CN117082476A - A C-V2V massive MIMO multicast and unicast robust collaborative transmission method - Google Patents

Abstract

The invention relates to a C-V2V large-scale MIMO multicast and unicast robust cooperative transmission method. In the method, all cellular users and V2V-Rx send uplink detection signals, and priori statistical channel information of each user is obtained; all cellular users and V2V-Rx send uplink pilot signals, a base station and each V2V-Tx carry out channel estimation by using prior statistical channel information and the uplink pilot signals to obtain posterior statistical channel information of each user, wherein the posterior statistical channel information comprises channel mean value and variance, and the influence of channel aging caused by user mobility is considered; the base station and each V2V-Tx carry out low-complexity robust linear precoding design by utilizing posterior statistical channel information, and implement the cooperative transmission of cellular network multicasting and V2V communication to unicast in a downlink, thereby achieving the effects of cooperative transmission weighting and rate maximization. The method can implement robust precoding with low computational complexity in various typical mobile scenes, greatly improve the rate of cooperative transmission and improve the spectrum efficiency of a wireless communication system.

Description

A robust coordinated transmission method for C-V2V massive MIMO multicast and unicast

Technical Field

The present invention relates to a C-V2V massive MIMO multicast and unicast robust cooperative transmission method, and in particular to a C-V2V massive MIMO cellular network multicast and vehicle communication used in a moving vehicle network for communication, which is robust to the cooperative transmission of imperfect channel statistical information of a base station and a vehicle transmitter in a unicast scenario.

Background Art

With the development of 5G communication technology and the diversified needs of services, vehicle-to-vehicle (V2V) and vehicle-to-network (V2N) communications have achieved rapid development in recent years. In order to meet the future needs of V2V communication applications, it is necessary to deeply tap and utilize spatial wireless resources and greatly improve the spectrum utilization and power utilization of V2V wireless communications. Therefore, introducing large-scale MIMO systems into V2V communications has become a very potential solution. Configuring large-scale antenna arrays (more than dozens) at the vehicle receiving end to deeply tap and utilize spatial dimension resources has become one of the development trends of future V2V wireless communications.

In the C-V2V massive MIMO system, due to the higher mobile speed and faster channel fading, more frequent channel information acquisition operations are required, which will greatly increase the pilot overhead. In the case of limited pilot resources, it becomes more difficult to obtain instantaneous channel state information, and the base station side and the transmitter of each V2V communication pair cannot always know the high-quality instantaneous CSI. In addition, there are many challenges in the practical application of large-scale MIMO systems, such as power amplifier nonlinearity, transceiver I/Q imbalance, quantization error, etc. At the same time, due to factors such as channel estimation error and channel aging, the base station side and the transmitter of each V2V communication pair usually cannot obtain perfect CSI. Considering a C-V2V massive MIMO downlink, the precoding design and multicast-unicast coordinated transmission that are robust to imperfect CSI at the base station side and the V2V transmitter in the C-V2V massive MIMO downlink transmission are studied. The present invention provides a C-V2V massive MIMO multicast and unicast robust coordinated transmission method.

Summary of the invention

Purpose of the invention: To address the deficiencies of the above-mentioned technologies, a C-V2V large-scale MIMO multicast and unicast collaborative transmission method is provided, which is robust to imperfect CSI at the base station and the V2V transmitter, and can obtain good gain in various typical mobile scenarios.

Technical solution: To achieve the above-mentioned invention object, the C-V2V massive MIMO multicast and unicast coordinated transmission method of the present invention configures a base station, multiple cellular users and multiple V2V communication pairs in a cellular network, each V2V communication pair includes a V2V-Tx and a V2V-Rx, the number of antennas equipped on the base station side is M, the number of antennas equipped on the V2V-Tx is N, the number of cellular users is K, the number of V2V communication pairs is D, the kth cellular user and the dth V2V-Rx are equipped with N _k and N _d antennas respectively, so as to They represent the cellular user set, V2V-Tx set and V2V-Rx set respectively;

First, all cellular users and all V2V-Rx in the cellular network send uplink detection signals to the base station and the matching V2V-Tx at the same time. The base station and each V2V-Tx obtain the prior statistical channel information of each cellular user and all V2V-Rx according to the received detection signals.

