CN113709755B - Heterogeneous network fair coexistence method based on RIS technology - Google Patents

Heterogeneous network fair coexistence method based on RIS technology Download PDF

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CN113709755B
CN113709755B CN202110981508.4A CN202110981508A CN113709755B CN 113709755 B CN113709755 B CN 113709755B CN 202110981508 A CN202110981508 A CN 202110981508A CN 113709755 B CN113709755 B CN 113709755B
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cellular base
wifi
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user
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CN113709755A (en
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陈琪美
杨洋
李瑞雪
江昊
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Wuhan University WHU
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/336—Signal-to-interference ratio [SIR] or carrier-to-interference ratio [CIR]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/345—Interference values
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/06—Testing, supervising or monitoring using simulated traffic
    • 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
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Abstract

The invention provides a heterogeneous network fair coexistence method based on an RIS technology. Based on the constructed heterogeneous network system under the assistance of the RIS, constructing a throughput model of each cellular base station, an interference power model of the WiFi users and a saturated throughput model of each WiFi user; constructing an optimization target based on a cellular base station throughput model and a WiFi user saturated throughput model, and constructing a constraint condition by combining the interference power of an access channel of the WiFi user, the transmitting power of the cellular base station, the limit relation between the amplitude and the phase of each reflecting element on the RIS and the value of an access selection variable of the WiFi user; under the limitation of constraint conditions, the minimum value of the weighted throughput of each cellular base station and the WiFi access point in the heterogeneous network is maximized by optimizing the beam vectors of all the cellular base stations, the reflection coefficient diagonal array of the reconfigurable intelligent reflecting surface and the access selection variables of all the WiFi users. The fair coexistence method provided by the invention can effectively offset transmission interference and improve the spectrum efficiency.

Description

Heterogeneous network fair coexistence method based on RIS technology
Technical Field
The invention belongs to the technical field of mobile internet, and particularly relates to a heterogeneous network fair coexistence method based on RIS technology.
Background
With the rapid development of the mobile internet industry, various intelligent terminals and emerging applications emerge endlessly. Meanwhile, the continuously enlarged user scale and the explosively increased data traffic bring new challenges to the development of wireless communication networks, and a new technical means needs to be explored to expand spectrum resources and improve the capacity of a communication system in the current 5G mobile communication.
At present, the application of Multiple schemes such as Multiple Input Multiple Output (MIMO) technology, Non-Orthogonal Multiple Access (NOMA) technology, Ultra Dense Networking (UDN) deployment and the like can ensure higher user connection density, thereby improving system capacity and spectral efficiency. In addition, researchers are exploring the feasibility of broadening existing spectrum resources. Currently, licensed frequency spectrum resources are allocated to be empty by each large operator, and the deployment of the existing communication technology to unlicensed frequency bands, millimeter waves and other high frequency bands becomes a main direction for communication industry research.
In the 2.4GHz and 5GHz Unlicensed frequency bands which are not fully utilized, people firstly put forward two new schemes of long term evolution (LTE in Unlicensed Spectrum, LTE-U) of the Unlicensed frequency bands and Licensed-Assisted Access (LAA), so that the Unlicensed frequency band resources are fully utilized, and the network capacity is enlarged; meanwhile, in order to solve the network deployment problem of various industries in the 5G era, a new air interface Unlicensed frequency band communication (NR-U) technology is developed, and an Unlicensed frequency band auxiliary communication framework with global universality is constructed by using multiple frequency bands such as 2.4GHz, 5GHz, millimeter waves and the like and adopting multiple deployment modes such as Dual Connectivity (DC), Carrier Aggregation (CA), independent deployment (standard, SA) and the like.
With the continuous widening of spectrum resources, various wireless access technologies such as NR-U, LTE-U, LAA, WiFi and the like exist in a 5G unlicensed frequency band, and a heterogeneous network system comprising various access networks such as a wireless personal area network, a wireless local area network, a wireless metropolitan area network, a public mobile communication network and the like is formed. How to solve the problems of asynchronism, incompatibility and the like among different access networks, effectively avoiding conflict and interference among the networks, fairly and reasonably distributing frequency spectrum resources, realizing friendly coexistence among heterogeneous networks and becoming the core problem of 5G license-free frequency band communication.
However, the existing coexistence mechanism related to the heterogeneous network mostly adopts the time division multiplexing scheme, and both the spectrum efficiency and the communication performance of the existing coexistence mechanism related to the heterogeneous network are difficult to meet the requirements of the future communication system. Therefore, it is necessary to develop advanced spatial multiplexing technology for heterogeneous network systems. As a promising emerging technology in 6G, an RIS composed of a large number of passive reflection elements with low cost, easy deployment, and mobility can intelligently improve a wireless communication environment by actively controlling the reflection of signals, increase the available spatial freedom of wireless communication, widen a coverage area, improve spectrum efficiency, and improve communication performance. The RIS technology is integrated into a heterogeneous network, and the transformation of the amplitude or the phase of an incident signal is reasonably induced by a reflecting element, so that the transmission interference can be effectively counteracted, and the fair coexistence is realized.
Disclosure of Invention
In order to solve the technical problem, the invention discloses a heterogeneous network fair coexistence method based on an RIS technology, which takes a 5G NR-U cellular communication system and a WiFi system as examples to construct an RIS-assisted unlicensed frequency band heterogeneous network model, and cancels transmission interference by reasonably configuring an RIS phase, thereby improving the spectrum efficiency and promoting the fair coexistence among heterogeneous networks.
