CN112383332A - Honeycomb base station communication system based on intelligent reflection surface - Google Patents

Honeycomb base station communication system based on intelligent reflection surface Download PDF

Info

Publication number
CN112383332A
CN112383332A CN202011206723.9A CN202011206723A CN112383332A CN 112383332 A CN112383332 A CN 112383332A CN 202011206723 A CN202011206723 A CN 202011206723A CN 112383332 A CN112383332 A CN 112383332A
Authority
CN
China
Prior art keywords
base station
user
macro base
intelligent
ris
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.)
Granted
Application number
CN202011206723.9A
Other languages
Chinese (zh)
Other versions
CN112383332B (en
Inventor
梁应敞
王俊
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.)
University of Electronic Science and Technology of China
Original Assignee
University of Electronic Science and Technology of China
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 University of Electronic Science and Technology of China filed Critical University of Electronic Science and Technology of China
Priority to CN202011206723.9A priority Critical patent/CN112383332B/en
Publication of CN112383332A publication Critical patent/CN112383332A/en
Application granted granted Critical
Publication of CN112383332B publication Critical patent/CN112383332B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

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
    • H04B7/0452—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
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
    • H04B7/0842—Weighted combining
    • H04B7/0848—Joint weighting
    • H04B7/0857—Joint weighting using maximum ratio combining techniques, e.g. signal-to- interference ratio [SIR], received signal strenght indication [RSS]
    • 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
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
    • H04B7/0842—Weighted combining
    • H04B7/086—Weighted combining using weights depending on external parameters, e.g. direction of arrival [DOA], predetermined weights or beamforming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • H04L27/2647—Arrangements specific to the receiver only
    • H04L27/2655—Synchronisation arrangements
    • H04L27/2689—Link with other circuits, i.e. special connections between synchronisation arrangements and other circuits for achieving synchronisation
    • H04L27/2691—Link with other circuits, i.e. special connections between synchronisation arrangements and other circuits for achieving synchronisation involving interference determination or cancellation
    • 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 belongs to the technical field of wireless communication, and relates to a cellular base station communication system based on an intelligent reflection surface. The invention provides a novel cellular base station communication system architecture based on an intelligent reflection surface, wherein the intelligent reflection surface is used for assisting communication of macro base station users and transmitting information to a plurality of users, interference is avoided, spectrum utilization efficiency is greatly improved, and the intelligent reflection surface consumes less energy and has higher energy efficiency. The intelligent reflective surface can further optimize the communication system by passive beam forming. The scheme is simple to implement, can prove to realize the spectrum efficiency higher than that of allocating the special communication resources for respective systems, and has strong application value.

