Orthogonal time-frequency-space hybrid multiple access method

Abstract

The invention discloses an orthogonal time-frequency-space hybrid multiple access method which is applied to an OFTS system. The OTFS system comprises a base station and a plurality of groups of users, each group of users comprises two users, resource multiplexing is carried out between the two users through NOMA, and inter-group interference is avoided between different groups of users through TDMA; the base station constructs a time allocation coefficient and power allocation joint optimization problem, and the objective function of the optimization problem is maximized system energy efficiency; solving the optimization problem to obtain a time distribution coefficient for each group of users and power of two users in each group of users; and the base station pre-allocates the length of a transmission time slot for each group of users according to the time allocation coefficient, performs power allocation on the users in each group according to the obtained power, and then transmits information to each group of users. Compared with the prior art, the energy efficiency is effectively improved, the complexity of the system is reduced, and the reliability of the system is improved.

Description

Technical Field

[0001] The present invention relates to the field of hybrid multiple access, and in particular to an orthogonal time-frequency-space hybrid multiple access method. Background Art

[0002] The development of mobile communication systems has led to higher demands for communication quality in high-speed mobile environments. Orthogonal frequency division multiplexing (OFDM), a widely used technology in mobile communications, suffers from a sharp decline in performance in rapidly time-varying channels due to Doppler shift, which destroys the orthogonality between subcarriers. Recently proposed by scholars, Orthogonal Time-Frequency Modulation (OTFS) performs modulation and channel modeling in the Delay-Doppler (DD) domain. This allows all information symbols to achieve full time-frequency gain, making OTFS more robust than OFDM in dual-frequency channels.

[0003] Furthermore, the number of users accessing mobile communication networks is increasing. Given limited time-frequency resources, traditional orthogonal multiple access (OMA) technology struggles to provide services to a large number of users. Non-orthogonal multiple access, on the other hand, multiplexes more user information on orthogonal resources via the power domain. Therefore, compared to OMA, NOMA can enable more user access while improving energy efficiency and spectrum utilization. Current research includes: directly superimposing user information in the DD domain via the power domain; and dividing users into high-speed and low-speed users, modulating high-speed users in the DD domain and low-speed users in the time-frequency domain, with the two superimposed via the power domain.

[0004] However, when NOMA multiplexes too many users on the same orthogonal resource, it is easy to cause the serial interference cancellation at the receiving end to be not perfect, causing error propagation in the system and resulting in performance degradation. Summary of the Invention

[0005] The purpose of the present invention is to provide an orthogonal time-frequency-space hybrid multiple access method to further improve the energy efficiency of the system.

[0006] In order to achieve the above tasks, the present invention adopts the following technical solutions:

[0007] An orthogonal time-frequency-space hybrid multiple access method is applied to the OFTS system. The OTFS system includes a base station and multiple user groups, each of which includes two users. Resources are multiplexed between the two users through NOMA, and inter-group interference is avoided between different groups of users through TDMA.

[0008] The base station constructs a joint optimization problem for time allocation coefficients and power allocation. The objective function of the optimization problem is to maximize the system energy efficiency. By solving the optimization problem, the time allocation coefficient for each group of users and the power of the two users in each group are obtained.

[0009] The base station pre-allocates the length of the transmission time slot for each group of users according to the time allocation coefficient, distributes power to the users in each group according to the obtained power, and then sends information to each group of users.

[0010] Furthermore, the process of the base station sending information to each group of users is as follows:

[0011] The base station superimposes the user information of each group of users and performs OTFS modulation to obtain the corresponding time domain information;

[0012] After the base station completes the allocation of transmission time slots and power to each group of users, it sends the time domain information of each group of users to the users through the time-frequency dual-selective channel in the corresponding transmission time slot;

[0013] Each group of users performs OTFS demodulation on the received time domain signal to obtain the corresponding user information sent by the base station.

