CN111726151A - Resource allocation method and device based on wireless energy-carrying communication - Google Patents

Resource allocation method and device based on wireless energy-carrying communication Download PDF

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CN111726151A
CN111726151A CN202010508838.7A CN202010508838A CN111726151A CN 111726151 A CN111726151 A CN 111726151A CN 202010508838 A CN202010508838 A CN 202010508838A CN 111726151 A CN111726151 A CN 111726151A
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iterative algorithm
objective function
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precoding
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朱政宇
陈鹏飞
孙钢灿
贾少波
郭亚博
王飞
马梦园
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
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B5/00—Near-field transmission systems, e.g. inductive or capacitive transmission systems
    • H04B5/70—Near-field transmission systems, e.g. inductive or capacitive transmission systems specially adapted for specific purposes
    • H04B5/79—Near-field transmission systems, e.g. inductive or capacitive transmission systems specially adapted for specific purposes for data transfer in combination with power transfer
    • 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

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Abstract

The invention provides a resource allocation method and a device based on wireless energy-carrying communication, which maximize 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 DDA0002527639570000011
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 proposed problem into a convex problem through a low-complexity iterative algorithm based on a ZF technology, and an iterative algorithm is proposed to solve the problem. The invention broadcasts multicast and unicast waves in millimeterThe wave is combined with SWIPT, and a resource allocation method and a resource allocation device based on wireless energy-carrying communication are provided. Millimeter waves fill more antennas with smaller physical dimensions; the SWIPT interference power is converted into energy of a receiving end, and energy efficiency is improved; with the hybrid precoding scheme, the number of RF chains is much smaller than the number of antennas.

Description

Resource allocation method and device based on wireless energy-carrying communication
Technical Field
The invention relates to the technical field of communication, in particular to a resource allocation method and device based on wireless energy-carrying communication.
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 with smaller physical size, forming a huge multiple-input multiple-output (mMIMO) millimeter wave system. 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 approaches to 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 are willing to receive the same content. Typically, these users request self-defined content when they use multicast content simultaneously. Taking the object-based broadcast (OBB) scenario as an example, each subscribed user is willing to receive both public messages via multicast and private messages via unicast. For this reason, combining multicast and unicast transmission may be an efficient and effective solution compared to conventional frequency/time division multiplexing.
Disclosure of Invention
Aiming at the defects in the prior art, the invention relates to a method for maximizing the energy efficiency of a system by combining multicast unicast wave millimeter waves and SWIPT on the basis of a resource allocation method based on wireless energy-carrying communication, 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 resource allocation method based on wireless energy-carrying communication, the method comprising:
s1: codebook-based analog precoding based on full-connection and sub-connection structure design
Figure BDA0002527639550000021
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 proposed problem into a convex problem through a low-complexity iterative algorithm based on a ZF technology, and an iterative algorithm is proposed to solve the problem.
Preferably, the step S1 specifically includes:
s11: is defined by searching as
Figure BDA0002527639550000022
To obtain the analog precoding. For a fully connected structure, the analog precoding for the kth user may be selected as
Figure BDA0002527639550000023
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 BDA0002527639550000031
Preferably, the step S2 specifically includes:
the following variables are initialized:
Figure BDA0002527639550000032
minimum harvested energy, P, for the k-th usermaxMaximum transmission power of BS, ηEEFor system energy efficiency, PtotalFor total power consumption, the k-th user common signal SINR is
Figure BDA00025276395500000310
Private signal SINR gammakObtaining an EE maximization problem model:
Figure BDA0002527639550000033
Figure BDA0002527639550000034
Figure BDA0002527639550000035
Figure BDA0002527639550000036
Figure BDA0002527639550000037
Figure BDA0002527639550000038
preferably, the step S3 specifically includes:
to solve (19), the fractional objective function is converted to a subtractive equation using theorem 1.