Description

Honeycomb base station communication system based on intelligent reflection surface
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a cellular base station communication system based on an intelligent reflection surface.
Background
With the rapid development of wireless communication technology, the number of communication devices and traffic volume has increased exponentially over the last decade, also bringing about a proliferation in cellular capacity demand. A method of increasing the capacity of a communication system generally comprises: more cells are deployed, available bandwidth is increased, and spectrum efficiency is improved. It is well known that reducing the size of a cell, deploying more cells, is the simplest and most efficient way to increase system capacity. Small cellular networks are used to cover blind areas and share traffic pressure by tightly deploying ad-hoc, low power, low cost micro base stations. In a multi-user network, users within the coverage of one cell share the available bandwidth, and therefore, reducing the cell size and deploying more cells reduces the number of users per cell, thereby increasing the bandwidth available to each user. Meanwhile, the distance between the user and the micro base station is reduced due to the reduction of the cell size, the signal to noise ratio of the user is improved, and the transmission rate of the user is further improved. Two major challenges are presented in terms of cell deployment, one is to design an effective interference management strategy to control multiple kinds of interference, including mutual interference between a micro cell and a macro base station, interference between adjacent micro cells, interference received by a micro base station from a macro base station terminal user, and the other is to reduce energy consumption and cost of cell deployment.
In recent years, with the breakthrough progress of research on radio frequency micro-electro-mechanical systems (MEMS) and metamaterials, a low-cost and efficient electromagnetic space reconstruction technology based on metamaterial reflection becomes possible, and the concept of intelligent reflection surfaces (RIS) is proposed in the academic community. The intelligent reflecting surface utilizes the highly controllable signal reflection characteristic, and can control the change of amplitude and phase in real time to adapt to the dynamic requirements of different channel environments. Compared with communication technologies such as amplification forwarding relay and the like, the intelligent reflecting surface does not need to actively generate a new signal, but only passively reflects an incident signal, and does not need an additional energy source. The intelligent reflecting surface utilizes the existing reflected signals, simplifies radio frequency links and devices required by transmission, and can realize large-scale multiple-input multiple-output (MIMO) like reflecting beam forming in a short time. The intelligent reflective surface can be deployed densely and quickly while achieving controllable cost and low energy consumption without introducing additional interference. The intelligent reflecting surface becomes a new technology which is attractive in future wireless communication, and particularly gives priority to indoor communication application scenes with high-density user gathering (such as communication applications in places such as stadiums, large shopping malls, exhibition centers, airports and the like). Currently, most of research on intelligent reflective surfaces is applied to conventional communication systems, such as multi-user downlink communication, bidirectional relay networks, etc., to improve coverage, provide security, and improve energy efficiency and spectral efficiency.
Disclosure of Invention
The invention provides a novel cellular base station communication system based on an intelligent reflection surface, which is called as an intelligent reflection communication system for short, and relates to a system composition structure, a working principle and a passive beam forming design method.
The intelligent reflective surface may be used to convey information by modulating information onto signals of the base station in addition to being used as a passive relay to assist other conventional communication systems. Specifically, assuming that the intelligent reflection surface adopts BPSK modulation, when the transmitted symbol is +1, it uses + Φ*To reflect the signal, using-phi when the transmitted symbol is-1*To reflect the signal, here phi*Is the optimized phase shift matrix. Due to the excellent reflection characteristic of the intelligent reflection surface and the capability of sending information, the intelligent reflection surface is introduced into a small cellular network to replace a micro base station, and information of a plurality of users is loaded on signals of a macro base station by utilizing phase offset which can be adjusted in real time, so that information transmission of the plurality of users is realized. Here the intelligent reflective surface is similar to a conventional micro base station and the transmitted information is obtained by a wired backhaul. But the intelligent reflective surface consumes less energy and is more energy efficient than the micro base station. Meanwhile, the intelligent reflection surface transmits information by passively reflecting signals instead of generating new signals, so that the signals of the intelligent reflection surface do not interfere with the macro base station service user but form a multipath, the transmission rate of the macro base station user is improved, and the problem of interference management in the traditional small cellular network is solved
The technical scheme of the invention is as follows: a cellular base station communication system based on an intelligent reflection surface is characterized by comprising a macro base station configured with a single antenna, an intelligent reflection surface (RIS) provided with N reflection units, a single-antenna user 0 served by the macro base station and K single-antenna users 1-K; the macro base station sends a main signal, the RIS receives the main signal sent by the macro base station, and the main signal needs to be sent by adjusting the coefficient of each reflection unitInformation to be sent to K users is loaded on a main signal of a macro base station to realize passive beam forming, and a precoding matrix corresponding to each user is phik＝diag{θk,1,θk,2,...,θk,NIn which θk,iCorresponding to the phase shift of the ith reflection unit to the kth user
Figure BDA0002757320980000021
And information transfer of a plurality of users is realized.
Further, the design method of the passive beamforming matrix at the RIS end is as follows: suppose that the macro base station transmitter transmits a signal s (n) with zero mean variance of 1 and the transmission power of ptRIS adopts BPSK modulation method, i.e. ck＝{+1,-1}，ckRepresenting information sent to user k and coming from the wired backhaul, the corresponding phase offset matrix is
Figure BDA0002757320980000022
The period of information symbol transmission of RIS is Q times of the period of information symbol transmission of macro base station end, Q is positive integer, and C is ═ C1,c2,...,cKIs the set of all user symbols, and the RIS introduces a phase offset matrix to the channel of
Figure BDA0002757320980000023
Ensuring minimum communication rate requirements for users served by macro base station
Figure BDA0002757320980000024
On the premise of optimizing the passive beamforming matrix of the RIS end by taking the maximum minimum communication rate of all users as a target
Figure BDA0002757320980000031
The optimization problem is established as follows:
Figure BDA0002757320980000032
Figure BDA0002757320980000033
Figure BDA0002757320980000034
|Φn,n|≤1,n＝1,2,...,N
Figure BDA0002757320980000035