Then, during the uplink training process, all cellular users and V2V-Rx send pilot signals, and the base station and each V2V-Tx use the prior statistical channel information and the received pilot signals to perform channel estimation, thereby obtaining the a posteriori statistical channel information of each user, which includes the channel mean and variance;

Finally, the base station and each V2V-Tx use the a posteriori statistical channel information including the channel mean and variance, perform low-complexity robust linear precoding through the MM algorithm and introduce deterministic equivalence, and implement the coordinated transmission of cellular network multicast and V2V communication to unicast in the downlink, so as to maximize the weighted sum rate of the coordinated transmission.

Preferably, during downlink transmission, the base station sends the same information to all cellular users, while the V2V-Tx in each V2V communication pair sends different information to the corresponding V2V-Rx.

Preferably, a time correlation coefficient is introduced into the posterior statistical channel model, and the time correlation coefficient is related to the moving speed of the cellular user and reflects the channel changes between different time blocks.

Preferably, in the robust precoding transmission, the base station and each V2V-Tx perform linear precoding design for cellular users and all V2V-Rx according to the weighted traversal and rate maximization criteria of the coordinated transmission of cellular network multicast and V2V communication to unicast, including the following steps: giving the optimal solution of weighted sum rate by iteration, the input value is the a priori statistical channel information obtained by the aforementioned uplink detection and the a posteriori statistical channel information obtained by uplink training, and the output value is the optimal precoding matrix. The present invention is the first to apply the MM algorithm and determinism to the C-V2V precoding scenario.

Preferably, the original optimization problem with cooperative transmission weighted traversal and rate as the objective function cannot be solved directly. By introducing the MM algorithm, a substitute function for the objective function in the original optimization problem is found, thereby constructing a new equivalent optimization problem. The equivalent optimization problem is a concave quadratic optimization problem and can be solved directly. Since it is equivalent to the original optimization problem, the optimal solution obtained is also the optimal solution of the original optimization problem.

Preferably, the computational complexity of the algorithm is reduced by replacing the rates in the concave quadratic optimization problem with their deterministic equivalents.

Preferably, the matrix inversion lemma is used to avoid the large-dimensional matrix inversion in the process of solving the concave quadratic optimization problem, thereby reducing the computational complexity of the algorithm. The MM algorithm, deterministic equivalence, and matrix inversion lemma described above are all tools for optimizing the precoding algorithm, and the results are reflected in the iterative formula used in the final precoding algorithm steps. In the specific implementation, it is only necessary to run the given precoding algorithm.

Preferably, the a posteriori statistical channel information of the cellular user and the transmitting vehicle user includes the channel mean and variance. In the special scenario where the channel means of all users are zero, the base station and each V2V-Tx adopt beam domain transmission, that is, when the base station sends a multicast signal, the optimal transmission direction should be consistent with the eigenvector of the base station covariance matrix, and in the V2V link, the transmission direction of the unicast signal should be consistent with the eigenvector of the covariance matrix of the corresponding V2V-Tx.

Preferably, in the special scenario where the mean of all user channels is zero, when beam domain transmission is used, the original coordinated transmission weighted sum rate optimization problem is simplified to a beam domain power allocation optimization problem. The information reflected by the precoding matrix is the transmission direction of the signal and the transmission power of each signal transmission direction. Since the transmission direction of the signal in beam domain transmission has been determined, it is only necessary to obtain the optimal transmission power in each direction to obtain the optimal solution of the precoding matrix.

