CN112383332A - Honeycomb base station communication system based on intelligent reflection surface - Google Patents
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- H04B7/0842—Weighted combining
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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
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 userAnd 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 isThe 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 ofEnsuring minimum communication rate requirements for users served by macro base stationOn the premise of optimizing the passive beamforming matrix of the RIS end by taking the maximum minimum communication rate of all users as a targetThe optimization problem is established as follows:
|Φn,n|≤1,n=1,2,...,N
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 signThe 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:
|Φn,n|≤1,n=1,2,...,N
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,for additive white gaussian noise power at user 0,is the additive white gaussian noise power at user k.
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 userThereby 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 isAnd the phase shift matrix introduced into the channel by the intelligent reflecting surface isThe 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:
wherein u isk(n) is an average of 0 and a power ofAdditive 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:
the corresponding average rates are:
Rk,s=EC{log2(1+γk,s(C)}≤log2{1+EC[γk,s(C)]} (3)
memory mi=hkΦil, then
Therefore, the upper bound on the average rate is:
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:
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:
the corresponding rates are:
for macro user 0 served by macro base station, its received signal is:
wherein u is0(n) is an average of 0 and a power ofWhite 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:
the average signal-to-noise ratio can be expressed as:
the upper bound on the average rate for user 0 is then:
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 stationOn 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 targetThe specific optimization problem is as follows:
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:
two possible types of solutions to the above optimization problem are given below for verifying the performance of the intelligent reflective communication system.
|cΨej|≤1,j=1,...,N, (15)
wherein ej=(0,0,...,1,0,...,0)T. Simultaneously, the method comprises the following steps:
the problem can be translated into:
with the introduction of the auxiliary variable T, the problem translates into:
the first solution is as follows: the SDP algorithm. Let vec (Ψ) ═ υ, υHXi then the problem (18) can be converted into:
wherein
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 newUsing 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
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 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*;
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:
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-:
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*;
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
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 ofChannel g0,h1~hKL obey a Leise distribution with a Leise factor of 10, with the non-direct path set toThe 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 receiverThe 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,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 isThe 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 ofEnsuring minimum communication rate requirements for users served by macro base stationTo maximize the minimum communication rate of all usersOptimizing RIS end passive beam forming matrix as targetThe optimization problem is established as follows:
|Φn,n|≤1,n=1,2,...,N
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 signThe 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:
|Φn,n|≤1,n=1,2,...,N
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,for additive white gaussian noise power at user 0,is the additive white gaussian noise power at user k;
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