The technical scheme adopted by the invention is as follows: a heterogeneous network fair sharing method based on RIS technology comprises the following steps:
step 1: constructing an RIS-assisted heterogeneous network system;
step 2: constructing each cellular user signal model under the coverage of each cellular base station, constructing each cellular user signal-to-interference-plus-noise ratio model under the coverage of each cellular base station according to each cellular user signal model under the coverage of each cellular base station, and constructing a throughput model of the cellular base station according to all cellular user signal-to-interference-plus-noise ratio models under the coverage of the cellular base stations;
and step 3: respectively constructing an interference power model of WiFi users and a saturated throughput model of each WiFi user;
and 4, step 4: establishing an optimization target through a cellular base station throughput model and a WiFi user saturation throughput model; constructing constraint conditions by combining the interference power when the WiFi user accesses the channel, the transmitting power of the cellular base station, the limit relationship between the amplitude and the phase of each reflecting element on the RIS and the value of the WiFi user access selection variable; under the limitation of constraint conditions, the minimum value of the weighted throughput of each cellular base station and the WiFi access point in the heterogeneous network is maximized by optimizing the beam vectors of all cellular base stations, optimizing the reflection coefficient diagonal array of the reconfigurable intelligent reflecting surface and optimizing the access selection variables of all WiFi users, so that the optimized beam vectors of all cellular base stations, the optimized reflection coefficient diagonal array of the reconfigurable intelligent reflecting surface and the optimized access selection variables of all WiFi users are obtained;
preferably, the RIS-assisted heterogeneous network system in step 1 includes N cellular base stations, N × K cellular users, a WiFi access module, I WiFi users, and a reconfigurable intelligent reflector;
the intelligent reflecting surface is provided with R reflecting elements;
the cellular base stations are respectively connected with the corresponding K cellular users in sequence in a wireless mode;
the WiFi access module is respectively connected with the I WiFi users in sequence in a wireless mode;
the reconfigurable intelligent reflecting surface establishes indirect link signals between the cellular base station and the cellular users and between the cellular base station and the WiFi users by generating reflecting paths to assist the communication between heterogeneous network systems;
preferably, in step 2, the signal model of each cellular user under the coverage of each cellular base station is:
Figure GDA0003292271000000031
Figure GDA0003292271000000032
wherein N represents the number of cellular base stations, K represents the number of cellular users under each cellular base station, and R represents the number of reflecting elements in the reconfigurable intelligent reflecting surface;
Figure GDA0003292271000000033
direct channel vectors representing the nth cell site to the kth cell user under the coverage of the nth cell site,
Figure GDA0003292271000000034
direct channel vectors representing the kth cellular user under the coverage of the mth cellular base station to the nth cellular base station; gnChannel vector, g, for the nth cellular base station to the reconfigurable intelligent reflecting surfacemChannel vectors from the mth cellular base station to the reconfigurable intelligent reflecting surface;
Figure GDA0003292271000000035
channel vectors from the reconfigurable intelligent reflecting surface to the kth cellular user covered by the nth cellular base station; theta represents a reflection coefficient diagonal matrix of the reconfigurable intelligent reflecting surface, wherein ArRepresenting the amplitude, θ, of the r-th reflecting element on the RISrDenotes the phase of the R-th reflecting element on the RIS, R ∈ [1, R]；
Figure GDA0003292271000000036
Representing beam vectors of the kth cellular user under the coverage of the nth cellular base station to the nth cellular base station,
Figure GDA0003292271000000037
representing beam vectors of the ith cellular user under the coverage of the nth cellular base station to the nth cellular base station,
Figure GDA0003292271000000038
a beam vector representing the ith cellular user under the coverage of the mth cellular base station to the mth cellular base station;
Figure GDA0003292271000000039
representing the transmission signals from the nth cellular base station to the kth cellular user under the coverage of the nth cellular base station,
Figure GDA00032922710000000310
denotes the n-thThe transmission signal from the cellular base station to the i-th cellular user under the coverage of the n-th cellular base station,
Figure GDA0003292271000000041
a transmission signal representing the mth cellular base station to the ith cellular user under the coverage of the mth cellular base station;
Figure GDA0003292271000000042
representing the gaussian random noise received by the kth cellular user under the coverage of the nth cellular base station,
Figure GDA0003292271000000043
and the standard deviation of the Gaussian random noise received by the k cellular user under the coverage of the n cellular base station is shown.
