CN114885339A - MassiveMIMO optimization-based heterogeneous network interference coordination method - Google Patents
MassiveMIMO optimization-based heterogeneous network interference coordination method Download PDFInfo
- Publication number
- CN114885339A CN114885339A CN202210356772.3A CN202210356772A CN114885339A CN 114885339 A CN114885339 A CN 114885339A CN 202210356772 A CN202210356772 A CN 202210356772A CN 114885339 A CN114885339 A CN 114885339A
- Authority
- CN
- China
- Prior art keywords
- angle
- channel
- mimo
- matrix
- angular
- 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.)
- Pending
Links
Images
Classifications
-
- 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
- H04B17/345—Interference values
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/382—Monitoring; Testing of propagation channels for resource allocation, admission control or handover
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/391—Modelling the propagation channel
- H04B17/3912—Simulation models, e.g. distribution of spectral power density or received signal strength indicator [RSSI] for a given geographic region
-
- 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
-
- 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
- H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
- H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
- H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
- H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
- H04B7/0621—Feedback content
- H04B7/0634—Antenna weights or vector/matrix coefficients
-
- 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
- H04B7/0802—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 antenna selection
- H04B7/0817—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 antenna selection with multiple receivers and antenna path selection
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/22—Scatter propagation systems, e.g. ionospheric, tropospheric or meteor scatter
-
- 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
- H04W16/22—Traffic simulation tools or models
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- Mathematical Physics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Quality & Reliability (AREA)
- Radio Transmission System (AREA)
Abstract
The invention relates to the technical field of 5G, and discloses a massiveMIMO optimization-based heterogeneous network interference coordination method, which needs to select different network deployment scenes. According to the massiveMIMO optimization-based heterogeneous network interference coordination method, a reasonable MIMO wireless channel model is provided according to the array structures of antennas at a transmitting end and a receiving end, the departure angle and the angle expansion of a transmitting signal, the arrival angle and the angle expansion of a receiving signal, an angle power spectrum, a Doppler power spectrum and other parameters, the angle expansion is fixed, and the correlation coefficient is reduced along with the increase of the distance between the antennas; when the antenna spacing is constant, the correlation coefficient decreases with increasing angular spread. When the angle expansion is smaller, the correlation coefficient is slowly reduced along with the increase of the antenna spacing, when the angle expansion is larger, the correlation coefficient is rapidly reduced along with the increase of the antenna spacing when the antenna spacing is very small (smaller than 1), and when the antenna spacing is larger than 1, the change of the correlation coefficient is relatively smooth.
Description
Technical Field
The invention relates to the technical field of 5G, in particular to a massiveMIMO optimization-based heterogeneous network interference coordination method.
Background
The MIMO technology is considered as one of the major breakthroughs in the modern communication technology because the capacity of the wireless channel can be increased by times without increasing the bandwidth of the transmission channel, and is becoming a research hotspot in the field of wireless communication. The MIMO technology is an important approach for achieving high data rate transmission, improving transmission quality, and increasing system capacity in future wireless communication systems. The MIMO channel model is necessary both in the theoretical research phase of MIMO technology and in the application phase of MIMO systems.
Disclosure of Invention
The invention aims to provide a MassiveMIMO optimization-based heterogeneous network interference coordination method to solve the problems in the background art.
