A user grouping and power allocation method in a millimeter wave mMIMO-NOMA system

Abstract

The application belongs to the technical field of wireless communication, and particularly relates to a user grouping and power allocation method under a millimeter wave mMIMO-NOMA system; the method comprises the following steps: receiving a user downlink signal and representing a user achievable rate according to the user downlink signal; adopting a cluster head selection algorithm to obtain a cluster head of each beam and putting all the cluster heads into a cluster head set; performing analog precoding and user grouping according to the cluster head set to obtain an equivalent channel vector of the user and a user grouping result; performing digital precoding according to the equivalent channel vector of the user and the user grouping result to obtain a digital precoding vector; constructing a power allocation and power segmentation factor joint optimization problem according to the achievable rate; adopting an iterative optimization algorithm to transform the non-convex power allocation and power segmentation factor joint optimization problem into a convex optimization problem; solving the convex optimization problem to obtain an optimal power allocation scheme; and the application can maximize the achievable rate and improve the spectral efficiency and energy efficiency of the system.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, specifically relating to a user packet and power allocation method in a millimeter-wave mMIMO-NOMA system. Background Technology

[0002] The combination of millimeter wave and massive MIMO technologies meets the needs of future 5G networks, and their integration holds great potential, such as enhancing system throughput, expanding coverage, and improving spectral and energy efficiency. In traditional MIMO systems, each antenna requires a dedicated RF chain to achieve fully digital signal processing, inevitably leading to prohibitive hardware costs and energy consumption. Therefore, Hybrid Precoding (HP) was proposed, addressing the issues of significant performance loss and the increased number of RF chains. Driven by the pursuit of higher spectral efficiency, Non-Orthogonal Multiple Access (NOMA) technology was introduced. Compared to traditional Orthogonal Multiple Access (OMA), NOMA offers significant performance advantages in spectral and energy efficiency. The combination of NOMA and millimeter wave massive MIMO technology allows each beam to support multiple devices simultaneously in time and frequency. Leveraging NOMA's in-beam superposition coding (SC) and continuous interference cancellation (SIC) technologies, multiplexing gain is improved, saving on RF chains and phase shifter networks, thereby effectively reducing operating costs and energy consumption.

[0003] Besides spectral efficiency, energy efficiency is also a key performance indicator. Green communication is an important part of the next generation of communication concepts. Compared with green energy sources such as wind and solar power, which are more affected by the environment, wireless energy harvesting technology can provide wireless devices with a more controllable and stable energy supply. Therefore, SWIPT technology has attracted great interest from researchers in recent years. The principle of SWIPT is to use a power-divided receiver to extract information and energy from the received radio frequency signal. This makes it possible to explore more energy-efficient networks, especially for the Internet of Things (IoT) with millions of wireless devices, where this energy-saving characteristic is particularly important. However, to promote efficient SWIPT in multi-user systems, the trade-off between information rate and harvested energy must be carefully considered. This is because inter-user interference is generally detrimental to information decoding (ID) but beneficial to energy harvesting (EH).

[0004] Existing technologies have implemented joint transceiver and power shunting designs for multi-cell, multi-user downlink SWIPT systems based on the mean square error (MSE) criterion, which optimizes system energy efficiency. However, the following challenges still exist:

[0005] 1. Due to the presence of multiple users within the NOMA beam, the existence of beam interference makes it impossible to directly apply power allocation schemes for SWIPT-MIMO systems.

[0006] 2. Due to interference both within and between NOMA beams, an inappropriate user grouping scheme can severely degrade system performance.

[0007] 3. For SWIPT, inter-beam interference is beneficial for energy harvesting but detrimental to information decoding, so a trade-off needs to be made and a suitable power division factor needs to be selected. For the entire MIMO-NOMA system, the impact of inter-beam interference needs to be eliminated as much as possible, so the coupling problem between power allocation and power division factor needs to be solved.