Theorem 1: maximum EE q*Obtained only in the following formula
Figure BDA0002527639550000039
When in use
Figure BDA0002527639550000041
And is
Figure BDA0002527639550000042
When the current is over;
preferably, the step S4 specifically includes:
solving the following optimization problem for a given q
Figure BDA0002527639550000043
s.t.(11b)-(11f)， (13b)
(13a) The optimum value of (a) is represented as t (q). According to theorem 1, the following definitions apply
Figure BDA0002527639550000044
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.
Two new variables g are definedkAnd okWherein
Figure BDA0002527639550000045
Thus, the optimization problem (13) can be rewritten as
Figure BDA0002527639550000046
Figure BDA0002527639550000047
Figure BDA0002527639550000048
Figure BDA0002527639550000049
Figure BDA00025276395500000410
Figure BDA00025276395500000411
Figure BDA00025276395500000412
(11e). (15h)
Next, Schur's complement theorem, first order Taylor series expansion, etc. are applied to convert them to convex. The rewrite optimization problem is
Figure BDA0002527639550000051
s.t.(5b)-(5f)，(11e) (16b)
Wherein
Figure BDA0002527639550000052
And
Figure BDA0002527639550000053
respectively represent the (i-1) th]In the next iteration d0、pk、gkAnd zkFinally the above problem is solved by a convex solver (e.g. CVX).
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 BDA0002527639550000054
A modeling module: the method is used for constructing the problems of joint digital multicast, unicast precoding and power splitting 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-section iterative algorithm for an outer ring, and the inner ring converts the proposed problem into a convex problem through a low-complexity iterative algorithm based on a ZF technology, so that an iterative algorithm is proposed for solving.
Preferably, the analog precoding module specifically includes:
a first analog pre-coding module for defining as by searching
Figure BDA0002527639550000055
To obtain an analog precoding. For a fully connected structure, the analog precoding for the kth user may be selected as
Figure BDA0002527639550000056
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 for the sub-connection structure. The analog precoding of sub-array i at the kth user can be selected as
Figure BDA0002527639550000057
Preferably, the modeling module specifically includes:
a modeling module for initializing the following variables:
Figure BDA0002527639550000061
minimum harvested energy, P, for the k-th usermaxMaximum transmission power of BS, ηEEFor system energy efficiency, PtotalFor the total power consumption, the common signal SINR of the kth user is
Figure BDA0002527639550000062
The private signal SINR is gammakAnd obtaining an original EE maximization problem model:
Figure BDA0002527639550000063
Figure BDA0002527639550000064
Figure BDA0002527639550000065
Figure BDA0002527639550000066
Figure BDA0002527639550000067
Figure BDA0002527639550000068
preferably, the function transformation module specifically includes:
and the function conversion module is used for solving (17) and converting the fractional target function into a subtraction equation by using theorem 1.
Theorem 1: maximum EE q*Obtained only in the following formula
Figure BDA0002527639550000069
When in use
Figure BDA00025276395500000610
And is
Figure BDA00025276395500000611
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 BDA0002527639550000071
s.t.(17b)-(17f)， (19b)
(19a) The optimum value of (a) is represented as t (q). According to theorem 1, the following definitions apply
Figure BDA0002527639550000072
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.
Inner loop solving module, two new variables g are definedkAnd okWherein
Figure BDA0002527639550000073
Figure BDA0002527639550000074
Thus, the optimization problem (19) can be rewritten as
Figure BDA0002527639550000075
Figure BDA0002527639550000076
Figure BDA0002527639550000077
Figure BDA0002527639550000078
Figure BDA0002527639550000079
Figure BDA00025276395500000710
Figure BDA00025276395500000711
(17e). (21h)
Next, Schur's complement theorem, first order Taylor series expansion, etc. are applied to convert them to convex. The rewrite optimization problem is
Figure BDA0002527639550000081
s.t.(5b)-(5f)，(17e). (22b)
Wherein
Figure BDA0002527639550000082
And
Figure BDA0002527639550000083
respectively represent the (i-1) th]In the next iteration d0、pk、gkAnd zkFinally the above problem is solved by a convex solver (e.g. CVX).
