CN109905917B - Wireless energy-carrying NOMA communication system wireless resource allocation method - Google Patents
Wireless energy-carrying NOMA communication system wireless resource allocation method Download PDFInfo
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Abstract
The invention discloses a wireless resource allocation method in a NOMA communication system based on wireless energy carrying, which comprises the following steps: 1) Based on the principle of 'maximization of system spectral efficiency', respectively calculating the length of a user uplink pilot sequence, a regularization parameter, a power division coefficient and a power distribution coefficient by using an iterative method; 2) Calculating the absolute value of the difference between the spectral efficiency of the current system and the spectral efficiency of the previous system after the iteration, and judging whether the absolute value is less than or equal to a convergence judgment threshold or not; if so, stopping calculation, wherein the iteration result is the optimal value, otherwise, continuing iteration; 3) And the user realizes the optimal distribution of the system wireless resources by utilizing the optimal uplink pilot frequency sequence length, the optimal regularization parameter, the optimal power distribution coefficient and the optimal power division coefficient. The method has the advantage that each user can coordinate information decoding and energy collection of the receiving end by using the optimal power division coefficient.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to a wireless resource allocation method in a NOMA communication system based on wireless energy carrying.
Background
With the rapid development of the fifth generation mobile communication technology, the conventional orthogonal Multiple Access technology has not been able to satisfy the connection of massive users in the future 5G network, so a Non-orthogonal Multiple Access (NOMA) technology has come to work, and the main reason for using the NOMA technology is that it can provide services for Multiple users in the same timeslot and frequency resource, support a large number of connectable users while effectively saving spectrum resources, however, using NOMA will bring more interference, and at the same time, the fairness of users must be considered. Another key objective in the future 5G network is to improve energy efficiency to the maximum extent, and in the traditional data transmission process, the wireless signal energy transmitted by the base station is regarded as useless energy, which causes waste of resources. In recent years, a new technology capable of simultaneously transmitting wireless information and energy, namely wireless portable communication, has appeared. Wireless energy-carrying communication is one of the key technologies for realizing green communication. The wireless energy-carrying communication is not only suitable for low-power application, but also suitable for high-power consumption scenes. However, research shows that the energy collected by the receiving end in the simultaneous transmission of energy and information is in a negative relation with the achievable rate, and therefore, how to reasonably allocate radio resources to coordinate the energy collection and information decoding of the receiving end is important.
Disclosure of Invention
Aiming at the problems, the invention provides a wireless resource allocation method in a NOMA communication system based on wireless energy carrying, which is based on the principle of 'system spectrum efficiency maximization', and calculates the optimal uplink pilot frequency sequence length, regularization parameters, power division coefficients and power allocation coefficients under the constraint of ensuring the fairness of system users and effectively utilizing the energy collected by the users so as to realize the optimal allocation of wireless resources in the system, and the specific implementation steps are as follows:
a step (101): based on the principle of 'system spectral efficiency maximization', respectively calculating the length of an uplink pilot sequence of a user, a regularization parameter, a power division coefficient and a power distribution coefficient by using an iteration method, and calculating the system spectral efficiency of the current iteration times according to the length of the uplink pilot sequence, the regularization parameter, the power division coefficient and the power distribution coefficient iterated each time;
a step (102): calculating the absolute value of the difference between the spectral efficiency of the system after the current iteration and the spectral efficiency of the system after the previous iteration, judging whether the absolute value is less than or equal to a convergence judgment threshold, if so, stopping the calculation, wherein the iteration result at the moment is the optimal value, and if not, returning to the step (101) to continue the iteration;
step (103): and the base station sends signals to each user based on the optimal power distribution coefficient, and each user uses one part of the received signals for energy collection and the other part for information decoding based on the optimal power division coefficient.
Further, in the aforementioned method for allocating radio resources in a NOMA communication system based on wireless energy transfer, in step (101), the iterative algorithm includes the following steps:
step (201): initializing the system user uplink pilot sequence length, regularization parameter, power division coefficient and power distribution coefficient, respectively symbolized as
β (0) 、
Simultaneously setting convergence judgment threshold E =10 -8 Iteration number i =0;
a step (202): by using
β (0) 、
Calculating an initial value of the spectral efficiency of the system;
step (203): calculating the system spectral efficiency of the (i + 1) th iteration based on the principle of' maximizing the system spectral efficiency
A step (204): judgment of
If yes, executing step (205) if the judgment result is yes, and executing step (206) if the judgment result is no;
a step (205): length of up pilot sequence from i +1 th iteration
Regularization parameter beta (i+1) Power division coefficient
And power distribution coefficient
The optimal value is obtained, and wireless transmission is carried out according to the optimal value;
step (206): and executing i = i +1, and returning to the step (203) to continue the iteration.
Further, in the foregoing method for allocating radio resources in a NOMA communication system based on wireless energy carrying, in step (202), the specific calculation steps of the initial value of the system spectral efficiency are as follows:
step (301): calculating a channel estimation parameter at the moment by using the initial values of the uplink pilot sequence length, the regularization parameter and the power division parameter of the system user, thereby acquiring the non-ideal channel state information of all the users of the system;
a step (302): calculating a sending pre-coding matrix at the moment by utilizing a non-ideal estimation channel matrix of a strong user and a regularization parameter initial value;
step (303): calculating the effective power of the users in the cluster and the inter-cluster interference by using the real channel vectors of the users in the cluster and the transmitted pre-coding matrix;
a step (304): and calculating the initial value of the spectral efficiency of the system by using the effective power and the inter-cluster interference of the users in the cluster and the initial values of the length of the uplink pilot sequence, the regularization parameter, the power segmentation parameter and the power distribution parameter.
Further, in the foregoing method for allocating radio resources in a NOMA communication system based on wireless energy carrying, in step (301), the obtaining of the non-ideal channel state information of all users of the system is specifically implemented as follows:
in a single-cell multi-user NOMA downlink wireless communication system based on wireless energy carrying, one part of energy collected by a user is used for uplink channel estimation and circuit consumption in a channel estimation stage, and the other part of energy is used for circuit consumption in an information transmission stage; suppose a base station is provided with N antennas, a user has a single antenna, and the total number of users is K (K)>N) in which>Represents greater than; there are N clusters in the system, each cluster having two users, where the better channel gain is the strong user, denoted by (N, 1), the worse channel gain is the weak user, denoted by (N, 2), and N is e [1,2]Epsilon represents belonging; meanwhile, the number of the uplink pilot frequency sent to the base station by each user is assumed to be T t The channel coherence interval is T, and T is a fixed value; channel usage is approximately constant, T t Is used for uplink channel estimation, T-T t Is used for the transmission of downlink data information, the complex gaussian noise power being σ 2 (ii) a Using the formula E n，i ＝(T-T t )ω(1-p s )β n，iρ Calculating the energy E collected by the ith user in the nth cluster n，i Where ω ∈ [0,1 ]]Represents the energy conversion efficiency, p s Representing the power division coefficient of each user, wherein rho is the signal-to-noise ratio of a downlink; will be provided with
Substitution calculation formula E n，i In, calculating
Energy collected by the user
Using formulas
Calculating the uplink transmission power of the ith user in the nth cluster as
Wherein
Is a circuit consumption of the user, and
and p 0 Is a constant; by using
Calculating the channel estimation parameter tau of the ith user in the nth cluster n，i Will be
Substituted into the calculation formula τ n，i Calculating
Channel estimation parameters of time
Thereby obtaining
Estimated channel gain for all users, where x is the superscript (i) Representing the result of the i-th iteration of x;
suppose that the true channel gain of the ith user in the nth cluster is modeled as
Wherein i ∈ [1,2 ]]，
Representing the real channel gain between the base station and the ith user in the nth cluster, and the vector size is 1 multiplied by N; beta is a n，i Is a constant, represents the large-scale fading factor of the ith user in the nth cluster,
representing a 1 XN complex Gaussian random vector whose elements all obey a mean of 0, a variance
Independent and same distribution of the components; the estimated channel gain of the users is available at the base station, and all users send uplink pilot frequencies to the base stationThe station receives the uplink pilot frequency to carry out channel estimation and estimates the parameters of the channel
Substitution formula
Obtaining the estimated channel gain of the ith user in the nth cluster
The vector size is 1 × N; wherein T is n，i Is an NxN deterministic non-negative definite matrix representing the transmit correlation matrix of the base station antennas, and T n，i ＝I N ，I N Is an N x N identity matrix and,
representing a 1 XN complex Gaussian random vector whose elements all obey a mean of 0, a variance
Independently of one another, τ n，i The channel estimation parameter for the ith user in the nth cluster represents the accuracy of the channel estimation, τ n，i ∈[0，1](ii) a Superscript (·) H Represents the conjugate transpose operation of the matrix,
which represents the square root of the arithmetic operation,
represents the square root operation of a matrix, (·) 2 Represents a quadratic; and fast fading real channel gains of strong users and weak users in the same cluster are modeled as follows:
wherein N belongs to [1,2]，
And
are all 1 x N in size,
representing a 1 XN complex Gaussian random vector whose elements all obey a mean of 0, a variance
The independent and same distribution of the water-soluble polymer,
and
independently of each other, [ theta ] n Is a constant value, and θ n ∈[0，1]And represents an error coefficient.
Further, in the foregoing method for allocating radio resources in a NOMA communication system based on wireless energy carrying, in step (302), the calculation of the transmission precoding matrix is specifically implemented as follows:
generating a channel estimation matrix from the channel gains of all strong users using the estimated channel gains of all strong users
The matrix size is N multiplied by N; the sending end adopts regularized zero-forcing pre-coding, the regularization parameter is beta, and the method utilizes
And β, calculating
Wherein (·) -1 Representing an inverse operation of a matrix; using a formula
Calculating a transmit precoding matrix G, wherein G satisfies tr (GG) H )≤NP，P>0,tr (·) represents the tracing operation of the matrix. Using the formula tr (GG) H ) NP less than or equal to, calculating
Wherein epsilon represents a normalization parameter meeting the constraint of base station transmitting power, N is the number of base station antennas, and P is the total transmitting power of the base station; let beta = beta (0) Finding β = β (0) A temporal precoding matrix G.
Further, in the foregoing method for allocating radio resources in a NOMA communication system based on wireless energy carrying, in step (303), the calculation of the effective power of users in a cluster and the inter-cluster interference is specifically implemented as follows:
for NOMA communication systems based on wireless energy portability,
removing strong user estimated channel vector in nth cluster
To obtain
The matrix size is (N-1) xN; using a formula
Calculating the real channel gains of the strong user and the weak user in the nth cluster as
And
according to G,
And
using a formula
Calculating the effective power U of the nth cluster strong user s By the formula
Calculating the effective power U of the nth cluster weak user w (ii) a Using formulas
Computing interference of other clusters in the system to strong users in the nth cluster by using
And calculating the interference of other clusters in the system to the n cluster weak user.
Further, in the foregoing method for allocating radio resources in a NOMA communication system based on wireless energy carrying, in step (304), the initial value of the system spectral efficiency is calculated as follows:
using the following formula
Calculating the spectral efficiency R of a system sp (ii) a Wherein alpha is n，1 The constant mu is greater than 0, E is the power distribution coefficient of the strong user in the nth cluster x { f (x) } denotes the expectation of f (x) with respect to variable x, |, computation 2 Represents the square operation of vector modulus, sigma (-) represents the summation operation, log (-) represents the logarithm operation; will be provided with
Substituting into the system spectral efficiency calculation formula R sp In, meterCalculating the initial value of the system spectral efficiency
Further, in the aforementioned method for allocating radio resources in a NOMA communication system based on wireless energy carrying, in step (203), the system spectral efficiency of the (i + 1) th iteration is calculated
The concrete implementation is as follows: firstly, the length of the uplink pilot sequence is utilized by the ith iteration result
Regularization parameter beta (i) Power division coefficient
The following equation is used:
calculating the power distribution coefficient of the strong user in the nth cluster of the (i + 1) th iteration
Where ρ is the downlink signal-to-noise ratio, R min Represents the minimum channel capacity of the weak user, and R min Is a constant; secondly, using the parameter beta (i) 、
Calculating the system spectral efficiency R sp And calculating optimal using one-dimensional search
Then, using the parameters
Calculating the spectral efficiency R of a system sp And calculating beta using a one-dimensional search (i+1) (ii) a Then, using the parameters
Calculating the spectral efficiency R of a system sp And calculated using a one-dimensional search
Finally, calculating the initial value of the system spectral efficiency
In the same way, will
Substitution into the calculation formula R sp In (1) calculating the system spectral efficiency of the (i + 1) th iteration
Further, in the method for allocating radio resources in a NOMA communication system based on wireless energy carrying, in step (102), a maximum number of iterations is set, it is first determined whether the current number of iterations is greater than the maximum number of iterations, and if the determination result is yes, the calculation is stopped; if the judgment result is negative, calculating the absolute value of the difference between the current and the previous system spectral efficiency after iteration, and judging whether the value is less than or equal to the convergence judgment threshold, if the judgment result is positive, stopping the calculation, wherein the length of the uplink pilot sequence, the regularization parameter, the power division coefficient and the power distribution coefficient after the current iteration are the optimal values, if the judgment result is negative, returning to the step (101) to continue the iteration until the absolute value of the difference between the current and the previous system spectral efficiency after iteration is less than or equal to the convergence judgment threshold.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
(1) The method adopts RZF precoding, compared with ZF precoding, the performance is obviously improved, and the same cluster is establishedThe fast fading true channel gain model of the medium-strong user and the weak user is realized by adjusting the error coefficient theta n The system performance is improved, and meanwhile, the calculation complexity is reduced;
(2) Based on the principle of 'system spectral efficiency maximization', under the constraint of ensuring system user fairness and effectively utilizing user collection energy, the method utilizes an iteration method to calculate the optimal uplink pilot frequency sequence length, regularization parameters, power division coefficients and power distribution coefficients, so that the inter-cluster interference of the system is reduced to the minimum, the energy collected by the users is effectively utilized, and meanwhile, each user utilizes the optimal power division coefficient to coordinate information decoding and energy collection of a receiving end, and the system spectral efficiency is remarkably improved.
Drawings
Fig. 1 is a flow chart illustrating a method for allocating radio resources in a NOMA communication system based on wireless energy carrying according to the present invention.
Fig. 2 is a flowchart illustrating a specific implementation step of the iterative algorithm in step (101) of fig. 1.
FIG. 3 is a flowchart illustrating an exemplary implementation of the initial value of the spectral efficiency calculated in step 202 of FIG. 2.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, the present invention provides a method for allocating radio resources in a NOMA communication system based on wireless energy transfer, the NOMA communication system based on wireless energy transfer comprises a base station and a plurality of users, the base station is equipped with multiple antennas, each user has a single antenna; the method calculates the optimal uplink pilot sequence length, regularization parameters, power division coefficients and power distribution coefficients under the constraint of ensuring the fairness of system users and effectively utilizing the energy collected by the users according to the principle of 'maximizing the spectral efficiency of the system', thereby realizing the optimal distribution of wireless resources, and specifically comprises the following steps:
a step (101): based on the principle of 'maximization of system spectral efficiency', respectively calculating the length of an uplink pilot sequence of a user, a regularization parameter, a power division coefficient and a power distribution coefficient by using an iteration method, and calculating the system spectral efficiency of the current iteration times according to the length of the uplink pilot sequence, the regularization parameter, the power division coefficient and the power distribution coefficient which are iterated each time;
a step (102): calculating the absolute value of the difference between the current and the previous system spectral efficiency after iteration, and judging whether the absolute value is less than or equal to a convergence judgment threshold; if so, stopping calculation, wherein the iteration result is the optimal value, otherwise, continuing iteration;
step (103): and the base station sends signals to each user based on the optimal power distribution coefficient, and each user uses one part of the received signals for energy collection and the other part for information decoding based on the optimal power division coefficient.
Specifically, as shown in fig. 2, the iterative algorithm in step (101) is performed as follows:
step (201): initializing the uplink pilot sequence length, regularization parameter, power division coefficient and power distribution coefficient of the system user, and respectively symbolizing
Simultaneously setting convergence judgment threshold E =10 -8 Iteration number i =0;
step (202): by using
Calculating an initial value of the system spectral efficiency; the specific calculation steps of the initial value of the system spectral efficiency are shown in fig. 3:
step (301): calculating the channel estimation parameters at the moment by using the initial values of the length of the uplink pilot sequence, the regularization parameters and the power division parameters of the system user, thereby acquiring the non-ideal channel state information of all the users of the system; the concrete implementation is as follows:
in a single-cell multi-user NOMA downlink wireless communication system based on wireless energy carrying, a base station is assumed to be provided with N antennas, a user has a single antenna, and the total number of users is K (K)>N) in which>Means greater than; there are N clusters in the system, each cluster having two users, where the better channel gain is the strong user, denoted by (N, 1), the worse channel gain is the weak user, denoted by (N, 2), N e [1,2]Epsilon represents belonging; meanwhile, the quantity of the uplink pilot frequency sent to the base station by each user is assumed to be T t The channel coherence interval is T, and T is a fixed value; channel usage is approximately constant, T t Is used for uplink channel estimation, T-T t Is used for the transmission of downlink data information, the complex gaussian noise power being σ 2 (ii) a Using the formula E n，i ＝(T-T t )ω(1-p s )β n，i P calculating the energy E collected by the ith user in the nth cluster n，i Where ω ∈ [0,1 ]]Represents the energy conversion efficiency, p s Representing the power division coefficient of each user, wherein rho is the signal-to-noise ratio of a downlink; will be provided with
Substitution calculation formula E n，i In, calculating
Energy collected by the user
Using a formula
Calculating the uplink transmission power of the ith user in the nth cluster as
Wherein
Is a circuit consumption of the user, and
and p 0 Is a constant; by using
Calculating the channel estimation parameter tau of the ith user in the nth cluster n，i Will be
Substituted into the calculation formula tau n，i Calculating
Channel estimation parameters in time
Thereby obtaining
Estimated channel gain for all users, where x is the superscript (i) Representing the result of x after the ith iteration;
suppose that the true channel gain of the ith user in the nth cluster is modeled as
Wherein i ∈ [1,2 ]]，
Representing the real channel gain between the base station and the ith user in the nth cluster, with a vector size of 1 xN, β n，i Is a constant value, represents the large-scale fading factor of the ith user in the nth cluster,
representing a 1 XN complex Gaussian random vector whose elements all obey a mean of 0, a variance
Independent and same distribution of the components; the estimated channel gains for the users are available at the base station, all users transmit uplink pilots to the base station,the base station receives the uplink pilot frequency to carry out channel estimation and estimates the parameters of the channel
Substituted formula
Obtaining the estimated channel gain of the ith user in the nth cluster
The vector size is 1 × N; wherein T is n，i Is an NxN deterministic non-negative definite matrix representing the transmit correlation matrix of the base station antenna, and T n，i ＝I N ，I N Is an N x N identity matrix and,
representing a 1 XN complex Gaussian random vector whose elements all obey a mean of 0, a variance
Independently and identically distributed, τ n，i A parameter is estimated for the channel of the ith user in the nth cluster, indicating the accuracy of the channel estimation, τ n，i ∈[0，1]Upper label (.) H Represents the conjugate transpose operation of the matrix,
which represents the square root of the arithmetic,
represents the square root operation of a matrix, (·) 2 Represents a square; fast fading real channel gain modeling of strong users and weak users in the same cluster is as follows:
wherein
And
are all 1 x N in size,
representing a 1 XN complex Gaussian random vector whose elements all obey a mean of 0, a variance
The independent and same distribution of the water-soluble polymer,
and
are independent of each other; theta.theta. n Is a constant value, and θ n ∈[0，1]Representing an error coefficient;
step (302): calculating a sending pre-coding matrix at the moment by utilizing a non-ideal estimated channel matrix of a strong user and a regularization parameter initial value; the specific implementation of calculating the transmission precoding matrix is as follows:
generating a channel estimation matrix from the channel gains of all strong users using the estimated channel gains of all strong users
The matrix size is NXN, the sending end adopts regularization zero-forcing pre-coding, the regularization parameter is beta, and the method utilizes
And β, calculating
Wherein (·) -1 To representPerforming inverse operation on the matrix; using formulas
Calculating a transmit precoding matrix G, wherein G satisfies tr (GG) H )≤NP，P>0,tr (·) represents the tracing operation of the matrix; using the formula tr (GG) H ) NP less than or equal to, calculating
Wherein epsilon represents a normalization parameter meeting the constraint of the transmitting power of the base station, N is the number of base station antennas, and P is the total transmitting power of the base station; let beta = beta (0) Calculating β = β (0) A temporal precoding matrix G;
step (303): calculating the effective power of the users in the cluster and the inter-cluster interference by using the real channel vectors of the users in the cluster and the transmitted pre-coding matrix; the effective power and the inter-cluster interference of the users in the cluster are calculated as follows:
for NOMA communication systems based on wireless energy portability,
removing strong user estimated channel vector in nth cluster
To obtain
The matrix size is (N-1) xN; using formulas
Calculating the real channel gains of the strong user and the weak user in the nth cluster as
And
according to G,
And
using formulas
Calculating the effective power U of the nth cluster strong user s By the formula
Calculating the effective power U of the nth cluster weak user w (ii) a Using formulas
Computing interference of other clusters in the system to strong users in the nth cluster, using
Calculating the interference of other clusters in the system to the nth cluster weak user;
a step (304): calculating the initial value of the spectral efficiency of the system by using the effective power and the inter-cluster interference of the users in the cluster, and the initial values of the length of the uplink pilot sequence, the regularization parameter, the power division parameter and the power distribution parameter; the initial value of the spectral efficiency of the computing system is specifically realized as follows:
user uplink pilot sequence length using ith iteration
Regularization parameter beta (i) Power division coefficient
And power distribution coefficient
Using the following formula
The system spectral efficiency of the ith iteration can be calculated
Wherein alpha is n，1 The constant mu is greater than 0, E is the power distribution coefficient of the strong user in the nth cluster x { f (x) } denotes the expectation of f (x) with respect to variable x, |, computation 2 Represents the square operation of the vector mode, sigma (phi) represents the summation operation, and log (phi) represents the logarithm operation; therefore, will
Substituting into the system spectral efficiency calculation formula R sp In the method, the initial value of the system spectral efficiency is calculated
A step (203): based on the principle of 'maximizing the system spectral efficiency', firstly, the length of an uplink pilot sequence is determined according to the ith iteration result
Regularization parameter beta (i) Power division factor
The following equation is used:
calculating the power distribution coefficient of the strong user in the nth cluster of the (i + 1) th iteration
Where ρ is the downlink signal-to-noise ratio, R min Represents the minimum channel capacity of the weak user, and R min Is a constant; secondly, using the parameter beta (i） 、
Calculating the system spectral efficiency R sp And calculating optimal using a one-dimensional search
Then, using the parameters
Calculating the system spectral efficiency R sp And calculating beta using a one-dimensional search (i+1) (ii) a Then, the parameters are utilized
Calculating the system spectral efficiency R sp And calculated using a one-dimensional search
Finally, calculating the initial value of the system spectral efficiency
In the same way, will
Substitution into the calculation formula R sp In (1), calculating the system spectral efficiency of the (i + 1) th iteration
A step (204): judgment of
If yes, executing step (205) if the judgment result is yes, and executing step (206) if the judgment result is no;
a step (205): length of uplink pilot sequence from i +1 th iteration
Regularization parameter beta (i+1) Power division coefficient
And power distribution coefficient
The optimal value is obtained, and wireless transmission is carried out according to the optimal value;
step (206): and executing i = i +1, and returning to the step (203) to continue the iteration.
Specifically, in step (102), a maximum iteration number S is set, the current iteration number S = i +1, whether the current iteration number S is greater than the maximum iteration number S is determined, and if the determination result is yes, the calculation is stopped; if not, calculating the absolute value of the difference between the current and the previous system spectral efficiency after iteration, judging whether the absolute value is less than or equal to a convergence judgment threshold, if so, stopping the calculation, and at the moment, iterating the length of the uplink pilot sequence currently
Regularization parameter beta (i+1) Power division coefficient
And power distribution coefficient of strong user in nth cluster
If the judgment result is negative, returning to the step (101) to continue iteration until the absolute value of the difference between the current and the previous system spectral efficiency is less than or equal to the convergence judgment threshold;
specifically, in step (103), the optimal pilot sequence length of the system user calculated in step (102) is used, the user sends an uplink pilot, the base station performs channel estimation according to the received pilot signal to obtain non-ideal channel state information of all users, and then calculates a regularized zero-forcing precoding matrix in combination with optimal regularization parameters, the base station sends signals to each user based on the optimal power distribution coefficient, each user uses one part of the received signals for energy collection based on the optimal power division coefficient, and the other part of the received signals for information decoding, thereby realizing optimal distribution of system wireless resources.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are also within the scope of the present invention.
Claims (3)
1. A wireless resource allocation method in a NOMA communication system based on wireless energy carrying is characterized in that: the method comprises the following specific steps:
a step (101): based on the principle of 'maximization of system spectral efficiency', respectively calculating the length of a user uplink pilot sequence, a regularization parameter, a power division coefficient and a power distribution coefficient by using an iterative method; calculating the system spectrum efficiency of the current iteration times according to the length of the uplink pilot sequence iterated each time, the regularization parameter, the power division coefficient and the power distribution coefficient;
in step (101), the iterative algorithm comprises the steps of:
step (201): initializing the system user uplink pilot sequence length, regularization parameter, power division coefficient and power distribution coefficient, respectively symbolized as
β (0) 、
Simultaneously setting convergence judgment threshold E =10 -8 Iteration number i =0;
step (202): by using
β (0) 、
Calculating an initial value of the spectral efficiency of the system;
the specific calculation steps of the initial value of the system spectral efficiency are as follows:
step (301): calculating the channel estimation parameters at the moment by using the initial values of the length of the uplink pilot sequence, the regularization parameters and the power division parameters of the system user, thereby acquiring the non-ideal channel state information of all the users of the system;
the specific implementation of acquiring the non-ideal channel state information of all the users of the system is as follows:
in a single-cell multi-user NOMA downlink wireless communication system based on wireless energy carrying, one part of energy collected by a user is used for uplink channel estimation and circuit consumption in a channel estimation stage, and the other part of energy is used for circuit consumption in an information transmission stage; suppose a base station is provided with N antennae, a user has a single antenna, and the total number of users is K, K>N is wherein>Represents greater than; there are N clusters in the system, each cluster having two users, where the better channel gain is the strong user, denoted by (N, 1), the worse channel gain is the weak user, denoted by (N, 2), N is E [1,2, \ 8230, N]Epsilon represents belonging to; meanwhile, the number of the uplink pilot frequency sent to the base station by each user is assumed to be T t The channel coherence interval is T, and T is a fixed value; channel usage is approximately constant, T t Is used for uplink channel estimation, T-T t Is used for the transmission of downlink data information, the complex gaussian noise power being σ 2 (ii) a Using the formula E n,i ＝(T-T t )ω(1-p s )β n,i Rho calculating the energy E collected by the ith user in the nth cluster n,i Wherein ω ∈ [0,1 ]]Represents the energy conversion efficiency, p s Representing the power division coefficient of each user, wherein rho is the signal-to-noise ratio of a downlink; will be provided with
Substitution calculation formula E n,i In, calculate
Energy collected by the user
Using formulas
Calculating the uplink transmission power of the ith user in the nth cluster as
Wherein
Is a circuit consumption of the user, and
and p 0 Is a constant; by using
Calculating the channel estimation parameter tau of the ith user in the nth cluster n,i Will be
Substituted into the calculation formula τ n,i Calculating
Channel estimation parameters in time
Thereby obtaining
Estimated channel gain for all users in time, where x is the superscript (i) Representing the result of the i-th iteration of x;
suppose that the true channel gain of the ith user in the nth cluster is modeled as
Wherein i ∈ [1,2 ]]，
Representing the real channel gain between the base station and the ith user in the nth cluster, and the vector size is 1 multiplied by N; beta is a beta n,i Is a constant value, represents the large-scale fading factor of the ith user in the nth cluster,
representing a 1 XN complex Gaussian random vector whose elements all obey a mean of 0, a variance
Independent and same distribution of the components; the estimated channel gain of the user is available in the base station, all users send the uplink pilot frequency to the base station, the base station receives the uplink pilot frequency to carry out channel estimation, and the channel estimation parameters are obtained
Substituted formula
Obtaining the estimated channel gain of the ith user in the nth cluster
The vector size is 1 × N; wherein T is n,i Is an NxN deterministic non-negative definite matrix representing the transmit correlation matrix of the base station antennas, and T n,i ＝I N ，I N Is an N x N identity matrix and,
representing a 1 XN complex Gaussian random vector whose elements all obey a mean of 0, a variance
Independently of one another, τ n,i A parameter is estimated for the channel of the ith user in the nth cluster, indicating the accuracy of the channel estimation, τ n,i ∈[0,1](ii) a Superscript (·) H Represents the conjugate transpose operation of the matrix,
which represents the square root of the arithmetic operation,
square root operations representing matrices, (-) 2 Represents a quadratic; and fast fading real channel gains of strong users and weak users in the same cluster are modeled as follows:
wherein N belongs to [1,2, \8230 ], N]，
And
are all 1 x N in size,
representing a 1 x complex gaussian random vector, whose elements all obey a 0 mean,variance (variance)
The independent and same distribution of the water-soluble polymer,
and
independently of each other, [ theta ] n Is a constant value, and θ n ∈[0,1]Representing an error coefficient;
step (302): calculating a sending pre-coding matrix at the moment by utilizing a non-ideal estimation channel matrix of a strong user and a regularization parameter initial value;
the specific implementation of calculating the transmission precoding matrix is as follows:
generating a channel estimation matrix from the channel gains of all strong users using the estimated channel gains of all strong users
The matrix size is nxn; the transmitting end adopts regularized zero-forcing pre-coding, the regularization parameter is beta, and the method utilizes
And β, calculating
Wherein (·) -1 Representing an inverse operation of a matrix; using formulas
Calculating a transmission precoding matrix G, wherein G satisfies tr (GC) H )≤NP，P>0,tr (·) represents the tracing operation of the matrix; using the formula tr (GG) H ) NP less than or equal to, calculating
Wherein epsilon represents a normalization parameter meeting the constraint of base station transmitting power, N is the number of base station antennas, and P is the total transmitting power of the base station; let beta = beta (0) Calculating β = β (0) A temporal precoding matrix G;
a step (303): calculating the effective power of the users in the cluster and the inter-cluster interference by using the real channel vectors of the users in the cluster and the transmitted pre-coding matrix;
the effective power and the inter-cluster interference of the users in the cluster are calculated as follows:
for NOMA communication systems based on wireless energy portability,
removing strong user estimated channel vector in nth cluster
To obtain
The matrix size is (N-1) xN; using a formula
Calculating the real channel gains of the strong user and the weak user in the nth cluster as
And
according to G,
And
using a formula
Calculating the effective power U of the nth cluster strong user s By the formula
Calculating the effective power U of the nth cluster weak user w (ii) a Using formulas
Computing interference of other clusters in the system to strong users in the nth cluster by using
Calculating the interference of other clusters in the system to the nth cluster weak user;
step (304): calculating the initial value of the spectral efficiency of the system by using the effective power and the inter-cluster interference of the users in the cluster and the initial values of the length of the uplink pilot sequence, the regularization parameter, the power segmentation parameter and the power distribution parameter;
the initial value of the spectral efficiency of the computing system is specifically realized as follows:
using the following formula
Calculating the spectral efficiency R of a system sp (ii) a Wherein alpha is n,1 For the strong user power distribution coefficient in the nth cluster, the constant mu is greater than 0,
E x { f (x) } denotes the expectation of f (x) with respect to variable x, |, computation 2 Represents the square operation of the vector mode, sigma (phi) represents the summation operation, and log (phi) represents the logarithm operation; will be provided with
β (0) 、
Substituting into the system spectral efficiency calculation formula R sp In the method, the initial value of the system spectral efficiency is calculated
Step (203): calculating the system spectral efficiency of the (i + 1) th iteration based on the principle of' maximizing the system spectral efficiency
A step (204): judgment of
If yes, executing step (205), if no, executing step (206);
a step (205): length of uplink pilot sequence from i +1 th iteration
Regularization parameter beta (i+1) Power division coefficient
And power distribution coefficient
The optimal value is obtained, and wireless transmission is carried out according to the optimal value;
step (206): executing i = i +1, and returning to the step (203) to continue the iteration;
a step (102): calculating the absolute value of the difference between the spectral efficiency of the system after the current iteration and the spectral efficiency of the system after the previous iteration, judging whether the absolute value is less than or equal to a convergence judgment threshold, if so, stopping the calculation, wherein the iteration result at the moment is the optimal value, so that the optimal pilot frequency sequence length, the optimal regularization parameter, the optimal power division coefficient and the optimal power distribution coefficient are obtained; if the judgment result is negative, returning to the step (101) to continue iteration;
step (103): and the base station sends signals to each user based on the optimal power distribution coefficient, and each user uses one part of the received signals for energy collection and the other part for information decoding based on the optimal power division coefficient.
2. The method of allocating radio resources in a wireless energy-carrying-based NOMA communication system according to claim 1, wherein: in step (203), the system spectral efficiency of the (i + 1) th iteration is calculated
The concrete implementation is as follows: firstly, the length of the uplink pilot sequence is utilized by the ith iteration result
Regularization parameter beta (i) Power division factor
The following formula is utilized:
calculating the power distribution coefficient of the strong user in the nth cluster of the (i + 1) th iteration
Where p is the downlink signal-to-noiseRatio, R min Represents the minimum channel capacity of the weak user, and R min Is a constant; secondly, using the parameter beta (i) 、
Calculating the system spectral efficiency R sp And calculating optimal using a one-dimensional search
Then, using the parameters
Calculating the spectral efficiency R of a system sp And calculating beta using a one-dimensional search (i+1) (ii) a Then, using the parameters
β (i+1) ，
Calculating the system spectral efficiency R sp And calculated using a one-dimensional search
Finally, calculating the initial value of the system spectral efficiency
In the same way, will
β (i+1) 、
Substitution calculation formula R sp In (1) calculating the system spectral efficiency of the (i + 1) th iteration
3. The method of allocating radio resources in a wireless energy-carrying-based NOMA communication system according to claim 1, wherein: in the step (102), the maximum iteration number is set, whether the current iteration number is larger than the maximum iteration number is judged, and if the judgment result is yes, the calculation is stopped; if the judgment result is negative, calculating the absolute value of the difference between the current and the previous system spectral efficiency after iteration, and judging whether the value is less than or equal to the convergence judgment threshold, if the judgment result is positive, stopping the calculation, wherein the length of the uplink pilot sequence, the regularization parameter, the power division coefficient and the power distribution coefficient after the current iteration are the optimal values, if the judgment result is negative, returning to the step (101) to continue the iteration until the absolute value of the difference between the current and the previous system spectral efficiency after iteration is less than or equal to the convergence judgment threshold.
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