n no))} {H h}_{m m,, u u} {W W}_{m m}^{((n no))})),, m m = = 11,, 22;; - - - - - - ((99))$

${d d}_{u u,, k k} = = vec vec (((({Σ Σ}_{m m &NotEqual; &NotEqual; u u}^{22} \frac{{ρ ρ}_{m m}}{{ρ ρ}_{u u}} {H h}_{m m,, u u} {W W}_{m m}^{((n no))} {W W}_{m m}^{H h,, ((n no))} {H h}_{m m,, u u}^{H h} + + \frac{{σ σ}_{u u}^{22}}{{ρ ρ}_{u u}} I I)) {f f}_{u u,, k k}^{((n no))})) . .$

式(6)中矩阵Q中的行向量为The row vector in matrix Q in formula (6) is

有益效果：1)本发明方法更加全面地考虑了基站对D2D链路的干扰以及D2D发送用户对无线移动网络链路的干扰。充分利用所有的传输信道和干扰信道信息，联合优化无线移动网络链路和D2D链路的发送预编码矩阵和接收矩阵。Beneficial effects: 1) The method of the present invention more comprehensively considers the interference of the base station to the D2D link and the interference of the D2D transmitting user to the wireless mobile network link. Make full use of all transmission channel and interference channel information, and jointly optimize the transmission precoding matrix and reception matrix of the wireless mobile network link and the D2D link.

2)采用本发明方法中计算出的发送预编码矩阵和接收矩阵可以达到最小化小区接收信号均方误差以降低接收误比特率和以及最大化两条链路的和速率的目标。对实际系统的性能有很大的提高。2) By adopting the transmitting precoding matrix and receiving matrix calculated in the method of the present invention, the goal of minimizing the mean square error of the cell receiving signal to reduce the receiving bit error rate and maximizing the sum rate of the two links can be achieved. The performance of the actual system has been greatly improved.

附图说明 Description of drawings

图1是本发明提出的移动通信网络下设备间通信的最优收发联合处理方法的系统框图。FIG. 1 is a system block diagram of an optimal sending and receiving joint processing method for communication between devices in a mobile communication network proposed by the present invention.

图2是本发明提出的最优化均方误差算法(算法一)和迫零、最大化SLNR算法的误比特性能曲线。图中，基站和UE1拥有4根发送天线，UE2和UE3有2根接收天线。两条链路都传输单流数据。Fig. 2 is the bit error performance curve of the optimal mean square error algorithm (algorithm 1) proposed by the present invention and the zero forcing and maximizing SLNR algorithm. In the figure, the base station and UE1 have 4 transmitting antennas, and UE2 and UE3 have 2 receiving antennas. Both links carry single-stream data.

图3是本发明提出的最大化系统容量算法(算法二)、迫零(ZF)、最大化信泄露噪比(SLNR)算法在小区含有D2D链路是的容量曲线。图3还包含了小区不含D2D链路时的容量曲线。图中，基站和UE1拥有4根发送天线，UE2和UE3有2根接收天线。两条链路都传输双流数据。Fig. 3 is a capacity curve of the algorithm for maximizing system capacity (algorithm 2), zero-forcing (ZF), and maximizing signal-leakage-to-noise ratio (SLNR) proposed by the present invention when the cell contains a D2D link. Figure 3 also includes the capacity curve when the cell does not contain a D2D link. In the figure, the base station and UE1 have 4 transmitting antennas, and UE2 and UE3 have 2 receiving antennas. Both links carry dual-stream data.

具体实施方式 Detailed ways

下面结合附图对本发明作更进一步的说明。The present invention will be further described below in conjunction with the accompanying drawings.

如图1所示：本发明的具体实施步骤如下：As shown in Figure 1: the specific implementation steps of the present invention are as follows:

1)D2D(Device-to-Device)发送用户UE1(User Equipment)和D2D接收用户UE2分别拥有N₁根发送天线和M₁根接收天线，基站拥有N₂根发送天线，网络用户UE3拥有M₂根接收天线。D2D发送用户和基站的发送功率分别为P₁、P₂，它们分别发送多流数据x₁和x₂(维度分别为S₁×1和S₂×1)给各自的接收机。S₁和S₂分别表示D2D链路和无线移动网络链路传输的数据流数。1) D2D (Device-to-Device) sending user UE1 (User Equipment) and D2D receiving user UE2 have N ₁ sending antennas and M ₁ receiving antennas respectively, the base station has N ₂ sending antennas, and network user UE3 has M ₂ Root receiving antenna. The transmission powers of the D2D transmitting user and the base station are respectively P ₁ and P ₂ , and they respectively transmit multi-stream data x ₁ and x ₂ (dimensions are S ₁ ×1 and S ₂ ×1 respectively) to their respective receivers. S ₁ and S ₂ represent the number of data streams transmitted by the D2D link and the wireless mobile network link, respectively.