[0014] Furthermore, the joint optimization problem of time allocation coefficient and power allocation is specifically expressed as:

[0015]

[0016] stC1:α k,1 +α k,2 ≤P max

[0017]

[0018] C3:t k log2(1+γ k,c )≥R min

[0019] Where J is the objective function, C1, C2, C3 are different constraints; P max represents the maximum power, η represents the energy conversion efficiency of the base station, P c Represents the fixed power consumption of the base station circuit, R min Indicates the user's minimum channel transmission rate; R k,1 and R k,2 represents the channel capacity of the first and second users in the kth group of users, t k represents the time allocation coefficient allocated to the kth group of users, K represents the number of user groups; γ k,c represents the received signal-to-noise ratio of the cth user in the kth group, c = 1, 2; α k,1 With α k,2 The work allocated to the first user and the second user in the kth group respectively.

[0020] Furthermore, the joint optimization problem of time allocation coefficient and power allocation is a non-convex optimization problem, and the arithmetic inequality, continuous convex approximation and second-order cone constraint methods are used to transform the original non-convex optimization problem into a convex optimization problem for solution.

[0021] Furthermore, the relaxation factor r is first introduced, and the objective function J is converted into r by introducing new constraints. The newly introduced constraints are:

[0022]

[0023] Introduce the auxiliary variable b to decouple the numerator and denominator:

[0024]

[0025] Introducing auxiliary variables To satisfy:

[0026]

[0027] Here, the subscripts,k,1,and,k,2,represent the auxiliary variables corresponding to the first and second users in the,k,th group, respectively;

[0028] Formula (16) is transformed into a convex constraint condition:

[0029]

[0030] Formula (18) can be transformed into a convex constraint through a first-order Taylor expansion:

[0031]

[0032] In the formula, iter represents the number of iterations. Represents t obtained in the iter-1th iteration optimization k ,α k,1 ,α k,2 The value of

[0033] In formula (17), a new slack variable is introduced to transform it:

[0034]

[0035] Among them, a k,1 ,a k,2 is a newly introduced auxiliary variable, so the constructed formula (21) is a linear constraint and a convex constraint; and formula (22) also needs to introduce auxiliary variables to decouple the numerator and denominator:

[0036] α k,1 ‖H k,2 ‖ 2 +MNσ2 ≤z k (twenty three)

[0037] α k,2 ‖H k,2 ‖ 2 ≥z k (a k,2 -1) (24)

[0038] Among them, z k is a newly introduced auxiliary variable; and Equation (24) can be transformed into a convex constraint by performing a first-order Taylor expansion on the right side of the inequality:

[0039]

[0040] in, is the z obtained in the iter-1th iteration optimization k ,a k,1 ,a k,2 The value of

[0041] By introducing the auxiliary variable β k,1 ,β k,2 , so that it satisfies:

[0042]

[0043] Then Equation (17) can be restated as:

[0044]

[0045] The formula in formula (27) also needs to introduce auxiliary variables first The constraints in formula (27) are equivalently expressed as the following three constraints:

[0046]

[0047] Equation (28) can be transformed into a linear convex constraint through a first-order Taylor expansion:

[0048]

[0049] Among them, r (iter-1) 、b (iter-1) Represents the values ​​of auxiliary variables r and b obtained in the iter-1th iterative optimization;

[0050] Formula (29) and Formula (30) can be expressed as a second-order cone convex constraint form:

[0051]

[0052] Based on the relationship of the auxiliary variables above, the constraints can be expressed as:

[0053]

[0054] At this point, we get the following convex optimization problem:

[0055]

[0056] stC1:α k,1 +α k,2 ≤P max

[0057]

[0058] C3:(19),(20),(21),(23),(25),(26),(31),(32),(33)

[0059] The above convex optimization problem is solved using the CVX toolkit.

[0060] A terminal device comprises a processor, a memory and a computer program stored in the memory; when the processor executes the computer program, the orthogonal time-frequency-space hybrid multiple access method is implemented.

[0061] A computer-readable storage medium stores a computer program; when the computer program is executed by a processor, the orthogonal time-frequency-space hybrid multiple access method is implemented.

