A communication optimization method combining beamforming and channel allocation

Abstract

The present invention discloses a communication optimization method of joint beamforming and channel allocation, comprising: allocating channels between a controller and a user based on an ultra-reliable low-latency communication system model; completing data transmission between the controller and the channel based on the allocated channels; constructing a decoding error rate model based on the user's received signal, and constructing an optimization model with an optimal beamforming vector that minimizes the decoding error rate as the optimization target; introducing auxiliary variables, and converting the optimization model into a convex problem model through continuous convex approximation; iteratively solving the convex problem model based on an interior point method to obtain an optimal beamforming vector matrix; converting the optimal beamforming vector matrix into an optimal beamforming vector between the controller and the user through singular value decomposition; completing the beamforming of the ultra-reliable low-latency communication system model based on the optimal beamforming vector; and allocating channels between the controller and the user again. The present invention can effectively improve communication stability.

Description

Technical Field

[0001] The invention relates to a communication optimization method combining beamforming and channel allocation, and belongs to the technical field of wireless communications. Background Art

[0002] Supporting mission-critical Internet of Things (IoT) devices is a considerable challenge for the sixth generation (6G) wireless networks. Applications that require ultra-reliable low-latency communication (URLLC) services include autonomous factory manufacturing, vehicle communications, smart grid automation, remote surgery, etc. In smart grid automation, the transmission delay is expected to be less than 1ms, while ensuring a packet error probability of 10-9. The low-latency transmission in URLLC requires a shorter data block length. Under the condition of short data block length, the traditional Shannon capacity formula is no longer applicable. At the same time, in order to adapt to business needs, it is necessary to consider channel allocation between multi-frequency users to minimize interference between users. Summary of the invention

[0003] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a communication optimization method of joint beamforming and channel allocation, which can effectively improve the reliability of an ultra-reliable low-latency communication (URLLC) network.

[0004] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0005] The present invention provides a communication optimization method for joint beamforming and channel allocation, comprising:

[0006] Establish an ultra-reliable and low-latency communication system model for controllers and users;

[0007] Allocate channels between controllers and users based on an ultra-reliable low-latency communication system model;

[0008] Complete data transmission between the controller and the channel based on the allocated channel;

[0009] A decoding error rate model is constructed based on the user's received signal, and an optimization model is constructed with the optimal beamforming vector that minimizes the decoding error rate as the optimization goal;

[0010] Auxiliary variables are introduced to transform the optimization model into a convex problem model through continuous convex approximation;

[0011] The convex problem model is iteratively solved based on the interior point method to obtain the optimal beamforming vector matrix;

[0012] The optimal beamforming vector matrix is ​​transformed into the optimal beamforming vector between the controller and the user through singular value decomposition;

[0013] Complete beamforming of the ultra-reliable low-latency communication system model based on the optimal beamforming vector;

[0014] Based on the ultra-reliable low-latency communication system model after beamforming, the channels between the controller and the users are allocated again.

[0015] Preferably, the ultra-reliable low-latency communication system model is established based on a controller and users, and the controller adopts a MIMO system, which provides services for users with a single antenna on channels at different frequencies; each of the users occupies at most one subcarrier, and the same subcarrier can carry transmission data of multiple users.

[0016] Preferably, the channel allocation includes:

[0017] Calculate the correlation between each user and each channel:

[0018]

[0019] in, and is the channel vector of the nth user on the kth subcarrier, h m,k The conjugate transpose of ;

[0020] Assign the most relevant users to the corresponding channels;

[0021] Compute the orthogonality of each user with respect to the first user on each occupied channel:

[0022]

[0023] in, For size N b The unit matrix, N b The number of antennas configured for the controller;

[0024] Assign the users with the highest orthogonality to the corresponding channels.

[0025] Preferably, the received signal of the user is:

[0026]

[0027] Among them, m,k is the received signal of the mth user on the kth subcarrier, s m,k 、s n,k is the transmitted signal of the mth and nth users on the kth subcarrier, w m,k 、w n,k is the beamforming vector of the mth and nth users on the kth subcarrier, is the channel vector of the mth user on the kth subcarrier, z m,k is the additive Gaussian white noise from the controller to the mth user on the kth subcarrier, the mean of the additive Gaussian white noise is 0, and the variance is is the set of users transmitting data on the kth subcarrier, is the set of users transmitting data on all subcarriers,

[0028] Preferably, the decoding error rate model is:

[0029]

[0030] Among them, R m,k is the theoretical information rate of the mth user on the kth subcarrier, D m,k is the amount of data transmitted by the mth user on the kth subcarrier, L is the number of code blocks for data transmission between the controller and the user; γ m,k is the signal-to-noise ratio of the mth user on the kth subcarrier,

[0031]

[0032] V m,k is the channel dispersion degree, ε m,k is the decoding error rate of the mth user on the kth subcarrier, Q(·) is the Q function,

[0033] The construction optimization model is:

[0034]

[0035]

[0036]

[0037] Among them, P k is the maximum power constraint of the kth subcarrier.