In order to achieve the purpose, the invention provides the following technical scheme: the heterogeneous network interference coordination method based on MassiveMIMO optimization comprises the following steps:
1) firstly, different network deployment scenes need to be selected, and meanwhile, UE equipment is allowed to be connected with a base station, so that the network communication quality of a user is conveniently constructed and is often calculated by throughput;
2) measuring the use throughput of a user according to the allocation of UE, and dividing a macro cell and a micro cell in different cells, wherein the network coverage of the cells in the isomorphic network deployment is a co-located area provided by LTE and 5GNR base stations, which are overlapped on a model, so that the base station in the layout mode is defined as a macro base station, otherwise, in the heterogeneous network scene deployment, for convenience of statistics and capacity observation gain, an LTEeNB is continuously set as a main cell of the Men;
3) in order to facilitate the specific analysis of the MIMO space-time channel, multipath is analyzed from the angle of a mathematical model;
4) considering two uniform linear antenna arrays (ULAs) with the number of transmitting end antennas being N and the number of receiving end antennas being M, assuming that the antennas are omnidirectional radiation antennas;
5) in the study of MIMO channel models, it is generally assumed that there are few spatially independent primary reflectors in the far-field region, one primary reflector has a primary path containing a large number of incoming waves caused by the structure of local scatterers near the receiver and transmitter with small relative delays that cannot be separated by the receiver, i.e., an unresolvable path, which will lead to space-time fading due to non-zero angular spread;
6) based on the simulation of the MIMO channel model, firstly selecting a simulated MIMO wireless channel scene;
7) then selecting antenna array structure of transmitting end and receiving end, i.e. number of antennas, antenna spacing and topological structure of array (uniform linear array ULA or other structure), etc. of transmitting end and receiving end, then inputting corresponding channel parameters including Doppler power spectrum, angle power spectrum (PAS), arrival angle (AOA), departure Angle (AOD), Angle Spread (AS) and so on of channel, respectively calculating space correlation matrix R of transmitting and receiving ends of MIMO channel Rx And R Tx And obtaining an overall correlation matrix R of the MIMO channel MIMO Then to R MIMO Carrying out corresponding matrix decomposition to obtain a MIMO channel spatial correlation matrix C; then, according to PDP of channel, making power distribution of each branch, then producing related fading coefficient, finally according to calculation result, obtaining coefficient matrix A of each tap in tap delay line simulation model l And finally obtaining an MIMO channel matrix H;
8) the MIMO channel matrix may be generated by the following method:
firstly, according to the above stepsGenerating correlation matrix R for MIMO channel receiving and transmitting ends Rx And R Tx Then according to formula
The overall correlation matrix of the resulting MIMO channel, consisting of R MIMO Performing corresponding matrix decomposition to obtain a symmetric mapping matrix C, wherein C is a space correlation formation matrix of the MIMO channel, namely: r MIMO ＝CC T (ii) a Finally, the coefficient matrix of the MIMO channel tap is calculated according to the following formula:
wherein, ν ec (·) represents arranging an M × N matrix into a vector of 1 × MN;
namely the fading coefficient of the MIMO channel; p l Power of the first resolvable path; a is MN×1 ＝[a 1 ,a 2 ,...a MN ] T 。
Preferably, a common 3GPP Release-147-0 is selected in the step 1 to build a homogeneous or heterogeneous network.
Preferably, in step 2, on one hand, the positions of the 5 ggnbs are randomly and irregularly distributed, so that the hot spot capacity and coverage enhancement provided by the cell can be displayed in the cell; on the other hand, the irregularities in shape determine the difference in performance, and the choice of conductor and material medium also limits the loss of bandwidth.
Preferably, the parameters of the MIMO wireless channel in step 3 mainly include a power azimuth spectrum, angle expansion, an average wave arrival angle at a receiving end and an average wave removal angle at a transmitting end, configuration of multiple transmitting and receiving antennas, and the like;
(1) angular power spectrum
The angular power spectrum (PAS), or so-called angular spectral distribution probability density function, is
The angular spectral distribution of the power spectral density of a signal is an important spatial characteristic of a wireless channel and is mainly determined by the characteristics of a propagation environment, commonly used angular spectral distributions comprise cosine distribution proposed by Lee and Salz assumed uniform distribution, Aszetly considers that Gaussian distribution is closer to the test result of the angular spectral distribution of a GSM system, Pedersen indicates that Laplace distribution is more consistent with the angular spectral distribution of a DCS1800 system, Weibull distribution is consistent with indoor angular spectral distribution and the like,
(2) spread of scattering angle
The scattering angle spectrum distribution macroscopically describes the distribution characteristics of multipath scattering, an important parameter of various distributions is the angular spread of scattering, which describes the scattering degree of scattering, determines the separability of signals to a great extent, and is a very important channel space characteristic parameter, and there are a plurality of ways for defining the angular spread, two of which are common:
a. defining the mean value of the spread angle as the angular spread (σ) rms )
In the formula:
here, the
Is shown at an angle
Is controlled by the power of the received power,
the standard deviation defining the energy distribution within the spread angle is the angular spread (σ)
In the formula (I), the compound is shown in the specification,
here, the
Is the power azimuth spectrum of the scattering multipath,
represents the average angle of arrival or the average angle of departure;
the spread angle is the spread angular domain of the scattered multipath signal in space and is expressed by 2 delta, namely the wave arrival azimuth limitation
For a uniform angular spectral distribution, the angle is spread to
For gaussian and lagrange angular distributions, the angular spread is given by equation (3.12).
Preferably, the average wave arrival angle and the average wave removal angle in the step 4 are as follows: in many previous studies, the average arrival direction and the average departure direction were assumed to be perpendicular to the array axis, while other directions were ignored; in fact, the influence of the average arrival angle and the average de-wavering angle on the spatial characteristics of the channel is not negligible, and the deviation of the average arrival angle and the de-wavering angle from the normal direction of the array will result in the enhancement of the correlation of multipath signals, the reduction of separability and the degradation of channel performance.