[0062] Compared with the prior art, the present invention has the following technical features:

[0063] Compared with OTFS orthogonal multiple access and OTFS fixed time allocation hybrid TDMA-NOMA access, the present invention effectively improves energy efficiency; compared with OTFS-NOMA, it reduces system complexity and increases system reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 is a convergence diagram in one embodiment of the present invention;

[0065] Figure 2 This is a comparison chart of energy efficiency between one embodiment of the present invention and the existing NOMA fixed time allocation and OMA optimized time allocation methods. DETAILED DESCRIPTION

[0066] The present invention provides an orthogonal time-frequency-space hybrid multiple access method, which is a hybrid TDMA-NOMA access scheme for an OTFS system. The method uses the allocated time slot length of each group of users and the user allocated power as optimization variables, constructs an optimization problem with maximizing the energy efficiency of the system as the objective function, and uses methods such as arithmetic inequalities, continuous convex approximation (SCA), and second-order cone constraints (SOC) to transform the original non-convex optimization problem into a convex optimization problem for solution.

[0067] The OTFS system includes one base station and K groups of users. Each group of users includes two users, and the two users reuse resources through NOMA. Different groups use TDMA to avoid inter-group interference. In addition, the two users in each group are divided into near users and far users, and the first user in each group is defined as the near user, and the second user as the far user. This scheme aims to establish an optimization problem for maximizing channel capacity based on the channel state information of each user fed back by the base station. The power allocation of each TDMA user group and the time allocation coefficient between user groups are obtained through the optimization problem. In the subsequent information transmission stage, the system allocates power and time according to the calculated power and time allocation coefficients to maximize channel capacity, thereby improving system performance.

[0068] The technical solution of the present invention is:

[0069] Step 1: The base station superimposes the kth group of user information in the kth (k=1, 2..., K)th time slot and performs OTFS modulation to obtain the corresponding time domain information. The specific process is as follows:

[0070] The information that the base station is going to send to the kth group of users is mapped to the constellation and then superimposed in the DD domain to obtain the kth group of information symbols:

[0071]

[0072] Where, l = 0, 1, ..., M-1, z = 0, 1, ..., N-1; and They respectively represent the information symbols after constellation mapping of the information sent by the base station to the first user and the second user in the k-th group of users; represents the M×N dimensional complex space, l and z represent the delay index value and Doppler index value respectively, M and N represent the maximum delay index value and the maximum Doppler index value respectively; α k,1 With α k,2 are the powers allocated to the first user and the second user respectively.

[0073] The information symbols of the kth group of users are transformed into time domain information after OTFS modulation:

[0074]

[0075] s k represents the time domain information of the kth group of users after OTFS modulation; F N represents the N-dimensional Fourier transform matrix, the superscript H represents the conjugate transpose; I M represents the M-dimensional identity matrix; represents the Kronecker product; definition Then the vector It can be expressed as where vec(·) represents a matrix vectorized operation.

[0076] Step 2: After allocating time to each group of users according to the time allocation coefficient, the base station will k The time domain signal r is sent to the kth group of users through the time-frequency dual-selective channel, and the time domain signal r received by the cth user in the kth group of users is k,c Expressed as:

[0077] r k,c =H k,c s k +n k,c (3)

[0078] Where c is the user index value, and the value 1 or 2 represents the first or second user in the kth group of users; n k,c represents the received noise vector of the cth user in the kth group of users; H k,c represents the equivalent channel matrix between the base station and the cth user in the kth group of users; H k,c Specifically, it can be expressed as:

[0079]

[0080] In the above formula, P k,c represents the total number of multipaths from the base station to the cth user in the kth group, They represent the i-th (i=0,1...,P k,c ) paths’ fading coefficients, delay taps, and Doppler taps, is the delay matrix, and its specific form is as follows:

[0081]

[0082] is the Doppler frequency shift matrix, which can be expressed as:

[0083]

[0084] Among them, e is a natural constant and j is an imaginary unit.