2）D2D链路与无线移动网络链路采用非正交方式共享时频资源，即D2D设备对之间通信的物理信道与某个无线移动网络用户的物理资源重叠。在TDD(TimeDivision Duplexing)系统中，为了使基站获得完整的信道信息来实现最优收发设计，基站可以通过上行信道估计获得基站至UE3的下行信道H₂以及基站至UE2的下行干扰信道H_2，1；通过上述相同的信道估计方式，UE1同样可以获得它至UE2的传输信道H₁和它与UE3之间的干扰信道H_1，2。最后UE1将H₁和H_1,2反馈给基站。2) D2D links and wireless mobile network links share time-frequency resources in a non-orthogonal manner, that is, the physical channel for communication between D2D device pairs overlaps with the physical resources of a certain wireless mobile network user. In a TDD (Time Division Duplexing) system, in order for the base station to obtain complete channel information to achieve optimal transceiver design, the base station can obtain the downlink channel _H2 from the base station to UE3 and the downlink interference channel H2 from the base station to UE2 through uplink channel estimation _{, 1} ; through the same channel estimation method as above, UE1 can also obtain the transmission channel H ₁ to UE2 and the interference channel H _1,2 between it and UE3. Finally, UE1 feeds back H ₁ and H _1,2 to the base station.

3)基站根据获得的完整的四类信道信息通过最小化接收信号均方误差降低误比特率或者最大化信道信道容量准则优化基站、UE1的预编码矩阵和UE2、UE3的接收矩阵。由于上述最优化问题是非凸问题且不存在闭式解，所以本专利提供了两种可选的迭代算法。u=1、u=2分别表示D2D链路和无线移动网络链路。为方便叙述，将最小化接收信号均方误差迭代算法称作算法一，如图2所示，而将最大化信道容量算法称作算法二，如图3所示。算法一的迭代计算方法如下：3) The base station optimizes the precoding matrix of the base station and UE1 and the receiving matrices of UE2 and UE3 by minimizing the mean square error of the received signal to reduce the bit error rate or maximizing the channel capacity criterion according to the obtained complete four types of channel information. Since the above optimization problem is a non-convex problem and there is no closed-form solution, this patent provides two optional iterative algorithms. u=1 and u=2 represent the D2D link and the wireless mobile network link respectively. For the convenience of description, the iterative algorithm for minimizing the received signal mean square error is called Algorithm 1, as shown in Figure 2, and the algorithm for maximizing channel capacity is called Algorithm 2, as shown in Figure 3. The iterative calculation method of Algorithm 1 is as follows:

步骤3：将步骤2计算得到的

代入功率约束条件下面式(3)求得参数

其中

in

步骤4：将步骤2和步骤3中计算得到的和

代入下面式(1)可以计算得到第n+1次迭代过程中的发送预编码矩阵 Step 4: Calculated in Step 2 and Step 3 and

步骤5：重复步骤2到步骤4直到

和

收敛。Step 5: Repeat Step 2 to Step 4 until

and

convergence.

和

分别代表了矩阵

和

主对角线上的第t个元素。其中，

G_{u}^{(n + 1)} = U_{u}^{H, (n + 1)} H_{u}^{H} F_{u}^{(n + 1)} F_{u}^{H, (n + 1)} H_{u}^{H} U_{u}^{(n + 1)},

和

分别是

and

respectively represent the matrix

and

The tth element on the main diagonal. in,

G_{u}^{(no + 1)} = u_{u}^{h, (no + 1)} h_{u}^{h} f_{u}^{(no + 1)} f_{u}^{h, (no + 1)} h_{u}^{h} u_{u}^{(no + 1)},

and

respectively

Diagonal and unitary matrices for eigendecomposition.