Preferably, in the special scenario where the mean of all user channels is zero, in the algorithm for solving the optimization problem of beam domain power allocation, the inversion of the large-dimensional matrix is completed by inverting the element by element, thereby greatly reducing the computational complexity of the algorithm. In the special scenario where the mean of all user channels is zero, since the optimization problem is transferred from the antenna domain to the beam domain, the large-dimensional matrix involved in the inversion in the original antenna domain is a diagonal matrix in the beam domain, and the inversion of the diagonal matrix is very simple, and each diagonal element can be inverted one by one, thereby avoiding the inversion of the large-dimensional matrix with high computational complexity. In the specific implementation, it can be calculated according to the formula in the given beam domain precoding algorithm.

Beneficial effects:

The robust cooperative transmission method for C-V2V massive MIMO base station multicast and V2V link unicast proposed in the present invention takes into account the impact of channel estimation error, channel aging and spatial correlation, is universal for a variety of typical mobile scenarios, and significantly improves the spectrum efficiency of the system. The proposed precoding design method for maximizing the weighted sum rate of cooperative transmission greatly reduces the number of large-dimensional matrix inversions and can be implemented with lower computational complexity. Through further research on special scenarios where the channel mean in the posterior statistical channel state information is zero, the original rate optimization problem is equivalently transformed into a simpler beam domain power allocation problem, and the algorithm can solve the large-dimensional matrix inversion in an element-by-element inversion manner, thereby greatly reducing the computational complexity of the algorithm. For the first time, the present invention introduces a time correlation coefficient in the posterior statistical channel model in the C-V2V scenario, reflecting the channel changes between different time blocks.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of the C-V2V massive MIMO multicast and unicast robust cooperative transmission method of the present invention.

FIG2 is a schematic diagram of a C-V2V massive MIMO wireless communication system to which the method of the present invention is applicable.

DETAILED DESCRIPTION

The specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings:

As shown in FIG1 and FIG2 , an embodiment of the present invention discloses a C-V2V massive MIMO multicast and unicast robust cooperative transmission method, in which a base station, multiple cellular users and multiple V2V communication pairs are configured in a cellular network, each V2V communication pair includes a transmitting vehicle user (V2V-Tx) and a receiving vehicle user (V2V-Rx), the number of antennas equipped on the base station side is M, the number of antennas equipped on the V2V-Tx is N, the number of cellular users is K, the number of V2V communication pairs is D, the kth cellular user and the dth V2V-Rx are equipped with N _k and N _d antennas respectively, so that They represent the cellular user set, V2V-Tx set and V2V-Rx set respectively.

Here are the steps:

First, all cellular users and all V2V-Rx in the cellular network send uplink detection signals to the base station and the matching V2V-Tx at the same time. The base station and each V2V-Tx obtain the prior statistical channel information of each cellular user and all V2V-Rx according to the received detection signals, that is, the channel power matrix of each user;

Then, during the uplink training process, all cellular users and V2V-Rx send pilot signals, and the base station and each V2V-Tx use the prior statistical channel information and the received pilot signals to perform channel estimation, thereby obtaining the a posteriori statistical channel information of each user, which includes the channel mean and variance;

Finally, the base station and each V2V-Tx use the a posteriori statistical channel information including the channel mean and variance, perform low-complexity robust linear precoding through the MM algorithm and introduce deterministic equivalence, and implement the coordinated transmission of cellular network multicast and V2V communication to unicast in the downlink, so as to maximize the weighted sum rate of the coordinated transmission.

The embodiments of the present invention are further described in detail below in conjunction with a specific system model.