Preferably, the configuration of the transceiving multiple antennas in step 4: the multi-antenna system is an important component of a wireless system, is a tool for transmitting signals and capturing multipath in a communication system, and can seriously affect the spatial characteristics of a channel in a configuration form; the directional pattern, gain, polarization, spacing, cross-talk, spatial layout, etc. of the multiple antenna elements need to be considered carefully.
Preferably, the doppler spread in step 4 is: caused by relative motion between the transceiving ends or motion of scatterers, channel time-varying and intersymbol interference (ISI) may result.
Preferably, the scene simulated in step 6 refers to a typical urban area, a severe urban area, a suburban area, or a rural area.
Compared with the prior art, the invention has the beneficial effects that: according to the massiveMIMO optimization-based heterogeneous network interference coordination method, a reasonable MIMO wireless channel model is provided according to the array structures of antennas at a transmitting end and a receiving end, the departure angle and the angle expansion of a transmitting signal, the arrival angle and the angle expansion of a receiving signal, an angle power spectrum, a Doppler power spectrum and other parameters, the angle expansion is fixed, and the correlation coefficient is reduced along with the increase of the distance between the antennas; when the antenna spacing is constant, the correlation coefficient is reduced along with the increase of the angle expansion, when the angle expansion is smaller, the correlation coefficient is slowly reduced along with the increase of the antenna spacing, when the angle expansion is larger, the correlation coefficient is rapidly reduced along with the increase of the antenna spacing when the antenna spacing is very small (smaller than 1), and when the antenna spacing is larger than 1, the change of the correlation coefficient is relatively smooth.
Drawings
FIG. 1 is a UE allocation diagram of a network relay station according to the present invention;
FIG. 2 is a diagram of a data model of a MIMO channel according to the present invention;
FIG. 3 is a simulation flow chart of the MIMO channel model of the present invention;
FIG. 4 is a diagram illustrating the generation of correlated fading in a MIMO channel according to the present invention;
FIG. 5 is a graph of the relationship between the patterns and the number of arrays in accordance with the present invention;
FIG. 6 is a graph of the directivity pattern versus wavelength of the present invention;
FIG. 7 is a graph of patterns versus distance in accordance with the present invention;
FIG. 8 is a graph of the relationship between the antenna spacing and the correlation coefficient for different angular extensions according to the present invention;
fig. 9 is a diagram of the correlation coefficient between the antenna spacing and the channel at different average angles of arrival according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-9, the present invention provides a technical solution: the heterogeneous network interference coordination method based on MassiveMIMO optimization comprises the following steps:
1) firstly, different network deployment scenes need to be selected, and meanwhile, UE equipment is allowed to be connected with a base station, so that the network communication quality of a user is conveniently constructed and is often calculated by throughput;
2) measuring the use throughput of a user according to the allocation of UE, and dividing a macro cell and a micro cell in different cells, wherein the network coverage of the cells in the isomorphic network deployment is a co-located area provided by LTE and 5GNR base stations, which are overlapped on a model, so that the base stations in the layout mode are defined as macro base stations, otherwise, in the heterogeneous network scene deployment, for convenience of statistics and capacity gain observation, an LTEeNB is continuously set as a main cell of the Men, and on one hand, the positions of the 5GgNB are randomly and irregularly distributed, so that the hot spot capacity and coverage enhancement provided by the cells can be displayed and processed in the cells; on the other hand, irregularities in shape determine the difference in its properties. Such as different directional angles supplying different transmit powers, etc.