和接收矩阵

and receiving matrix

步骤2：n=n+1，然后根据下面(5)式二次规划计算出第n+1次迭代中更新向量z；z的表达式为Step 2: n=n+1, and then calculate the update vector z in the n+1th iteration according to the quadratic programming of the following formula (5); the expression of z is

其中，和

分别表示链路u预编码矩阵和接收矩阵在地n+1次迭代过程中的更新值；u=1、2分别表示D2D链路(UE1和UE2之间的通信链路)和移动网络下行链路(基站到UE3的通信链路)；

表示拉直运算，T表示矩阵转置。 in, and

respectively represent the update values of the link u precoding matrix and receiving matrix in the n+1 iteration process; u=1 and 2 respectively represent the D2D link (the communication link between UE1 and UE2) and the mobile network downlink Road (communication link from base station to UE3);

Represents a straightening operation, and T represents a matrix transpose.

和

and

步骤4：更新

和

and

最大化信道容量准则的计算公式如式下面(4)所示，下面式(5)是式(4)的近似式且为二次规划问题；The formula for maximizing the channel capacity criterion is shown in (4) below, and the following formula (5) is an approximate formula of formula (4) and is a quadratic programming problem;

其中，式(4)中下标u和m都是标识链路的变量且u,m∈{1,2},u≠m，大写H表示矩阵的共轭转置操作，I表示单位阵。w_u，k和f_u,k分别是发送预编码矩阵W_u和接收矩阵F_u的第k列向量；ρ_u是链路u的发送功率约束因子，保证功率恒为P_u。为链路u的第k个数据流的接收信干噪比，将它泰勒展开后用一阶多项式近似忽略高阶项后可以将(4)式的最优化问题等价的转化为如下的凸优化问题：Among them, the subscripts u and m in formula (4) are variables that identify the link and u,m∈{1,2},u≠m, the uppercase H represents the conjugate transpose operation of the matrix, and I represents the identity matrix. w _{u, k} and f _{u, k} are the k-th column vectors of the transmit precoding matrix _Wu and receive matrix _Fu respectively; ρ _u is the transmit power constraint factor of link u, and the guaranteed power is always P _u . is the receiving signal-to-interference-noise ratio of the kth data stream of link u, after Taylor expansion, the optimization problem of equation (4) can be equivalently transformed into the following convex Optimization:

s.t. Qz＝e (5)s.t. Qz＝e (5)

-τ1≤z≤τ1-τ1≤z≤τ1

e=[P₁/ρ₁ P₂/ρ₂]^T e=[P ₁ /ρ ₁ P ₂ /ρ ₂ ] ^T

p_u，k表达式中的

g_x，u,k和g_y，u，k如下所示，p _{u, k} in the expression

g _{x, u, k} and g _{y, u, k} are as follows,

式(7)中

和

分别为的分子和分母，变量a_u，i，b_u,i，c_m和d_u，k定义如下In formula (7)

and

respectively The numerator and denominator of , the variables a _u,i , b _u,i , c _m and d _u,k are defined as follows

式(6)中矩阵Q中的行向量为The row vector in the matrix Q in formula (6) is

以上所述仅是本发明的优选实施方式，应当指出：对于本技术领域的普通技术人员来说，在不脱离本发明原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也应视为本发明的保护范围。The above is only a preferred embodiment of the present invention, it should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, some improvements and modifications can also be made. It should be regarded as the protection scope of the present invention.

Claims

1. an optimal sending and receiving combined processing method for communication between devices under a mobile communication network, characterized in that: the method is carried out in the following steps:

1) The D2D transmitting user UE1 and the D2D receiving user UE2 have N ₁ transmitting antennas and M ₁ receiving antennas respectively, the base station has N ₂ transmitting antennas, and the network user UE3 has M ₂ receiving antennas. The powers are P ₁ and P ₂ respectively, and they respectively send multi-stream data x ₁ and x ₂ to their respective receivers, and their dimensions are S ₁ ×1 and S ₂ ×1 respectively, and S ₁ and S ₂ respectively represent the D2D chain Number of data streams transmitted over road and wireless mobile network links;