[0008] In summary, there is an urgent need for an optimization method that jointly optimizes power allocation and SWIPT power splitting factor for multi-beam scenarios. This method can maximize the achievable rate while ensuring fairness as much as possible, thereby improving spectral efficiency and energy efficiency. Summary of the Invention

[0009] To address the shortcomings of existing technologies, this invention proposes a user grouping and power allocation method for millimeter-wave mMIMO-NOMA systems, which includes:

[0010] S1: Receives user downlink signals and indicates the user's reachable rate based on the user downlink signals;

[0011] S2: Use a cluster head selection algorithm to obtain the cluster head of each beam and put all cluster heads into the cluster head set;

[0012] S3: Perform simulated precoding and user grouping based on the cluster head set to obtain the user's equivalent channel vector and user grouping results;

[0013] S4: Perform digital precoding based on the user's equivalent channel vector and user grouping results to obtain the digital precoding vector;

[0014] S5: Construct a joint optimization problem of power allocation and power partitioning factor based on the user reachability rate;

[0015] S6: The non-convex power allocation and power division factor joint optimization problem is transformed into a convex optimization problem by using an iterative optimization algorithm; the convex optimization problem is solved to obtain the optimal power allocation scheme.

[0016] Preferably, the process of calculating the user's reachable rate includes:

[0017] The power split receiver divides the user's downlink signal into an energy harvesting signal and an information decoding signal;

[0018] The information decoding signal is subjected to crosstalk interference removal to obtain the remaining signal after some interference is eliminated;

[0019] The achievable rate for the user is calculated based on the remaining signal and noise plus interference.

[0020] Furthermore, the formula for calculating the user's reachable rate is expressed as:

[0021]

[0022] Among them, R m,n d represents the achievable rate of the nth user in the mth beam. m Represents a digital precoding vector. p represents the equivalent channel vector of the nth user in the mth beam. m,n θ represents the transmit power of the nth user in the mth beam. m,n This represents the signal-to-noise ratio plus interference for the nth user in the mth beam.

[0023] Preferably, the process of obtaining the cluster head of each beam using a cluster head selection algorithm includes:

[0024] S21: Select the user with the highest channel gain as the cluster head of the first beam;

[0025] S22: Select users among the remaining users whose channel correlation with the currently selected cluster head is less than the channel correlation threshold as candidate users for the next beam cluster head; if the number of candidate users is 0, proceed to step S24.

[0026] S23: Select the user with the highest channel gain from the candidate users as the cluster head of the next beam, and return to step S22;

[0027] S24: If there are unassigned beams, increase the channel correlation threshold.

[0028] Preferably, in step S3, the formula for obtaining the user's equivalent channel vector is:

[0029]

[0030] in, This represents the equivalent channel vector for the nth user. Let A represent the channel vector of the nth user, and let A represent the analog precoding matrix.

[0031] Preferably, in step S3, the formula for grouping users is:

[0032]

[0033] in, This indicates that the user has been grouped into a beam. This represents the equivalent channel vector for the nth user. This represents the channel vector of the cluster head in the m-th beam. This represents the channel vector of the nth user.

[0034] Preferably, the joint optimization problem of power allocation and power partitioning factor is expressed as:

[0035]

[0036] stC1:P m,n ≥0

[0037]

[0038]

[0039]

[0040] in, P represents the minimum reachable rate for the nth user in the mth beam. m,n β represents the transmit power of the nth user in the mth beam. m,n N represents the power division factor for the nth user in the mth beam. RF S represents the number of radio frequency chains. m P represents the number of users within the beam. t R represents the maximum total transmission power of the base station. m,n This represents the achievable rate of the nth user in the mth beam. This represents the minimum data rate requirement for the nth user in the mth beam. This represents the energy received by the user. This represents the minimum collected energy of the nth user in the mth beam.