wherein R iskIs the user k solves ckRate of (A), R0Is an upper bound on the average rate of user 0, Rk,sIs the upper bound of the average rate of the user k solution s (n), the vector sign
Figure BDA0002757320980000036
The first constraint is the QoS requirement of the communication rate of the user served by the macro base station, the second constraint ensures that each user in the small cellular network can realize the serial interference elimination, the last constraint is the passive reflection constraint of the RIS, and the detailed expression of the optimization problem is as follows:
Figure BDA0002757320980000037
Figure BDA0002757320980000038
Figure BDA0002757320980000039
|Φn,n|≤1,n＝1,2,...,N
Figure BDA00027573209800000310
wherein l is a macro groupChannel between station and intelligent reflective surface, hkFor the channel between the intelligent reflecting surface and the user k in the microcell, h0For the channel between the intelligent reflective surface and macro-user 0, g0For the channel between macro base station and macro user 0, gkFor the channel between the macro base station and the user k in the micro cell,
Figure BDA00027573209800000311
for additive white gaussian noise power at user 0,
Figure BDA00027573209800000312
is the additive white gaussian noise power at user k.
By solving the above optimization problem
Figure BDA00027573209800000313
The invention has the beneficial effects that: the invention provides a novel cellular base station communication system architecture based on an intelligent reflection surface, wherein the intelligent reflection surface is used for assisting communication of macro base station users and transmitting information to a plurality of users, interference is avoided, spectrum utilization efficiency is greatly improved, and the intelligent reflection surface consumes less energy and has higher energy efficiency. The intelligent reflective surface can further optimize the communication system by passive beam forming. The scheme is simple to implement, can prove to realize the spectrum efficiency higher than that of allocating the special communication resources for respective systems, and has strong application value.
Drawings
FIG. 1 illustrates a schematic diagram of an application scenario of an intelligent reflective communication system;
FIG. 2 illustrates a block diagram of an intelligent reflective communication system;
FIG. 3 shows a diagram of an intelligent reflective communication frame structure;
FIG. 4 is a diagram of an algorithm verification system location setting;
FIG. 5 is a diagram of the multi-user minimum SNR performance of the system of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and simulation examples.
As shown in fig. 1, which is a schematic view of an application scenario of the intelligent reflective communication system in the small cellular network, one macro base station realizes wide area coverage, and a plurality of intelligent reflective surfaces realize accurate coverage of the small cellular network, and form integrated three-dimensional coverage together with the macro base station. Considering that different spectrum resource blocks are adopted in different micro cells, there is no interference between them, and the diagram of fig. 1 can be reduced to the system structure diagram shown in fig. 2. The technical scheme adopted by the invention is an intelligent reflection communication system which comprises a macro base station provided with a single antenna, an intelligent reflection surface (RIS) provided with N reflection units, a single-antenna user 0 served by the macro base station and K single-antenna users 1-K. Let l be a channel between the macro base station and the intelligent reflection surface, and h be a channel between the intelligent reflection surface and the user k in the micro cellkThe channel between the smart reflective surface and macro user 0 is h0The channel between macro base station and macro user 0 is g0The channel between the macro base station and the user k in the micro cell is gk. Assuming that a macro base station transmitter transmission signal s (n) is a signal with zero mean variance 1 and transmission power is pt. The intelligent reflection surface receives signals sent by the macro base station, and loads information needing to be sent to K users in the micro cell to main signals of the macro base station by adjusting the coefficient of each reflection unit of the intelligent reflection surface, so that passive beam forming is realized, and the precoding matrix corresponding to each user is phik＝diag{θk,1,θk,2,...,θk,NIn which θk,iCorresponding to the phase shift of the ith reflection unit to the kth user
Figure BDA0002757320980000041
Thereby realizing the information transfer of a plurality of users in the micro cell. Assuming that the intelligent reflecting surface assumes a BPSK modulation scheme, i.e. ck{ +1, -1}, where ckRepresenting the information sent to user k and coming from the wired backhaul. The phase shift matrix we can design is
Figure BDA0002757320980000042
And the phase shift matrix introduced into the channel by the intelligent reflecting surface is
Figure BDA0002757320980000043
The users in the micro cell simultaneously receive the information s (n) of the macro base station and their respective information by using joint detection. The expression of the signal received by user k in the micro cell is:
Figure BDA0002757320980000051
wherein u isk(n) is an average of 0 and a power of
Figure BDA0002757320980000052
Additive White Gaussian Noise (AWGN). It is assumed here that the period of the information symbol transmitted by the intelligent reflection surface is an integral multiple of the period of the information symbol transmitted by the macro base station side, that is: t isc＝QTsWherein T iscIs the symbol period, T, of the RIS signalsIs the symbol period of the macro base station signal, Q is a positive integer, and the frame structure of the system is shown in fig. 3.
C ═ C1,c2,...,cKIs the set of all the microcell user symbols. After receiving the signal reflected by RIS, user k jointly detects the main link signal s (n) from macro base station and its own signal ckFirstly, detecting s (n), then making Serial Interference Cancellation (SIC) to eliminate Interference of main link signal, and then detecting ck. The signal to noise ratio (SNR) at detection of s (n) is:
Figure BDA0002757320980000053
the corresponding average rates are:
Rk,s＝EC{log2(1+γk,s(C)}≤log2{1+EC[γk,s(C)]} (3)
memory mi＝hkΦil, then
Figure BDA0002757320980000054
Therefore, the upper bound on the average rate is:
Figure BDA0002757320980000055
after SIC is done to eliminate the interference of the main link, the user k detects the signal c of the user kkThe SIC completion signal is:
Figure BDA0002757320980000056
since the symbol period of the RIS is Q times the symbol period of the macro base station, it is used to solve for ckThe signal to interference plus noise ratio (SINR) is the SINR after Maximum Ratio Combining (MRC), and the combined SINR expression is:
Figure BDA0002757320980000061
the corresponding rates are:
Figure BDA0002757320980000062
for macro user 0 served by macro base station, its received signal is:
Figure BDA0002757320980000063
wherein u is0(n) is an average of 0 and a power of
Figure BDA0002757320980000064
White additive gaussian noise. User 0 pays attention to information s (n) of the primary link, and does not need information ckBut it may also do joint reception to obtain ckAt the cost of increased receiver complexity, and gains from multiple paths. User 0 detects the signal-to-noise ratio of s (n) as:
Figure BDA0002757320980000065
the average signal-to-noise ratio can be expressed as:
Figure BDA0002757320980000066
the upper bound on the average rate for user 0 is then:
Figure BDA0002757320980000067