In view of the above technical solutions, the present invention provides a method and an apparatus for resource allocation based on wireless energy-carrying communication, so as 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.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the 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 the 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 illustrating a resource allocation method based on wireless energy-carrying communication according to the present invention;
FIG. 4 is the convergence performance of a low complexity algorithm based on ZF technique under different structures;
FIG. 5 is a comparison graph of simulation of the effect of initial values on different structures according to the present invention as the number of iterations increases;
FIG. 6 is NRFWhen the maximum transmitting power of the base station is gradually increased as 4, EE simulation comparison graphs with different structures are adopted in the invention;
FIG. 7 is a comparative illustration of EE simulation of different structures in the present invention when the maximum transmission power of the base station is gradually increased when the EE is maximized;
FIG. 8 is a comparison chart of EE simulation 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. 9 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. 10 is a simulation diagram of the trade-off between EE and SE for a sub-array configuration;
fig. 11 is a schematic structural diagram of a resource allocation apparatus based on wireless portable communication according to 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 described in detail and completely with reference to the drawings in the embodiments of the present invention. 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 resource allocation method based on wireless energy-carrying communication, which is a method for maximizing the energy efficiency of a system by combining multicast single-broadcast wave millimeter waves and SWIPT. 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: in order to reduce the complexity of joint design, we first apply codebook-based method to design the analog precoding of full-connected and subarray-connected structure
Figure BDA0002527639550000091
S2, constructing a combined digital multicast, unicast pre-coding and power splitting rate optimization problem to maximize the energy efficiency η of the systemEEWhile taking into account the maximum transmit power P at the BSmaxAnd minimum harvested energy of receiver
Figure BDA0002527639550000092
S3: converting the fractional objective function into a subtractive objective function by using a theorem, and providing a dual-cycle iterative algorithm for solving the fractional objective function;
s4: and the outer ring adopts a classic dual-section algorithm, the inner ring converts the proposed problem into a convex problem through a low-complexity iterative algorithm based on the ZF technology, and an iterative algorithm is proposed 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 BDA0002527639550000101
And
Figure BDA0002527639550000102
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 BDA0002527639550000103
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 BDA0002527639550000104
Wherein
Figure BDA0002527639550000105
And xkRespectively representing the downlink channel vector, the digital precoding vector and the dedicated signal of the k-th user.
Figure BDA0002527639550000106
And x0Respectively the number of the k-th userA precoding vector and a common signal. n iskIs an Additive White Gaussian Noise (AWGN).
Figure BDA0002527639550000107
A precoding matrix is simulated. For fully connected structures, F is written as
Figure BDA0002527639550000108
Wherein
Figure BDA0002527639550000109
Is an analog precoding vector associated with the k-th RF chain, and
Figure BDA00025276395500001010
also, for a sublinker structure, F can be represented as
Figure BDA00025276395500001011
In the formula (I), the compound is shown in the specification,
Figure RE-GDA0002584942400000109
by using
Figure RE-GDA00025849424000001010
Representing the analog precoding vector associated with the k-th RF chain. N is a radical ofUSB＝NTX/NRF. Rho 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 BDA0002527639550000113
Harvested energy
Figure BDA0002527639550000114
In the formula, η ∈ (0,1) represents energy conversion efficiency. The received signal for the ID may be expressed as
Figure BDA0002527639550000115
Wherein the content of the first and second substances,
Figure BDA0002527639550000116
is additive noise caused by the ID.
The achievable SINR of the common signal at the kth user can be expressed as
Figure BDA0002527639550000117
At the kth user, the achievable SINR of the private signal may be expressed as
Figure BDA0002527639550000118
For millimeter wave channels, a widely used geometric channel mode is adopted
Figure BDA0002527639550000119
Where L is the number of paths and,
Figure BDA00025276395500001110
the complex gain of the l-th path is indicated.
Figure BDA00025276395500001111
Is the antenna array response vector for user k.
When a uniform linear array is used,
Figure BDA00025276395500001112
can be expressed as
Figure BDA0002527639550000121
Is defined by searching as
Figure BDA0002527639550000122
To obtain an analog precoding. For a fully connected structure, the analog precoding for the kth user may be chosen as
Figure BDA0002527639550000123
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 BDA0002527639550000124
Wherein
Figure BDA0002527639550000125
Have become subarray-based codebooks.