Step 2, constructing a signal to interference plus noise ratio model of each cellular user under the coverage of each cellular base station is as follows:
Figure GDA0003292271000000044
wherein N represents the number of cellular base stations, and K represents the number of cellular users under each cellular base station;
Figure GDA0003292271000000045
direct channel vectors representing the nth cell site to the kth cell user under the coverage of the nth cell site,
Figure GDA0003292271000000046
direct channel vectors representing the kth cellular user under the coverage of the mth cellular base station to the nth cellular base station; gnChannel vector from nth cellular base station to reconfigurable intelligent reflecting surface, gmChannel vectors from the mth cellular base station to the reconfigurable intelligent reflecting surface;
Figure GDA0003292271000000047
for reconstructing intelligent reflecting surface to k < th > under coverage of n < th > cellular base stationA channel vector for a cellular user; theta represents a reflection coefficient diagonal matrix of the reconfigurable intelligent reflecting surface, wherein ArRepresenting the amplitude, theta, of the r-th reflecting element on the RISrDenotes the phase of the R-th reflecting element on the RIS, R ∈ [1, R]；
Figure GDA0003292271000000048
Representing beam vectors of the kth cellular user under the coverage of the nth cellular base station to the nth cellular base station,
Figure GDA0003292271000000049
representing beam vectors of the ith cellular user under the coverage of the nth cellular base station to the nth cellular base station,
Figure GDA00032922710000000410
a beam vector representing the ith cellular user under the coverage of the mth cellular base station to the mth cellular base station;
Figure GDA00032922710000000411
representing the transmission signals from the nth cellular base station to the kth cellular user under the coverage of the nth cellular base station,
Figure GDA00032922710000000412
representing the transmission signals from the nth cellular base station to the ith cellular user under the coverage of the nth cellular base station,
Figure GDA00032922710000000413
a transmission signal representing the mth cellular base station to the ith cellular user under the coverage of the mth cellular base station;
Figure GDA00032922710000000414
representing the gaussian random noise received by the kth cellular user under the coverage of the nth cellular base station,
Figure GDA00032922710000000415
and the standard deviation of the Gaussian random noise received by the k cellular user under the coverage of the n cellular base station is shown.
Step 2, the throughput model of each cellular base station is as follows:
Figure GDA0003292271000000051
n∈[1,N]
wherein, FRnA throughput model for the nth cellular base station, N representing the number of cellular base stations, and K representing the number of cellular users per cellular base station;
preferably, the interference power model of the WiFi user in step 3 is:
Figure GDA0003292271000000052
Figure GDA0003292271000000053
wherein N represents the number of cellular base stations, K represents the number of cellular users under each cellular base station, and R represents the number of reflecting elements in the reconfigurable intelligent reflecting surface; h isn,iRepresents the direct channel vector, H, from the nth cellular base station to the ith WiFi useriChannel vector for RIS to ith WiFi user; gnChannel vectors from the nth cellular base station to the reconfigurable intelligent reflecting surface; theta represents a reflection coefficient diagonal matrix of the reconfigurable intelligent reflecting surface, wherein ArRepresenting the amplitude, θ, of the r-th reflecting element on the RISrDenotes the phase of the R-th reflecting element on the RIS, R ∈ [1, R]；
Figure GDA0003292271000000054
Representing beam vectors of the kth cellular user under the coverage of the nth cellular base station to the nth cellular base station.
By reasonably setting the reflection coefficient of the RIS, the interference suffered by the WiFi user can be selectively offset, so that the interference power detected by the WiFi user is smaller than a certain threshold value. At this point, the WiFi user will consider the channel to be empty and start DC-basedThe CSMA/CA mechanism in the F protocol competes for access. At the same time, we introduce the variable xiIndicating whether the ith WiFi user accesses the channel or not when xiWhen 1, the ith WiFi user is successfully accessed.
And 3, the saturated throughput model of the WiFi user is as follows:
Figure GDA0003292271000000055
wherein L ispMean packet size, T, representing WiFi data TransmissionσIndicating the average slot length, TsIndicating the average time of channel occupancy, T, at successful transmissioncRepresenting the average time of channel occupancy due to collision by WiFi users.
Figure GDA0003292271000000061
The probability that at least one device in the channel is transmitting is specifically expressed as:
Figure GDA0003292271000000062
wherein, F is the total number of WiFi users accessing the channel, and τ represents the WiFi user access probability.
Figure GDA0003292271000000063
The probability of successful transmission for the WiFi user is specifically expressed as:
Figure GDA0003292271000000064
wherein, F is the total number of WiFi users accessing the channel, and τ represents the WiFi user access probability.
When the throughput of the WiFi user reaches a maximum, the access probability τ may be represented by the following equation:
Figure GDA0003292271000000065
wherein, TσIndicating the average slot length, TcThe average time of channel occupation caused by collision of WiFi users is shown, and F is the total number of WiFi users accessing the channel.