1. C-V2V massive MIMO system configuration and communication process

In the C-V2V large-scale MIMO system model, the base station is located in the center of the cell, and all cellular users and V2V communication pairs are distributed in this area. The base station side and the V2V receiving end are configured with an antenna array containing more than dozens of antenna units. The large-scale antenna array can adopt a linear array, a circular array, a plate array or other array structures. This embodiment adopts a uniform linear array. Each antenna unit can adopt an omnidirectional antenna or a sector antenna. When each antenna unit adopts an omnidirectional antenna, a 120-degree sector antenna and a 60-degree sector antenna, the spacing between each antenna can be configured to be 1/2 wavelength, wavelength and 1 wavelength. Each antenna unit can use a single-polarization or multi-polarization antenna. Communication adopts time division duplex transmission mode. Assume that the number of antennas equipped on the base station side is M, the number of antennas equipped on the V2V-Tx is N, the number of cellular users is K, the number of V2V communication pairs is D, and the kth cellular user and the dth V2V-Rx are equipped with N _k and N _d antennas respectively. They represent the cellular user set, V2V-Tx set and V2V-Rx set respectively.

In this case, the C-V2V massive MIMO multicast and unicast robust cooperative transmission process includes the following three steps:

i. Channel detection: The base station and each V2V-Tx obtain the prior statistical channel information of each cellular user based on the received detection signal.

ii. Channel training: Cellular users and V2V-Rx periodically send pilot signals to the base station and the corresponding V2V-Tx. The base station and each V2V-Tx use the prior statistical channel information and the received pilot signal to perform channel estimation and obtain the a posteriori statistical channel information of each user, including the channel mean and variance.

iii. Precoded transmission: The base station and each V2V-Tx use the obtained a posteriori statistical channel information to perform robust linear precoding and implement the coordinated transmission of cellular network multicast and V2V communication to unicast in the downlink.

2. Prior Statistical Channel Information

The acquisition of the prior statistical channel information of each user is completed by the uplink channel detection process. Assume that the time resource is divided into several time slots, each time slot contains several time blocks, and each time block has T symbols. Consider that the channel fades flat in each time block, that is, the channel remains unchanged in a time block, but changes between different coherent time blocks. Assume that in each time slot, the first time block is used for uplink training and the remaining time blocks are used for downlink data transmission.

make and They represent the downlink channel from the base station to the kth cellular user, the downlink channel from the base station to the dth VRx, the downlink channel from the d'th VTx to the kth cellular user, and the downlink channel from the d'th VTx to the dth VRx in the nth time block in the time slot s, respectively. The expressions are as follows:

in, and are all definite unitary matrices, V _M and V _N represent the M-dimensional and N-dimensional DFT matrices respectively, and are all deterministic matrices whose elements are non-negative. and are all complex Gaussian random matrices whose elements are independent and identically distributed (mean of each element is zero and variance is 1). s is ignored in the subscripts for simplicity.

The Gauss-Markov process is used to represent the channel changes between different time blocks. The channel matrix in the (n+1)th time block has the following expression:

in, and is the time correlation coefficient. This coefficient is related to the moving speed of base station users and vehicles, reflecting the channel changes between different time blocks, and can be obtained using the Jake autocorrelation model, that is, Where J ₀ (·) is the first kind of zero-order Bessel function, v _k is the mobile speed of the kth cellular user, f _c is the carrier frequency, and c represents the speed of light. Other correlation coefficients can be obtained by the same method. Define the channel power matrix

These channel power matrices are the a priori statistical channel information obtained by the base station and each VTx during the uplink detection process. This a priori statistical channel information is a long-term statistical characteristic, and its variation is related to the specific application scenario. Its typical statistical time window is several times or dozens of times the short-term transmission time window, and the acquisition of relevant information is also carried out over a larger time width. Assume that the number of channel samples is S, and The sth sample of The sample covariance matrix can be obtained as

By eigenvalue decomposition Can get Then the channel power matrix It can be calculated by the following formula:

3. A posteriori statistical channel information

According to the channel reciprocity in TDD communication, the channel state information of the downlink can be obtained by the uplink training process. The uplink training in each time slot is performed in the first time block of the time slot. represents the receiving matrix of the base station in uplink training, then

in, and are the uplink training matrices sent by the kth cellular user and the dth VRx in the first time block, is an additive Gaussian white noise matrix whose elements are independent and identically distributed (the mean of each element is zero and the variance is ). will receive the matrix Vectorization, that is

make express The covariance matrix of

Assuming that the pilot sequences of all transmitting antennas are orthogonal, we can get Time The expression of MMSE estimation is as follows:

Among them, the matrix The elements are defined as

Then we can get

in,

Finally, we get the following expression:

in,

and

Similarly, represents the receiving matrix of the dth VTx in uplink training, then

and The covariance matrix of There are the following expressions:

The covariance matrices of the MMSE estimate and the estimation error are

and

in,

Therefore, the following expression can be obtained:

in,

and

Formula (1), Formula (2), Formula (3) and Formula (4) are the a posteriori statistical channel information obtained by the base station and each VTx through channel estimation, including the channel mean and variance. and The base station and the transmitters of all vehicle communication pairs know their corresponding and in and It can be seen from equations (1) to (4) that the a posteriori statistical channel information can be obtained by the base station and each VTx using the a priori statistical channel information and the pilot signal received during the uplink training process.

4. Robust precoding transmission

Consider the multicast and unicast coordinated transmission in time slot s, let and They represent the multicast signal sent by the base station and the unicast signal sent by the dth VTx in the downlink of the nth time block in time slot s. and They are uncorrelated and have the following statistical properties: make represents the signal received by the kth cellular user, represents the signal received by the dth VRx, then and The expressions are as follows:

and

in, and are the precoding matrices of the base station and the d-th VTx respectively, and

is the additive white Gaussian noise vector. Assume that all cellular users and VRx know the instantaneous channel state information and precoding matrix. When decoding the multicast signal, the sum of the interference and noise for the kth cellular user is

Assuming it as the worst-case Gaussian noise, the corresponding covariance is

Similarly, when the dth VRx decodes the unicast signal, the sum of interference and noise is

It is also regarded as the worst-case Gaussian noise, with a covariance of

Assume that the kth cellular user is known The dth VRx is known In a multicast transmission from a base station to all cellular users, the multicast signal sent by the base station must be decoded by all cellular users. For a cellular link, the achievable ergodic multicast rate is defined as

Among them, the achievable ergodic multicast rate of the kth cellular user is for

For unicast transmission of V2V links, the unicast signal sent by the d-th VTx is decoded by the corresponding VRx in the same communication pair. For the d-th VTx, its achievable ergodic unicast rate can be expressed as

In cooperative transmission, the weighted sum of the multicast rate of cellular users and the unicast rate of the V2V link is used as the objective function, and the following rate optimization problem is constructed:

Among them, η∈(0,1) is the weight factor, P ^c and are the power constraints of the base station and the dth VTx respectively. Next, our goal is to find the precoding matrix that can maximize the weighted sum rate in equation (5): and

Let f denote the objective function in equation (5). represents a fixed point combination of precoding matrices in the i-th iteration. Definition and for

Define the function _g1 as

in, and is a constant, and

Then g ₁ is the function f in The minimization function at . This function can be used to update the precoding matrix to

The limit point provided in equation (6) is a stable point of the precoding matrix in equation (5), and the optimization problem in equation (6) is a concave quadratic optimization problem, and its optimal solution can be obtained by the Lagrange multiplier method. Define its Lagrangian function as

Among them, λ, λ ₁ ,…,λ _D are Lagrange multipliers. From the first-order optimal condition of the above formula, we can get

and

Let λ ^o = 0 and The optimal solution

The same method can also be used to obtain the optimal solution

In order to calculate the optimal solution of equation (7) and equation (8), we need to first find

definition

Then we can get the following expression:

Then there is

and The first part of can be found in the same way, but the second part is very complicated and has no closed form expression. We will solve this problem by using deterministic equivalence.

and The deterministic equivalent expression of is as follows:

or

in,

and and It can be obtained by the following iterative equation

and

but and The certainty is equivalent to the following:

Note that in each In the calculation of , there is an M×M inverse process In each pair In the calculation of , there is an N×N inverse process Therefore, when the values of M and N are very large, the computational complexity increases dramatically. According to the matrix inversion lemma, and Rewritten as:

By using equations (17) and (18), we can avoid calculating The M×M inverse and calculation of The N×N inversion is performed, and the computational complexity is greatly reduced.

