CN111726156A - NOMA-based resource allocation method and device - Google Patents

NOMA-based resource allocation method and device Download PDF

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CN111726156A
CN111726156A CN202010508826.4A CN202010508826A CN111726156A CN 111726156 A CN111726156 A CN 111726156A CN 202010508826 A CN202010508826 A CN 202010508826A CN 111726156 A CN111726156 A CN 111726156A
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user
noma
objective function
detection matrix
codebook
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郝万明
林宇
王宁
朱政宇
孙钢灿
李哲
徐金雷
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Zhengzhou University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0426—Power distribution
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The invention provides a resource allocation method and a device based on NOMA, wherein the method comprises the following steps: establishing an uplink non-orthogonal multiple access millimeter wave system model of which a base station adopts a fully-connected sparse radio frequency chain antenna structure; a codebook-based analog beam design and improved NOMA decoding scheme is proposed; jointly optimizing a detection matrix at the BS and the transmission power at a user to construct a maximum minimum user Energy Efficiency (EE) optimization problem; the fractional objective function is converted into a subtractive objective function, a double-loop iteration algorithm is provided for solving the fractional objective function, the inner loop iteratively updates the detection matrix and the transmitting power, and the outer loop adopts double-section iteration to obtain the optimal solution of the problem. The invention provides an uplink MIMO-NOMA millimeter wave system which is researched and provides a codebook-based analog beam design to reduce pilot frequency overhead of channel estimation; the joint optimization design of the transmission power of the user and the detection matrix of the BS obtains better performance in the aspects of spectrum efficiency and energy efficiency.

Description

NOMA-based resource allocation method and device
Technical Field
The invention belongs to the technical field of communication, and particularly relates to a resource allocation method and device based on NOMA.
Background
Millimeter wave (mmWave) technology has become a promising solution to meet the rapidly increasing capacity demands of wireless networks. However, millimeter wave signals suffer from severe propagation loss due to the high carrier frequency. To compensate for losses, Base Stations (BSs) typically use a large number of antennas to provide large array gain. However, due to high power consumption, it is not practical to implement dedicated Radio Frequency (RF) chains for all antenna elements. Therefore, in order to reduce energy consumption and hardware cost, advanced sparse radio frequency chain antenna structures are adopted. In order to improve the spectrum utilization of wireless networks, non-orthogonal multiple (NOMA) technology is considered a promising solution. This scheme studies the power domain NOMA technique. The mmWave Multiple Input Multiple Output (MIMO) technology is combined with the NOMA technology to form an mmWave-MIMO-NOMA system, and an effective scheme is provided for meeting the requirements of high capacity and high service quality of a wireless network.
Millimeter wave MIMO-NOMA systems face two major challenges. The first is user clustering, i.e., how users are partitioned, forming NOMA clusters. To date, most clustering schemes have been designed with full knowledge of the Channel State Information (CSI). Another challenge is related to uplink Energy Efficiency (EE) optimization, which is an important indicator for evaluating system performance. Unlike those dedicated to downlink EEs, uplink EE optimization is more challenging because the power allocation of users and the beam design of the base station must be considered together. On the other hand, for downlink MIMO-NOMA, the variable is often a beamforming matrix, and semi-definite programming (SDP) is usually employed to solve such problems. In contrast, for the problem under consideration, the detection matrix at the BS and the power values at the users need to be optimized. Furthermore, since the signal to interference plus noise ratio (SINR) of a user is a function of its multiplier, these two different variables are coupled in the problem formulation. Thus, SDP is no longer applicable. Furthermore, in the downlink, there is only a total power constraint, while in the uplink, each user has its own power constraint. Therefore, existing downlink solutions cannot be used to solve the problem under consideration.
Disclosure of Invention
Because the current millimeter wave MIMO-NOMA system faces the optimization problem of how to divide users to form NOMA clustering and uplink Energy Efficiency (EE), the invention provides a resource allocation method and a resource allocation device based on NOMA.
In a first aspect, the present invention provides a method for allocating resources based on NOMA, including:
s1: establishing an uplink non-orthogonal multiple access millimeter wave system model of which a base station adopts a fully-connected sparse radio frequency chain antenna structure;
s2: a codebook-based analog beam design and improved NOMA decoding scheme is proposed;
s3: jointly optimizing a detection matrix at the BS and the transmission power at the user to construct a maximum minimum user Energy Efficiency (EE) optimization problem;
s4: the fractional objective function is converted into a subtractive objective function, a double-loop iteration algorithm is provided to solve the fractional objective function, the inner loop iteratively updates the detection matrix and the transmitting power, and the outer loop adopts double-section iteration to obtain the optimal solution of the problem.