Preferably, the optimization goal in step 4 is to maximize the minimum value of the weighted throughput of each cellular base station and WiFi access point in the heterogeneous network, which is specifically defined as follows:
Figure GDA0003292271000000066
step 4, the equality constraint conditions are as follows:
w＝[w1,...,wn,...,wN]
wn＝[wn,1,...,wn,m,...,wn,N]T
Figure GDA0003292271000000067
Figure GDA0003292271000000068
x＝[x1,...,xI]T
step 4, the inequality constraint conditions are as follows:
Figure GDA0003292271000000069
Figure GDA0003292271000000071
Figure GDA0003292271000000072
Figure GDA0003292271000000073
wherein N represents the number of cellular base stations, K represents the number of cellular users under each cellular base station, I represents the number of WiFi users, and R represents the number of reflective elements in the reconfigurable intelligent reflective surface; w denotes the beam vector of all cellular base stations, wnAll beam vectors, w, representing the nth cellular base stationn,mRepresenting beam vectors of all cellular users covered from the nth to the mth cellular base station,
Figure GDA0003292271000000074
representing beam vectors from the nth cellular base station to the kth cellular user of the mth cellular base station, theta represents a reflection coefficient diagonal matrix of the reconfigurable intelligent reflecting surface, and x represents access selection variable values of all WiFi users; rhonRepresents the weight, p, of the nth cellular system in the heterogeneous networkwRepresenting the weight of the WiFi system in the heterogeneous network, and setting numbers from 0 to 1 according to application scenes; FRnA throughput model representing the nth cellular base station, WR representing the saturated throughput of all WiFi users; x is a radical of a fluorine atomiIndicating channel access selection, y, for the ith WiFi useriRepresenting the interference power, Γ, of the ith WiFi userwRepresenting an interference threshold for allowing WiFi users to access the channel;
Figure GDA0003292271000000075
representing the beam vectors, P, from the nth cell site to the kth cell user of the nth cell sitenRepresents the maximum transmission power of the nth cellular base station; a. therRepresenting the amplitude, theta, of the r-th reflecting element on the RISrDenotes the phase of the R-th reflecting element on the RIS, R ∈ [1, R]；
Step 4, the optimization target is a non-convex function, and auxiliary variable conversion is required to be introduced; integer variables are involved in channel access selection constraints of WiFi users, and auxiliary variables are required to be introduced for equivalent replacement;
in the optimization objective function, the throughput of the WiFi users is only related to the total number F of the WiFi users accessing the channel, the WiFi user access probability is a transcendental function, and a double-layer circular optimization algorithm is introduced to solve:
the outer loop carries out exhaustive search on the total number of WiFi user access, and the inner loop establishes sub-problems for three optimization variables and solves the sub-problems in sequence:
solving the power sub-problem of the cellular base station by a binary search method to obtain beam vectors of all the cellular base stations;
solving the RIS reflected beam subproblem by a continuous convex approximation method to obtain the reflection coefficient of each element in the RIS;
solving a WiFi user selection variable subproblem through a penalty function method to obtain all WiFi user access selection variables;
based on the optimized beam vectors w of all the cellular base stations obtained in the step 4, the optimized reflection coefficient diagonal matrix theta on the intelligent reflecting surface can be reconstructed, the optimized access selection variables x of all the WiFi users are obtained, the system reconfigures the wireless communication environment, the network signal coverage range of each base station is adjusted, the user edge throughput is improved, channel interference is counteracted, the network communication efficiency is improved, and the system communication performance is improved.
Compared with the prior art, the invention has the beneficial effects that: aiming at the problem of fair coexistence of 5G unlicensed frequency band heterogeneous networks, the invention introduces a novel RIS technology, constructs an RIS-assisted unlicensed frequency band heterogeneous network system, intelligently improves the wireless communication environment by actively controlling the reflection of signals, and provides a heterogeneous network friendly coexistence method for fairly distributing space gain. Compared with the existing method, the fair coexistence method provided by the invention can effectively offset transmission interference and improve the spectrum efficiency.
Drawings
FIG. 1: the invention is a structure diagram of an RIS-assisted unlicensed band heterogeneous network.
FIG. 2: the method is a solving step diagram of the target optimization problem of the embodiment of the invention.
Detailed description of the preferred embodiments
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides a heterogeneous network fair sharing method based on an RIS technology by taking a heterogeneous network composed of NR-U and a WiFi system as an example, which comprises the following steps:
step 1: constructing an RIS-assisted heterogeneous network system;
referring to fig. 1, the RIS-assisted heterogeneous network system includes N × 4 cellular base stations, N × K cellular users, a WiFi access module, I × 8 WiFi users, and a reconfigurable intelligent reflector;
the intelligent reflecting surface is provided with 50 reflecting elements;
the cellular base stations are respectively connected with corresponding 8 cellular users in sequence in a wireless mode;
the WiFi access module is respectively connected with the I WiFi users in sequence in a wireless mode;
the reconfigurable intelligent reflecting surface establishes indirect link signals between the cellular base station and the cellular users and between the cellular base station and the WiFi users by generating reflecting paths to assist the communication between heterogeneous network systems;
step 2: constructing each cellular user signal model under the coverage of each cellular base station, constructing each cellular user signal-to-interference-plus-noise ratio model under the coverage of each cellular base station according to each cellular user signal model under the coverage of each cellular base station, and constructing a throughput model of the cellular base station according to all cellular user signal-to-interference-plus-noise ratio models under the coverage of the cellular base stations;
step 2, the signal model of each cellular user covered by each cellular base station is as follows:
Figure GDA0003292271000000091
Figure GDA0003292271000000092
wherein N represents the number of cellular base stations, K represents the number of cellular users under each cellular base station, and R represents the number of reflecting elements in the reconfigurable intelligent reflecting surface;
Figure GDA0003292271000000093
representing the direct channel vectors of the nth cell site to the kth cell user under the coverage of the nth cell site,
Figure GDA0003292271000000094
direct channel vectors representing the kth cellular user under the coverage of the mth cellular base station to the nth cellular base station; gnChannel vector from nth cellular base station to reconfigurable intelligent reflecting surface, gmChannel vectors from the mth cellular base station to the reconfigurable intelligent reflecting surface;
Figure GDA0003292271000000095
channel vectors from the reconfigurable intelligent reflecting surface to the kth cellular user covered by the nth cellular base station; theta represents a reflection coefficient diagonal matrix of the reconfigurable intelligent reflecting surface, wherein ArRepresenting the amplitude, theta, of the r-th reflecting element on the RISrDenotes the phase of the R-th reflecting element on the RIS, R ∈ [1, R]；
Figure GDA0003292271000000096
Representing beam vectors of the kth cellular user under the coverage of the nth cellular base station to the nth cellular base station,
Figure GDA0003292271000000097
representing beam vectors of the ith cellular user under the coverage of the nth cellular base station to the nth cellular base station,
Figure GDA0003292271000000098
waves representing the ith cellular user under the coverage of the mth cellular base station to the mth cellular base stationA beam vector;
Figure GDA0003292271000000099
representing the transmission signals from the nth cellular base station to the kth cellular user under the coverage of the nth cellular base station,
Figure GDA00032922710000000910
representing the transmission signals from the nth cellular base station to the ith cellular user under the coverage of the nth cellular base station,
Figure GDA00032922710000000911
a transmission signal representing the mth cellular base station to the ith cellular user under the coverage of the mth cellular base station;
Figure GDA00032922710000000912
representing the gaussian random noise received by the kth cellular user under the coverage of the nth cellular base station,
Figure GDA0003292271000000101
and the standard deviation of the Gaussian random noise received by the k cellular user under the coverage of the n cellular base station is shown.