the invention provides an intelligent reflection surface end passive beam forming optimization design method aiming at an intelligent reflection communication system serving a small cellular network, wherein an original micro base station is replaced by an intelligent reflection surface to serve a plurality of users in the small cellular network. Ensuring minimum communication rate requirements for users served by macro base station
Figure BDA0002757320980000068
On the premise of optimizing the passive beamforming matrix at the end of the intelligent reflection surface by taking the minimum communication speed of all users in the maximized small-sized cellular network as a target
Figure BDA0002757320980000071
The specific optimization problem is as follows:
Figure BDA0002757320980000072
the first constraint is the QoS requirement of the communication rate of the user served by the macro base station, the second constraint ensures that each user in the small cellular network can realize the serial interference elimination, and the last constraint is the passive reflection constraint of the intelligent reflection surface. The optimization problem can be expressed in detail as:
Figure BDA0002757320980000073
two possible types of solutions to the above optimization problem are given below for verifying the performance of the intelligent reflective communication system.
Has a diameter ofi＝diag(θi,1,θi,2,...,θi,N) Memory for recording
Figure BDA0002757320980000074
c＝(c1,c2,...,cK)，
Figure BDA0002757320980000075
Thus, the third constraint may become:
|cΨej|≤1,j＝1,...,N, (15)
wherein ej＝(0,0,...,1,0,...,0)T. Simultaneously, the method comprises the following steps:
Figure BDA0002757320980000076
the problem can be translated into:
Figure BDA0002757320980000081
with the introduction of the auxiliary variable T, the problem translates into:
Figure BDA0002757320980000082
the first solution is as follows: the SDP algorithm. Let vec (Ψ) ═ υ, υHXi then the problem (18) can be converted into:
Figure BDA0002757320980000083
wherein
Figure BDA0002757320980000091
This problem can be solved in a two-step procedure to separate the optimization variables T and xi, while relaxing the constraint of rank 1, which is more difficult to handle. The method comprises the following specific steps: first find TupAnd Tlow，TupMakes the problem unfeasible, TlowMaking the problem feasible. Calculate a new
Figure BDA0002757320980000092
Using in combination TnewReplacing T in the problem and solving the problem, if the problem is feasible, updating the value Tlow＝TnewAnd obtaining a Xie of the problem, and updating the value T if the problem is not feasibleup＝TnewRepeating the process until Tup-TlowLess than a threshold value. For a certain T, the problem is an SDP problem, which can be found in the document "Grant M, Boyd S.CVX: Matlab software for differentiated context programming, version 2.1[ J]Standard optimization solver tool CVX in 2014 "to solve. After solving for xi, a solution with rank 1 is required, using the literature "Sidiropoulos N D, Davidson T N, Luo Z Q. Transmit beamforming for physical-layer multicasting [ J]Processing xi by Gaussian random method in IEEE Transactions on Signal Processing,2006,54(6):2239 and 2251 ", obtaining upsilon, and obtaining matrix psi by upsilon, thereby obtaining
Figure BDA0002757320980000093
The SDP algorithm specifically comprises the following steps:
step S11. give TupAnd TlowWherein T isupMakes problem (19) unfeasible, TlowMake problem (19) feasible;
step S12. when T is reachedup-TlowAbove a given threshold value epsilon, the following steps S13-S15 are performed, otherwise the loop is skipped;
step S13. calculation
Figure BDA0002757320980000094
Step S14. use TnewReplacing T in the problem (19) and solving the problem;
step S15, if the problem (19) is feasible, updating Tlow＝TnewAnd xi, otherwise update Tup＝Tnew；
S16, obtaining the solved optimal XI solution at the end of the cycle*；
Step S17, a xi part is decomposed*Method for obtaining rank 1 solution upsilon by Gaussian random method*；
Step S18. passing through upsilon*Generating Ψ*By means of Ψ*Generating
Figure BDA0002757320980000101
Step S19, returning the optimal solution
Figure BDA0002757320980000102
Because the algorithm adopting the Gaussian random method is influenced by the excessive restriction of the optimization problem, the algorithm adopting the continuous convex approximation is provided, and the Taylor expansion is used for approximation, so that the situation that the rank-one solution is obtained by the Gaussian random method is avoided, and the rank-one solution is recorded as the SCA algorithm. The problem (18) can be converted into:
Figure BDA0002757320980000103
for function in upsilontDeveloped using the literature "Sun Y, Babu P, Palomar D P.Majorization-minimization algorithms in signal processing,communications,and machine learning[J]The Taylor expansion property in IEEE Transactions on Signal Processing,2016,65(3): 794-:
Figure BDA0002757320980000104
wherein M isk＝λk,maxI，λk,maxIs FkThe maximum eigenvalue of (c). Problem (22) similar to problem (19) may be employed
The two-step process is solved. The SCA algorithm comprises the following specific steps:
step S21. initializing upsilontIs upsilon0；
S22, solving an optimization problem (22) by using a two-step flow method to obtain an optimal solution T;
step S23. give T and upsilont-1Iteratively solving the optimization problem (22) until a two-norm of a difference between the solution obtained t times and the solution obtained t-1 times is calculated to be less than a certain threshold value;
s24, obtaining the solved optimal solution upsilon after circulation is finished*；
Step S25. passing through upsilon*Generating Ψ*By means of Ψ*Generating
Figure BDA0002757320980000111
S26, returning the optimal solution
Figure BDA0002757320980000112
The simulation results are given below to verify the superiority of the system in transmission efficiency and the possibility of the optimization algorithm scheme. As shown in fig. 4, we consider a simulation location design in which a macro base station is located at a coordinate origin (0m,0m), an intelligent reflection surface is located at (50m,0m), a primary user is located at (0m,40m), and two secondary users are distributed in a circle with a radius of 10m and a circle center of (50m,15 m). The number of the reflection units of the intelligent reflection surface is 16 × 4. Consider the following large scale fading
Figure BDA0002757320980000113
Where ρ is2Represents the large-scale fading coefficient of the channel, d represents the transmission distance, β represents the channel fading coefficient in the case of 1m, and η represents the spatial fading index. The specific simulation parameters are set as follows, beta is-30 dB, and the channel g0,h1～hKL has a spatial fading index η of 2, h0The spatial fading index η of (2.5), g1～gKThe spatial fading index η of (1) is 3.6. Channel h0,g1～gKSubject to Rayleigh distribution, all elements subject to small-scale fading of
Figure BDA0002757320980000114
Channel g0,h1～hKL obey a Leise distribution with a Leise factor of 10, with the non-direct path set to
Figure BDA0002757320980000115
The LoS path is generated using a steering vector. The difference Q of the period multiples of the secondary user and the primary user is 10, and the noise of the receiver
Figure BDA0002757320980000116
The number of channel realizations is 1000.
Fig. 5 compares the performance of the intelligent reflective communication system with the minimum signal-to-noise ratio of multiple users under different intelligent reflective surface reflection strategies. The reference algorithm is to align the phase shift matrix of each user to the channel of the intelligent reflective surface to each user. Particularly, the result shows that the intelligent reflective communication system of the small-scale cellular network can realize the effective transmission of multi-user information, and the performance is obviously superior to the performance of the given reference algorithm.