Preferably, the step S1 specifically includes:
the number of radio frequency chains of the uplink mmWave system is much lower than the number of antennas, where the BS is equipped with N antennas and M (M ≦ N) RF chains, each RF chain being connected to all antennas through N phase shifters, one antenna for each user.
Preferably, the step S2 specifically includes:
selecting an analog beam matrix from a predefined codebook using a Discrete Fourier Transform (DFT) codebook; applying beam alignment to user clustering, wherein users belonging to the same cluster are served by NOMA, and effective CSI is obtained through beam alignment; a widely used geometric mmWave channel model with G scatterers is used.
Preferably, the step S3 specifically includes:
a decoding scheme which only depends on effective channel strength and does not depend on user clusters/groups is provided to eliminate the interference among users, meet the transmitting power and rate of the users and maximally realize the minimum EE of the users, which can be expressed as follows:
Figure BDA0002527630890000031
Figure BDA0002527630890000032
Figure BDA0002527630890000033
Figure BDA0002527630890000034
where (1b) represents the minimum rate requirement for each user, (1c) is the maximum transmit power constraint for the user, and (1d) represents the normalized power constraint for the hybrid detection vector and analog beams at the BS.
Preferably, the step S4 specifically includes:
the obtained mathematical model (1) is non-convex and difficult to directly solve, the (1a) is transformed into a subtraction form, an effective double-loop iterative algorithm is provided, and
Figure BDA0002527630890000036
the optimal EE solution, W, defined as problem (1)*And P*Respectively, a corresponding optimal detection matrix and power allocation matrix. Problem (1) can be rewritten as:
Figure BDA0002527630890000035
the detection matrix and the transmitting power are updated iteratively by the inner loop, and the optimal solution of the problem (1) is obtained by the outer loop by adopting an iterative algorithm based on two segments.
In another aspect, the present invention provides a NOMA-based resource allocation apparatus, including:
the model establishing module is used for establishing an uplink non-orthogonal multiple access millimeter wave system model of which the base station adopts a fully-connected sparse radio frequency chain antenna structure;
a beam design module for codebook based analog beam design and improved NOMA decoding scheme to reduce pilot overhead for channel estimation;
an equation construction module for jointly optimizing a detection matrix at the BS and a transmit power at the user to construct a maximum minimum user Energy Efficiency (EE) optimization equation;
and the iteration processing module is used for converting the fractional objective function into a subtractive objective function, and provides a double-loop iteration algorithm to solve the fractional objective function, the inner loop iteratively updates the detection matrix and the transmitting power, and the outer loop adopts double-section iteration to obtain the optimal solution of the problem.
Preferably, the modeling module comprises:
the first modeling unit is used for allocating N antennas and M (M is less than or equal to N) RF chains at the BS according to the condition that the number of the radio frequency chains of the uplink non-orthogonal multiple access millimeter wave system is far lower than that of the antennas, each RF chain is connected to all the antennas through N phase shifters, and each user is allocated with one antenna;
a second modeling unit for selecting an analog beam matrix from a predefined codebook using a Discrete Fourier Transform (DFT) codebook; applying beam alignment to user clustering to obtain effective CSI through beam alignment; a geometric mmWave channel model with G scatterers was used.
Preferably, the equation construction module specifically includes:
constructing a maximum minimum user Energy Efficiency (EE) optimization equation using jointly optimizing a detection matrix at the BS and transmit power at the user:
Figure BDA0002527630890000041
Figure BDA0002527630890000042
Figure BDA0002527630890000043
Figure BDA0002527630890000044
preferably, the iterative processing module specifically includes:
and converting the fractional objective function into a subtractive objective function, and providing a double-loop iteration algorithm to solve, wherein the inner loop iteratively updates the detection matrix and the transmitting power, and the outer loop adopts double-section iteration to solve the optimal solution of the problem.
The invention provides a resource allocation method and a device based on NOMA, which reduce pilot frequency overhead by providing a codebook-based analog beam design and an improved NOMA decoding scheme, formulate the maximum and minimum user energy efficiency EE optimization problem by jointly optimizing a detection matrix at a BS and the maximum transmitting power at a user, convert a fractional objective function into a reduced objective function, provide a double-loop iteration algorithm to solve the fractional objective function, iteratively update the detection matrix and the transmitting power in an inner loop, and obtain the optimal solution of the problem by adopting double-section iteration in an outer loop.