Step 2, constructing a signal to interference plus noise ratio model of each cellular user under the coverage of each cellular base station is as follows:
Figure GDA0003292271000000102
wherein N represents the number of cellular base stations, K represents the number of cellular users under each cellular base station, and R represents the number of reflecting elements in the reconfigurable intelligent reflecting surface;
Figure GDA0003292271000000103
representing the direct channel vectors of the nth cell site to the kth cell user under the coverage of the nth cell site,
Figure GDA0003292271000000104
represents the mth beeDirect channel vectors of kth cellular users under the coverage of the cellular base station to the nth cellular base station; gnChannel vector from nth cellular base station to reconfigurable intelligent reflecting surface, gmChannel vectors from the mth cellular base station to the reconfigurable intelligent reflecting surface;
Figure GDA0003292271000000105
channel vectors from the reconfigurable intelligent reflecting surface to the kth cellular user covered by the nth cellular base station; theta represents a reflection coefficient diagonal matrix of the reconfigurable intelligent reflecting surface, wherein ArRepresenting the amplitude, θ, of the r-th reflecting element on the RISrDenotes the phase of the R-th reflecting element on the RIS, R ∈ [1, R]；
Figure GDA0003292271000000106
Representing beam vectors of the kth cellular user under the coverage of the nth cellular base station to the nth cellular base station,
Figure GDA0003292271000000107
representing beam vectors of the ith cellular user under the coverage of the nth cellular base station to the nth cellular base station,
Figure GDA0003292271000000108
a beam vector representing the ith cellular user under the coverage of the mth cellular base station to the mth cellular base station;
Figure GDA0003292271000000109
representing the transmission signals from the nth cellular base station to the kth cellular user under the coverage of the nth cellular base station,
Figure GDA00032922710000001010
representing the transmission signals from the nth cellular base station to the ith cellular user under the coverage of the nth cellular base station,
Figure GDA00032922710000001011
a transmission signal representing the mth cellular base station to the ith cellular user under the coverage of the mth cellular base station;
Figure GDA00032922710000001012
representing the gaussian random noise received by the kth cellular user under the coverage of the nth cellular base station,
Figure GDA00032922710000001013
and the standard deviation of the Gaussian random noise received by the k cellular user under the coverage of the n cellular base station is shown.
Step 2, the throughput model of each cellular base station is as follows:
Figure GDA0003292271000000111
n∈[1,N]
wherein, FRnA throughput model for the nth cellular base station, where N represents the number of cellular base stations and K represents the number of cellular users under each cellular base station;
and 3, step 3: respectively constructing an interference power model of WiFi users and a saturated throughput model of each WiFi user;
step 3, the interference power model of the WiFi user is as follows:
Figure GDA0003292271000000112
Figure GDA0003292271000000113
wherein N represents the number of cellular base stations, K represents the number of cellular users under each cellular base station, and R represents the number of reflective elements in the reconfigurable intelligent reflective surface; h isn,iRepresents the direct channel vector, H, from the nth cellular base station to the ith WiFi useriChannel vector for RIS to ith WiFi user; gnChannel vectors from the nth cellular base station to the reconfigurable intelligent reflecting surface; theta represents a reflection coefficient diagonal matrix of the reconfigurable intelligent reflecting surface, wherein ArOn the RISAmplitude of the r-th reflecting element, thetarDenotes the phase of the R-th reflecting element on the RIS, R ∈ [1, R]；
Figure GDA0003292271000000114
Representing beam vectors of the kth cellular user under the coverage of the nth cellular base station to the nth cellular base station.
By reasonably setting the reflection coefficient of the RIS, the interference suffered by the WiFi user can be selectively offset, so that the interference power detected by the WiFi user is smaller than a certain threshold value. At this time, the WiFi user will consider the channel to be empty and start to contend for access based on the CSMA/CA mechanism in the DCF protocol. At the same time, we introduce the variable xiIndicating whether the ith WiFi user accesses the channel or not when xiWhen 1, the ith WiFi user is successfully accessed.