[0038] Preferably, the introducing of auxiliary variables and converting the optimization model into a convex problem model through continuous convex approximation iterations comprises:

[0039] Introduce the auxiliary variable g of the decoding error rate m,k :

[0040] ε m,k =Q(g m,k )

[0041]

[0042] The decoding error rate ε m,k Minimize the optimization objective and convert it into auxiliary variable g m,k Maximization is the optimization goal:

[0043]

[0044] Introduce auxiliary variable z k , and m,k :

[0045]

[0046]

[0047]

[0048] The auxiliary variable g m,k Maximization is converted into auxiliary variable z as the optimization objective k Maximization is the optimization goal:

[0049]

[0050]

[0051]

[0052]

[0053] In the tth iteration, The upper bound of is:

[0054]

[0055] in, for The approximation of the t-th convex approximation is, and x m,k and m,k At the value of the t-th convex approximation; then:

[0056]

[0057] Introducing semi-positive variables but:

[0058] log2(1+γ m,k )=α m,k -β m,k

[0059] in,

[0060]

[0061]

[0062] W n,k ≥0

[0063] Rank(W m,k )=1

[0064] Perform a first-order Taylor expansion:

[0065]

[0066] in, β m,k The t-th convex approximation of , then:

[0067]

[0068] Introduce auxiliary variable a m,k ,b m,k ,c m,k ,x m,k ,y m,k , the auxiliary variable z k The optimization objective is to maximize:

[0069]

[0070]

[0071]

[0072]

[0073]

[0074]

[0075] Among them, a m,k represents the convex approximation of the received user signal power, b m,k represents the convex approximation of the received interference and noise power, c m,k represents the convex approximation of the receiving user’s signal-to-interference-noise ratio, x m,k represents the convex approximation of the received user information rate power, y m,k represents the convex approximation of the channel dispersion of the receiving user;

[0076] At the tth iteration, and The lower bound of is:

[0077]

[0078]

[0079] in, express The convex approximation of express The convex approximation of

[0080] The auxiliary variable z k Maximization is transformed into a convex problem model as the optimization objective:

[0081]

[0082]

[0083]

[0084] Preferably, the iteratively solving the convex problem model based on the interior point method to obtain the optimal beamforming vector matrix includes:

[0085] Let t = t + 1, Perform iterative operations until the interior point method converges to the preset error δ or the iteration reaches the specified maximum number of iterations T, and output the final optimal beamforming vector matrix

[0086] Compared with the prior art, the present invention has the following beneficial effects:

[0087] The present invention provides a communication optimization method for joint beamforming and channel allocation, which can meet the low latency requirements of ultra-reliable low-latency communication systems and reduce the decoding error probability of users, thereby improving communication reliability; at the same time, multiple users can work in the same frequency band, thereby improving spectrum utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 It is a flow chart of a communication optimization method of joint beamforming and channel allocation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0089] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0090] like Figure 1 As shown, the present invention provides a communication optimization method for joint beamforming and channel allocation, comprising the following steps:

[0091] 1. Establish an ultra-reliable and low-latency communication system model for controllers and users;

[0092] The ultra-reliable low-latency communication system model is built based on the controller and users. The controller adopts the MIMO system, which provides services for users with single antennas on channels with different frequencies. Each user occupies at most one subcarrier, and the same subcarrier can carry the transmission data of multiple users.

[0093] 2. Allocate channels between the controller and users based on the ultra-reliable low-latency communication system model;

[0094] Channel assignments include:

[0095] Calculate the correlation between each user and each channel:

[0096]

[0097] in, and is the channel vector of the nth user on the kth subcarrier, h m,k The conjugate transpose of ;

[0098] Assign the most relevant users to the corresponding channels;

[0099] Compute the orthogonality of each user with respect to the first user on each occupied channel:

[0100]

[0101] in, For size N b The unit matrix, N b The number of antennas configured for the controller;

[0102] Assign the users with the highest orthogonality to the corresponding channels.