Secondly, the choice of conductor and material medium also limits the loss of bandwidth. Particularly, the millimeter wave frequency band is increased along with the increase of the application frequency of the circuit, and the signal wavelength is correspondingly shortened; the ratio of the cross-sectional area to the line width of the transmission line is constant under the condition that the transmission line is not closed, so that the radiation loss of the circuit can be ignored;
3) in order to analyze the MIMO space-time channel specifically, multipath is analyzed from the perspective of a mathematical model, and one band-pass signal is as follows:
where s (t) -the equivalent low-pass signal; f. of c Carrier frequency, hypothetical channelComprising L paths, the received band-pass signal and equivalent low-pass signal can be represented as
Where rho l -attenuation coefficient of the l-th path; theta l (t) -phase shift of the l-th path; tau is l (t) -delay of the l-th path,
θ l (t)＝2πf l (t)-2π(f c +f l )τ l (t) (3.4)
in the formula f l -doppler shift of the l-th path, where the first term is the phase shift due to doppler shift and the second term is the phase shift due to delay, we will introduce rayleigh fading model and rice fading model to describe the signal variation in the narrow-band multipath environment (non-frequency-selective), and for non-frequency-selective channels, the delay spread is small with respect to the symbol period, so there are the following assumptions:
s(t-τ l (t))≈s(t) (3.5)
if there are L multipaths present in the channel, the received signal can be represented as:
defining the complex multiplication coefficient as:
then there are:
a(t)＝a R (t)+ja I (t)＝a(t)e jφ(t) (3.8)
4) considering two uniform linear antenna arrays (ULA) with the number of antennas at the transmitting end being N and the number of antennas at the receiving end being M, assuming that the antennas are omnidirectional radiation antennas, the transmission signal on the antenna array at the transmitting end is recorded as:
s(t)＝[s 1 (t)，s 2 (t)，...s N (t)] T (3.13)
s N (t) represents the transmission signal on the nth transmitting antenna element, the symbol [ ·] T Representing the transpose of the vector (or matrix), and likewise, the received signal on the receiving-end antenna array can be represented as:
y(t)＝[y 1 (t)，y 2 (t)，...y M (t)] T (3.14)
a wideband MIMO wireless channel matrix describing the connection of a transmitting end and a receiving end can be represented as:
wherein H (τ) ∈ C M×N And is and
to describe the time delay tau of antenna array at both transmitting and receiving ends l Complex channel transmission coefficient matrix, hmn, of l Represents a complex transmission coefficient from the nth transmit antenna to the mth receive antenna, L represents the number of resolvable antennas;
the relationship between the transmitted signal vector s (t) and the received signal vector y (t) can be expressed as (excluding noise):
y(t)＝∫H(τ)s(t-τ)dτ (3.16)
or:
s(t)＝∫H T (τ)y(t-τ)dτ； (3.17)
5) in the study of MIMO channel models, it is generally assumed that there are few spatially independent primary reflectors in the far-field region, one primary reflector has a primary path containing a large number of incoming waves due to the structure of local scatterers near the receiver and transmitter, which have small relative delays, the receiver cannot separate, i.e., an unresolvable path, since the angular spread is not zero, which leads to space-time fading,
due to the effect of scatterers near the transmitter and receiver, many indistinguishable paths with small time delays will be generated, so that the angular spread is not zero, assuming that the AOA and AOD of the p-th resolvable path are respectively
And
is a quantity that reflects the position of the antenna array and the primary reflector; defining the azimuth angles of the transmitting array and the receiving array as
And
then the angle spread of the first resolvable path at the receiving end
Is composed of
In the formula (I), the compound is shown in the specification,
representing the arrival angle corresponding to the l-th indistinguishable path in the p-th resolvable path; l denotes the number of indistinguishable paths, for angular spread of the originating end
In the same way, the method can obtain,
assuming that the receiving antenna is in the far-field region of the transmitting antenna, the signal of the receiving antenna can be assumed to be a plane wave, and the additional time delay of the received signal of the r-th receiving antenna relative to the 1 st receiving antenna is
In the formula (d) Rx Is the distance between adjacent antennas, corresponding to an additional phase shift of the received signal of the r-th receiving antenna relative to the 1 st receiving antenna
Is composed of
Propagation response vector of uniform linear array at receiving end
Can be expressed as:
same propagation response vector capable of obtaining uniform linear array of sending end
Can be expressed as:
additional time delay of transmission signal of mth transmission antenna relative to 1 st transmission antenna
Comprises the following steps:
therefore, corresponding additional phase shift
Comprises the following steps:
because the decision time is limited, not all the arriving reflected waves of the signals can be separated, and if the mobile station or scatterer moves, the path length of each local scatterer changes, time-varying complex fading is generated, and for a given rate v, the maximum frequency offset is f d Space-time fading coefficient beta between mth transmitting antenna and mth receiving antenna of pth resolvable path p,m,r (t) is:
each arriving path experiences attenuation v p,l Suppose v p,l Is generated by a random process, and σ ν 1, the AOD is generally considered to be uniformly distributed in 0-2 pi in simulation, so that a classical power spectrum can be obtained,
in the case of a fixed m and r,
and
characterizing fading characteristics of a time domain; at a fixed time t, v corresponds to different m and r p,m,r And
then the spatial characteristics of the arrays are reflected and their correlation is propagated by the response vectors of the two arrays
And
determining, recording the time delay tau of the resolvable multipath generated by the p-th space main scatterer p And generally, it is assumed that independent processes between them are independent, and different propagation environments correspond to different ones
The distribution of the water content is carried out,
when the local scatterers are less, due to the action of the local scatterers around the transmitter, when the distance between the main reflector and the receiver is relatively large, the angle spread of the arrival angle of the receiving antenna is smaller, and at the moment, the receiving end only causes time fading without space fading; when a large number of local scatterers are arranged around the receiving antenna, a large angle is expanded, and at the moment, the receiving end generates space-time fading;
6) simulating an MIMO channel model, namely selecting a simulated MIMO wireless channel scene, wherein the simulated scene refers to channel propagation environments such as typical urban areas, severe urban areas, suburbs or villages;
7) then selecting antenna array structure of transmitting end and receiving end, i.e. number of antennas, antenna spacing and topological structure of array (uniform linear array ULA or other structure), etc. of transmitting end and receiving end, then inputting corresponding channel parameters including Doppler power spectrum, angle power spectrum (PAS), arrival angle (AOA) and departure angle of channelAngle (AOD), Angle Spread (AS), etc., respectively calculating spatial correlation matrix R of both ends of MIMO channel receiving and transmitting Rx And R Tx And obtaining an overall correlation matrix R of the MIMO channel MIMO Then to R MIMO Carrying out corresponding matrix decomposition to obtain a MIMO channel spatial correlation matrix C; then, according to PDP of channel, making power distribution of each branch, then producing related fading coefficient, finally according to calculation result, obtaining coefficient matrix A of each tap in tap delay line simulation model l And finally obtaining an MIMO channel matrix H;
8) the MIMO channel matrix may be generated by the following method:
firstly, generating correlation matrix R of MIMO channel receiving and transmitting ends according to the above steps Rx And R Tx Then according to formula
The overall correlation matrix of the resulting MIMO channel, consisting of R MIMO Performing corresponding matrix decomposition to obtain a symmetric mapping matrix C, wherein C is a space correlation formation matrix of the MIMO channel, namely: r MIMO ＝CC T (ii) a Finally, the coefficient matrix of the MIMO channel tap is calculated according to the following formula:
wherein, ν ec (·) represents arranging an M × N matrix into a vector of 1 × MN;
namely the fading coefficient of the MIMO channel; p is l Power of the first resolvable path; a is MN×1 ＝[a 1 ,a 2 ,...a MN ] T ；
9) In the simulation, a typical urban environment is selected, the antenna structure is a uniform linear array, and the number of antennas (N) at the transmitting end T ) 2, the number of antennas at the receiving end is (N) R ) 4, the type of angular power spectrum (PAS) is laplacian distribution. When the receiving endAnd the antenna spacing of the transmitting end is respectively 5 lambda and 0.5 lambda, the angle expansion is respectively 5 degrees and 30 degrees, and when the AOA and the AOD are both 0, the obtained channel matrix is as follows:
the correlation matrix at the transmitting end is:
the correlation matrix at the receiving end is:
the spatial correlation matrix of the channel is:
10) the influence of each parameter on the MIMO channel characteristic can be seen by changing the channel parameters while keeping other parameters unchanged, the antenna spacing of the transmitting and receiving ends is respectively changed into 6 lambda and 1.5 lambda, and the correlation of the channel is reduced along with the increase of the distance d between the antennas from the matrix obtained by simulation.
The correlation matrix at the transmitting end is:
the correlation matrix at the receiving end is:
the spatial correlation matrix of the channel is:
11) the following matrix is the correlation matrix for the channel when the other parameters are unchanged and the angular spread becomes 40 degrees, and comparing these several matrices with the first one in this section, it can be seen that: the correlation coefficient decreases with increasing angular spread.
The correlation matrix at the transmitting end is:
the correlation matrix at the receiving end is:
the spatial correlation matrix of the channel is:
12) the following matrices are other parameters invariant, and the AOA and AOD become the channel correlation matrix at 20 degrees, which is also compared with the first ones in this section, and can be seen: the larger the AOA and AOD, the larger the correlation coefficient.