Claims (2)

1. A cellular base station communication system based on an intelligent reflection surface is characterized by comprising a macro base station configured with a single antenna and a system with N reflection surfacesThe system comprises an intelligent reflecting surface (RIS) of a unit, a single antenna user 0 served by a macro base station and K single antenna users 1-K; the macro base station sends a main signal, the RIS receives the main signal sent by the macro base station, and loads information to be sent to K users onto the main signal of the macro base station by adjusting the coefficient of each reflection unit, so as to realize passive beam forming, and the precoding matrix corresponding to each user is phik＝diag{θk,1,θk,2,...,θk,NIn which θk,iCorresponding to the phase shift of the ith reflection unit to the kth user and satisfying the thetak,i|＝1,
Figure FDA0002757320970000011
And i, realizing information transfer of a plurality of users.
2. The cellular base station communication system based on intelligent reflecting surface as claimed in claim 1, wherein the design method of RIS end passive beamforming matrix is: suppose that the macro base station transmitter transmits a signal s (n) with zero mean variance of 1 and the transmission power of ptRIS adopts BPSK modulation method, i.e. ck＝{+1,-1}，ckRepresenting information sent to user k and coming from the wired backhaul, the corresponding phase offset matrix is
Figure FDA0002757320970000012
The period of information symbol transmission of RIS is Q times of the period of information symbol transmission of macro base station end, Q is positive integer, and C is ═ C1,c2,...,cKIs the set of all user symbols, and the RIS introduces a phase offset matrix to the channel of
Figure FDA0002757320970000013
Ensuring minimum communication rate requirements for users served by macro base station
Figure FDA0002757320970000014
To maximize the minimum communication rate of all usersOptimizing RIS end passive beam forming matrix as target
Figure FDA0002757320970000015
The optimization problem is established as follows:
Figure FDA0002757320970000016
Figure FDA0002757320970000017
Figure FDA0002757320970000018
|Φn,n|≤1,n＝1,2,...,N
Figure FDA0002757320970000019
wherein R iskIs the user k solves ckRate of (A), R0Is an upper bound on the average rate of user 0, Rk,sIs the upper bound of the average rate of the user k solution s (n), the vector sign
Figure FDA00027573209700000110
The first constraint is the QoS requirement of the communication rate of the user served by the macro base station, the second constraint ensures that each user in the small cellular network can realize the serial interference elimination, the last constraint is the passive reflection constraint of the RIS, and the detailed expression of the optimization problem is as follows:
Figure FDA0002757320970000021
Figure FDA0002757320970000022
Figure FDA0002757320970000023
|Φn,n|≤1,n＝1,2,...,N
Figure FDA0002757320970000024
wherein l is a channel between the macro base station and the intelligent reflection surface, hkFor the channel between the intelligent reflecting surface and the user k in the microcell, h0For the channel between the intelligent reflective surface and macro-user 0, g0For the channel between macro base station and macro user 0, gkFor the channel between the macro base station and the user k in the micro cell,
Figure FDA0002757320970000025
for additive white gaussian noise power at user 0,
Figure FDA0002757320970000026
is the additive white gaussian noise power at user k;
by solving the above optimization problem
Figure FDA0002757320970000027
CN202011206723.9A 2020-11-03 2020-11-03 Honeycomb base station communication system based on intelligent reflection surface Active CN112383332B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011206723.9A CN112383332B (en) 2020-11-03 2020-11-03 Honeycomb base station communication system based on intelligent reflection surface

