CN111740766A - Codebook-based beam design method and device - Google Patents

Codebook-based beam design method and device Download PDF

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CN111740766A
CN111740766A CN202010508820.7A CN202010508820A CN111740766A CN 111740766 A CN111740766 A CN 111740766A CN 202010508820 A CN202010508820 A CN 202010508820A CN 111740766 A CN111740766 A CN 111740766A
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user
codebook
detection matrix
objective function
iteration
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朱政宇
徐金雷
申凌峰
贾少波
李双志
郭新
王忠勇
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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
    • H04B7/0413—MIMO systems
    • H04B7/0452—Multi-user MIMO systems
    • 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
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • 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
    • 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
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    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention provides a wave beam design method and a device based on a codebook, 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 is proposed; 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; converting the fractional objective function into a subtractive objective function, and providing a double-loop iterative algorithm to solve the fractional objective function; the inner loop adopts a low-complexity iterative algorithm based on ZF technology to design a detection matrix, 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

Codebook-based beam design method and device
Technical Field
The invention belongs to the technical field of communication, and particularly relates to a codebook-based beam design method and device.
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 may no longer be 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 codebook-based beam design method and a codebook-based beam design device.
In a first aspect, the present invention provides a codebook-based beam design method, 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: converting the fractional objective function into a subtractive objective function, and providing a double-loop iterative algorithm to solve the fractional objective function; the inner loop adopts a low-complexity iterative algorithm based on ZF technology to design a detection matrix, 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 BDA0002527630790000031
Figure BDA0002527630790000032
Figure BDA0002527630790000033
Figure BDA0002527630790000034
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 resulting mathematical model (1) is non-convex and is difficult to solve directly. Transforming (1a) into a subtractive form, an efficient two-cycle iterative algorithm is proposed, which will
Figure BDA0002527630790000036
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 BDA0002527630790000035
the inner ring provided designs a detection matrix by adopting a low-complexity iterative algorithm based on a ZF technology, iteratively updates the detection matrix and the transmitting power, and the outer ring obtains the optimal solution of the problem (1) by adopting a dual-section-based iterative algorithm.
In another aspect, the present invention further provides a codebook-based beam design 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, providing a dual-cycle iteration algorithm to solve the fractional objective function, designing a detection matrix by adopting a low-complexity iteration algorithm based on a ZF technology in an inner ring, iteratively updating the detection matrix and the transmitting power, and obtaining the optimal solution of the problem by adopting dual-section iteration in an outer ring.
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 BDA0002527630790000041
Figure BDA0002527630790000042
Figure BDA0002527630790000043
Figure BDA0002527630790000044
preferably, the iterative processing module specifically includes:
the fractional objective function is converted into a subtractive objective function, a dual-cycle iterative algorithm is provided for solving, an inner ring adopts a low-complexity iterative algorithm based on a ZF technology to design a detection matrix, the detection matrix and the transmitting power are iteratively updated, and an outer ring adopts dual-section iteration to solve the optimal solution of the problem.
The invention provides a beam design method and a device based on a codebook, which reduce pilot frequency overhead by providing a simulated beam design based on the codebook 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 subtractive objective function, provide a dual-cycle iterative algorithm to solve the fractional objective function, design the detection matrix by adopting a low-complexity iterative algorithm based on a ZF technology in an inner ring, iteratively update the detection matrix and the transmitting power, and obtain the optimal solution of the problem by adopting dual-section iteration in an outer ring.
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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 codebook-based beam design method 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 spectral efficiency versus iteration number provided by an embodiment of the present invention;
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 codebook-based beam design 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 only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flowchart of a codebook-based beam design 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: converting the fractional objective function into a subtractive objective function, and providing a double-loop iterative algorithm to solve the fractional objective function; the inner loop adopts a low-complexity iterative algorithm based on ZF technology to design a detection matrix, 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 BDA0002527630790000071
Figure BDA0002527630790000072
in the range of [ - π, π]Uniform distribution is adopted; at the same timeThe signal-to-noise ratio is defined as
Figure BDA0002527630790000073
And assuming that all users have 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 BDA0002527630790000074
Further, step S2 includes: fig. 3 is a polar coordinate diagram of N-4 and K-8 codebook array factors provided by an embodiment of the present invention to select an analog beam matrix from a predefined codebook. Using a Discrete Fourier Transform (DFT) codebook, defined as
Figure BDA0002527630790000075
Wherein:
Figure BDA0002527630790000076
a set of BS antennas is represented as,
Figure BDA0002527630790000077
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 BDA0002527630790000078
Where smi and Pmi respectively represent transmission signal and power (user (m, i)) at the ith user of the mth analog beam, satisfy
Figure BDA0002527630790000079
V is the analog beam matrix.
Figure BDA00025276307900000710
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 BDA0002527630790000081
In the formula (I), the compound is shown in the specification,
Figure BDA0002527630790000082
is provided with
Figure BDA0002527630790000083
The complex gain of the g-th path of (1);
Figure BDA0002527630790000084
is the azimuth angle of the g-th path,
Figure BDA0002527630790000085
representing the direction vector of the antenna array, can be written as
Figure BDA0002527630790000086
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 BDA0002527630790000087
(2) Can be rewritten as
Figure BDA0002527630790000088
Wherein
Figure BDA0002527630790000089
In order to further eliminate the interference among users, a decoding scheme which only depends on the effective channel strength and does not depend on user clusters/groups is provided; gamma raymiIndicates the SINR of the user (m, i).
Figure BDA00025276307900000810
Wherein
Figure BDA00025276307900000811
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 BDA00025276307900000812
EE of User (m, i) is represented by
Figure BDA0002527630790000091
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 BDA0002527630790000092
Figure BDA0002527630790000093
Figure BDA0002527630790000094
Figure BDA0002527630790000095
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 BDA0002527630790000096
Defined as the optimal EE solution, W, of the problem (10)*And P*Respectively corresponding optimal detection matrix and power distribution matrix
Figure BDA0002527630790000097
Where Ω is the set of all feasible solutions that satisfy (10b) - (10 d). With respect to the optimal solution, there is the following theorem:
theorem 1 optimal solution (W) of problem (10)*,P*) It is possible to obtain if and only if:
Figure BDA0002527630790000098
the above theorem is proven both in terms of necessity and sufficiency. First, the necessity was demonstrated. Let { W, P } be any feasible solution of (12), having
Figure BDA0002527630790000101
According to (13), obtaining
Figure BDA0002527630790000102
Thus, { W*,P*Is also the optimal solution of (12).
Second, proof of adequacy: assume { W, P } and { W }*,P*Are respectively the feasible solution and the optimal solution of (12), and can be obtained
Figure BDA0002527630790000103
Rewriting (15)
Figure BDA0002527630790000104
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 BDA0002527630790000105
(12) Still difficult to solve, for this reason the following functions are defined:
Figure BDA0002527630790000106
theorem 2:
Figure BDA0002527630790000107
is provided with ηEEIs strictly monotonically decreasing function.
For any
Figure BDA0002527630790000108
And
Figure BDA0002527630790000109
is to assume
Figure BDA00025276307900001010
And (W)1，P1),(W2，P2) Is the corresponding optimal solution; then, there are
Figure BDA0002527630790000111
For practical systems, when ηEEWhen the content is equal to 0, the content,
Figure BDA0002527630790000112
when ηEEWhen the size of the particles is large enough,
Figure BDA0002527630790000113
classical dichotomy can be used to solve
Figure BDA0002527630790000114
To obtain
Figure BDA0002527630790000115
For given η'EEThe following problem (19) needs to be solved to obtain
Figure BDA0002527630790000116
Figure BDA0002527630790000117
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 BDA0002527630790000118
Figure BDA0002527630790000119
(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, and optimization is very difficult at the same time, and a double-loop iterative algorithm is provided to solve the problem, wherein an inner loop adopts a ZF-based algorithm according to the existence ofEffective channel strength, arranging two users in each beam group in descending order, i.e.
Figure BDA00025276307900001110
Generating a detection vector based on the effective channel of the strong user, thus defining
Figure BDA00025276307900001111
And W ═ HHH)-1HHDetecting vector wmCan be expressed as
Figure BDA00025276307900001112
Where V (m) is defined as the m-th row of V. The detection signal of the mth beam at the BS may be expressed as
Figure BDA0002527630790000121
The same decoding order as that in step S3 is adopted. Also, will
Figure BDA0002527630790000122
Defined as the second set of users in all clusters having weaker effective channels than users (m, i), and thus the SINR of users (m, i) can be calculated as
Figure BDA0002527630790000123
And the obtained secret ratio can be expressed as
Figure BDA0002527630790000124
Then, the following max-min EE optimization problem is restated as
Figure BDA0002527630790000125
Figure BDA0002527630790000126
Figure BDA0002527630790000127
According to the scheme proposed previously, the (25) is directly converted into:
Figure BDA0002527630790000128
Figure BDA0002527630790000129
(25b),(25c). (26c)
second, given P and t are optimized for W, the W obtained*A, (20) can be simplified to
Figure BDA00025276307900001210
Figure BDA00025276307900001211
Figure BDA00025276307900001212
Figure BDA00025276307900001213
Wherein
Figure BDA0002527630790000131
R is to bemi(W*P) is rewritten to
Figure BDA0002527630790000132
Wherein
Figure BDA0002527630790000133
And
Figure BDA0002527630790000134
to this end, the constraint (27b) may be expressed as
Figure BDA0002527630790000135
Due to the fact that
Figure BDA0002527630790000136
And
Figure BDA0002527630790000137
are convex with P, (29) are the difference of the convex constraints, (27) are a DC planning problem, and the constrained concave-convex process (CCCP) is used to solve the DC planning. On this basis, first, the approximation transforms (29) into a convex constraint by a first order Taylor expansion
Figure BDA0002527630790000138
Wherein
Figure BDA0002527630790000139
And
Figure BDA00025276307900001310
finally, (27) conversion to
Figure BDA00025276307900001311
Figure BDA00025276307900001312
Figure BDA00025276307900001316
Figure BDA00025276307900001313
In the formula
Figure BDA00025276307900001314
The problem (32) is a standard convex optimization problem that can be solved by the interior point method, using iterative solution (32) to obtain a solution for (27). From an initial point of view
Figure BDA00025276307900001315
Initially, the optimal P can be found by solving (32)*Then, with P*Updating
Figure BDA0002527630790000141
And solving (32), performing the iteration until convergence.
According to the technical scheme, the invention provides the Max-Min uplink millimeter wave MIMO-NOMA system energy-saving resource allocation method based on the codebook, and the optimal user energy efficiency is obtained by jointly optimizing the detection matrix at the BS and the transmission power at the user under the constraint of the predefined minimum rate and the maximum transmission power of each user.
FIG. 4 gives at setting η'EEWhen 0, the spectral efficiency is related to the number of inner loop iterations.
Curve convergence requires 4 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 codebook-based beam design approach proposed (scheme 1) when equal to 0, does not eliminate the baseline scheme (scheme 3) spectral efficiency SE versus signal-to-noise ratio SNR, in contrast to the SE results (scheme 2) provided by conventional uplink decoding order that depends on the strength of the active channel and user group or cluster, and the weak interference between another cluster. Of all the considered solutions, solution 1 is always the best, followed by solutions 2 and 3. The inventionThe gap between the approach provided by the example and scheme 2 indicates the efficiency of interference cancellation in terms of intensity, 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 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 codebook-based beam design 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 dual-cycle iteration algorithm to solve the fractional objective function, designing a detection matrix by adopting a low-complexity iteration algorithm based on a ZF technology in an inner ring, iteratively updating the detection matrix and the transmitting power, and obtaining the optimal solution of the problem by adopting dual-section iteration in an outer ring.
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.
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 BDA0002527630790000151
Figure BDA0002527630790000161
Figure BDA0002527630790000162
Figure BDA0002527630790000163
in this embodiment, the function equation constructing module specifically includes:
the fractional objective function is converted into a subtractive objective function, a dual-cycle iterative algorithm is provided for solving, an inner ring adopts a low-complexity iterative algorithm based on a ZF technology to design a detection matrix, the detection matrix and the transmitting power are iteratively updated, and an outer ring adopts dual-section iteration to solve the optimal solution of the problem.