And 3, the saturated throughput model of the WiFi user is as follows:
Figure GDA0003292271000000115
wherein L ispMean packet size, T, representing WiFi data TransmissionσIndicating the average slot length, TsIndicating the average time of channel occupancy, T, at successful transmissioncRepresenting the average time of channel occupancy due to collision by WiFi users.
prt is the probability that at least one device in the channel is transmitting, and is specifically expressed as:
Figure GDA0003292271000000121
wherein, F is the total number of WiFi users accessing the channel, and τ represents the WiFi user access probability.
Figure GDA0003292271000000122
The probability of successful transmission for the WiFi user is specifically expressed as:
Figure GDA0003292271000000123
wherein, F is the total number of WiFi users accessing the channel, and τ represents the WiFi user access probability.
When the throughput of the WiFi user reaches a maximum, the access probability τ may be represented by the following equation:
Figure GDA0003292271000000124
wherein, TσIndicating the average slot length, TcWhich represents the average time of channel occupancy due to collision of WiFi users, F is the total number of WiFi users accessing the channel.
And 4, step 4: establishing an optimization target through a cellular base station throughput model and a WiFi user saturated throughput model; constructing constraint conditions by combining the interference power when the WiFi user accesses the channel, the transmitting power of the cellular base station, the limit relationship between the amplitude and the phase of each reflecting element on the RIS and the value of the WiFi user access selection variable; under the limitation of constraint conditions, the minimum value of the weighted throughput of each cellular base station and the WiFi access point in the heterogeneous network is maximized by optimizing the beam vectors of all cellular base stations, optimizing the reflection coefficient diagonal array of the reconfigurable intelligent reflecting surface and optimizing the access selection variables of all WiFi users, so that the optimized beam vectors of all cellular base stations, the optimized reflection coefficient diagonal array of the reconfigurable intelligent reflecting surface and the optimized access selection variables of all WiFi users are obtained;
step 4, the optimization objective is to maximize the minimum value of the weighted throughput of each cellular base station and WiFi access point in the heterogeneous network, and is specifically defined as follows:
Figure GDA0003292271000000125
step 4, the equality constraint conditions are as follows:
w＝[w1,...,wn,...,wN]
wn＝[wn,1,...,wn,m,...,wn,N]T
Figure GDA0003292271000000131
Figure GDA0003292271000000132
x＝[x1,...,xI]T
step 4, the inequality constraint conditions are as follows:
Figure GDA0003292271000000133
Figure GDA0003292271000000134
Figure GDA0003292271000000135
Figure GDA0003292271000000136
wherein N represents the number of cellular base stations, K represents the number of cellular users under each cellular base station, I represents the number of WiFi users, and R represents the number of reflecting elements in the reconfigurable intelligent reflecting surface; w denotes the beam vector of all cellular base stations, wnAll beam vectors, w, representing the nth cellular base stationn,mRepresenting beam vectors of all cellular users covered from the nth to the mth cellular base station,
Figure GDA0003292271000000137
representing the nth cell site to the mth cell siteThe wave beam vector of the kth cellular user is represented by theta, a reflection coefficient diagonal matrix of the reconfigurable intelligent reflecting surface is represented by x, and access selection variable values of all WiFi users are represented by x; rhonRepresents the weight, p, of the nth cellular system in the heterogeneous networkwRepresenting the weight of the WiFi system in the heterogeneous network, and setting numbers from 0 to 1 according to application scenes; FRnA throughput model representing the nth cellular base station, WR representing the saturated throughput of all WiFi users; x is the number ofiIndicating channel access selection, y, for the ith WiFi useriRepresenting the interference power, Γ, of the ith WiFi userwRepresenting an interference threshold for allowing WiFi users to access the channel;
Figure GDA0003292271000000138
representing the beam vectors, P, from the nth cell site to the kth cell user of the nth cell sitenRepresents the maximum transmission power of the nth cellular base station; a. therRepresenting the amplitude, θ, of the r-th reflecting element on the RISrDenotes the phase of the R-th reflecting element on the RIS, R ∈ [1, R]；
Step 4, the optimization target is a non-convex function, and auxiliary variable conversion is required to be introduced; integer variables are involved in channel access selection constraints of WiFi users, and auxiliary variables are required to be introduced for equivalent replacement;
in the optimization objective function, the throughput of the WiFi users is only related to the total number F of the WiFi users accessing the channel, the WiFi user access probability is a transcendental function, and a double-layer circular optimization algorithm is introduced to solve:
the outer loop exhaustively searches the total number of WiFi user accesses, and the inner loop sequentially solves the sub-problems established by aiming at three optimization variables:
solving the power sub-problem of the cellular base station by a binary search method to obtain beam vectors of all the cellular base stations;
solving the RIS reflected beam subproblem by a continuous convex approximation method to obtain the reflection coefficient of each element in the RIS;
solving a WiFi user selection variable subproblem through a penalty function method to obtain all WiFi user access selection variables;
based on the optimized beam vectors w of all the cellular base stations obtained in the step 4, the optimized reflection coefficient diagonal matrix theta on the intelligent reflecting surface can be reconstructed, the optimized access selection variables x of all the WiFi users are obtained, the system reconfigures the wireless communication environment, the network signal coverage range of each base station is adjusted, the user edge throughput is improved, channel interference is counteracted, the network communication efficiency is improved, and the system communication performance is improved.
Please refer to fig. 2, a step diagram for solving the optimization problem, and the detailed solving process is not repeated.