The correlation matrix at the transmitting end is:
the correlation matrix at the receiving end is:
the spatial correlation matrix of the channel is:
although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in the embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. The heterogeneous network interference coordination method based on MassiveMIMO optimization is characterized by comprising the following steps:
1) firstly, different network deployment scenes need to be selected, and meanwhile, UE equipment is allowed to be connected with a base station, so that the network communication quality of a user is conveniently constructed and is often calculated by throughput;
2) measuring the use throughput of a user according to the allocation of UE, and dividing a macro cell and a micro cell in different cells, wherein the network coverage of the cells in the isomorphic network deployment is a co-located area provided by LTE and 5GNR base stations, which are overlapped on a model, so that the base station in the layout mode is defined as a macro base station, otherwise, in the heterogeneous network scene deployment, for convenience of statistics, capacity gain is observed, and the LTEeNB is continuously set as a main cell of the Men;
3) in order to analyze MIMO space-time channels in detail, multipath is analyzed from the perspective of a mathematical model, and a band-pass signal is as follows:
where s (t) -the equivalent low-pass signal; f. of c The received band-pass signal and the equivalent low-pass signal can be represented as a carrier frequency, assuming that the channel comprises L paths
Where rho l -attenuation coefficient of the l-th path; theta l (t) -phase shift of the l-th path; tau is l (t) -delay of the l-th path,
θ l (t)＝2πf l (t)-2π(f c +f l )τ l (t) (3.4)
in the formula f l The first term is the phase shift generated by the doppler shift, and the second term is the phase shift generated by the time delay, we will introduce the rayleigh fading model and the rice fading model to describe the signal variation in the narrow-band multipath environment (non-frequency selectivity), and for the non-frequency selective channel, the time delay spread is small relative to the symbol period, so there are the following assumptions:
s(t-τ l (t))≈s(t) (3.5)
if there are L multipaths present in the channel, the received signal can be represented as:
defining the complex multiplication coefficient as:
then there are:
a(t)＝a R (t)+ja I (t)＝a(t)e jφ(t) (3.8)
4) considering two uniform linear antenna arrays (ULA) with the number of antennas at the transmitting end being N and the number of antennas at the receiving end being M, assuming that the antennas are omnidirectional radiation antennas, the transmission signal on the antenna array at the transmitting end is recorded as:
s(t)＝[s 1 (t)，s 2 (t)，...s N (t)] T (3.13)
s N (t) represents the transmission signal on the nth transmitting antenna element, the symbol [ ·] T Representing the transpose of the vector (or matrix), and likewise, the received signal on the receiving-end antenna array can be represented as:
y(t)＝[y 1 (t)，y 2 (t)，...y M (t)] T (3.14)
a wideband MIMO wireless channel matrix describing the connection of a transmitting end and a receiving end can be represented as:
wherein H (τ) ∈ C M×N And is and
to describe the time delay tau of antenna array at both transmitting and receiving ends l Complex channel transmission coefficient matrix, hmn, of l Represents a complex transmission coefficient from the nth transmit antenna to the mth receive antenna, L represents the number of resolvable antennas;
the relationship between the transmitted signal vector s (t) and the received signal vector y (t) can be expressed as (excluding noise):
y(t)＝∫H(τ)s(t-τ)dτ (3.16)
or:
s(t)＝∫H T (τ)y(t-τ)dτ； (3.17)
5) in the study of MIMO channel models, it is generally assumed that there are few spatially independent primary reflectors in the far-field region, one primary reflector has a primary path containing a large number of incoming waves caused by the structure of local scatterers near the receiver and transmitter with small relative delays that cannot be separated by the receiver, i.e., an unresolvable path, which will lead to space-time fading due to non-zero angular spread;
6) based on the simulation of the MIMO channel model, firstly selecting a simulated MIMO wireless channel scene;
7) then selecting antenna array structure of transmitting end and receiving end, i.e. number of antennas, antenna spacing and topological structure of array (uniform linear array ULA or other structure), etc. of transmitting end and receiving end, then inputting corresponding channel parameters including Doppler power spectrum, angle power spectrum (PAS), arrival angle (AOA), departure Angle (AOD), Angle Spread (AS) and so on of channel, respectively calculating space correlation matrix R of transmitting and receiving ends of MIMO channel Rx And R Tx And obtaining an overall correlation matrix R of the MIMO channel MIMO Then to R MIMO Carrying out corresponding matrix decomposition to obtain a MIMO channel spatial correlation matrix C; then, according to PDP of channel, making power distribution of each branch, then producing related fading coefficient, finally according to calculation result, obtaining coefficient matrix A of each tap in tap delay line simulation model l And finally obtaining an MIMO channel matrix H;
8) the MIMO channel matrix may be generated by the following method:
firstly, generating correlation matrix R of MIMO channel receiving and transmitting ends according to the above steps Rx And R Tx Then according to formula
The overall correlation matrix of the resulting MIMO channel, consisting of R MIMO Performing corresponding matrix decomposition to obtain a symmetric mapping matrix C, which is the spatial phase of the MIMO channelThe matrix is formed, namely: r is MIMO ＝CC T (ii) a Finally, the coefficient matrix of the MIMO channel tap is calculated according to the following formula:
wherein, ν ec (·) indicates arranging an M × N matrix into a vector of 1 × MN;
namely the fading coefficient of the MIMO channel; p l Power of the first resolvable path; a is MN×1 ＝[a 1 ,a 2 ,...a MN ] T 。
2. The MassiveMIMO optimization-based heterogeneous network interference coordination method according to claim 1, characterized in that: and selecting common 3GPP Release-147-0 to build a homogeneous or heterogeneous network in the step 1.