Claims (8)

1. A codebook-based beam design method, 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: converting the fractional objective function into a subtractive objective function, and providing a double-loop iterative algorithm to solve the fractional objective function; the inner loop adopts a low-complexity iterative algorithm based on ZF technology to design a detection matrix, 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 FDA0002527630780000021
Figure FDA0002527630780000022
Figure FDA0002527630780000023
Figure FDA0002527630780000024
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 FDA0002527630780000025
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 FDA0002527630780000026
the inner ring provided designs a detection matrix by adopting a low-complexity iterative algorithm based on a ZF technology, iteratively updates the detection matrix and the transmitting power, and the outer ring obtains the optimal solution of the problem (1) by adopting a dual-section-based iterative algorithm.
6. An apparatus for codebook-based beam design, 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, providing a dual-cycle iteration algorithm to solve the fractional objective function, designing a detection matrix by adopting a low-complexity iteration algorithm based on a ZF technology in an inner ring, iteratively updating the detection matrix and the transmitting power, and adopting an optimal solution of an iteration algorithm based on two sections in an outer ring.
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 FDA0002527630780000031
Figure FDA0002527630780000032
Figure FDA0002527630780000033
Figure FDA0002527630780000034
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 ring adopts a low-complexity iteration algorithm based on a ZF technology to design a detection matrix, the detection matrix and the transmitting power are iteratively updated, and the outer ring adopts double-section iteration to solve the optimal solution of the problem.
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