[0041] Preferably, the process of transforming the joint optimization problem of power allocation and power partitioning factor into a convex optimization problem includes: confirming the remaining signal of the user after partial interference elimination based on the digital precoding vector and the user's equivalent channel vector; rewriting the achievable rate of the user based on the remaining signal of all users after partial interference elimination using the minimum mean square error test to obtain the objective function of the rewritten optimization problem; generating a new objective function based on the rewritten objective function using an iterative optimization algorithm; and rewriting the constraints of the optimization problem by introducing new variables and constraints to obtain the convex optimization problem.

[0042] The beneficial effects of this invention are as follows: First, compared to simple single-beam scenarios, this invention focuses on multi-beam scenarios, which aligns with the practical applications of NOMA and is more practically effective. Second, compared to traditional MIMO systems, this invention introduces SWIPT technology, which can utilize the energy generated by interference. The user grouping scheme and hybrid precoding of this invention significantly reduce inter-beam interference. Simultaneously, the optimization method of jointly optimizing power allocation and SWIPT power division factors maximizes the achievable rate while ensuring fairness as much as possible, thereby improving spectral efficiency and energy efficiency. Attached Figure Description

[0043] Figure 1 This is a flowchart of the user grouping and power allocation method in the millimeter-wave mMIMO-NOMA system of the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] This invention proposes a user grouping and power allocation method for millimeter-wave mMIMO-NOMA systems, such as... Figure 1 As shown, the method includes the following:

[0046] S1: Receives user downlink signals and indicates the user's reachable rate based on the user downlink signals.

[0047] This invention considers two hybrid precoding architectures in the downlink scenario of a millimeter-wave massive MIMO-NOMA system with SWIPT: a fully connected architecture and a sub-connected architecture. A base station equipped with N antennas and N_RF radio frequency chains simultaneously serves K users equipped with power-divided receivers, and it is assumed that the channel state information between the base station and the users is known.

[0048] In millimeter-wave massive MIMO systems, power domain multiplexing is achieved using in-beam NOMA. It is assumed that the number of beams equals the number of RF chains, i.e., G = N_RF; the received signal (downlink signal) of the nth user in the m-th beam is y. m,n It can be represented as:

[0049]

[0050] Where A represents the analog precoding matrix, d represents the channel vector of the nth user in the mth beam;i d m p represents the digital precoding vectors of the i-th and m-th beams, respectively. i,j S represents the transmit power of the j-th user in the i-th beam. i,j S m,n V represents the transmitted signal of the j-th user in the i-th beam and the transmitted signal of the n-th user in the m-th beam, respectively; m,n Let the noise of the nth user in the mth beam have a mean of 0 and a covariance of . The complex Gaussian distribution of p; m,n This represents the transmit power of the nth user in the mth beam.

[0051] For a uniform panel array (UPA), the channel vector h of the nth user in the m-th beam is... m,n It can be represented as:

[0052]

[0053] Among them, L m,n This represents the number of paths for the nth user in the mth beam; and These represent the complex gain, azimuth, and elevation angles of the l-th path, respectively. The response vector of the antenna array is defined as:

[0054]

[0055]

[0056]

[0057] Where λ represents the signal wavelength, N1 and d1 represent the number and spacing of horizontal antennas, and N2 and d2 represent the number and spacing of vertical antennas. Usually, d1 = d2 = λ / 2 is taken; J(n) = {0, 1, ..., n-1}.

[0058] The power split receiver will divide the user's downlink signal y m,n It is divided into two parts: one part is forwarded to the information decoder for information decoding, and the other part is processed for energy harvesting. Let β... m,n β is the power division factor for the nth user in the mth beam, 0 < β m,n <1, then the information decoding signal It can be represented as:

[0059]

[0060] Among them, u m,nThe noise caused by the power divider has a mean of 0 and a covariance of τ. v 2 The complex Gaussian distribution.

[0061] Signals used for energy harvesting It can be represented as:

[0062]

[0063] The energy received by the receiver is represented as:

[0064]

[0065] Where 0 < ρ < 1 represents the energy conversion efficiency. This represents the energy collected by the nth user in the mth beam.

[0066] Perform crosstalk cancellation (SIC) at the receiver. For the equivalent channel gain, assume Furthermore, since the nth signal can eliminate the interference of all j-th (j>n) user signals, the remaining signal of the nth user in the m-th beam after partial interference elimination can be expressed as:

[0067]

[0068] Then the signal-to-interference-plus-noise ratio μ of the nth user in the mth beam m,n Represented as:

[0069]

[0070] The interference plus noise is represented as follows:

[0071]

[0072] The reachable rate for a user can be expressed as:

[0073] R m,n =log2(1+μ m,n )

[0074] S2: Use a cluster head selection algorithm to obtain the cluster head of each beam and put all cluster heads into the cluster head set.

[0075] The process of obtaining the cluster head for each beam using the cluster head selection algorithm includes:

[0076] S21: Select the user with the highest channel gain as the cluster head of the first beam;

[0077] S22: Select users among the remaining users whose channel correlation with the currently selected cluster head is less than the channel correlation threshold as candidate users for the next beam cluster head; if the number of candidate users is 0, proceed to step S24.

[0078] S23: Select the user with the highest channel gain from the candidate users as the cluster head of the next beam, and return to step S22;

[0079] S24: If there are unassigned beams, increase the channel correlation threshold.

[0080] S3: Perform simulated precoding and user grouping based on the cluster head set to obtain the user's equivalent channel vector and user grouping results.

[0081] Based on the cluster head set, the gain of the fully connected architecture is maximized to obtain the analog precoding vector; the analog precoding vector of the user in the cluster head set. Represented as:

[0082]

[0083] in, angle(h γ(m) (i) represents the phase angle, h γ(m) (i) represents the channel vector, and B represents the bandwidth.

[0084] All analog precoding vectors constitute the analog precoding matrix A. Based on the analog precoding matrix and the user's channel vector, the equivalent channel vector is obtained, represented as:

[0085] The formula for grouping users is:

[0086]

[0087] in, This indicates that the user has been grouped into a beam. This represents the equivalent channel vector for the nth user. This represents the channel vector of the cluster head in the m-th beam. This represents the channel vector of the nth user.

[0088] S4: Perform digital precoding based on the user's equivalent channel vector and user grouping results to generate a digital precoding vector.

[0089] After analog precoding and user grouping are completed, the equivalent channel vector of the nth user in the mth beam can be denoted as:

[0090] This invention employs zero-forcing precoding, and performs digital precoding design based on the equivalent channel vectors of each beam. Assuming that user n of a beam has the highest equivalent channel gain, each group selects the user with the maximum equivalent channel gain, and the equivalent channel vector matrix formed by their equivalent channel vectors is expressed as:

[0091]

[0092] in, This represents the equivalent channel vector of user n in the m-th beam.

[0093] Generate a size of N rf ×N rf The digital precoding matrix and the normalized digital precoding vector are:

[0094]

[0095]

[0096] S5: Construct a joint optimization problem of power allocation and power partitioning factor based on the user reachability rate.

[0097] To ensure effective SIC (Series Intrusion Cancellation), the signal of the nth user should be detectable at all users with indices less than n in the same beam. If the achievable rate of the nth user in the mth beam is... If the minimum rate for users in the same beam is given, then the minimum rate for all NOMA users can be expressed as:

[0098]

[0099] The joint optimization problem of power allocation and power partitioning factor based on the user reachability rate is expressed as:

[0100]

[0101] stC1:P m,n ≥0

[0102]

[0103]

[0104]

[0105] in, P represents the minimum reachable rate for the nth user in the mth beam. m,n β represents the transmit power of the nth user in the mth beam. m,n N represents the power division factor for the nth user in the mth beam. RFS indicates the number of radio frequency connections. m P represents the number of users within the beam. t R represents the maximum total transmission power of the base station. m,n This represents the achievable rate of the nth user in the mth beam. This represents the minimum rate standard for the nth user in the mth beam. This indicates that the user has received energy. This represents the minimum collected energy for the nth user in the mth beam. C1 indicates that the allocated power must be positive, and C2 indicates that the total received power is less than P. t P t C3 represents the maximum total transmission power of the base station, C4 represents the minimum rate threshold constraint, and C5 represents the QoS constraint for energy collection for each user.