2) The D2D link and the wireless mobile network link share time-frequency resources in a non-orthogonal manner, that is, the physical channel for communication between D2D device pairs overlaps with the physical resources of a certain wireless mobile network user; in the TDD system, in order to make The base station obtains complete channel information to realize optimal transceiver design. The base station can obtain the downlink channel _H2 from the base station to UE3 and the downlink interference channel _H2,1 from the base station to UE2 through uplink channel estimation; through the same channel estimation method as above, UE1 It can also obtain the transmission channel H ₁ to UE2 and the interference channel H _1,2 between it and UE3, and finally UE1 feeds back H ₁ and H _1,2 to the base station;

3) The base station optimizes the base station, the precoding matrix of UE1 and the receiving matrix of UE2 and UE3 by minimizing the mean square error of the received signal to reduce the bit error rate or maximizing the channel capacity criterion according to the obtained complete four types of channel information;

4) The base station calculates the transmit precoding matrix and receive matrix of link 1 and link 2 according to the criterion of minimizing the mean square error of the received signal or maximizing the channel capacity as W ₁ , F ₁ and W ₂ , F ₂ respectively. Transfer W ₁ and F ₁ to the D2D communication device pair through the control channel;

5) Finally, UE1 uses the transmission precoding matrix W ₁ to weight the data stream x ₁ and send it to UE2, while the transmission precoding matrix W ₂ is used by the base station to weight the downlink signal x ₂ and send it to UE3.

2. The optimal transceiving joint processing method for communication between devices in a mobile communication network according to claim 1, characterized in that: the iterative algorithm for minimizing the mean square error of received signals in the step 3) is called Algorithm 1, and the algorithm The iterative calculation method of is as follows:

Step 1: Let the number of iterations variable n=0, initialize the transmission precoding matrix according to the singular value decomposition results of the transmission channels H ₁ and H ₂

u=1,2; the subscript u is a variable identifying the link, where u=1 and u=2 represent the D2D link and the mobile network downlink respectively;

Step 2: n=n+1, then calculate the receiving matrix of link u in the n+1th iteration process according to the following formula (2)

Step 3: Calculated in step 2 Substituting the power constraints into the following equation (3) to obtain the parameters

in is the Lagrangian multiplier introduced to ensure the establishment of power constraints;

Step 4: Calculated in Step 2 and Step 3

and

Step 5: Repeat Step 2 to Step 4 until and

convergence;

{W W}_{u u}^{((n no + + 11))} = = {(({H h}_{u u}^{H h} {F f}_{u u}^{((n no + + 11))} {F f}_{u u}^{H h,, ((n no + + 11))} {H h}_{u u} + + {H h}_{u u,, m m}^{H h} {F f}_{m m}^{((n no + + 11))} {F f}_{m m}^{H h,, ((n no + + 11))} {H h}_{u u,, m m} + + {μ μ}_{u u}^{((n no + + 11))} I I))}^{- - 11} {H h}_{u u}^{H h} {F f}_{u u}^{((n no + + 11))} - - - - - - ((11))

{F f}_{u u}^{((n no + + 11))} = = {(({H h}_{u u} {W W}_{u u}^{((n no))} {W W}_{u u}^{H h,, ((n no))} {H h}_{u u}^{H h} + + {H h}_{m m,, u u}^{H h} {W W}_{m m}^{((n no))} {W W}_{m m}^{H h,, ((n no))} {H h}_{m m,, u u} + + {σ σ}_{u u}^{22} I I))}^{- - 11} {H h}_{u u} {W W}_{u u}^{((n no))} - - - - - - ((22))

{Σ Σ}_{i i = = 11}^{{N N}_{u u}} \frac{{G G}_{u u}^{[[tt tt]],, ((n no + + 11))}}{[[{μ μ}_{u u}^{((n no + + 11))} + + {Δ Δ}_{u u}^{[[tt tt]],, ((n no + + 11))}]]} = = Pu Pu - - - - - - ((33))

In formulas (1) to (3), the subscripts u and m are variables that identify links and u, m∈{1,2}, u≠m, while n represents the number of iterations variable, and capital H represents the total number of matrices. Yoke transpose operation, I means unit matrix;

and

respectively represent the matrix

and

The tth element on the main diagonal; where,

G_{u}^{(no + 1)} = u_{u}^{h, (no + 1)} h_{u}^{h} f_{u}^{(no + 1)} f_{u}^{h, (no + 1)} h_{u}^{h} u_{u}^{(no + 1)},

and

respectively Diagonal and unitary matrices for eigendecomposition.