3. The MassiveMIMO optimization-based heterogeneous network interference coordination method according to claim 1, characterized in that: in the step 2, on one hand, the positions of the 5GgNB are randomly and irregularly distributed, so that the hot spot capacity and coverage enhancement provided by the cell can be displayed and processed in the cell; on the other hand, the irregularities in shape determine the difference in performance, and the choice of conductor and material medium also limits the loss of bandwidth.
4. The MassiveMIMO optimization-based heterogeneous network interference coordination method according to claim 1, characterized in that: the parameters of the MIMO wireless channel in the step 3 mainly comprise a power azimuth angle spectrum, angle expansion, an average wave arrival angle of a receiving end and an average wave removal angle of a transmitting end, configuration of transmitting and receiving multiple antennas and the like;
(1) angular power spectrum
The angular power spectrum (PAS), or so-called angular spectral distribution probability density function, is
The angular spectral distribution of the power spectral density of a signal is an important spatial characteristic of a wireless channel and is mainly determined by the characteristics of a propagation environment, commonly used angular spectral distributions comprise cosine distribution proposed by Lee and Salz assumed uniform distribution, Aszetly considers that Gaussian distribution is closer to the test result of the angular spectral distribution of a GSM system, Pedersen indicates that Laplace distribution is more consistent with the angular spectral distribution of a DCS1800 system, Weibull distribution is consistent with indoor angular spectral distribution and the like,
(2) spread of scattering angle
The scattering angle spectrum distribution macroscopically describes the distribution characteristics of multipath scattering, an important parameter of various distributions is the angular spread of scattering, which describes the scattering degree of scattering, determines the separability of signals to a great extent, and is a very important channel space characteristic parameter, and there are a plurality of ways for defining the angular spread, two of which are common:
a. defining the mean value of the spread angle as the angular spread (σ) rms )
In the formula:
here, the
Is shown at an angle
Is controlled by the power of the received power,
the standard deviation defining the energy distribution within the spread angle is the angular spread (σ)
In the formula (I), the compound is shown in the specification,
here, the
Is the power azimuth spectrum of the scattering multipath,
represents the average angle of arrival or the average angle of departure;
the spread angle is the spread angular domain of the scattered multipath signal in space and is expressed by 2 delta, namely the wave arrival azimuth limitation
For a uniform angular spectral distribution, the angle is spread to
For gaussian and lagrange angular distributions, the angular spread is given by equation (3.12).
5. The MassiveMIMO optimization-based heterogeneous network interference coordination method according to claim 4, wherein: the average wave arrival angle and the average wave removal angle in the step 4 are as follows: in many previous studies, the average arrival direction and the average departure direction were assumed to be perpendicular to the array axis, while other directions were ignored; in fact, the influence of the average arrival angle and the average de-wavering angle on the spatial characteristics of the channel is not negligible, and the deviation of the average arrival angle and the de-wavering angle from the normal direction of the array will result in the enhancement of the correlation of multipath signals, the reduction of separability and the degradation of channel performance.
6. The MassiveMIMO optimization-based heterogeneous network interference coordination method according to claim 4, wherein: the configuration of the transceiving multiple antennas in the step 4: the multi-antenna system is an important component of a wireless system, is a tool for transmitting signals and capturing multipath in a communication system, and can seriously affect the spatial characteristics of a channel in a configuration form; the directional pattern, gain, polarization, spacing, cross-talk, spatial layout, etc. of the multiple antenna elements need to be considered carefully.
7. The MassiveMIMO optimization-based heterogeneous network interference coordination method according to claim 4, wherein: doppler spread in said step 4: caused by relative motion between the transceiving ends or motion of scatterers, channel time-varying and intersymbol interference (ISI) may result.