It should be understood that the above description of the preferred embodiments is given for clarity and not for any purpose of limitation, and that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (1)

1. A heterogeneous network fair coexistence method based on RIS technology is characterized by comprising the following steps:
step 1: constructing an RIS-assisted heterogeneous network system;
step 2: constructing each cellular user signal model under the coverage of each cellular base station, constructing each cellular user signal-to-interference-plus-noise ratio model under the coverage of each cellular base station according to each cellular user signal model under the coverage of each cellular base station, and constructing a throughput model of the cellular base station according to all cellular user signal-to-interference-plus-noise ratio models under the coverage of the cellular base stations;
and step 3: respectively constructing an interference power model of WiFi users and a saturated throughput model of each WiFi user;
and 4, step 4: establishing an optimization target through a cellular base station throughput model and a WiFi user saturated throughput model; constructing constraint conditions by combining the interference power when the WiFi user accesses the channel, the transmitting power of the cellular base station, the limit relationship between the amplitude and the phase of each reflecting element on the RIS and the value of the WiFi user access selection variable; under the limitation of constraint conditions, the minimum value of the weighted throughput of each cellular base station and the WiFi access point in the heterogeneous network is maximized by optimizing the beam vectors of all cellular base stations, optimizing the reflection coefficient diagonal array of the reconfigurable intelligent reflecting surface and optimizing the access selection variables of all WiFi users, so that the optimized beam vectors of all cellular base stations, the optimized reflection coefficient diagonal array of the reconfigurable intelligent reflecting surface and the optimized access selection variables of all WiFi users are obtained;
the RIS-assisted heterogeneous network system in the step 1 comprises N cellular base stations, N × K cellular users, a WiFi access module, I WiFi users and a reconfigurable intelligent reflecting surface;
the intelligent reflecting surface is provided with R reflecting elements;
the cellular base stations are respectively connected with the corresponding K cellular users in sequence in a wireless mode;
the WiFi access module is respectively connected with the I WiFi users in sequence in a wireless mode;
the reconfigurable intelligent reflecting surface establishes indirect link signals between the cellular base station and the cellular users and between the cellular base station and the WiFi users by generating reflecting paths to assist the communication between heterogeneous network systems;
step 2, the signal model of each cellular user covered by each cellular base station is as follows:
Figure FDA0003588914950000011
Figure FDA0003588914950000021
wherein N represents the number of cellular base stations, K represents the number of cellular users under each cellular base station, and R represents the number of reflecting elements in the reconfigurable intelligent reflecting surface;
Figure FDA0003588914950000022
indicating the nth cellular base station toThe direct channel vector of the kth cellular user under the coverage of the nth cellular base station,
Figure FDA0003588914950000023
direct channel vectors representing the kth cellular user under the coverage of the mth cellular base station to the nth cellular base station; gnChannel vector from nth cellular base station to reconfigurable intelligent reflecting surface, gmChannel vectors from the mth cellular base station to the reconfigurable intelligent reflecting surface;
Figure FDA0003588914950000024
channel vectors from the reconfigurable intelligent reflecting surface to the kth cellular user covered by the nth cellular base station; theta represents a reflection coefficient diagonal matrix of the reconfigurable intelligent reflecting surface, wherein ArRepresenting the amplitude, theta, of the r-th reflecting element on the RISrDenotes the phase of the R-th reflecting element on the RIS, R ∈ [1, R]；
Figure FDA0003588914950000025
Representing beam vectors of the kth cellular user under the coverage of the nth cellular base station to the nth cellular base station,
Figure FDA0003588914950000026
representing beam vectors of the ith cellular user under the coverage of the nth cellular base station to the nth cellular base station,
Figure FDA0003588914950000027
a beam vector representing the ith cellular user under the coverage of the mth cellular base station to the mth cellular base station;
Figure FDA0003588914950000028
representing the transmission signals from the nth cellular base station to the kth cellular user under the coverage of the nth cellular base station,
Figure FDA0003588914950000029
indicating the nth cellular base station toThe transmission signal of the l cellular user under the coverage of the n cellular base station,
Figure FDA00035889149500000210
a transmission signal representing the mth cellular base station to the ith cellular user under the coverage of the mth cellular base station;
Figure FDA00035889149500000211
representing the gaussian random noise received by the kth cellular user under the coverage of the nth cellular base station,
Figure FDA00035889149500000212
the standard deviation of Gaussian random noise received by a kth cellular user under the coverage of the nth cellular base station is represented;
step 2, constructing a signal to interference plus noise ratio model of each cellular user under the coverage of each cellular base station is as follows:
Figure FDA00035889149500000213
step 2, the throughput model of each cellular base station is as follows:
Figure FDA0003588914950000031
n∈[1,N]
wherein, FRnA throughput model for the nth cellular base station, N representing the number of cellular base stations, and K representing the number of cellular users per cellular base station;
step 3, the interference power model of the WiFi user is as follows:
Figure FDA0003588914950000032
Figure FDA0003588914950000033