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In order to more clearly illustrate the embodiments or technical solutions of the present invention in the prior art, the drawings used in the description of the embodiments or prior art are briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart illustrating a method for allocating resources based on NOMA according to an embodiment of the present invention;
FIG. 2 is a diagram of an uplink millimeter wave MIMO-NOMA model with a sparse radio frequency chain antenna structure according to an embodiment of the present invention;
fig. 3 is a polar coordinate diagram of an array factor of an N-4, K-8 codebook provided in an embodiment of the present invention;
fig. 4 is a graph of the spectrum efficiency versus the number of iterations provided in the embodiment of the present invention ("first iteration" indicates that the detection matrix V is iteratively updated, and "second iteration" indicates that the power distribution matrix P is iteratively updated);
FIG. 5 is a graph of energy efficiency versus number of outer loop iterations provided by an embodiment of the present invention;
FIG. 6 is a graph of secret ratio versus signal-to-noise ratio for two basic schemes and proposed methods provided by embodiments of the present invention;
FIG. 7 is a graph of NOMA and conventional OMA spectral efficiency versus signal-to-noise ratio provided by an embodiment of the present invention;
FIG. 8 is a graph of energy efficiency versus signal-to-noise ratio for two basic solutions provided by embodiments of the present invention and the proposed method;
FIG. 9 is a graph of energy efficiency versus signal-to-noise ratio for NOMA and OMA as provided by an embodiment of the present invention;
fig. 10 is a schematic structural diagram of a NOMA-based resource allocation apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 shows a flowchart of a NOMA-based resource allocation method provided in an embodiment of the present invention, including:
s1: establishing an uplink non-orthogonal multiple access millimeter wave system model of which a base station adopts a fully-connected sparse radio frequency chain antenna structure;
s2: a codebook-based analog beam design and improved NOMA decoding scheme is proposed;
s3: jointly optimizing a detection matrix at the BS and the transmission power at the user to construct a maximum minimum user Energy Efficiency (EE) optimization problem;
s4: the fractional objective function is converted into a subtractive objective function, a double-loop iteration algorithm is provided to solve the fractional objective function, the inner loop iteratively updates the detection matrix and the transmitting power, and the outer loop adopts double-section iteration to obtain the optimal solution of the problem.
In this embodiment, an uplink non-orthogonal multiple access millimeter wave system model is established, and antennas and RF chains are equipped at a BS according to a sparse radio frequency chain structure of the model, a codebook-based analog beam design and an improved NOMA decoding scheme are proposed to reduce pilot overhead, and a dual-cycle iterative algorithm is proposed to solve a detection matrix at a jointly optimized BS and a problem of constructing a maximum minimum user Energy Efficiency (EE) optimization at a user's transmit power.
Specifically, step S1 includes:
the method of this embodiment is applied to the uplink mmWave system model shown in fig. 2, where the system parameters include: a base station is provided with N-32 antennas and M-4 radio frequency chains, each RF chain is connected to all antennas through N phase shifters, each user has one antenna, and a plurality of two user beam groups are formed by enough users; assuming that the number of clusters in the mmWave channel is G-8,
Figure BDA0002527630890000071
Figure BDA0002527630890000072
in the range of [ - π, π]Uniform distribution is adopted; meanwhile, the signal-to-noise ratio is defined as
Figure BDA0002527630890000073
And assume all usersHave the same maximum transmit power; the inefficiency of the power amplifier is set to 1/0.38 and the circuit power consumption per user is set to Pc100 mW; assume that the minimum rate requirements for all users are the same and are set to
Figure BDA0002527630890000074
Further, step S2 includes: fig. 3 is a polar coordinate diagram of N-4 and K-8 codebook array factors according to an embodiment of the present invention, where an analog beam matrix is selected from a predefined codebook, and a Discrete Fourier Transform (DFT) codebook is used, which is defined as
Figure BDA0002527630890000075
Wherein:
Figure BDA0002527630890000076
a set of BS antennas is represented as,
Figure BDA0002527630890000077
is the set of beam patterns in the codebook and the DFT codebook F is the N × K matrix.
Applying beam alignment to user clusters, users belonging to the same cluster will be served by NOMA, obtaining valid CSI by beam alignment, each column of codebook F representing one beam direction, i.e. F ═ F1,...,fK}。
There are M RF chains, and the detection signal of the M-th analog beam at the BS can be expressed as
Figure BDA0002527630890000078
Wherein s ismiAnd PmiThe transmission signal and power (user (m, i)) at the ith user, which respectively represent the mth analog beam, satisfy
Figure BDA0002527630890000079
V is the matrix of the analog beams,
Figure BDA00025276308900000710
a detection vector, h, representing a user (m, i) (i ∈ {1,2})miRepresenting the channel coefficients from the users (m, i) to the BS.
A widely used geometric channel model with G scatterers, channel h, is adoptedmiCan be written as
Figure BDA0002527630890000081
In the formula (I), the compound is shown in the specification,
Figure BDA0002527630890000082
is provided with
Figure BDA0002527630890000083
The complex gain of the g-th path of (1);
Figure BDA0002527630890000084
is the azimuth angle of the g-th path,
Figure BDA0002527630890000085
representing the direction vector of the antenna array, can be written as
Figure BDA0002527630890000086
Where d and λ represent the inter-antenna distance and signal wavelength, respectively.
Defining an effective channel between the BS and the user (m, i) as
Figure BDA0002527630890000087
(2) Can be rewritten as
Figure BDA0002527630890000088
Wherein
Figure BDA0002527630890000089
For further eliminationInter-user interference, a decoding scheme is proposed that relies only on effective channel strength and not on user clusters/groups; gamma raymiIndicates the SINR of the user (m, i).
Figure BDA00025276308900000810
Wherein
Figure BDA00025276308900000811
Representing users with a weaker effective channel than User (m, i)'s, the achievable rate can be written as
Rmi(W，P)＝log2(1+γmi), (7)
For User (m, i), the total power consumption includes circuit power consumption and transmit power, and the total power consumption includes circuit power consumption and transmit power, which can be expressed as
Figure BDA00025276308900000812
EE of User (m, i) is represented by
Figure BDA0002527630890000091
The aim is to achieve the minimum EE of the user to the maximum extent under the condition of meeting the requirements of the transmission power and the speed of the user, and the minimum EE is expressed as follows:
Figure BDA0002527630890000092
Figure BDA0002527630890000093
Figure BDA0002527630890000094
Figure BDA0002527630890000095
where (10b) represents the minimum rate requirement for each user, (10c) is the maximum transmit power constraint for the user, and (10d) represents the normalized power constraint for the hybrid detection vector and analog beams at the BS.
Further, step S3 includes:
problem (10) is a non-convex optimization problem that is difficult to solve directly, and the classification of (10) into generalized fractional programming, (10a) is transformed into a subtractive form, jointly optimizing the detection matrix at the BS and the maximum transmit power at the user, will yield a solution that will yield a solution for the problem (10) in terms of the maximum transmit power of the user
Figure BDA0002527630890000096
Defined as the optimal EE solution, W, of the problem (10)*And P*Respectively, a corresponding optimal detection matrix and power allocation matrix.
Figure BDA0002527630890000097
Where Ω is the set of all feasible solutions satisfying (10b) - (10d), there are the following theorems for the optimal solution:
theorem 1 optimal solution (W) of problem (10)*,P*) It is possible to obtain if and only if:
Figure BDA0002527630890000098
the above theorem is proved from the aspects of both necessity and sufficiency; first, the necessity was demonstrated; let { W, P } be any feasible solution of (12), having
Figure BDA0002527630890000101
According to (13), obtaining
Figure BDA0002527630890000102
Thus, { W*,P*Is also the optimal solution of (12).
Second, proof of adequacy: assume { W, P } and { W }*,P*Are separately arrangedIs a feasible solution and an optimal solution of (12), and can be obtained
Figure BDA0002527630890000103
Rewriting (15)
Figure BDA0002527630890000104
Thus, { W*,P*Is also the optimal solution of (10).
Theorem 1 demonstrates that the solution to the problem (10) can be obtained by the solution (12) because it cannot be obtained in advance
Figure BDA0002527630890000105
(12) Still difficult to solve, for this reason the following functions are defined:
Figure BDA0002527630890000106
theorem 2:
Figure BDA0002527630890000107
is provided with ηEEIs strictly monotonically decreasing function.