[0103] 3. Complete data transmission between the controller and the channel based on the allocated channel;

[0104] 4. Construct a decoding error rate model based on the user's received signal, and construct an optimization model with the optimal beamforming vector that minimizes the decoding error rate as the optimization goal;

[0105] 4.1. The user's receiving signal is:

[0106]

[0107] Among them, m,k is the received signal of the mth user on the kth subcarrier, s m,k 、s n,k is the transmitted signal of the mth and nth users on the kth subcarrier, w m,k 、w n,kis the beamforming vector of the mth and nth users on the kth subcarrier, is the channel vector of the mth user on the kth subcarrier, z m,k is the additive Gaussian white noise from the controller to the mth user on the kth subcarrier, the mean of the additive Gaussian white noise is 0, and the variance is is the set of users transmitting data on the kth subcarrier, is the set of users transmitting data on all subcarriers,

[0108] 4.2. The decoding error rate model is:

[0109]

[0110] Among them, R m,k is the theoretical information rate of the mth user on the kth subcarrier, D m,k is the amount of data transmitted by the mth user on the kth subcarrier, L is the number of code blocks for data transmission between the controller and the user; γ m,k is the signal-to-noise ratio of the mth user on the kth subcarrier,

[0111]

[0112] V m,k is the channel dispersion degree, ε m,k is the decoding error rate of the mth user on the kth subcarrier, Q(·) is the Q function,

[0113] 4.3. Construct the optimization model as follows:

[0114]

[0115]

[0116]

[0117] Among them, P k is the maximum power constraint of the kth subcarrier.

[0118] 5. Introduce auxiliary variables and transform the optimization model into a convex problem model through continuous convex approximation;

[0119] Transformed into convex problem model includes:

[0120] Introduce the auxiliary variable g of the decoding error rate m,k :

[0121] ε m,k =Q(gm,k )

[0122]

[0123] The decoding error rate ε m,k Minimize the optimization objective and convert it into auxiliary variable g m,k Maximization is the optimization goal:

[0124]

[0125] Introduce auxiliary variable z k , and m,k :

[0126]

[0127]

[0128]

[0129] The auxiliary variable g m,k Maximization is converted into auxiliary variable z as the optimization objective k Maximization is the optimization goal:

[0130]

[0131]

[0132]

[0133]

[0134] In the tth iteration, The upper bound of is:

[0135]

[0136] in, for The approximation of the t-th convex approximation is, and x m,k and m,k At the value of the t-th convex approximation; then:

[0137]

[0138] Introducing semi-positive variables but:

[0139] log2(1+γ m,k )=α m,k -β m,k

[0140] in,

[0141]

[0142]

[0143] W n,k ≥0

[0144] Rank(W m,k )=1

[0145] Perform a first-order Taylor expansion:

[0146]

[0147] in, β m,k The t-th convex approximation of , then:

[0148]

[0149] Introduce auxiliary variable a m,k ,b m,k ,c m,k ,x m,k ,y m,k , the auxiliary variable z k The optimization objective is to maximize:

[0150]

[0151]

[0152]

[0153]

[0154]

[0155]

[0156] Among them, a m,k represents the convex approximation of the received user signal power, b m,k represents the convex approximation of the received interference and noise power, c m,k represents the convex approximation of the receiving user’s signal-to-interference-noise ratio, x m,k represents the convex approximation of the received user information rate power, y m,k represents the convex approximation of the channel dispersion of the receiving user;

[0157] At the tth iteration, and The lower bound of is:

[0158]

[0159]

[0160] in, express The convex approximation of express The convex approximation of

[0161] The auxiliary variable z k Maximization is transformed into a convex problem model as the optimization objective:

[0162]

[0163]

[0164]

[0165] 6. Iteratively solve the convex problem model based on the interior point method to obtain the optimal beamforming vector matrix;

[0166] Let t = t + 1, Perform iterative operations until the interior point method converges to the preset error δ or the iteration reaches the specified maximum number of iterations T, and output the final optimal beamforming vector matrix

[0167] 7. Use singular value decomposition to transform the optimal beamforming vector matrix Transformed into the optimal beamforming vector between the controller and the user

[0168] 8. Complete beamforming of the ultra-reliable low-latency communication system model based on the optimal beamforming vector;

[0169] 9. The ultra-reliable low-latency communication system model after beamforming is used to allocate channels between the controller and users again.