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011206723.9A CN112383332B (en) 2020-11-03 2020-11-03 Honeycomb base station communication system based on intelligent reflection surface

Publications (2)

Publication Number Publication Date
CN112383332A true CN112383332A (en) 2021-02-19
CN112383332B CN112383332B (en) 2021-08-24

Family

ID=74577610

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011206723.9A Active CN112383332B (en) 2020-11-03 2020-11-03 Honeycomb base station communication system based on intelligent reflection surface

Country Status (1)

Country Link
CN (1) CN112383332B (en)

Cited By (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112995989A (en) * 2021-04-30 2021-06-18 电子科技大学 5G application-oriented safe transmission method based on large-scale reconfigurable intelligent surface
CN113055816A (en) * 2021-03-23 2021-06-29 南通大学 Multi-intelligent-reflector-assisted two-hop relay wireless communication method and system based on position information
CN113098575A (en) * 2021-03-29 2021-07-09 东南大学 Intelligent reflection surface assisted multi-cell downlink transmission design method for improving edge rate
CN113286292A (en) * 2021-05-18 2021-08-20 广东工业大学 Combined optimization method and system for hidden rate of reconfigurable intelligent surface auxiliary communication
CN113364494A (en) * 2021-05-06 2021-09-07 西安交通大学 IRS (inter-Range instrumentation System) assisted MISO (multiple input single output) system performance optimization method for hardware distortion
CN113612505A (en) * 2021-07-16 2021-11-05 中国计量大学上虞高等研究院有限公司 Reconfigurable intelligent surface-based space-time shift keying method
CN113691295A (en) * 2021-08-18 2021-11-23 北京理工大学 IRS-based interference suppression method in heterogeneous network
CN113825203A (en) * 2021-09-22 2021-12-21 湖南智领通信科技有限公司 Multipath signal transmission method and device based on time synchronization
CN113938175A (en) * 2021-09-01 2022-01-14 华中科技大学 Intelligent reflector assistance-based two-way relay communication method
US20220052764A1 (en) * 2020-08-14 2022-02-17 Huawei Technologies Co., Ltd. Media-based reconfigurable intelligent surface-assisted modulation
CN114978263A (en) * 2022-05-20 2022-08-30 西安电子科技大学 Loop interference suppression device and method based on assistance of intelligent reflecting surface
WO2022213301A1 (en) * 2021-04-07 2022-10-13 Qualcomm Incorporated Network communications between reconfigurable intelligent surfaces
CN115395990A (en) * 2022-08-02 2022-11-25 清华大学 Self-evolution reconfigurable intelligent surface based on self-supervision neural differential mapping and control method
CN116015452A (en) * 2023-03-24 2023-04-25 南昌大学 Transmission-reflection combined multi-RIS visible light communication method and system
WO2023130397A1 (en) * 2022-01-07 2023-07-13 北京小米移动软件有限公司 Precoding method/apparatus/device and storage medium