For any
Figure BDA0002527630890000108
And
Figure BDA0002527630890000109
is to assume
Figure BDA00025276308900001010
And (W)1，P1),(W2，P2) Is the corresponding optimal solution; then, there are
Figure BDA0002527630890000111
For practical systems, when ηEEWhen the content is equal to 0, the content,
Figure BDA0002527630890000112
when ηEEWhen the size of the particles is large enough,
Figure BDA0002527630890000113
classical dichotomy can be used to solve
Figure BDA0002527630890000114
To obtain
Figure BDA0002527630890000115
For given η'EEThe following problem (19) needs to be solved to obtain
Figure BDA0002527630890000116
Figure BDA0002527630890000117
s.t(10b)-(10d). (19b)
The objective function (19a) is non-smooth, the constraint (10) is non-convex, and an auxiliary variable t is introduced to rewrite (19) to
Figure BDA0002527630890000118
Figure BDA0002527630890000119
(10b)-(10d).(20c)
Further, step S4 specifically includes:
problem (20) is a non-convex problem with three variables { W, P, t }. W and P are coupled, optimization is very difficult at the same time, a double-loop iterative algorithm is provided to solve the problem, the inner loop iteratively updates the transmitting power of the detection matrix, and the outer loop adopts a dichotomy.
The optimization problem is first solved (21) given feasible P:
Figure BDA00025276308900001110
Figure BDA00025276308900001111
Figure BDA00025276308900001112
(10d). (21d)
in the formula (I), the compound is shown in the specification,
Figure BDA0002527630890000121
constraints (21b) and (21c) are non-convex, and the non-convex constraint is transformed into convex by generally adopting a continuous convex approximation technique, and an auxiliary variable matrix Z is introduced into the non-convex constraint matrix Z ═ Zmi]M×2To obtain
Figure BDA0002527630890000122
Figure BDA0002527630890000123
Figure BDA0002527630890000124
Figure BDA0002527630890000125
(10d). (22e)
(22d) Is a non-convex constraint, introduces an auxiliary variable matrix Q ═ Qmi]M×2Dividing (22d) into two constraints
Figure BDA0002527630890000126
Figure BDA0002527630890000127
Definition of
Figure BDA0002527630890000128
And
Figure BDA0002527630890000129
by using
Figure BDA00025276308900001210
Linearization of u (w)m) Can be expressed as
Figure BDA00025276308900001211
Wherein
Figure BDA00025276308900001212
Is u (w)m) In that
Figure BDA00025276308900001213
And
Figure BDA00025276308900001214
the derivative of (c). Defining functions
Figure BDA00025276308900001215
And obtain
Figure BDA0002527630890000131
Thus, there are
Figure BDA0002527630890000132
Based on the above analysis, the (22) is transformed into the following optimization problem:
Figure BDA0002527630890000133
Figure BDA0002527630890000134
Figure BDA0002527630890000135
Figure BDA0002527630890000136
Figure BDA0002527630890000137
(10d). (27f)
the objective function z is linear, constraints (27b), (27d), (27e) and (27f) are convex constraints, (27c) is a linear constraint, and (27) is a convex optimization problem that can be solved with a numerical convex solver, iteratively solving (27) until convergence.
Second, given P and t are optimized for W, the W obtained*A, (20) can be simplified to
Figure BDA0002527630890000138
Figure BDA0002527630890000139
Figure BDA00025276308900001310
Figure BDA00025276308900001311
Wherein
Figure BDA0002527630890000141
R is to bemi(W*P) is rewritten to
Figure BDA0002527630890000142
Wherein
Figure BDA0002527630890000143
And
Figure BDA0002527630890000144
for this reason, the constraint (28b) is represented as
Figure BDA0002527630890000145
Due to the fact that
Figure BDA0002527630890000146
And
Figure BDA0002527630890000147
are both convex with P, (30) are differences in convex constraints, (28) are a DC planning problem; a constrained concave-convex process (CCCP) is used to solve the DC plan; on this basis, first, the approximation is transformed (30) into a convex constraint by a first order Taylor expansion
Figure BDA0002527630890000148
Wherein
Figure BDA0002527630890000149
And
Figure BDA00025276308900001410
finally, (28) conversion to
Figure BDA00025276308900001411
Figure BDA00025276308900001412
Figure BDA00025276308900001413
Figure BDA00025276308900001414
In the formula
Figure BDA00025276308900001415
The problem (33) is a standard convex optimization problem that can be solved by the interior point method, using iterative solutions (33) to obtain a solution (28). From an initial point of view
Figure BDA00025276308900001416
Initially, the optimal P can be found by solving (33)*(ii) a Then, with P*Updating
Figure BDA0002527630890000151
And solving (33) the above iterations until convergence.
According to the technical scheme, the invention provides the energy-saving resource allocation method of the uplink millimeter wave MIMO-NOMA system based on Max-Min of codebook, and under the constraint of the predefined minimum rate and the maximum transmitting power of each user, the optimal user energy efficiency is obtained by jointly optimizing the detection matrix at the BS and the transmitting power at the user.