According to the above deterministic equivalent expressions, the precoding update expressions of equations (7) and (8) can be rewritten as

and

The specific steps of the C-V2V massive MIMO robust precoding algorithm are given below:

1) Initialization: Set i = 0. Randomly generate the initial value of the precoding matrix that meets the power constraint

2) Calculated by formula (9) and

3) Using the calculation results of step 2, calculate according to formula (11) and

4) Using the calculation results of step 2 and step 3, calculate according to formula (10) and According to formula (17) and formula (18), and According to formula (14), formula (15) and formula (16), calculate and

5) Based on the calculation result of step 4, update the precoding matrix by equation (19) and equation (20): and Let i=i+1.

6) Repeat steps 2 to 5 until the rate difference between two adjacent iterations reaches a preset threshold.

5. Precoding design when the channel mean is zero

For any and When the channel mean of all users is zero, that is, and

Sometimes, there are

in, and is a matrix with non-zero main diagonal elements and zero elements elsewhere, and is a permutation matrix. The row vectors are defined as and as follows:

The permutation matrix and are set to make and The elements in are arranged in descending order. Since the channel mean of all users is zero, the expression can be rewritten as follows:

Define the function:

All of the above functions evaluate to diagonal matrices, and

From the above expression, we can and Rewrite as

in, and They are all diagonal matrices, and their values are related to the precoding matrix.

When the channel mean of all users is zero, beam domain transmission can maximize the sum rate, and after introducing deterministic equivalence, beam domain transmission is still the optimal solution to the proposed coordinated transmission weighted sum rate optimization problem. At this time, the optimal precoder can be rewritten as

That is, only and The original precoding design problem is simplified to an optimal problem of beam domain power allocation. From the above expression, the precoding algorithm can use the commutativity of permutation matrix and diagonal matrix to simplify the calculation, and in each iteration process, the inversion of large-dimensional matrix can be obtained element by element, which greatly reduces the computational complexity of the algorithm. Equation (24) and Equation (25) give the expressions required in the iterative algorithm as follows:

and

The specific steps of the C-V2V massive MIMO beam-domain precoding algorithm when the channel mean is zero are given below:

1) Initialization: Set i = 0. Generate initial value The main diagonal element values of each matrix are all 1, and the elements at other positions are all 0, and then these matrices are normalized to meet the power constraint value.

2) Calculated by formula (21) and

3) Using the calculation results of step 2, calculate according to formula (22) and

4) Using the calculation results of steps 2 and 3, calculate the values of the parameters in equation (24).

5) According to the calculation results of step 4, update the matrix by formula (25) Let i=i+1.

6) Repeat steps 2 to 5 until the rate difference between two adjacent iterations reaches a preset threshold value, and then the optimal precoding matrix is obtained by equation (23).