wherein N represents the number of cellular base stations, K represents the number of cellular users under each cellular base station, and R represents the number of reflecting elements in the reconfigurable intelligent reflecting surface; h is a total ofn,iRepresents the direct channel vector, H, from the nth cellular base station to the ith WiFi useriChannel vector for RIS to ith WiFi user; g is a radical of formulanChannel vectors from the nth cellular base station to the reconfigurable intelligent reflecting surface; theta represents a reflection coefficient diagonal matrix of the reconfigurable intelligent reflecting surface, wherein ArRepresenting the amplitude, theta, of the r-th reflecting element on the RISrDenotes the phase of the R-th reflecting element on the RIS, R ∈ [1, R]；
Figure FDA0003588914950000034
Beam vectors representing the kth cellular user under the coverage of the nth cellular base station to the nth cellular base station;
by reasonably setting the reflection coefficient of the RIS, the interference suffered by the WiFi user can be selectively offset, and the detected interference power is smaller than a certain threshold value; at this time, the WiFi user considers that the channel is empty and starts to access based on the CSMA/CA mechanism in the DCF protocol; at the same time, we introduce the variable xiIndicating whether the ith WiFi user accesses the channel or not when xiWhen the number of the users is 1, the users are successfully accessed by the ith WiFi user;
and 3, the saturated throughput model of the WiFi user is as follows:
Figure FDA0003588914950000035
wherein L ispMean packet size, T, representing WiFi data TransmissionσIndicating the average slot length, TsIndicating the average time of channel occupancy, T, at successful transmissioncRepresents the average time of channel occupation due to collision of WiFi users;
Figure FDA0003588914950000041
the probability that at least one device in the channel is transmitting is specifically expressed as:
Figure FDA0003588914950000042
wherein, F is the total number of WiFi users accessing the channel, and tau represents the access probability of the WiFi users;
Figure FDA0003588914950000043
the probability of successful transmission for the WiFi user is specifically expressed as:
Figure FDA0003588914950000044
wherein, F is the total number of WiFi users accessing the channel, and tau represents the access probability of the WiFi users;
when the throughput of the WiFi user reaches a maximum, the access probability τ may be represented by the following equation:
Figure FDA0003588914950000045
wherein, TσIndicating the average slot length, TcRepresenting the average time occupied by the channel due to collision of the WiFi users, wherein F is the total number of the WiFi users accessing the channel;
step 4, the optimization objective is to maximize the minimum value of the weighted throughput of each cellular base station and WiFi access point in the heterogeneous network, and is specifically defined as follows:
Figure FDA0003588914950000046
step 4, equality constraint conditions are as follows:
w＝[w1,...,wn,...,wN]
wn＝[wn,1,...,wn,m,...,wn,N]T
Figure FDA0003588914950000047
Figure FDA0003588914950000048
x＝[x1,...,xI]T
step 4, the inequality constraint conditions are as follows:
Figure FDA0003588914950000049
Figure FDA00035889149500000410
Figure FDA0003588914950000051
Figure FDA0003588914950000052
wherein N represents the number of cellular base stations, K represents the number of cellular users under each cellular base station, I represents the number of WiFi users, and R represents the number of reflective elements in the reconfigurable intelligent reflective surface; w denotes the beam vector of all cellular base stations, wnAll beam vectors, w, representing the nth cellular base stationn,mRepresenting beam vectors of all cellular users covered from the nth to the mth cellular base station,
Figure FDA0003588914950000053
representing beam vectors from the nth cellular base station to the kth cellular user of the mth cellular base station, theta represents a reflection coefficient diagonal matrix of the reconfigurable intelligent reflecting surface, and x represents access selection variable values of all WiFi users; rhonRepresents the weight, p, of the nth cellular system in the heterogeneous networkwRepresenting the weight of the WiFi system in the heterogeneous network, and setting numbers from 0 to 1 according to application scenes; FRnA throughput model representing the nth cellular base station, WR representing the saturated throughput of all WiFi users; x is the number ofiIndicating channel access selection, y, for the ith WiFi useriRepresenting the interference power, Γ, of the ith WiFi userwRepresenting an interference threshold for allowing WiFi users to access the channel;
Figure FDA0003588914950000054
representing the beam vectors, P, from the nth cell site to the kth cell user of the nth cell sitenRepresents the maximum transmission power of the nth cellular base station; a. therRepresenting the amplitude, theta, of the r-th reflecting element on the RISrDenotes the phase of the R-th reflecting element on the RIS, R ∈ [1, R]；
4, the optimization target is a non-convex function, and auxiliary variable conversion is required to be introduced; integer variables are involved in channel access selection constraints of WiFi users, and auxiliary variables are required to be introduced for equivalent replacement;
in the optimization objective function, the throughput of the WiFi users is only related to the total number F of the WiFi users accessing the channel, the WiFi user access probability is a transcendental function, and a double-layer circular optimization algorithm is introduced to solve:
the outer loop exhaustively searches the total number of WiFi user accesses, and the inner loop sequentially solves the sub-problems established by aiming at three optimization variables:
solving the power sub-problem of the cellular base station by a binary search method to obtain beam vectors of all the cellular base stations;
solving the RIS reflected beam subproblem by a continuous convex approximation method to obtain the reflection coefficient of each element in the RIS;
solving a WiFi user selection variable subproblem through a penalty function method to obtain all WiFi user access selection variables;
based on the optimized beam vectors w of all the cellular base stations obtained in the step 4, the optimized reflection coefficient diagonal matrix theta on the intelligent reflecting surface can be reconstructed, the optimized access selection variables x of all the WiFi users are obtained, the system reconfigures the wireless communication environment, the network signal coverage range of each base station is adjusted, the user edge throughput is improved, channel interference is counteracted, the network communication efficiency is improved, and the system communication performance is improved.
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