FIG. 4 gives at setting η'EETo show the convergence performance of the scheme at 0, the relation of spectral efficiency to the number of iterations (including iterations of P and W), first, here, "first iteration" means iteratively updating the detection matrix W, i.e. solving the problem (21), and "second iteration" means iteratively updating the power allocation matrix P, i.e. solving the problem (28). It can be observed that the convergence speed of the two steps is fast. The first convergence requires 4 iterations and the second convergence requires only 2 iterations.
Fig. 5 shows the relationship between the energy efficiency EE and the number of iterations of the outer loop, and since a two-stage method is adopted, the energy efficiency EE curve fluctuates, reaches a peak value in the 2 nd iteration, reaches a valley value in the 4 th iteration, and converges after about 8 iterations.
FIG. 6 gives setting η'EEThe MOMA-based resource allocation method proposed when 0 (scheme 1) and method depend on the effective channel and userThe weak interference between SE results (scheme 2) provided by the conventional uplink decoding order of the strength of a group or cluster and another cluster does not eliminate the baseline scheme (scheme 3) spectral efficiency SE versus signal-to-noise ratio SNR. Of all the considered solutions, solution 1 is always the best, followed by solutions 2 and 3. The gap between the method provided by the embodiment of the present invention and scheme 2 indicates the efficiency of interference cancellation according to strength, especially at high signal-to-noise ratios. The gap between scheme 2 and scheme 3 illustrates the necessity to eliminate weak interference between clusters.
Fig. 7 presents a graph of the spectral efficiency SE of the NOMA scheme compared to the spectral efficiency SE of the conventional OMA scheme, where users belonging to the same beam group are time division duplex split access services. The results show that the method provided by the embodiment of the present invention has a higher signal-to-noise ratio than the conventional OMA scheme.
Fig. 8 presents a graph of energy efficiency EE versus signal-to-noise ratio SNR for the three schemes. Comparison of these three protocols shows that: EE increases first and then saturates as the signal-to-noise ratio increases. At low signal-to-noise ratios, a small increase in signal-to-noise ratio results in a large increase in SE and hence EE. In contrast, at high signal-to-noise ratios, a large increase in signal-to-noise ratio results in only a small increase in SE. Therefore, the extra available power cannot be used to increase EE.
Fig. 9 gives an energy efficiency EE diagram for the NOMA scheme and OMA scheme. The method provided by the embodiments of the present invention has a higher EE than OMA.
Fig. 10 is a schematic structural diagram of a NOMA-based resource allocation apparatus according to an embodiment of the present invention;
the model establishing module is used for establishing an uplink non-orthogonal multiple access millimeter wave system model of which the base station adopts a fully-connected sparse radio frequency chain antenna structure;
a beam design module for codebook based analog beam design and improved NOMA decoding scheme to reduce pilot overhead for channel estimation;
an equation construction module for jointly optimizing a detection matrix at the BS and a transmit power at the user to construct a maximum minimum user Energy Efficiency (EE) optimization equation;
and the iteration processing module is used for converting the fractional objective function into a subtractive objective function, providing a double-loop iteration algorithm to solve the fractional objective function, iteratively updating the detection matrix and the transmitting power, and obtaining the optimal solution of the problem by adopting double-section iteration in the outer ring.
Preferably, the modeling module comprises:
and the first modeling unit is used for configuring N antennas and M (M is less than or equal to N) RF chains at the BS according to the condition that the number of the radio frequency chains of the uplink non-orthogonal multiple access millimeter wave system is far lower than that of the antennas. Each RF chain is connected to all antennas through N phase shifters, one antenna for each user;
a second modeling unit for selecting an analog beam matrix from a predefined codebook using a Discrete Fourier Transform (DFT) codebook; applying beam alignment to user clustering to obtain effective CSI through beam alignment; a geometric mmWave channel model with G scatterers was used.
In this embodiment, the function equation constructing module specifically includes:
jointly optimizing the detection matrix at the BS and the transmit power at the users to construct a maximum minimum user Energy Efficiency (EE) optimization equation:
Figure BDA0002527630890000171
Figure BDA0002527630890000172
Figure BDA0002527630890000173
Figure BDA0002527630890000174
in this embodiment, the function equation constructing module specifically includes:
and converting the fractional objective function into a subtractive objective function, and providing a double-loop iteration algorithm to solve, wherein the inner loop iteratively updates the detection matrix and the transmitting power, and the outer loop adopts double-section iteration to solve the optimal solution of the problem.