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140079146A1 (en) * 2012-09-18 2014-03-20 Samsung Electronics Co., Ltd. Method and apparatus for transmitting and receiving channel state information in wireless communication system
US20190140719A1 (en) * 2017-06-09 2019-05-09 At&T Intellectual Property I, L.P. Facilitation of rank and precoding matrix indication determinations for multiple antenna systems with aperiodic channel state information reporting in 5g or other next generation networks
CN110225538A (en) * 2019-06-21 2019-09-10 电子科技大学 The non-orthogonal multiple access communications design method of reflecting surface auxiliary
CN111313951A (en) * 2020-02-17 2020-06-19 南京邮电大学 IRS (inter-Range instrumentation Standard) auxiliary secure communication wireless transmission method based on non-ideal CSI (channel State information)
CN111355520A (en) * 2020-03-10 2020-06-30 电子科技大学 Design method of intelligent reflection surface assisted terahertz safety communication system
CN111698046A (en) * 2020-06-11 2020-09-22 电子科技大学 Green communication method based on reconfigurable intelligent reflecting surface
CN111818533A (en) * 2020-06-04 2020-10-23 浙江大学 Wireless communication system design method based on intelligent reflecting surface

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140079146A1 (en) * 2012-09-18 2014-03-20 Samsung Electronics Co., Ltd. Method and apparatus for transmitting and receiving channel state information in wireless communication system
US20190140719A1 (en) * 2017-06-09 2019-05-09 At&T Intellectual Property I, L.P. Facilitation of rank and precoding matrix indication determinations for multiple antenna systems with aperiodic channel state information reporting in 5g or other next generation networks
CN110225538A (en) * 2019-06-21 2019-09-10 电子科技大学 The non-orthogonal multiple access communications design method of reflecting surface auxiliary
CN111313951A (en) * 2020-02-17 2020-06-19 南京邮电大学 IRS (inter-Range instrumentation Standard) auxiliary secure communication wireless transmission method based on non-ideal CSI (channel State information)
CN111355520A (en) * 2020-03-10 2020-06-30 电子科技大学 Design method of intelligent reflection surface assisted terahertz safety communication system
CN111818533A (en) * 2020-06-04 2020-10-23 浙江大学 Wireless communication system design method based on intelligent reflecting surface
CN111698046A (en) * 2020-06-11 2020-09-22 电子科技大学 Green communication method based on reconfigurable intelligent reflecting surface

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
HUAYAN GUO: "Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks", 《IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS》 *
梁应敞: "智能无线通信技术研究概况", 《通信学报》 *