[0170] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A communication optimization method for joint beamforming and channel allocation, characterized in that: include: Establish an ultra-reliable and low-latency communication system model for controllers and users; Allocate channels between controllers and users based on an ultra-reliable low-latency communication system model; Complete data transmission between the controller and the channel based on the allocated channel; A decoding error rate model is constructed based on the user's received signal, and an optimization model is constructed with the optimal beamforming vector that minimizes the decoding error rate as the optimization goal; Auxiliary variables are introduced to transform the optimization model into a convex problem model through continuous convex approximation; The convex problem model is iteratively solved based on the interior point method to obtain the optimal beamforming vector matrix; The optimal beamforming vector matrix is ​​transformed into the optimal beamforming vector between the controller and the user through singular value decomposition; Complete beamforming of the ultra-reliable low-latency communication system model based on the optimal beamforming vector; The channels between the controller and the users are allocated again based on the ultra-reliable low-latency communication system model after beamforming; Wherein, the channel allocation includes: Calculate the correlation between each user and each channel: in, and is the channel vector of the nth user on the kth subcarrier, h m,k The conjugate transpose of ; Assign the most relevant users to the corresponding channels; Compute the orthogonality of each user with respect to the first user on each occupied channel: in, For size N b The unit matrix, N b The number of antennas configured for the controller; Assign the users with the highest orthogonality to the corresponding channels; The received signal of the user is: Among them, m,k is the received signal of the mth user on the kth subcarrier, s m,k 、s n,k is the transmitted signal of the mth and nth users on the kth subcarrier, w m,k 、w n,k is the beamforming vector of the mth and nth users on the kth subcarrier, is the channel vector of the mth user on the kth subcarrier, z m,k is the additive Gaussian white noise from the controller to the mth user on the kth subcarrier, the mean of the additive Gaussian white noise is 0, and the variance is is the set of users transmitting data on the kth subcarrier, is the set of users transmitting data on all subcarriers, Wherein, the decoding error rate model is: Among them, R m,k is the theoretical information rate of the mth user on the kth subcarrier, D m,k is the amount of data transmitted by the mth user on the kth subcarrier, L is the number of code blocks for data transmission between the controller and the user; γ m,k is the signal-to-noise ratio of the mth user on the kth subcarrier, V m,k is the channel dispersion degree, ε m,k is the decoding error rate of the mth user on the kth subcarrier, Q(·) is the Q function, The construction optimization model is: Among them, P k is the maximum power constraint of the kth subcarrier; The step of introducing auxiliary variables and converting the optimization model into a convex problem model through continuous convex approximation iterations includes: Introduce the auxiliary variable g of the decoding error rate m,k : e m,k =Q(g m,k ) The decoding error rate ε m,k Minimize the optimization objective and convert it into auxiliary variable g m,k Maximization is the optimization goal: Introduce auxiliary variable z k , and m,k : The auxiliary variable g m,k Maximization is converted into auxiliary variable z as the optimization objective k Maximization is the optimization goal: In the tth iteration, The upper bound of is: in, for The approximation of the t-th convex approximation is, and x m,k and m,k At the value of the t-th convex approximation; then: Introducing semi-positive variables but: log2(1+γ m,k )=a m,k -b m,k in, W n,k ≥0 Rank(W m,k )=1 Perform a first-order Taylor expansion: in, β m,k The t-th convex approximation of , then: Introduce auxiliary variable a m,k ,b m,k ,c m,k ,x m,k ,y m,k , the auxiliary variable z k The optimization objective is to maximize: Among them, a m,k represents the convex approximation of the received user signal power, b m,k represents the convex approximation of the received interference and noise power, c m,k represents the convex approximation of the receiving user’s signal-to-interference-noise ratio, x m,k represents the convex approximation of the received user information rate power, y m,k represents the convex approximation of the channel dispersion of the receiving user; At the tth iteration, and The lower bound of is: in, express The convex approximation of express The convex approximation of The auxiliary variable z k Maximization is transformed into a convex problem model as the optimization objective: The iterative solution of the convex problem model based on the interior point method to obtain the optimal beamforming vector matrix includes: make Perform iterative operations until the interior point method converges to the preset error δ or the iteration reaches the specified maximum number of iterations T, and output the final optimal beamforming vector matrix 2. A communication optimization method of joint beamforming and channel allocation according to claim 1, characterized in that: The ultra-reliable low-latency communication system model is established based on a controller and users. The controller adopts a MIMO system, which provides services for users with a single antenna on channels at different frequencies; each of the users occupies at most one subcarrier, and the same subcarrier can carry transmission data of multiple users.

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