3. The optimal sending and receiving joint processing method for communication between devices in a mobile communication network according to claim 1, characterized in that: the algorithm for maximizing channel capacity in the step 3) is called Algorithm 2, and the iterative calculation method of the algorithm as follows:

Step 1: Let the iteration number variable n=0, select a small enough positive number τ and the threshold, and initialize the transmission precoding matrix according to the singular value decomposition results of the transmission channels H ₁ and H ₂

and receiving matrix

Step 2: n=n+1, then calculate update vector z in the n+1th iteration according to following formula (5) quadratic programming; The expression of z is

in,

and

respectively represent the update values of the link u precoding matrix and receiving matrix in the n+1 iteration process; u=1 and 2 respectively represent the D2D link and the mobile network downlink;

Indicates straightening operation, T indicates matrix transposition;

Step 3: Use the update vector obtained in step 2 and introduce a linear search parameter β, β changes linearly in the interval [0,1], and record

and

And they are substituted into the following formula (4) to calculate cap(n+1, β) containing parameters, and finally obtain the β ^* of maximizing cap(n+1, β);

Step 4: Update

and

And obtain the optimal channel capacity in the n+1 iterative process and record it as cap(n+1)=cap(n+1,β ^* );

Step 5: Repeat steps 2 to 4 until cap(n+1)-cap(n)≤threshold;

Step 6: Finally, linearly weight W _u according to the power constraints;

The formula for maximizing the channel capacity criterion is shown in the following formula (4), and the following formula (5) is an approximate formula of the following formula (4) and is a quadratic programming problem;

\underset{Δ_{W, u}^{(no + 1)}, Δ_{f, u}^{(no + 1)}}{\max imize} Σ_{u = 1}^{2} Σ_{k = 1}^{S_{u}} \log_{2} (1 + γ_{u, k}^{(no + 1)})

(4)

s the s . . t t . . {γ γ}_{u u,, k k}^{((n no + + 11))} = = \frac{{f f}_{u u,, k k}^{H h,, ((n no + + 11))} {H h}_{u u} {w w}_{u u,, k k}^{((n no + + 11))} {w w}_{u u,, k k}^{H h,, ((n no + + 11))} {H h}_{u u}^{H h} {f f}_{u u,, k k}^{((n no + + 11))}}{{f f}_{u u,, k k}^{H h,, ((n no + + 11))} (({H h}_{u u} {Σ Σ}_{i i &NotEqual; &NotEqual; k k}^{{S S}_{u u}} {w w}_{u u,, i i}^{((n no + + 11))} {w w}_{u u,, i i}^{H h,, ((n no + + 11))} {H h}_{u u}^{H h} + + {Σ Σ}_{m m &NotEqual; &NotEqual; u u}^{22} \frac{{ρ ρ}_{m m}}{{ρ ρ}_{u u}} {H h}_{m m,, u u} {W W}_{m m}^{((n no + + 11))} {W W}_{m m}^{H h,, ((n no + + 11))} {H h}_{m m,, u u}^{H h} + + \frac{{σ σ}_{u u}^{22}}{{ρ ρ}_{u u}} I I)) {f f}_{u u,, k k}^{((n no + + 11))}}

tr tr (({W W}_{m m}^{((n no + + 11))} {W W}_{m m}^{H h,, ((n no + + 11))})) = = \frac{{P P}_{u u}}{{ρ ρ}_{u u}};; u u = = 1,2 1,2;; k k = = 11,, . . . . . .,, {S S}_{u u}

Among them, the subscripts u and m in formula (4) are variables that identify the link and u, m∈{1,2}, u≠m, the uppercase H represents the conjugate transpose operation of the matrix, and I represents the identity matrix; w _{u, k} and f _{u, k} are the k-th column vectors of the transmitting precoding matrix W _u and the receiving matrix F _u respectively; ρ _u is the transmitting power constraint factor of link u, and the guaranteed power is always P _u ; is the receiving signal-to-interference-noise ratio of the kth data stream of link u, after Taylor expansion, the optimization problem of equation (4) can be equivalently transformed into the following convex Optimization: