CN111726152A - Hybrid precoding method and device - Google Patents

Hybrid precoding method and device Download PDF

Info

Publication number
CN111726152A
CN111726152A CN202010508844.2A CN202010508844A CN111726152A CN 111726152 A CN111726152 A CN 111726152A CN 202010508844 A CN202010508844 A CN 202010508844A CN 111726152 A CN111726152 A CN 111726152A
Authority
CN
China
Prior art keywords
precoding
objective function
analog
module
sub
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
CN202010508844.2A
Other languages
Chinese (zh)
Inventor
朱政宇
马梦园
王梓晅
郝万明
孙钢灿
赵飞
王忠勇
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhengzhou University
Original Assignee
Zhengzhou University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Zhengzhou University filed Critical Zhengzhou University
Priority to CN202010508844.2A priority Critical patent/CN111726152A/en
Publication of CN111726152A publication Critical patent/CN111726152A/en
Withdrawn legal-status Critical Current

Links

Images

Classifications

    • 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
    • H04B7/0413—MIMO systems
    • H04B7/0426—Power distribution
    • 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 hybrid precoding method and a hybrid precoding device, which are used for maximizing the energy efficiency of a system and comprise the following steps: s1: codebook-based analog precoding based on full-connection and sub-connection structure design
Figure DDA0002527641630000011
S2: constructing a problem of combining digital multicast, unicast pre-coding and power splitting rate optimization; s3: converting the fractional objective function into a subtractive objective function, and providing a double-loop iterative algorithm to solve the fractional objective function; s4: and the outer ring adopts a classical two-section iterative algorithm to solve T (q), the inner ring converts the problem into a convex problem through a successive convex approximation technology, and an iterative algorithm is provided to solve the problem. The invention provides a hybrid precoding method and a hybrid precoding device by combining multicast unicast wave millimeter waves and SWIPT. Millimeter waves may fill more antennas with smaller physical dimensions; SWIPT interference power transfer in multi-user systemsThe energy efficiency can be improved by converting the energy of the receiving end; with analog/digital hybrid precoding, the number of RF chains required is much smaller than the number of antennas.

Description

Hybrid precoding method and device
Technical Field
The invention relates to the technical field of communication, in particular to a hybrid precoding method and a hybrid precoding device.
Background
Millimeter waves (30-300GHZ) have wider bandwidth and are considered to be a promising technology for meeting the data traffic exponential growth requirement in future wireless communication. In addition, because the wavelength of the millimeter wave band is shorter, more antennas can be filled by using smaller physical size, and a huge multiple-input multiple-output (mMIMO) millimeter wave system is formed. However, when all-digital signal processing is employed, the use of a large number of antennas results in significant energy consumption and hardware cost because each antenna requires a dedicated Radio Frequency (RF) chain. To address this problem, an analog/digital hybrid precoding scheme may be employed, where the number of RF chains required would be much smaller than the number of antennas. Based on the connectivity of the RF chains, when the number of RF chains is small, two structures are generally considered, one is a fully connected structure, and the other is a sub-array structure. For the former, each radio frequency chain is connected to all antennas through a large number of phase shifters, and a high Spectral Efficiency (SE) can be obtained. In contrast, for the latter, it is required that each RF chain will be connected with a subset of antennas and a small number of phase shifters, thereby obtaining high Energy Efficiency (EE).
On the other hand, Synchronized Wireless Information and Power Transfer (SWIPT) is also considered a promising technology for future wireless communication. In general, there are two practical schemes for SWIPT, namely power splitting and time switching. Through power splitting, the receiver splits the received radio frequency signal while performing information detection and energy collection, and as time switches, the receiver switches between information detection and energy collection at different times. In fact, SWIPT is a very effective solution for multi-user systems, where interference power can be converted into energy at the receiving end.
Since mmWave and SWIPT are both technology drivers for energy-efficient wireless communications, future cellular networks may potentially support a wide range of services with diverse needs. There is an increasing demand for multicast content delivery services over cellular networks, where a group of subscribed users intend to receive the same content. Typically, these users request custom content when they use multicast content simultaneously. Taking the object-based broadcast (OBB) scenario as an example, each subscribed user intends to receive both public messages via multicast and private messages via unicast. For this reason, combining multicast and unicast transmission may be an effective and efficient solution compared to conventional frequency/time division multiplexing.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for maximizing the energy efficiency of a system by combining multicast unicast wave millimeter waves and SWIPT on the basis of a hybrid precoding design, and simultaneously considers the maximum transmitting power at a BS and the minimum acquisition energy of a receiver.
In a first aspect, the present invention provides a hybrid precoding method, including:
s1: design of codebook-based analog precoding f from full-connection and sub-connection structuresk *；
S2: constructing a problem of combining digital multicast, unicast pre-coding and power splitting rate optimization;
s3: converting the fractional objective function into a subtractive objective function, and providing a double-loop iterative algorithm to solve the fractional objective function;
s4: and the outer ring adopts a classical two-section iterative algorithm to solve T (q), the inner ring converts the problem into a convex problem through a successive convex approximation technology, and an iterative algorithm is provided for solving the problem.
Preferably, the step S1 specifically includes:
s11: is defined by searching as
Figure BDA0002527641610000021
To obtain an analog precoding.For a fully connected structure, the analog precoding for the kth user may be chosen as
Figure BDA0002527641610000022
And generalizes the analog precoding scheme to algorithm 1.
S12: for the sub-join structure, a codebook is searched based on the sub-joins. The analog precoding of sub-array i at the kth user can be selected as
Figure BDA0002527641610000023
Preferably, the step S2 specifically includes:
the following variables are initialized:
Figure BDA0002527641610000031
minimum harvested energy, P, for the k-th usermaxFor BS maximum transmit power, ηEEFor system energy efficiency, PtotalFor total power consumption, the k-th user common signal SINR isPrivate signal SINR gammakObtaining an EE maximization problem model:
Figure BDA0002527641610000033
Figure BDA0002527641610000034
Figure BDA0002527641610000035
Figure BDA0002527641610000036
Figure BDA0002527641610000037
Figure BDA0002527641610000038
preferably, the step S3 specifically includes:
the fractional objective function is converted into a subtractive equation by using theorem 1.
Theorem 1: maximum EE q*Obtained only in the following formula
Figure BDA0002527641610000039
When in use
Figure BDA00025276416100000310
And is
Figure BDA00025276416100000311
When the current is over;
preferably, the step S4 specifically includes:
solving the following optimization problem for a given q
Figure BDA0002527641610000041
s.t.(11b)-(11f)， (13b)
The optimal value is denoted as t (q). According to theorem 1, the following definitions apply
Figure BDA0002527641610000042
T (q) is a strictly decreasing convex function with respect to q, T (q) > 0 at q → - ∞ and T (q) < 0 at q → ∞. Therefore, we can use the classical two-section method to find t (q) ═ 0.
Defining a new variable muk＝1/ρk,ωk＝1/(1-ρk) Then define
Figure BDA0002527641610000043
Δdk＝dk-dk.Setting new variables
Figure BDA0002527641610000044
And
Figure BDA0002527641610000045
to obtain z0τkAnd zkλkUpper bound of (2)
Figure BDA0002527641610000046
When in use
Figure BDA0002527641610000047
Is { z0,{τk},{zk},{λkI-1 iteration values. Finally, the following optimization problem is presented
Figure BDA0002527641610000048
Figure BDA0002527641610000049
Figure BDA00025276416100000410
(5e)-(5g),(11e)，(11f). (16d)
Finally, a standard convex optimization technology, such as an interior point method or a CVX tool box, is used for solving to obtain an optimal value { muk},{ωk},{τk},{λk},{dk},z0,{zkAnd solving the objective function.
In a second aspect, the present invention provides a hybrid precoding apparatus, the apparatus comprising:
a pre-coding module: design of codebook-based analog precoding based on full-connection and sub-connection structures
Figure BDA0002527641610000051
A modeling module: the method is used for constructing the problems of joint digital multicast, unicast precoding and power split rate optimization;
a function conversion module: the method is used for converting the fractional objective function into a subtractive objective function and providing a double-loop iterative algorithm to solve the fractional objective function;
a solving module: the method is used for solving T (q) by adopting a classical two-segment iterative algorithm for the outer ring, converting the problem into a convex problem by a successive convex approximation technology for the inner ring, and providing an iterative algorithm for solving.
Preferably, the analog precoding module specifically includes:
a first analog pre-coding module for defining as by searching
Figure BDA0002527641610000052
To obtain an analog precoding. For a fully connected structure, the analog precoding for the kth user may be selected as
Figure BDA0002527641610000053
And generalizes the analog precoding scheme to algorithm 1.
And the second analog pre-coding module is used for searching a codebook based on the sub-connection structure. The analog precoding of sub-array i at the kth user can be selected as
Figure BDA0002527641610000054
Preferably, the modeling module specifically includes:
a modeling module for initializing the variables
Figure BDA0002527641610000055
Minimum harvested energy, P, for the k-th usermaxMaximum transmission power of BS, ηEEFor system energy efficiency, PtotalFor total power consumption, the kth useCommon signal SINR of the user is
Figure BDA0002527641610000056
The private signal SINR is gammakObtaining an original EE maximization problem model:
Figure BDA0002527641610000057
Figure BDA0002527641610000058
Figure BDA0002527641610000061
Figure BDA0002527641610000062
Figure BDA0002527641610000063
Figure BDA0002527641610000064
preferably, the function transformation module specifically includes:
and the function conversion module is used for solving (17) and converting the fractional objective function into a subtraction equation by using theorem 1.
Theorem 1: maximum EE q*Obtained only in the following formula
Figure BDA0002527641610000065
When in use
Figure BDA0002527641610000066
And is
Figure BDA0002527641610000067
When the current is over;
preferably, the solving module specifically includes:
an outer loop solving module to solve the following optimization problem for a given q
Figure BDA0002527641610000068
s.t.(17b)-(17f)， (19)
(19a) The optimum value of (a) is represented as t (q). According to theorem 1, the following definitions apply
Figure BDA0002527641610000069
T (q) is a strictly decreasing convex function with respect to q, T (q) > 0 at q → - ∞ and T (q) < 0 at q → ∞. Therefore, we can use the classical two-section method to find t (q) ═ 0.
An inner loop solution module for defining two new variables muk＝1/ρk,ωk＝1/(1-ρk) And the following optimization problem is reconstructed and then defined
Figure BDA0002527641610000071
Δdk＝dk-dkSetting new variables
Figure BDA0002527641610000072
And
Figure BDA0002527641610000073
to obtain z0τkAnd zkλkUpper bound of (2)
Figure BDA0002527641610000074
When in use
Figure BDA0002527641610000075
Is { z0,{τk},{zk},{λkI-1 iteration values. Finally, the following optimization problem is presented
Figure BDA0002527641610000076
Figure BDA0002527641610000078
Figure BDA0002527641610000077
(5e)-(5g),(17e)，(17f). (22d)
Finally, a standard convex optimization technology, such as an interior point method or a CVX tool box, is used for solving to obtain an optimal value { muk},{ωk},{τk},{λk},{dk},z0,{zkAnd solving the objective function.
According to the technical scheme, the invention provides the hybrid precoding method and the hybrid precoding device, and the method combines multicast unicast wave millimeter waves and SWIPT to maximize the energy efficiency of the system. Meanwhile, the maximum transmitting power at the BS and the minimum acquired energy of the receiver are considered, so that the EE is maximized while the hardware cost and the energy consumption are reduced.
Drawings
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 schematic diagram of a downlink mmWave communication system;
FIG. 2 is a schematic diagram of two sparse RF chain structures of a base station;
fig. 3 is a flowchart of a hybrid precoding method provided in the present invention;
FIG. 4 is a diagram of a joint digital precoding and power split rate iterative algorithm when q is 0 and Pmax＝30Convergence performance under different structures at dBm;
FIG. 5 is a simulated comparison of convergence characteristics for different antenna configurations;
FIG. 6 is a comparison graph of the convergence simulation of the values of T (q) for different structures in the present invention as the number of iterations increases;
FIG. 7 is a graph showing the result when N isRFWhen the maximum transmitting power of the base station is gradually increased to 4, EE simulation comparison graphs with different structures are adopted in the invention;
FIG. 8 is a comparative illustration of EE simulation for different structures in the present invention when the maximum transmission power of the base station is gradually increased when the EE is maximized;
FIG. 9 is a comparative EE simulation diagram of different structures in the present invention when the maximum transmission power of the base station is gradually increased when the SE is maximized;
FIG. 10 is PmaxWhen the minimum acquisition energy is gradually increased when the energy is 30dBm, EE simulation comparison graphs with different structures are adopted in the invention;
FIG. 11 is a simulation diagram of the trade-off between EE and SE for a sub-array configuration;
fig. 12 is a schematic structural diagram of a hybrid precoding device provided in 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.
The invention provides a hybrid precoding method, which combines multicast unicast wave millimeter waves and SWIPT to maximize the energy efficiency of a system. While considering the maximum transmit power at the BS and the minimum harvested energy of the receiver. As shown in fig. 3, the method comprises the steps of:
s1: design of codebook-based simulation pre-prediction based on full-connection and sub-connection structuresEncoding
Figure BDA0002527641610000098
S2: constructing a problem of combining digital multicast, unicast pre-coding and power splitting rate optimization;
s3: converting the fractional objective function into a subtractive objective function, and providing a double-loop iterative algorithm to solve the fractional objective function;
s4: and the outer ring adopts a classical two-section iterative algorithm to solve T (q), and the inner ring converts the problem into a convex problem by a successive convex approximation technology, so that an iterative algorithm is provided for solving.
As shown in fig. 1, the method of the present embodiment is applied to a downlink mmWave communication system, the coverage area of the BS is 30 meters, and the path loss is modeled as 69.4+24log10(D) dB, where D represents the distance in meters, assuming that the mmWave channel has 8 paths, the base station is equipped with NTX256 antennas and NRF4RF chain, d λ/2, noise power
Figure BDA0002527641610000091
And
Figure BDA0002527641610000092
set to-80 dBm and-60 dBm, respectively, the energy conversion efficiency η is 0.5, the power amplifier's useless efficiency ξ is 0.38, and, in addition, set to PBB＝200mW，PRF＝300mW，PPS40mW with a minimum energy capture of
Figure BDA0002527641610000093
The number of users is set to K2.
In this embodiment, the specific process of step S1 is as follows:
the signal received by the kth user can be represented as
Figure BDA0002527641610000094
Wherein
Figure BDA0002527641610000095
And xkRespectively representing the downlink channel vector, the digital precoding vector and the dedicated signal of the k-th user.
Figure BDA0002527641610000096
And x0Respectively the digital precoding vector and the common signal of the kth user. n iskIs an Additive White Gaussian Noise (AWGN).
Figure BDA0002527641610000097
A precoding matrix is simulated. For fully connected structures, F is written as
Figure BDA0002527641610000101
Wherein
Figure BDA0002527641610000102
Is an analog precoding vector associated with the k-th RF chain, and
Figure BDA0002527641610000103
also, for a sublinker structure, F can be represented as
Figure BDA0002527641610000104
In the formula (I), the compound is shown in the specification,
Figure BDA0002527641610000105
by using
Figure BDA0002527641610000106
Representing the analog precoding vector associated with the k-th RF chain. N is a radical ofUSB＝NT/XNR。FRho of received signal power per userkThe ratio is divided into IDs, and the remaining 1-rhokThe ratio is converted to EH. Therefore, the reception signal for EH by the k-th user can be written
Figure BDA0002527641610000107
Harvested energy
Figure BDA0002527641610000108
In the formula, η ∈ (0,1) represents energy conversion efficiency. The received signal for the ID may be expressed as
Figure BDA0002527641610000109
Wherein the content of the first and second substances,
Figure BDA00025276416100001010
is additive noise caused by the ID.
The achievable SINR of the common signal at the kth user can be expressed as
Figure BDA00025276416100001011
At the kth user, the achievable SINR of the private signal may be expressed as
Figure BDA00025276416100001012
For millimeter wave channels, a widely used geometric channel mode is adopted
Figure BDA0002527641610000111
Where L is the number of paths and,
Figure BDA0002527641610000112
the complex gain of the l-th path is indicated.
Figure BDA0002527641610000113
Is the antenna array response vector for user k.
When a uniform linear array is used,
Figure BDA0002527641610000114
can be expressed as
Figure BDA0002527641610000115
Is defined by searching as
Figure BDA0002527641610000116
To obtain an analog precoding. For a fully connected structure, the analog precoding for the kth user may be chosen as
Figure BDA0002527641610000117
And generalizes the analog precoding selection scheme to algorithm 1.
For the sub-connection structure, we need to search the codebook based on the sub-arrays. For example, the analog precoding for sub-array i at the kth user may be selected as
Figure BDA0002527641610000118
Wherein
Figure BDA0002527641610000119
Have become subarray-based codebooks.
In this embodiment, the specific process of step S2 is as follows:
the following variables are initialized:
Figure BDA00025276416100001110
minimum energy collected for the kth user, PmaxMaximum transmission power of BS, ηEEFor the energy efficiency of the system, PtotalFor total power consumption, the common signal SINR of the kth user is
Figure BDA00025276416100001111
SINR of the private signal is represented as γk。
For a fully connected configuration, the circuit power consumption can be written as
PC＝PBB+NRFPRF+NRFNTXPPS, (35)
Wherein, PBB，PRF，PPSRepresenting the power consumption of the baseband, RF chain and phase shifter, respectively. Likewise, the circuit power consumption of the sub-array structure may be expressed as
PC＝PBB+NRFPRF+NTXPPS, (36)
Finally, the total power consumption is given as follows
Figure BDA0002527641610000121
Where ξ ≧ 1 is the low efficiency of the power amplifier.
Next, the EE of the system is defined as
Figure BDA0002527641610000122
Power splitting ratio optimization by joint
Figure BDA0002527641610000123
And digital precoding
Figure BDA0002527641610000124
To maximize the EE of the system, write as
Figure BDA0002527641610000125
Figure BDA0002527641610000126
Figure BDA0002527641610000127
Figure BDA0002527641610000128
Obtaining an original EE maximization problem model:
Figure BDA0002527641610000129
Figure BDA00025276416100001210
Figure BDA00025276416100001211
Figure BDA00025276416100001212
Figure BDA00025276416100001213
Figure BDA0002527641610000131
in this embodiment, the specific process of step S3 is as follows:
to solve (40), the fractional objective function is converted to a subtractive form using theorem 1, representing q*As the largest EE of the system, i.e.
Figure BDA0002527641610000132
Wherein { { ρ { {k},{vk},z0{,zk} should satisfy constraints (40b) - (40 f). Then, the following theorem applies.
Theorem 1: maximum EEq*Obtained only in the following formula
Figure BDA0002527641610000133
When in use
Figure BDA0002527641610000134
And is
Figure BDA0002527641610000135
When the current is over;
in this embodiment, the specific process of step S4 is as follows:
the following optimization problem for a given q needs to be solved
Figure BDA0002527641610000136
s.t.(40b)-(40f)， (43b)
(43a) The optimum value of (a) is represented as t (q). According to theorem 1, the following definitions apply
Figure BDA0002527641610000137
T (q) is a strictly decreasing convex function with respect to q, T (q) > 0 at q → - ∞ and T (q) < 0 at q → ∞. Therefore, t (q) can be found to be 0 using a classical two-section method.
Two new variables μ are definedk＝1/ρk,ωk＝1/(1-ρk) And reconstructs the following optimization problem
Figure BDA0002527641610000141
Figure BDA0002527641610000142
Figure BDA0002527641610000143
Figure BDA0002527641610000144
Figure BDA0002527641610000145
(40e)，(40f). (45f)
This is still a non-convex optimization problem due to the non-convex constraints (45b) and (45 d). Is provided with
Figure BDA0002527641610000146
Is a feasible solution, then d is definedk＝dk+Δdk
Figure BDA0002527641610000147
In the formula (I), the compound is shown in the specification,
Figure BDA0002527641610000148
in this case, (45b) - (45d) can be converted to
Figure BDA0002527641610000149
Figure BDA00025276416100001410
Figure BDA00025276416100001411
Then, a new variable is set
Figure BDA00025276416100001412
And
Figure BDA00025276416100001413
and restate the optimization as
Figure BDA0002527641610000151
Figure BDA0002527641610000152
Figure BDA0002527641610000153
Figure BDA0002527641610000154
Figure BDA0002527641610000155
(40e)，(40f)，(45e)，(49). (50f)
By obtaining z0τkAnd zkλkUpper bound of (2)
Figure BDA0002527641610000156
When in use
Figure BDA0002527641610000157
Is that
Figure BDA0002527641610000158
I-1 iteration values. Finally, the following optimization problem is presented
Figure BDA0002527641610000159
Figure BDA00025276416100001510
Figure BDA00025276416100001511
(40e)，(40f)，(45e)，(49)，(50c)，(50e). (52d)
Finally, a standard convex optimization technology, such as an interior point method or a CVX tool box, is used for solving to obtain an optimal value { muk},{ωk},{τk},{λk},{dk},z0,{zkAnd solving the objective function.
In addition, due to the obtained
Figure BDA00025276416100001512
Is the optimal solution for the ith iteration, iteratively updating these variables will increase or maintain the value of the objective function.
Fig. 4 shows convergence performance of a joint digital precoding and power splitting rate iterative algorithm under different antenna structures, including a digital structure, a full-connection structure and a sub-array structure, where q is set to 0 and P is set tomax30 dBm. The algorithm takes about 50 iterations to converge. Furthermore, SE in a digital architecture has been found to be the highest compared to the other two architectures, but with high power consumption and hardware complexity.
FIG. 5 and FIG. 6 show the convergence of the EE resource allocation algorithm based on the two-segment method under different antenna structures, respectively, and P is expressedmaxSet to 40 dBm. The problem is solved by combining digital precoding with a power split rate iterative algorithm (43). It can be observed from fig. 6 that EE tends to converge after 8 iterations. Furthermore, it can be seen that the EE under the subarray structure is higher than that under the other two structures. This is because its circuitry consumes less power because of the small number of radio frequency chains and phase shifters. In addition, fig. 6 shows that the value of t (q) must be zero according to theorem 1, which is also verified by fig. 5.
FIG. 7 is a graph of energy efficiency versus maximum transmit power of a base station, NRFObserved when 4, EE first follows PmaxIncreases with increasing P, then follows PmaxSaturation is increased. It will be appreciated that a higher SE can be achieved with a greater transmit power, but that the rate of improvement will be lower as the transmit power increases. Thus, as the transmit power continues to increase, EE will reach a point of diminishing returns. In addition, due to the huge power consumption of the radio frequency chain, the EE is highest under the subarray structure, and the EE is lowest under the digital structure.
Fig. 8 and 9 examine the EE of the system under different optimization schemes, which are the same under both optimization schemes when the maximum transmission power is the same. When the maximum transmission power increases, the EE reaching maximum value remains unchanged under the system's EE scheme at maximum, while the EE decreases under the system's EE scheme at maximum. In fact, the purpose of the maximize-time system EE solution is to maximize SE without regard to power consumption. As a result, EE can be reduced due to the larger transmission power.
FIG. 10 shows EE as a function of minimum harvested energy, PmaxSet to 45 dBm. It can be observed that
Figure BDA0002527641610000161
Relatively small, EE remains unchanged, e.g.
Figure BDA0002527641610000162
This is because the base station has redundant power supplies that can be used to meet the energy harvesting requirements. However, when
Figure BDA0002527641610000171
Larger, more power must be used to convert to energy, thereby reducing EE. Therefore, generally, EE and EH cannot be increased at the same time, and one must be sacrificed to increase the other.
FIG. 11 illustrates the trade-off between EE and SE for a subarray configuration, and it can be seen that EE increases with SE when SE is smaller. For larger SEs, EE will decrease, which means that a large SE will not result in a higher EE and vice versa. Therefore, there is a trade-off between EE and SE, especially for higher SE.
Fig. 12 is a schematic structural diagram of a hybrid precoding apparatus provided in the present invention, including:
a pre-coding module: design of codebook-based analog precoding based on full-connection and sub-connection structures
Figure BDA0002527641610000172
A modeling module: the method is used for constructing the problems of joint digital multicast, unicast precoding and power split rate optimization;
a function conversion module: the method is used for converting the fractional objective function into a subtractive objective function, and provides a double-loop iterative algorithm to solve the fractional objective function;
a solving module: the method is used for solving T (q) by adopting a classical two-segment iterative algorithm for the outer ring, converting the problem into a convex problem by a successive convex approximation technology for the inner ring, and providing an iterative algorithm for solving.
In this embodiment, the precoding module specifically includes:
a first pre-coding module, the search being defined as
Figure BDA0002527641610000173
To obtain an analog precoding. The analog precoding of the k-th user in the full-connection structure can be selected as
Figure BDA0002527641610000174
And generalizes the analog precoding scheme to algorithm 1.
And a second pre-coding module for searching a codebook based on the sub-connection for the sub-connection structure. The analog precoding of sub-array i at the kth user can be selected as
Figure BDA0002527641610000175
In this embodiment, the modeling module specifically includes:
the modeling module is used for initializing variables to obtain an original EE maximization problem model:
Figure BDA0002527641610000181
Figure BDA0002527641610000182
Figure BDA0002527641610000183
Figure BDA0002527641610000184
Figure BDA0002527641610000185
Figure BDA0002527641610000186
in this embodiment, the function transformation module specifically includes:
a function transformation module for solving the problem (53), transforming the fractional objective function into a subtractive equation using theorem 1:
theorem 1: maximum EEq*Obtained by the following formula alone
Figure BDA0002527641610000187
When in use
Figure BDA0002527641610000188
And is
Figure BDA0002527641610000189
When the current is over;
in this embodiment, the function solving module specifically includes:
an outer loop solving module to solve the following optimization problem for a given q
Figure BDA00025276416100001810
s.t.(53b)-(53f)， (55b)
(55a) The optimum value of (a) is represented as t (q). According to theorem 1, the following definitions apply
Figure BDA0002527641610000191
T (q) is a strictly decreasing convex function with respect to q, and t (q) > 0 at q → - ∞ and t (q) < 0 at q → ∞, so that t (q) ═ 0 is found using the classical bipartite method.
An inner loop solution module for defining a new variable μk＝1/ρk,ωk＝1/(1-ρk)，
Figure BDA0002527641610000192
And
Figure BDA0002527641610000193
wherein
Figure BDA0002527641610000194
Is { z0,{τk},{zk},{λkI-1 iteration values. Finally, the optimization problem turns into
Figure BDA0002527641610000195
Figure BDA0002527641610000196
Figure BDA0002527641610000197
(40e)，(40f)，(45e)，(49)，(50c)，(50e). (57d)
Finally, a convex optimization technology is used for obtaining an optimal value { muk},{ωk},{τk},{λk},{dk},z0,{zkAnd solving the objective function.
The above description is only for the specific embodiment 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 included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.

Claims (10)

1. A hybrid precoding method, the method comprising:
s1: codebook-based analog precoding based on full-connection and sub-connection structure design
Figure FDA0002527641600000016
S2: constructing a problem of combining digital multicast, unicast pre-coding and power splitting rate optimization;
s3: converting the fractional objective function into a subtractive objective function, and providing a double-loop iterative algorithm to solve the fractional objective function;
s4: and the outer ring adopts a classical two-section iterative algorithm to solve T (q), the inner ring converts the problem into a convex problem through a successive convex approximation technology, and an iterative algorithm is provided to solve the problem.
2. The hybrid precoding method as claimed in claim 1, wherein the step S1 specifically comprises:
s11: the search is defined as
Figure FDA0002527641600000011
The codebook of (a) obtains the analog precoding. Full-connected structure, the analog pre-coding of the k user can be selected as
Figure FDA0002527641600000012
And generalizes the analog precoding scheme to algorithm 1.
S12: for the sub-join structure, a codebook is searched based on the sub-joins. The analog precoding of sub-array i at the kth user can be selected as
Figure FDA0002527641600000013
3. The hybrid precoding method as claimed in claim 1, wherein the step S2 specifically comprises:
initializing variables to obtain an original EE maximization problem model:
Figure FDA0002527641600000014
Figure FDA0002527641600000015
Figure FDA0002527641600000021
Figure FDA0002527641600000022
Figure FDA0002527641600000023
Figure FDA0002527641600000024
4. the hybrid precoding method as claimed in claim 1, wherein the step S3 specifically comprises:
the fractional objective function is converted into a subtractive equation using theorem 1:
theorem 1: maximum EEq*Obtained by the following formula alone
Figure FDA0002527641600000025
When in use
Figure FDA0002527641600000026
And is
Figure FDA0002527641600000027
Then (c) is performed.
5. The hybrid precoding method as claimed in claim 1, wherein the step S4 specifically comprises:
the following optimization problem for a given q needs to be solved
Figure FDA0002527641600000028
s.t.(1b)-(1f)， (3b)
(3a) The optimal value of (1) is T (q), and is defined as follows according to theorem 1
Figure FDA0002527641600000031
T (q) is a strictly decreasing convex function with respect to q, and t (q) > 0 at q → - ∞ and t (q) < 0 at q → ∞, so that t (q) ═ 0 is found using the classical bipartite method.
Defining new variables, then transforming optimization problem into
Figure FDA0002527641600000032
Figure FDA0002527641600000033
Figure FDA0002527641600000034
Figure FDA0002527641600000035
Figure FDA0002527641600000036
Figure FDA0002527641600000037
Figure FDA0002527641600000038
(1e)，(1f). (5h)
Finally, a convex optimization technology is used for obtaining an optimal value { muk},{ωk},{τk},{λk},{dk},z0,{zkAnd solving the objective function.
6. A hybrid precoding apparatus, wherein the method comprises:
a pre-coding module: design of codebook-based analog precoding based on full-connection and sub-connection structures
Figure FDA0002527641600000039
A modeling module: the method is used for constructing the problems of joint digital multicast, unicast precoding and power split rate optimization;
a function conversion module: the method is used for converting the fractional objective function into a subtractive objective function and providing a double-loop iterative algorithm to solve the fractional objective function;
a solving module: the method is used for solving T (q) by adopting a classical two-segment iterative algorithm for an outer ring, and solving the problem by converting the problem into a convex problem through a successive convex approximation technology for an inner ring by providing an iterative algorithm.
7. The hybrid precoding device of claim 6, wherein the analog precoding module specifically comprises:
a first pre-analog pre-coding module, the search being defined as
Figure FDA0002527641600000041
The codebook of (a) obtains the analog precoding. Full-connected architecture, with the analog precoding selected for the kth user as
Figure FDA0002527641600000042
And generalizes the analog precoding scheme to algorithm 1.
And the second analog pre-coding module searches a codebook based on the sub-connection for the sub-connection structure. The analog precoding of sub-array i at the kth user can be selected as
Figure FDA0002527641600000043
8. The hybrid precoding device of claim 6, wherein the pre-modeling module specifically comprises:
the modeling module is used for initializing variables to obtain an original EE maximization problem model:
Figure FDA0002527641600000044
Figure FDA0002527641600000045
Figure FDA0002527641600000051
Figure FDA0002527641600000052
Figure FDA0002527641600000053
Figure FDA0002527641600000054
9. the hybrid precoding device of claim 6, wherein the function transformation module specifically comprises:
and the function conversion module is used for converting the fractional objective function into a subtraction equation by using theorem 1:
theorem 1: maximum EEq*Obtained by the following formula alone
Figure FDA0002527641600000055
When in use
Figure FDA0002527641600000056
And is
Figure FDA0002527641600000057
When the current is over;
10. the hybrid precoding device of claim 6, wherein the function solving module specifically comprises:
an outer loop solving module to solve the following optimization problem for a given q
Figure FDA0002527641600000058
s.t.(6b)-(6f)， (8b)
(8a) The optimal value is denoted as t (q). According to theorem 1, the following definitions apply
Figure FDA0002527641600000061
T (q) is a strictly decreasing convex function with respect to q, and t (q) > 0 at q → - ∞ and t (q) < 0 at q → ∞, so that t (q) ═ 0 is found using the classical bipartite method.
An inner loop solution module to define new variables and then to transform the optimization problem into
Figure FDA0002527641600000062
Figure FDA0002527641600000063
Figure FDA0002527641600000064
(5d)-(5g),(6e)，(6f). (10d)
Finally, a convex optimization technology is used for obtaining an optimal value { muk},{ωk},{τk},{λk},{dk},z0,{zkAnd solving the objective function.
CN202010508844.2A 2020-06-06 2020-06-06 Hybrid precoding method and device Withdrawn CN111726152A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010508844.2A CN111726152A (en) 2020-06-06 2020-06-06 Hybrid precoding method and device

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010508844.2A CN111726152A (en) 2020-06-06 2020-06-06 Hybrid precoding method and device

Publications (1)

Publication Number Publication Date
CN111726152A true CN111726152A (en) 2020-09-29

Family

ID=72566114

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010508844.2A Withdrawn CN111726152A (en) 2020-06-06 2020-06-06 Hybrid precoding method and device

Country Status (1)

Country Link
CN (1) CN111726152A (en)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104393956A (en) * 2014-11-26 2015-03-04 北京邮电大学 Maximizing and speed rate pre-coding method for simultaneous wireless information and power transfer system
US20150230266A1 (en) * 2014-02-10 2015-08-13 Korea Advanced Institute Of Science And Technology User scheduling and beamformer design method, apparatus, and storage medium based on two-stage beamformer for massive mimo downlink

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150230266A1 (en) * 2014-02-10 2015-08-13 Korea Advanced Institute Of Science And Technology User scheduling and beamformer design method, apparatus, and storage medium based on two-stage beamformer for massive mimo downlink
CN104393956A (en) * 2014-11-26 2015-03-04 北京邮电大学 Maximizing and speed rate pre-coding method for simultaneous wireless information and power transfer system

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
WANMING HAO ET AL.: "Hybrid Precoding Design for SWIPT Joint Multicast-Unicast mmWave System with Subarray Structure", 《IEEE》 *

Similar Documents

Publication Publication Date Title
CN112290995B (en) Beam design method based on safety energy efficiency in satellite-ground integrated network
CN111726151A (en) Resource allocation method and device based on wireless energy-carrying communication
CN111147112B (en) Energy maximization collection method based on MIMO-NOMA system
CN107046434B (en) Large-scale MIMO system analog-digital mixed precoding method
Kaushik et al. Joint bit allocation and hybrid beamforming optimization for energy efficient millimeter wave MIMO systems
CN111447631A (en) Satellite-ground combined beam forming and power distribution method based on non-orthogonal multiple access technology
CN109861731B (en) Hybrid precoder and design method thereof
CN110299937B (en) Beam forming method for uplink MIMO-NOMA wireless communication system
EP3403339A1 (en) Practical hybrid precoding scheme for multi-user massive mimo systems
CN110011712B (en) Millimeter wave large-scale multi-input multi-output-oriented hybrid precoding method
CN111726156A (en) NOMA-based resource allocation method and device
CN111835406A (en) Robust precoding method suitable for energy efficiency and spectral efficiency balance of multi-beam satellite communication
CN114070365B (en) Intelligent reflection surface assisted low-radio-frequency-complexity multi-user MIMO uplink spectrum efficiency optimization method
CN109068382B (en) NOMA cross-layer power distribution method based on time delay QoS
CN113824478B (en) Broadband millimeter wave multi-user large-scale MIMO uplink spectrum efficiency optimization method assisted by discrete lens antenna array
CN107809275A (en) A kind of Limited Feedback mixing method for precoding based on millimeter wave mimo system
CN109361434B (en) Millimeter wave MIMO mixed precoding method for base station cooperative transmission
CN113572503B (en) Low-complexity improved mixed beam forming method based on GP
CN108566236B (en) User terminal, base station, and hybrid beamforming transmission method and system
CN111740766A (en) Codebook-based beam design method and device
US11108458B2 (en) Method and apparatus for combining plurality of radio signals
CN114039638B (en) Combined design method of hybrid beam shaper and analog-to-digital converter
CN111010220A (en) Multi-user multi-stream downlink hybrid precoding method and system based on energy efficiency
CN111726152A (en) Hybrid precoding method and device
CN108631829B (en) Joint power distribution, precoding and decoding method and base station thereof

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
WW01 Invention patent application withdrawn after publication
WW01 Invention patent application withdrawn after publication

Application publication date: 20200929