[0085] Step 3: Each user in the kth group receives the time domain signal r k,c Perform OTFS demodulation to obtain the corresponding information sent by the base station.

[0086] The user performs OTFS demodulation on the received time domain information, and the signal demodulated by the cth user in the kth group is It can be expressed as:

[0087]

[0088] Among them F N Represents the conjugate transpose of the N-dimensional Fourier transform matrix; I M Represents the M-dimensional identity matrix.

[0089] Among them, the second user in the kth group of users directly decodes his own information, that is, the second user in the kth group passes Directly perform constellation demapping to obtain the user information transmitted by the base station to itself.

[0090] The first user in the kth group of users is first demodulated by OTFS to obtain Perform constellation demapping to obtain the information transmitted by the base station to the second user in the kth group, and then Subtract the second user's information from the received data, and then perform constellation demapping to obtain the user information sent by the base station.

[0091] The base station constructs a joint optimization problem of time allocation coefficient and power allocation for the OFTS system; by optimizing the power and time allocation coefficients, the performance of the signal transmission and reception system is improved; after the modeling and solution are completed to obtain the power of each group of users and the time allocation coefficient between groups, power and time are allocated to each group of users, and information modulation, transmission, reception and demodulation are performed according to steps 1 to 3.

[0092] (1) Analyze the user's received signal-to-noise ratio and channel capacity.

[0093] The relationship between the time domain information transmitted by the base station and the time domain signal received by the user in the DD domain can be expressed as:

[0094]

[0095] The received signal-to-noise ratio γ of the first user in the kth group k,1 It can be expressed as:

[0096]

[0097] in:

[0098]

[0099] |||| represents the F norm of the matrix, σ 2 Represents the noise variance, c=1,2.

[0100] The received signal-to-noise ratio of the second user in group k can be expressed as:

[0101]

[0102] The channel capacity of the first and second users in the kth group of users can be expressed as:

[0103] R k,1 =t k log2(1+γ k,1 ) (12)

[0104] R k,2 =t k log2(1+γ k,2 ) (13)

[0105] In the above formula, t k It represents the time allocation coefficient allocated to the kth group of users. This coefficient is the result of normalizing the transmission time slots allocated to the kth group of users.

[0106] (2) Construct a joint optimization problem of time allocation coefficient and power allocation with the goal of maximizing energy efficiency.

[0107]

[0108] Where J is the objective function, C1, C2, and C3 are the maximum power constraints, time allocation constraints, and minimum channel transmission rate constraints of the objective function; P max represents the maximum power, η represents the energy conversion efficiency of the base station, P c represents the fixed power consumption of the base station circuit, R min represents the minimum channel transmission rate of the user; constraint C2 is the result of time normalization for the convenience of representation.

[0109] (3) Solving the joint optimization problem of time allocation coefficient and power allocation.

[0110] Since the original optimization problem is a non-convex optimization problem and difficult to solve, the non-convex to convex operation is used to transform the original non-convex optimization problem into a convex optimization problem for solution.

[0111] First, since the objective function J is a non-convex function, the relaxation factor r is first introduced, and the objective function J is converted into r by introducing new constraints. The newly introduced constraints are:

[0112]

[0113] Since the newly introduced constraint in the above formula is in fractional form and difficult to solve, the numerator and denominator are decoupled by introducing an auxiliary variable b:

[0114]

[0115] Among them, the variable t in formula (16) k With α k,1 , α k,2 The convexity of the multiplication of is difficult to guarantee, so the auxiliary variable is introduced To satisfy:

[0116]

[0117] The subscripts k,1 and k,2 respectively denote the auxiliary variables corresponding to the first and second users in the kth group, and the same applies below.

[0118] Therefore, Equation (16) is transformed into a convex constraint:

[0119]

[0120] Formula (18) can be transformed into a convex constraint through a first-order Taylor expansion:

[0121]

[0122] In the formula, iter represents the number of iterations. Represents t obtained in the iter-1th iteration optimization k ,α k,1 ,α k,2 The numerical value of .