[0106] S6: The non-convex power allocation and power division factor joint optimization problem is transformed into a convex optimization problem by using an iterative optimization algorithm; the convex optimization problem is solved to obtain the optimal power allocation scheme.

[0107] The process of transforming the joint optimization problem of power allocation and power partitioning factor into a convex optimization problem based on the digital precoding vector and the user's equivalent channel gain is as follows:

[0108] Since the objective function P1 and constraints C3 and C4 are non-convex, they are transformed into a convex problem. The remaining signal after partial interference cancellation is confirmed based on the calculated digital precoding vector and the user's equivalent channel vector. Represented as a signal

[0109]

[0110] The achievable rate of each user is rewritten based on the remaining signal after partial interference removal from all users using the minimum mean square error test; specifically: the minimum mean square error (MMSE) test is used. Seeking S in the middle m,n The problem can be formalized as follows:

[0111]

[0112]

[0113] in, e represents the optimal equilibrium coefficient. m,n S represents the mean square error. m,n c represents the user's transmitted signal. m,n This represents the channel equalization coefficient.

[0114] The above formula can be written as:

[0115]

[0116] The optimal equilibrium coefficient is then obtained as follows:

[0117]

[0118] The minimum mean square error can be expressed as:

[0119]

[0120] According to the extension of the Woodbury matrix identity, we know that:

[0121]

[0122] The minimum mean square error is then expressed as:

[0123]

[0124] According to relevant theorems, the achievable rate of the nth user in the mth beam can be transformed into:

[0125]

[0126] Among them, a m,n The optimal value is e m,n The reciprocal of.

[0127] The optimization problem P1 can then be rewritten as:

[0128]

[0129] stC1,C2,C3,C4

[0130] A new objective function is generated based on the rewritten objective function using an iterative optimization algorithm. Specifically:

[0131] To solve the optimization problem P2, this invention introduces an iterative optimization algorithm for p m,n β m,n c m,n and a m,n Optimize separately; let the optimal solution after t-1 iterations be... and Then the t-th iteration and as follows:

[0132]

[0133]

[0134] in,

[0135] The optimization problem P2 can be transformed into:

[0136]

[0137]

[0138]

[0139]

[0140]

[0141] Wherein, the mean square error of the t-th iteration It can be represented as:

[0142]

[0143] By introducing new variables and constraints, and rewriting the constraints of the optimization problem, a convex optimization problem is obtained; specifically:

[0144] Introduction As a constraint The optimization problem P3 then becomes:

[0145]

[0146]

[0147] At this point, although the objective function has been transformed into a convex function, the constraints... It is still non-convex, and we introduce... and constraints Constraints Transform into:

[0148]

[0149]

[0150] According to the Schur complement lemma of the matrix, the constraints Rewritten in matrix form:

[0151]

[0152]

[0153] Therefore, optimization problem P1 is ultimately transformed into convex optimization problem P5:

[0154]

[0155]

[0156] Since the non-convex problem has been transformed into a convex optimization problem, it can be solved using a numerical convex program solver. The solution p is obtained iteratively. m,n β m,n c m,n and a m,n The optimal solution yields the optimal power allocation scheme, i.e., the optimal solution. and and the number of iterations T max The iterative optimization algorithm proposed in this invention will converge to at least a local optimum. By implementing the optimal power allocation scheme, the achievable rate can be maximized, thereby improving spectral efficiency and energy efficiency.