In this embodiment, the specific process of step S2 is as follows:
the following variables are initialized:
Figure BDA0002527639550000126
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 BDA0002527639550000127
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 BDA0002527639550000128
Where ξ ≧ 1 is the low efficiency of the power amplifier.
Next, the EE of the system is defined as
Figure BDA0002527639550000131
Power splitting ratio optimization by joint
Figure BDA0002527639550000132
And digital precoding
Figure BDA0002527639550000133
To maximize the EE of the system, write as
Figure BDA0002527639550000134
Figure BDA0002527639550000135
Figure BDA0002527639550000136
Figure BDA0002527639550000137
Obtaining an original EE maximization problem model:
Figure BDA0002527639550000138
Figure BDA0002527639550000139
Figure BDA00025276395500001310
Figure BDA00025276395500001311
Figure BDA00025276395500001312
Figure BDA00025276395500001313
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.
Denotes q＊As the largest EE of the system, i.e.
Figure BDA0002527639550000141
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 BDA0002527639550000142
When in use
Figure BDA0002527639550000143
And is
Figure BDA0002527639550000144
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 BDA0002527639550000145
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 BDA0002527639550000146
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.
The achievable SINR of the public and private signals at the kth user can be expressed as
Figure BDA0002527639550000147
Figure BDA0002527639550000151
Wherein p iskIndicating the unicast transmission power of the k-th user. In this case, only the transmit power p needs to be optimizedkAnd multicast precoding d0。
Next, two new variables g are definedkAnd okWherein
Figure BDA0002527639550000152
Thus, the optimization problem (43) can be rewritten as
Figure BDA0002527639550000153
Figure BDA0002527639550000154
Figure BDA0002527639550000155
Figure BDA0002527639550000156
Figure BDA0002527639550000157
Figure BDA0002527639550000158
Figure BDA0002527639550000159
(40e). (46h)
It can be observed that (46b) - (46f) are all non-convex constraints. Next, some approximation techniques are applied to convert them to convex.
First, combine
|hkdk|2＝(dk+Δdk)HHk(dk+Δdk)
≥2Re{(dk)HHkΔdk}+(dk)HHkdk，
(46d) Convertible into convex constraint
Figure BDA0002527639550000161
To process the (46e) and (46f) non-convex constraints, Schur's complement theorem is applied to convert them to constraints in the form of a convex-down matrix
Figure BDA0002527639550000162
Figure BDA0002527639550000163
In addition, define
Figure BDA0002527639550000164
Can be written as
Figure BDA0002527639550000165
In the formula (I), the compound is shown in the specification,
Figure BDA0002527639550000166
and
Figure BDA0002527639550000167
in addition to this, the present invention is,
Figure BDA0002527639550000168
Figure BDA0002527639550000169
and
Figure BDA00025276395500001610
respectively represent the (i-1) th]In the next iteration d0,pkAnd g andkthe value of (c). Thus, (46b) can be written as a following convex constraint
Figure BDA00025276395500001611
Finally, (46c) is denoted as
Figure BDA00025276395500001612
Can obtain zkgkUpper bound of (2)
Figure BDA00025276395500001613
Wherein
Figure BDA0002527639550000171
And
Figure BDA0002527639550000172
are respectively [ i-1 ]]Z at the time of the next iterationkAnd gkThe value of (c). Thereafter, (46c) may be converted into a convex down constraint
Figure BDA0002527639550000173
Therefore, iterative solution of the following convex optimization problem is required
Figure BDA0002527639550000174
s.t.(40e)，(47)，(48)，(49)，(51)，(54). (55b)
Finally, the above problem is solved by a convex solver (e.g., CVX).