[0123] In formula (17), since the fractional form of the received signal-to-interference-and-noise ratio is difficult to handle, a new slack variable is introduced to transform it:

[0124]

[0125] Among them, a k,1 ,a k,2 is a newly introduced auxiliary variable, so the constructed formula (21) is a linear constraint and a convex constraint; and formula (22) also needs to introduce auxiliary variables to decouple the numerator and denominator:

[0126] α k,1 ‖H k,2 ‖ 2 +MNσ 2 ≤z k (twenty three)

[0127] α k,2 ‖H k,2 ‖2 ≥z k (a k,2 -1) (24)

[0128] Among them, z k is a newly introduced auxiliary variable; therefore, the constructed formula (23) is a linear constraint and a convex constraint; and formula (24) can be transformed into a convex constraint by performing a first-order Taylor expansion on the right side of the inequality:

[0129]

[0130] in, is the z obtained in the iter-1th iteration optimization k ,a k,1 ,a k,2 The numerical value of .

[0131] By introducing the auxiliary variable β k,1 ,β k,2 , so that it satisfies:

[0132]

[0133] Then Equation (17) can be restated as:

[0134]

[0135] The formula in formula (27) also needs to introduce auxiliary variables first The constraints in formula (27) are equivalently expressed as the following three constraints:

[0136]

[0137] Equation (28) can be transformed into a linear convex constraint through a first-order Taylor expansion:

[0138]

[0139] Among them, r (iter-1) 、b (iter-1) Represents the values ​​of the auxiliary variables r and b obtained in the iter-1th iterative optimization.

[0140] Formula (29) and Formula (30) can be expressed as a second-order cone convex constraint form:

[0141]

[0142] Based on the relationship of the auxiliary variables above, the constraints can be expressed as:

[0143]

[0144] At this point, the original non-convex optimization problem can be transformed into the following convex optimization problem:

[0145]

[0146] stC1:α k,1 +α k,2 ≤P max

[0147]

[0148] C3:(19),(20),(21),(23),(25),(26),(31),(32),(33)

[0149] The above convex optimization problem can be solved using the CVX toolkit.

[0150] Based on the above problem, the power and time allocation coefficients can be solved. The base station allocates power to each group of users according to the required power, and pre-allocates the length of the information transmission time slot for each group of users according to the required time allocation coefficient. The base station then sends information to each user according to steps 1 to 3. This can improve the service quality of the base station when serving multi-user communications and increase the multiple access communication capacity.

[0151] Figure 1 The convergence of an embodiment of the method of the present invention is demonstrated; it can be seen from the figure that the method converges within a few iterations.

[0152] Figure 2 The paper compares the three schemes of hybrid TDMA-NOMA (NOMA optimized time allocation in the figure caption), NOMA with fixed time allocation (NOMA fixed time allocation in the figure caption), and orthogonal multiple access in the OTFS system; the figure shows the effectiveness of the invented method and the obvious improvement in energy efficiency.

[0153] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. An orthogonal time-frequency-space hybrid multiple access method, characterized in that: The method is applied to an OFTS system; the OTFS system includes a base station and multiple user groups, each user group includes two users, resources are multiplexed between the two users through NOMA, and inter-group interference is avoided between different groups of users through TDMA; The base station constructs a joint optimization problem for time allocation coefficients and power allocation. The objective function of the optimization problem is to maximize the system energy efficiency. By solving the optimization problem, the time allocation coefficient for each group of users and the power of the two users in each group are obtained. The base station pre-allocates the length of the transmission time slot for each group of users according to the time allocation coefficient, distributes power to the users in each group according to the obtained power, and then sends information to each group of users.

2. The orthogonal time-frequency-space hybrid multiple access method according to claim 1, characterized in that: The process of the base station sending information to each group of users is as follows: The base station superimposes the user information of each group of users and performs OTFS modulation to obtain the corresponding time domain information; After the base station completes the allocation of transmission time slots and power to each group of users, it sends the time domain information of each group of users to the users through the time-frequency dual-selective channel in the corresponding transmission time slot; Each group of users performs OTFS demodulation on the received time domain signal to obtain the corresponding user information sent by the base station.