[0157] The above-described embodiments further illustrate the purpose, technical solution, and advantages of the present invention. It should be understood that the above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made to the present invention within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A user grouping and power allocation method in a millimeter-wave mMIMO-NOMA system, characterized in that, include: S1: Receives user downlink signals and indicates the user's reachable rate based on the user downlink signals; S2: The cluster head of each beam is obtained using a cluster head selection algorithm, and all cluster heads are added to the cluster head set. The process of obtaining the cluster head of each beam using the cluster head selection algorithm includes: S21: Select the user with the highest channel gain as the cluster head of the first beam; S22: Select users among the remaining users whose channel correlation with the currently selected cluster head is less than the channel correlation threshold as candidate users for the next beam cluster head; if the number of candidate users is 0, proceed to step S24. S23: Select the user with the highest channel gain from the candidate users as the cluster head of the next beam, and return to step S22; S24: If there are unassigned beams, increase the channel correlation threshold; S3: Perform simulated precoding and user grouping based on the cluster head set to obtain the user's equivalent channel vector and user grouping results; the formula for obtaining the user's equivalent channel vector is: ； in, This represents the equivalent channel vector for the nth user. This represents the channel vector of the nth user. Represents the analog precoding matrix; The formula for grouping users is: ； in, This indicates that the user has been grouped into a beam. , This represents the equivalent channel vector for the nth user. This represents the channel vector of the cluster head in the m-th beam. This represents the channel vector of the nth user; S4: Perform digital precoding based on the user's equivalent channel vector and user grouping results to obtain the digital precoding vector; S5: Construct a joint optimization problem of power allocation and power partitioning factor based on the user's achievable rate; the joint optimization problem of power allocation and power partitioning factor is expressed as: ； in, This represents the achievable rate of the nth user in the mth beam. This represents the transmit power of the nth user in the mth beam. This represents the power division factor for the nth user in the mth beam. Indicates the number of radio frequency chains. Indicates the number of users within the beam. This indicates the maximum total transmission power of the base station. This represents the achievable rate of the nth user in the mth beam. This represents the minimum data rate requirement for the nth user in the mth beam. This represents the energy received by the user. This represents the minimum collected energy for the nth user in the mth beam; S6: The non-convex power allocation and power division factor joint optimization problem is transformed into a convex optimization problem by using an iterative optimization algorithm; the convex optimization problem is solved to obtain the optimal power allocation scheme.

2. The user grouping and power allocation method in a millimeter-wave mMIMO-NOMA system according to claim 1, characterized in that, The process of calculating the reachable rate for a user includes: The power split receiver divides the user's downlink signal into an energy harvesting signal and an information decoding signal; The information decoding signal is subjected to crosstalk interference removal to obtain the remaining signal after some interference is eliminated; The achievable rate for the user is calculated based on the remaining signal and the noise and interference.

3. The user grouping and power allocation method in a millimeter-wave mMIMO-NOMA system according to claim 2, characterized in that, The formula for calculating the reachable rate for a user is expressed as: ； in, This represents the achievable rate of the nth user in the mth beam. Represents a digital precoding vector. This represents the equivalent channel vector of the nth user in the mth beam. This represents the transmit power of the nth user in the mth beam. This represents the signal-to-noise ratio plus interference for the nth user in the mth beam.

4. The user grouping and power allocation method in a millimeter-wave mMIMO-NOMA system according to claim 1, characterized in that, The process of transforming the joint optimization problem of power allocation and power partitioning factor into a convex optimization problem includes: confirming the remaining signal of the user after partial interference elimination based on the digital precoding vector and the user's equivalent channel vector; rewriting the achievable rate of the user based on the remaining signal of all users after partial interference elimination using the minimum mean square error test to obtain the objective function of the rewritten optimization problem; generating a new objective function based on the rewritten objective function using an iterative optimization algorithm; and rewriting the constraints of the optimization problem by introducing new variables and constraints to obtain the convex optimization problem.

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