Claims (8)

1. A method for NOMA-based resource allocation, the method comprising:
s1: establishing an uplink non-orthogonal multiple access millimeter wave system model of which a base station adopts a fully-connected sparse radio frequency chain antenna structure;
s2: a codebook-based analog beam design and improved NOMA decoding scheme is proposed;
s3: jointly optimizing a detection matrix at the BS and the transmission power at the user to construct a maximum minimum user Energy Efficiency (EE) optimization problem;
s4: the fractional objective function is converted into a subtractive objective function, a double-loop iteration algorithm is provided to solve the fractional objective function, the inner loop iteratively updates the detection matrix and the transmitting power, and the outer loop adopts double-section iteration to obtain the optimal solution of the problem.
2. The method according to claim 1, wherein the step S1 specifically includes:
the number of radio frequency chains of the uplink mmWave system is much lower than the number of antennas, where the BS is equipped with N antennas and M (M ≦ N) RF chains, each RF chain being connected to all antennas through N phase shifters, one antenna for each user.
3. The method according to claim 1, wherein the step S2 specifically includes:
selecting an analog beam matrix from a predefined codebook using a Discrete Fourier Transform (DFT) codebook; applying beam alignment to user clustering, wherein users belonging to the same cluster are served by NOMA, and effective CSI is obtained through beam alignment; a widely used geometric mmWave channel model with G scatterers is used.
4. The method according to claim 1, wherein the step S3 specifically includes:
a decoding scheme which only depends on effective channel strength and does not depend on user clusters/groups is provided to eliminate the interference among users, meet the transmitting power and rate of the users and maximally realize the minimum EE of the users, which can be expressed as follows:
Figure FDA0002527630880000021
Figure FDA0002527630880000022
Figure FDA0002527630880000023
Figure FDA0002527630880000024
where (1b) represents the minimum rate requirement for each user, (1c) is the maximum transmit power constraint for the user, and (1d) represents the normalized power constraint for the hybrid detection vector and analog beams at the BS.
5. The method according to claim 1, wherein the step S4 specifically includes:
the obtained mathematical model (1) is non-convex and difficult to directly solve, the (1a) is transformed into a subtraction form, an effective double-loop iterative algorithm is provided, and
Figure FDA0002527630880000025
the optimal EE solution, W, defined as problem (1)*And P*Respectively, the corresponding optimal detection matrix and power allocation matrix, and the problem (1) can be rewritten as:
Figure FDA0002527630880000026
the detection matrix and the transmitting power are updated iteratively by the inner loop, and the optimal solution of the problem (1) is obtained by the outer loop by adopting an iterative algorithm based on two segments.
6. An apparatus for NOMA-based resource allocation, comprising:
the model establishing module is used for establishing an uplink non-orthogonal multiple access millimeter wave system model of which the base station adopts a fully-connected sparse radio frequency chain antenna structure;
a beam design module for codebook based analog beam design and improved NOMA decoding scheme to reduce pilot overhead for channel estimation;
an equation construction module for jointly optimizing a detection matrix at the BS and a transmit power at the user to construct a maximum minimum user Energy Efficiency (EE) optimization equation;
and the iteration processing module is used for converting the fractional objective function into a subtractive objective function, and provides a double-loop iteration algorithm to solve the fractional objective function, the inner loop iteratively updates the detection matrix and the transmitting power, and the outer loop adopts double-section iteration to obtain the optimal solution of the problem.
The modeling module includes:
the first modeling unit is used for allocating N antennas and M (M is less than or equal to N) RF chains at the BS according to the condition that the number of the radio frequency chains of the uplink non-orthogonal multiple access millimeter wave system is far lower than that of the antennas, each RF chain is connected to all the antennas through N phase shifters, and each user is allocated with one antenna;
a second modeling unit for selecting an analog beam matrix from a predefined codebook using a Discrete Fourier Transform (DFT) codebook; applying beam alignment to user clustering to obtain effective CSI through beam alignment; a geometric mmWave channel model with G scatterers was used.
7. The apparatus of claim 6, comprising:
an equation construction module that jointly optimizes a detection matrix at the BS and transmit power at the user to construct a maximum minimum user Energy Efficiency (EE) optimization equation:
Figure FDA0002527630880000031
Figure FDA0002527630880000032
Figure FDA0002527630880000033
Figure FDA0002527630880000034
8. the apparatus of claim 6, comprising:
and the iteration processing module is used for converting the fractional objective function into a subtractive objective function, and provides a double-loop iteration algorithm for solving, wherein the inner loop iteratively updates the detection matrix and the transmitting power, and the outer loop adopts double-section iteration to solve the optimal solution of the problem.