3. The orthogonal time-frequency-space hybrid multiple access method according to claim 1, characterized in that: The joint optimization problem of time allocation coefficient and power allocation is specifically expressed as: Where J is the objective function, C1, C2, C3 are different constraints; P max represents the maximum power, η represents the energy conversion efficiency of the base station, P c Represents the fixed power consumption of the base station circuit, R min Indicates the user's minimum channel transmission rate; R k,1 and R k,2 represents the channel capacity of the first and second users in the kth group of users, t k represents the time allocation coefficient allocated to the kth group of users, K represents the number of user groups; γ k,c represents the received signal-to-noise ratio of the cth user in the kth group, c = 1, 2; α k,1 With α k,2 The work allocated to the first user and the second user in the kth group respectively.

4. The orthogonal time-frequency-space hybrid multiple access method according to claim 3, characterized in that: The joint optimization problem of time allocation coefficient and power allocation is a non-convex optimization problem. The method of arithmetic inequality, continuous convex approximation and second-order cone constraint is used to transform the original non-convex optimization problem into a convex optimization problem for solution.

5. The orthogonal time-frequency-space hybrid multiple access method according to claim 4, characterized in that: First, the relaxation factor r is introduced, and the objective function J is transformed into r by introducing new constraints. The newly introduced constraints are: Introduce the auxiliary variable b to decouple the numerator and denominator: Introducing auxiliary variables Make it satisfy: Here, the subscripts,k,1,and,k,2,represent the auxiliary variables corresponding to the first and second users in the,k,th group, respectively; Formula (16) is transformed into a convex constraint condition: Formula (18) can be transformed into a convex constraint through a first-order Taylor expansion: In the formula, iter represents the number of iterations. Represents t obtained in the iter-1th iteration optimization k ,α k,1 ,α k,2 The value of In formula (17), a new slack variable is introduced to transform it: Among them, a k,1 ,a k,2 is a newly introduced auxiliary variable, so the constructed formula (21) is a linear constraint and a convex constraint; and formula (22) also needs to introduce auxiliary variables to decouple the numerator and denominator: a k,1 ‖H k,2 ‖ 2 +MNσ 2 ≤z k (23) a k,2 ‖H k,2 ‖ 2 ≥z k (a k,2 -1) (24) Among them, z k is a newly introduced auxiliary variable; and Equation (24) can be transformed into a convex constraint by performing a first-order Taylor expansion on the right side of the inequality: in, is the z obtained in the iter-1th iteration optimization k ,a k,1 ,a k,2 The value of By introducing the auxiliary variable β k,1 ,β k,2 , so that it satisfies: Then Equation (17) can be restated as: The formula in formula (27) also needs to introduce auxiliary variables first The constraints in formula (27) are equivalently expressed as the following three constraints: Equation (28) can be transformed into a linear convex constraint through a first-order Taylor expansion: Among them, r (iter-1) 、b (iter-1) Represents the values ​​of auxiliary variables r and b obtained in the iter-1th iterative optimization; Formula (29) and Formula (30) can be expressed as a second-order cone convex constraint form: Based on the relationship of the auxiliary variables above, the constraints can be expressed as: At this point, we get the following convex optimization problem: The above convex optimization problem is solved using the CVX toolkit.

6. A terminal device comprising a processor, a memory, and a computer program stored in the memory; characterized in that: When the processor executes the computer program, the orthogonal time-frequency-space hybrid multiple access method according to any one of claims 1 to 5 is implemented.

7. A computer-readable storage medium storing a computer program; wherein: When the computer program is executed by a processor, the orthogonal time-frequency-space hybrid multiple access method according to any one of claims 1 to 5 is implemented.

8. An OTFS system, characterized in that: The base station in the system realizes information transmission to users by using the orthogonal time-frequency-space hybrid multiple access method according to any one of claims 1-5.

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