Fig. 4 shows convergence performance of a ZF-based low-complexity algorithm under different antenna structures, where q is set to 0 and P is set to be a sub-array structure, and the convergence performance includes a digital structure, a full-connection structure, and a sub-array structure max30 dBm. Converge after approximately 5 iterations. Therefore, ZF-based methods can quickly get a solution to the problem with less loss of performance. Furthermore, SE in a digital architecture has been found to be the highest compared to the other two architectures, but is very power consuming and hardware complex.
FIG. 5 is a graph of the effect of different initial values on its solution. Where algorithm 5 and fully connected structures are considered. It can be seen from this figure that the algorithm always converges to the same point at different initial values. However, the initialization has little influence on the convergence speed.
FIG. 6 is a graph of energy efficiency versus maximum transmit power of a base station, N RF4. It can be observed that EE increases first and then follows PmaxSaturation is increased. It will be appreciated that greater transmit power may be achievedHigh SE, but the rate of improvement will be lower and 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. 7 and 8 examine the EE of the system under different optimization schemes, which are the same 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 system-at-maximization EE scheme is to maximize SE without regard to power consumption. As a result, EE can be reduced due to the larger transmission power.
FIG. 9EE vs. minimum energy harvested, PmaxSet to 45 dBm. It can be observed that
Figure BDA0002527639550000181
Relatively small, EE remains unchanged, e.g.
Figure BDA0002527639550000182
This is because the base station has redundant power supplies that can be used to meet the energy harvesting requirements. However, when
Figure BDA0002527639550000183
Larger, more power must be used to convert to energy, thereby reducing EE. Therefore, generally, SE and EH cannot be increased at the same time, and one must be sacrificed to increase the other.
The trade-off between EE and SE under the sub-array structure of fig. 10 can be seen as increasing EE 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. 11 is a schematic structural diagram of a resource allocation apparatus based on wireless portable communication according to the present invention;
a pre-coding module: for according to full connectionCodebook-based analog precoding for design of sub-connection structure
Figure BDA0002527639550000184
A modeling module: the method is used for constructing the problems of joint digital multicast, unicast precoding and power splitting 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-section iterative algorithm for an outer ring, converting the proposed problem into a convex problem by a low-complexity iterative algorithm based on a ZF technology for an inner ring, and solving by adopting an iterative algorithm.
In this embodiment, the precoding module specifically includes:
a first pre-coding module, the search being defined as
Figure BDA0002527639550000191
To obtain an analog precoding. The analog precoding of the k-th user in the full-connection structure can be selected as
Figure BDA0002527639550000192
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 BDA0002527639550000193
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 BDA0002527639550000194
Figure BDA0002527639550000195
Figure BDA0002527639550000196
Figure BDA0002527639550000197
Figure BDA0002527639550000198
Figure BDA0002527639550000199
in this embodiment, the function transformation module specifically includes:
a function transformation module for solving a problem (56) by transforming the fractional objective function into a subtractive equation using theorem 1:
theorem 1: maximum EEq*Obtained by the following formula alone
Figure BDA0002527639550000201
When in use
Figure BDA0002527639550000202
And is
Figure BDA0002527639550000203
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 BDA0002527639550000204
s.t.(56b)-(56f)， (58b)
(56a) The optimum value of (a) is represented as t (q). According to theorem 1, the following definitions apply
Figure BDA0002527639550000205
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 two new variables gkAnd okWherein
Figure BDA0002527639550000206
Figure BDA0002527639550000207
Thus, the optimization problem (58) can be rewritten as
Figure BDA0002527639550000208
Figure BDA0002527639550000209
Figure BDA00025276395500002010
Figure BDA00025276395500002011
Figure BDA00025276395500002012
Figure BDA0002527639550000211
Figure BDA0002527639550000212
(56e). (60h)
(60b) - (60f) are all non-convex constraints. Next, Schur's complement theorem, a first order Taylor series expansion, etc. are applied to convert them to convex. The rewrite optimization problem is
Figure BDA0002527639550000213
s.t.(47)，(48)，(49)，(51)，(54)，(56e). (61b)
Wherein
Figure BDA0002527639550000214
And
Figure BDA0002527639550000215
respectively represent the (i-1) th]In the next iteration d0、pk、gkAnd zkFinally the above problem is solved by a convex solver (e.g. CVX).