Cited By (25)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20220052764A1 (en) * 2020-08-14 2022-02-17 Huawei Technologies Co., Ltd. Media-based reconfigurable intelligent surface-assisted modulation
US11848709B2 (en) * 2020-08-14 2023-12-19 Huawei Technologies Co., Ltd. Media-based reconfigurable intelligent surface-assisted modulation
CN113055816B (en) * 2021-03-23 2021-11-26 南通大学 Multi-intelligent-reflector-assisted two-hop relay wireless communication method and system based on position information
CN113055816A (en) * 2021-03-23 2021-06-29 南通大学 Multi-intelligent-reflector-assisted two-hop relay wireless communication method and system based on position information
CN113098575A (en) * 2021-03-29 2021-07-09 东南大学 Intelligent reflection surface assisted multi-cell downlink transmission design method for improving edge rate
WO2022213301A1 (en) * 2021-04-07 2022-10-13 Qualcomm Incorporated Network communications between reconfigurable intelligent surfaces
CN112995989A (en) * 2021-04-30 2021-06-18 电子科技大学 5G application-oriented safe transmission method based on large-scale reconfigurable intelligent surface
CN112995989B (en) * 2021-04-30 2021-08-06 电子科技大学 5G application-oriented safe transmission method based on large-scale reconfigurable intelligent surface
CN113364494A (en) * 2021-05-06 2021-09-07 西安交通大学 IRS (inter-Range instrumentation System) assisted MISO (multiple input single output) system performance optimization method for hardware distortion
CN113364494B (en) * 2021-05-06 2022-08-16 西安交通大学 IRS (inter-Range instrumentation System) assisted MISO (multiple input single output) system performance optimization method for hardware distortion
WO2022233250A1 (en) * 2021-05-06 2022-11-10 西安交通大学 Irs-assisted miso system performance optimization method for hardware distortion
CN113286292A (en) * 2021-05-18 2021-08-20 广东工业大学 Combined optimization method and system for hidden rate of reconfigurable intelligent surface auxiliary communication
CN113612505A (en) * 2021-07-16 2021-11-05 中国计量大学上虞高等研究院有限公司 Reconfigurable intelligent surface-based space-time shift keying method
CN113612505B (en) * 2021-07-16 2022-08-23 中国计量大学上虞高等研究院有限公司 Reconfigurable intelligent surface-based space-time shift keying method
CN113691295A (en) * 2021-08-18 2021-11-23 北京理工大学 IRS-based interference suppression method in heterogeneous network
CN113691295B (en) * 2021-08-18 2023-02-24 北京理工大学 IRS-based interference suppression method in heterogeneous network
CN113938175B (en) * 2021-09-01 2022-08-09 华中科技大学 Intelligent reflector assistance-based two-way relay communication method
CN113938175A (en) * 2021-09-01 2022-01-14 华中科技大学 Intelligent reflector assistance-based two-way relay communication method
CN113825203B (en) * 2021-09-22 2023-07-04 湖南智领通信科技有限公司 Multipath signal transmission method and device based on time synchronization
CN113825203A (en) * 2021-09-22 2021-12-21 湖南智领通信科技有限公司 Multipath signal transmission method and device based on time synchronization
WO2023130397A1 (en) * 2022-01-07 2023-07-13 北京小米移动软件有限公司 Precoding method/apparatus/device and storage medium
CN114978263A (en) * 2022-05-20 2022-08-30 西安电子科技大学 Loop interference suppression device and method based on assistance of intelligent reflecting surface
CN114978263B (en) * 2022-05-20 2023-10-20 西安电子科技大学 Loop interference suppression device and method based on intelligent reflecting surface assistance
CN115395990A (en) * 2022-08-02 2022-11-25 清华大学 Self-evolution reconfigurable intelligent surface based on self-supervision neural differential mapping and control method
CN116015452A (en) * 2023-03-24 2023-04-25 南昌大学 Transmission-reflection combined multi-RIS visible light communication method and system

Also Published As

Publication number Publication date
CN112383332B (en) 2021-08-24

Similar Documents

Publication Publication Date Title
CN112383332B (en) Honeycomb base station communication system based on intelligent reflection surface
Zhou et al. Energy-efficient NOMA enabled heterogeneous cloud radio access networks
CN111314893B (en) Reflector assisted device-to-device communication system design method
Khan et al. Performance analysis of cloud radio access networks with distributed multiple antenna remote radio heads
Abdalla et al. UAVs with reconfigurable intelligent surfaces: Applications, challenges, and opportunities
CN113114317B (en) IRS-assisted phase shift optimization method for downlink multi-user communication system
CN114070365B (en) Intelligent reflection surface assisted low-radio-frequency-complexity multi-user MIMO uplink spectrum efficiency optimization method
CN112601242B (en) Intelligent reflection-surface-assisted two-cell NOMA uplink low-power-consumption transmission method
Li et al. Beamforming design and power allocation for transmissive RMS-based transmitter architectures
CN110299937A (en) A kind of Uplink MIMO-NOMA wireless communication system beam-forming method
CN114286312A (en) Method for enhancing unmanned aerial vehicle communication based on reconfigurable intelligent surface
Jiang et al. Wireless fronthaul for 5G and future radio access networks: Challenges and enabling technologies
CN109890036B (en) Self-return method of heterogeneous network
Jolly et al. An analysis on rate-splitting multiple access for IRS aided 6G communication
She et al. Security Energy Efficiency Analysis of CR-NOMA Enabled IoT Systems for Edge-cloud Environment
CN102195700B (en) Scheduling transmission method for collaborative cells against cell edge users of downlink
Deshpande et al. Resource allocation design for spectral-efficient URLLC using RIS-aided FD-NOMA system
Jian et al. Improving multiple-user capacity through downlink noma in oam systems
CN116321236A (en) RIS-assisted safe honeycomb-free large-scale MIMO system energy efficiency optimization method
Prabakar et al. Improving Spectral Efficiency of Small Cells with Multi-Variant Clustering and Interference Alignment
Bazrafkan et al. Performance comparison between a simple full-duplex multi-antenna relay and a passive reflecting intelligent surface
Lyu et al. Primary rate maximization in movable antennas empowered symbiotic radio communications
Kavianinia et al. Resource allocation of STAR-RIS assisted Full-Duplex systems
Chen et al. Joint Energy and Information Beamforming Design for RIS-assisted Wireless Powered Communication
Danish et al. Future prospects and challenges associated with intelligent reflecting surfaces enabled wireless communication

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
GR01 Patent grant
GR01 Patent grant