8. The MassiveMIMO optimization-based heterogeneous network interference coordination method according to claim 1, characterized in that: the simulated scene in step 6 refers to a typical urban area, a severe urban area, a suburban area, a rural area, or a channel propagation environment such as a rural area.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210356772.3A CN114885339A (en) | 2022-04-06 | 2022-04-06 | MassiveMIMO optimization-based heterogeneous network interference coordination method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210356772.3A CN114885339A (en) | 2022-04-06 | 2022-04-06 | MassiveMIMO optimization-based heterogeneous network interference coordination method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114885339A true CN114885339A (en) | 2022-08-09 |
Family
ID=82670636
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210356772.3A Pending CN114885339A (en) | 2022-04-06 | 2022-04-06 | MassiveMIMO optimization-based heterogeneous network interference coordination method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114885339A (en) |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110381534A (en) * | 2019-07-16 | 2019-10-25 | 重庆邮电大学 | A kind of cell selecting method and equipment based on network cooperation |
-
2022
- 2022-04-06 CN CN202210356772.3A patent/CN114885339A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110381534A (en) * | 2019-07-16 | 2019-10-25 | 重庆邮电大学 | A kind of cell selecting method and equipment based on network cooperation |
Non-Patent Citations (5)
| Title |
|---|
| RF技术社区: "LTE/5G双连接关键技术", HTTPS://RF.EEFOCUS.COM/ARICLE/ID-333152, pages 1 - 3 * |
| 文库吧: "MIMO信道建模(本科生毕业论文).doc", HTTPS://WWW.WENKUB.COM/DOC-301792318.HTML, pages 2 - 4 * |
| 百度: "基于5G条件下的异构网络信道干扰模型建立方法-时间证明", 百度, pages 1 * |
| 百度文库: "基于5G条件下的异构网络信道干扰模型建立方法", HTTPS://WENKU.BAIDU.COM/VIEW/3AA52EB6CE1755270722192E453610661FD95A5C.HTML?_WKTS_=1695692797897&BDQUERY=%E5%9F%BA%E4%BA%8E5G%E6%9D%A1%E4%BB%B6%E4%B8%8B%E7%9A%84%E5%BC%82%E6%9E%84%E7%BD%91%E7%BB%9C%E4%BF%A1%E9%81%93%E5%B9%B2%E6%89%B0%E6%A8%A1%E5%9E%8B, pages 1 - 38 * |
| 秦圈圈: "(本科生毕业论文)MIMO信道建模.doc", HTTPS://MAX.BOOK118.COM/HTML/2018/0912/7036152131001146.SHTM, pages 2 - 4 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Uwaechia et al. | A comprehensive survey on millimeter wave communications for fifth-generation wireless networks: Feasibility and challenges | |
| MacCartney et al. | Millimeter-wave base station diversity for 5G coordinated multipoint (CoMP) applications | |
| Mumtaz et al. | MmWave massive MIMO: a paradigm for 5G | |
| Samimi et al. | 28 GHz millimeter-wave ultrawideband small-scale fading models in wireless channels | |
| Bai et al. | Coverage and capacity of millimeter-wave cellular networks | |
| Thomä et al. | MIMO vector channel sounder measurement for smart antenna system evaluation | |
| Hur et al. | Multilevel millimeter wave beamforming for wireless backhaul | |
| EP1189364B1 (en) | Radio device | |
| US7800552B2 (en) | Antenna apparatus for multiple input multiple output communication | |
| Jeng et al. | Experimental studies of spatial signature variation at 900 MHz for smart antenna systems | |
| JP2008541639A (en) | Multi-input multi-output communication system | |
| Zou et al. | Beamforming codebook design and performance evaluation for 60GHz wireless communication | |
| CN114095318A (en) | Intelligent super-surface-assisted hybrid configuration millimeter wave communication system channel estimation method | |
| Mudonhi et al. | Indoor mmWave channel characterization with large virtual antenna arrays | |
| Jiang et al. | Dual-beam intelligent reflecting surface for millimeter and THz communications | |
| Khan et al. | Antenna beam-forming for a 60 Ghz transceiver system | |
| Abbas et al. | Millimeter wave communications over relay networks | |
| Ward et al. | Characterising the radio propagation channel for smart antenna systems | |
| Rappaport | 5G millimeter wave wireless: Trials, testimonies, and target rollouts | |
| Eisenbeis et al. | Hybrid beamforming analysis based on MIMO channel measurements at 28 GHz | |
| CN114885339A (en) | MassiveMIMO optimization-based heterogeneous network interference coordination method | |
| Norklit et al. | The angular aspect of wideband modelling and measurements | |
| Mateo et al. | A comprehensive study of low frequency and high frequency channel correlation | |
| Kühne et al. | Performance simulation of a 5G hybrid beamforming millimeter-wave system | |
| Haq et al. | Analysis on Channel Parameters and Signal Processing methods at mm-wave for 5G networks |
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 |