Claims (10)

1. A C-V2V large-scale MIMO multicast and unicast robust cooperative transmission method is characterized in that: configuring a base station, a plurality of cellular subscribers and a plurality of V2V communication pairs in a cellular network, each V2V communication pair comprising one V2V-Tx and one V2V-Rx;

firstly, all cellular users and all V2V-Rx in a cellular network respectively send uplink detection signals to a base station and a matched V2V-Tx at the same time, and the base station and each V2V-Tx acquire prior statistical channel information of each cellular user and all V2V-Rx according to the received detection signals;

then, in the uplink training process, all cellular users and V2V-Rx send pilot signals, a base station and each V2V-Tx carry out channel estimation by using prior statistical channel information and the received pilot signals, and posterior statistical channel information of each cellular user is obtained, wherein the posterior statistical channel information comprises channel mean and variance;

finally, the base station and each V2V-Tx perform low-complexity robust linear precoding by utilizing posterior statistical channel information comprising channel mean and channel variance through MM algorithm and introduced deterministic equivalence, and implement the cooperative transmission of cellular network multicast and V2V communication to unicast in the downlink, so that the weighted sum rate of the cooperative transmission is maximized.

2. The C-V2V massive MIMO multicast and unicast robust cooperative transmission method according to claim 1, characterized by: during downlink transmission, the base station transmits the same information to all cellular users, while V2V-Tx in each V2V communication pair transmits different information to the corresponding V2V-Rx, respectively.

3. The C-V2V massive MIMO multicast and unicast robust cooperative transmission method according to claim 1, characterized by: and introducing a time correlation coefficient into the posterior statistical channel model, wherein the time correlation coefficient is related to the moving speed of the cellular user, and reflecting the channel variation among different time blocks.

4. The C-V2V massive MIMO multicast and unicast robust cooperative transmission method according to claim 1, characterized by: in the robust precoding transmission, the base station and each V2V-Tx perform linear precoding design of cellular users and all V2V-Rx according to a coordinated transmission weighted traversal and rate maximization criterion of cellular network multicasting and V2V communication for unicast, comprising the following steps: and (3) giving an optimal solution of the weighted sum rate in an iterative mode, wherein input values are the priori statistical channel information obtained by uplink detection and the posterior statistical channel information obtained by uplink training, and output values are an optimal precoding matrix.

5. The C-V2V massive MIMO multicast and unicast robust cooperative transmission method of claim 4, wherein: the original optimization problem with the cooperative transmission weighted traversal and the speed as the objective function cannot be directly solved, and the original optimization problem is converted into a concave quadratic optimization problem which can be solved by a Lagrangian multiplier method by introducing an MM algorithm.

6. The C-V2V massive MIMO multicast and unicast robust cooperative transmission method of claim 5, wherein: the computational complexity of the algorithm is reduced by replacing the rate in the concave quadratic optimization problem with its deterministic equivalence.

7. The C-V2V massive MIMO multicast and unicast robust cooperative transmission method according to either of claim 5 or claim 6, characterized by: and the matrix inversion theory is utilized, so that the large-dimensional matrix inversion in the concave quadratic optimization problem solving process is avoided, and the calculation complexity of an algorithm is reduced.

8. The C-V2V massive MIMO multicast and unicast robust cooperative transmission method according to claim 1, characterized by: the posterior statistical channel information of the cellular user and the transmitting vehicle user comprises a channel mean value and a variance, and under the special scene that the channel mean value of all users is zero, the base station and each V2V-Tx are transmitted by adopting a wave beam domain, namely: when the base station transmits the multicast signal, the optimal transmission direction should be consistent with the feature vector of the covariance matrix of the base station, and in the V2V link, the transmission direction of the unicast signal should be consistent with the feature vector of the covariance matrix of the corresponding V2V-Tx.

9. The C-V2V massive MIMO multicast and unicast robust cooperative transmission method of claim 8, wherein: in the special scene that the average value of all user channels is zero, when the beam domain transmission is adopted, the original cooperative transmission weighting and rate optimization problem is simplified into a beam domain power allocation optimization problem.

10. The C-V2V massive MIMO multicast and unicast robust cooperative transmission method according to claim 9, characterized by: in the solving algorithm of the beam domain power distribution optimization problem under the special scene that the average value of all user channels is zero, the inversion of the large-dimensional matrix is completed in an element-by-element inversion mode, so that the calculation complexity of the algorithm is greatly reduced.