Claims (10)

1. A method for resource allocation based on wireless energy-carrying communication, the method comprising:
s1: codebook-based analog precoding based on full-connection and sub-connection structure design
Figure FDA0002527639540000011
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 proposed problem into a convex problem through a low-complexity iterative algorithm based on a ZF technology, and an iterative algorithm is proposed to solve the problem.
2. The method as claimed in claim 1, wherein the step S1 specifically includes:
s11: is defined by searching as
Figure FDA0002527639540000012
To obtain an analog precoding. For a fully connected structure, the analog precoding for the kth user may be chosen as
Figure FDA0002527639540000013
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 FDA0002527639540000014
3. The method as claimed in claim 1, wherein the step S2 specifically includes:
initializing variables to obtain an original EE maximization problem model:
Figure FDA0002527639540000015
Figure FDA0002527639540000021
Figure FDA0002527639540000022
Figure FDA0002527639540000023
Figure FDA0002527639540000024
Figure FDA0002527639540000025
4. the method as claimed in claim 1, wherein the step S3 specifically includes:
the fractional objective function is converted to a subtractive form using theorem 1:
theorem 1: maximum EEq*Obtained by the following formula alone
Figure FDA0002527639540000026
When in use
Figure FDA0002527639540000027
And is
Figure FDA0002527639540000028
Then (c) is performed.
5. The method as claimed in claim 1, wherein the step S4 specifically includes:
the following optimization problem for a given q needs to be solved
Figure FDA0002527639540000029
s.t.(1b)-(1f)， (3b)
(3a) The optimal value of (1) is T (q), and is defined as follows according to theorem 1
Figure FDA0002527639540000031
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.
Eliminating interference between multiple users by adopting zero-forcing precoding and defining variable gkAnd okWherein
Figure FDA0002527639540000032
Reconstructing the optimization problem into a solution by using Schur complementary theory and first-order Taylor series expansion
Figure FDA0002527639540000033
Figure FDA0002527639540000034
Figure FDA0002527639540000035
Figure FDA0002527639540000036
Figure FDA0002527639540000037
Figure FDA0002527639540000038
(1e). (5g)
Finally, the above problem is solved by a convex solver (e.g., CVX).
6. An apparatus for resource allocation based on wireless portable communication, the method comprising:
a pre-coding module: design of codebook-based analog precoding based on full-connection and sub-connection structures
Figure FDA0002527639540000041
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-section iterative algorithm in an inner ring, and solving by adopting an iterative algorithm which is based on a ZF technology and is used for converting the proposed problem into a convex problem in an outer ring through a low-complexity iterative algorithm.
7. The device according to claim 6, wherein the analog precoding module specifically comprises:
a first pre-analog pre-coding module defined by a search
Figure FDA0002527639540000042
To obtain an analog precoding. For a fully connected structure, the analog precoding for the kth user may be selected as
Figure FDA0002527639540000043
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 FDA0002527639540000044
8. The apparatus according to 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 FDA0002527639540000051
Figure FDA0002527639540000052
Figure FDA0002527639540000053
Figure FDA0002527639540000054
Figure FDA0002527639540000055
Figure FDA0002527639540000056
9. the apparatus according to 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 FDA0002527639540000057
When in use
Figure FDA0002527639540000058
And is
Figure FDA0002527639540000059
When the current is over;
10. the apparatus according to 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 FDA0002527639540000061
s.t.(6b)-(6f)， (8b)
(8a) The optimum value of (a) is represented as t (q). According to theorem 1, the following definitions apply
Figure FDA0002527639540000062
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 solving module for eliminating the interference between multiple users by adopting zero-forcing precoding and defining a variable gkAnd okWherein
Figure FDA0002527639540000063
Reconstructing the optimization problem into a problem by using Schur complementary theory, first-order Taylor series expansion and the like
Figure FDA0002527639540000064
s.t.(5b)-(5f)，(6e). (10b)
Finally, the above problem is solved by a convex solver (e.g., CVX).
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