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Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112290995A (en) * 2020-11-06 2021-01-29 郑州大学 Beam design method based on safety energy efficiency in satellite-ground integrated network
CN112637907A (en) * 2020-12-18 2021-04-09 温州大学 Combined optimization method for user multi-association and downlink power distribution in millimeter wave network
CN112929067A (en) * 2021-02-04 2021-06-08 重庆邮电大学 SCA-based IRS-NOMA system low-complexity beam forming method
CN112929068A (en) * 2021-02-04 2021-06-08 重庆邮电大学 SDR-based IRS-NOMA system beam forming optimization method
CN113163497A (en) * 2021-03-29 2021-07-23 南京航空航天大学 Computing efficiency optimization method in millimeter wave mobile edge computing system based on reconfigurable intelligent surface
CN113206694A (en) * 2021-04-22 2021-08-03 南京航空航天大学 Computing efficiency optimization method in millimeter wave mobile edge computing system based on hybrid beam forming
CN113225112A (en) * 2021-04-30 2021-08-06 内蒙古大学 Millimeter wave combined beam selection and power distribution optimization method
CN113541768A (en) * 2021-07-06 2021-10-22 桂林电子科技大学 NOMA-based LEO satellite communication system frequency point allocation method

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107947843A (en) * 2017-11-06 2018-04-20 上海交通大学 NOMA methods for the reduction pilot-frequency expense multiplex data of the extensive MIMO of millimeter wave
CN108811069A (en) * 2018-08-27 2018-11-13 重庆邮电大学 A kind of Poewr control method of the full duplex non-orthogonal multiple access system based on efficiency
CN110086515A (en) * 2019-04-25 2019-08-02 南京邮电大学 A kind of MIMO-NOMA system uplink Precoding Design method
WO2019195680A1 (en) * 2018-04-05 2019-10-10 Convida Wireless, Llc Configuration and signaling for ul noma operations

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107947843A (en) * 2017-11-06 2018-04-20 上海交通大学 NOMA methods for the reduction pilot-frequency expense multiplex data of the extensive MIMO of millimeter wave
WO2019195680A1 (en) * 2018-04-05 2019-10-10 Convida Wireless, Llc Configuration and signaling for ul noma operations
CN108811069A (en) * 2018-08-27 2018-11-13 重庆邮电大学 A kind of Poewr control method of the full duplex non-orthogonal multiple access system based on efficiency
CN110086515A (en) * 2019-04-25 2019-08-02 南京邮电大学 A kind of MIMO-NOMA system uplink Precoding Design method

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
WANMING HAO; MING ZENG; GANGCAN SUN;: "Codebook-Based Max–Min Energy-Efficient Resource Allocation for Uplink mmWave MIMO-NOMA Systems", 《 IEEE TRANSACTIONS ON COMMUNICATIONS ( VOLUME: 67, ISSUE: 12, DECEMBER 2019)》 *

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112290995A (en) * 2020-11-06 2021-01-29 郑州大学 Beam design method based on safety energy efficiency in satellite-ground integrated network
CN112637907A (en) * 2020-12-18 2021-04-09 温州大学 Combined optimization method for user multi-association and downlink power distribution in millimeter wave network
CN112637907B (en) * 2020-12-18 2022-07-12 温州大学 Combined optimization method for user multi-association and downlink power distribution in millimeter wave network
CN112929067A (en) * 2021-02-04 2021-06-08 重庆邮电大学 SCA-based IRS-NOMA system low-complexity beam forming method
CN112929068A (en) * 2021-02-04 2021-06-08 重庆邮电大学 SDR-based IRS-NOMA system beam forming optimization method
CN112929068B (en) * 2021-02-04 2022-06-10 重庆邮电大学 SDR-based IRS-NOMA system beam forming optimization method
CN112929067B (en) * 2021-02-04 2022-07-15 重庆邮电大学 SCA-based IRS-NOMA system low-complexity beam forming method
CN113163497A (en) * 2021-03-29 2021-07-23 南京航空航天大学 Computing efficiency optimization method in millimeter wave mobile edge computing system based on reconfigurable intelligent surface
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CN113541768A (en) * 2021-07-06 2021-10-22 桂林电子科技大学 NOMA-based LEO satellite communication system frequency point allocation method

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