CN110912630A - Airspace spectrum sensing method based on multiple antennas - Google Patents

Airspace spectrum sensing method based on multiple antennas Download PDF

Info

Publication number
CN110912630A
CN110912630A CN201911173825.2A CN201911173825A CN110912630A CN 110912630 A CN110912630 A CN 110912630A CN 201911173825 A CN201911173825 A CN 201911173825A CN 110912630 A CN110912630 A CN 110912630A
Authority
CN
China
Prior art keywords
spectrum
user
signal
matrix
space
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201911173825.2A
Other languages
Chinese (zh)
Other versions
CN110912630B (en
Inventor
陈云华
史治平
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
University of Electronic Science and Technology of China
Original Assignee
University of Electronic Science and Technology of China
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by University of Electronic Science and Technology of China filed Critical University of Electronic Science and Technology of China
Priority to CN201911173825.2A priority Critical patent/CN110912630B/en
Publication of CN110912630A publication Critical patent/CN110912630A/en
Application granted granted Critical
Publication of CN110912630B publication Critical patent/CN110912630B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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

Landscapes

  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention belongs to the technical field of cognitive radio, and particularly relates to a spatial domain spectrum sensing method based on multiple antennas. The invention takes the space angle dimension information as a new space spectrum sensing method of spectrum opportunity, estimates the arrival angle of the signal space angle dimension, and can avoid the communication direction of the main user or carry out null antenna beam design on the communication direction of the main user through the beam forming technology, so that the cognitive user can avoid the communication direction of the main user at the same frequency, the same time or even the same place and carry out spectrum access through different space angles, thereby increasing the system capacity and improving the spectrum utilization rate. The invention takes the space angle dimension information as a new spectrum opportunity to detect the spectrum cavity of the space angle dimension, and compared with the traditional dimension spectrum sensing algorithm, the invention increases the system capacity and improves the spectrum utilization rate although the realization complexity is increased.

Description

Airspace spectrum sensing method based on multiple antennas
Technical Field
The invention belongs to the technical field of cognitive radio, and particularly relates to a spatial domain spectrum sensing method based on multiple antennas.
Background
With the continuous development of wireless communication, spectrum resources become more scarce, which severely restricts the development of communication technology. To promote the development of wireless communication, it is necessary to improve the utilization rate of spectrum resources, wherein the Cognitive Radio (CR) technology is an effective method for solving the spectrum shortage and improving the utilization rate of spectrum resources. The basic idea of CR is spectrum sharing or spectrum reuse, which is characterized by allowing an unauthorized Cognitive User (CU) to access an authorized frequency band at an opportunity without interfering with the communication of an authorized Primary User (PU). To achieve this, the Cognitive User (CU) system must continuously detect whether an authorized Primary User (PU) is occupying a certain authorized frequency band, i.e. a spectrum sensing process.
Although the traditional dimensional spectrum sensing algorithm improves the detection performance to a certain extent, the detection is mainly carried out in the frequency dimension, the time dimension and the geographical latitude, and the spectrum development capability is limited. On the other hand, due to the rapid development of the multi-antenna technology and the application of the 5G large-scale antenna array, the mobile terminal and the base station have the angle identification capability, and the development of angle dimensional spectrum resources is promoted. If the arrival angle of the signal space angle dimension is estimated, the main user (PU) communication direction can be avoided or the null antenna beam design can be carried out on the main user communication direction through the beam forming technology, so that the cognitive user can avoid the communication direction of the main user at the same frequency, the same time or even the same place, and the spectrum access is carried out through different space angles, thereby increasing the system capacity and improving the spectrum utilization rate.
Disclosure of Invention
The invention provides a space domain spectrum sensing method based on multiple antennas, and aims to increase system capacity and improve spectrum utilization rate.
The technical scheme of the invention is as follows:
for a Cognitive User (CU) carrying multiple antennas, the antennas are isotropic M-element Uniform Circular Arrays (UCAs). Assuming that D (D is less than or equal to M) far-field main user signals in the space are incident to the M-element uniform circular array from different directions, and the circle center of the uniform circular array is taken as a reference point, the ith main user signal reaching the array element j is as follows:
Figure BDA0002289445980000011
wherein h isijIndicating the i-th primary user signal siChannel gain between (t) and jth receive antenna, zi(t) is the complex envelope of the ith primary user signal, containing signal information,
Figure BDA0002289445980000021
is the carrier of the spatial signal. Since the signal satisfies the narrowband assumption, zi(t-τ)≈zi(t) throughThe signal after the propagation delay τ can be expressed as:
Figure BDA0002289445980000022
then ideally the signal received by the jth array element can be expressed as:
Figure BDA0002289445980000023
wherein, tauijThe time delay of the ith main user signal reaching the array element j relative to the reference point, wj(t) variance σ over array element j2White additive gaussian noise.
The spatial domain spectrum sensing method comprises the following steps:
s1, the array antennas perform N times of sampling on the received signal, and the signal received by each array antenna of the cognitive user is represented as:
Figure BDA0002289445980000024
wherein,
Figure BDA0002289445980000025
for the phase difference caused by the propagation delay of the signal,
Figure BDA0002289445980000026
1,2, D denotes the ith primary user signal, 1,2, …, M denotes the jth receiving antenna, θiAnd
Figure BDA0002289445980000027
respectively indicates the azimuth angle and the elevation angle of the ith primary user signal, N is 0,1, …, N-1 indicates the nth sampling number, and λ indicates the wavelength,
Figure BDA0002289445980000028
Represents the carrier angular frequency; let the received data of M array antennas form an M × N dimensional matrix:
Figure BDA0002289445980000029
wherein,
Figure BDA00022894459800000210
the channel gain matrix of the receiving antenna of the main user and the cognitive user is shown,' indicates the dot product of the matrix,
Figure BDA00022894459800000211
in the form of a matrix of signals,
Figure BDA00022894459800000212
in order for the array to be popular,
Figure BDA0002289445980000031
Figure BDA0002289445980000032
is an additive noise matrix;
s2, calculating a sample covariance matrix
Figure BDA0002289445980000033
Figure BDA0002289445980000034
Obtaining an estimated autocorrelation function by sampling a sequence
Figure BDA0002289445980000035
Then to
Figure BDA0002289445980000036
Decomposing the eigenvalues to obtain M eigenvalues and corresponding eigenvectors thereof, thereby obtaining
Figure BDA0002289445980000037
Maximum eigenvalue of
Figure BDA0002289445980000038
Trace
Figure BDA0002289445980000039
And geometric mean of eigenvalues
Figure BDA00022894459800000310
S3, taking α E [0,1], calculating a test statistic T of the fusion detection algorithm:
Figure BDA00022894459800000311
obtaining false alarm probability P according to random matrix theoryfa：
Figure BDA00022894459800000312
Wherein,
Figure BDA00022894459800000313
σ2is the variance of white Gaussian noise w (n),
Figure BDA00022894459800000314
Figure BDA00022894459800000315
FTW(.) is a first order Tracy-Widom distribution; according to false alarm probability PfaDetermining a decision threshold gamma:
Figure BDA0002289445980000041
wherein
Figure BDA0002289445980000042
Is the inverse of the first-order Tracy-Widom distribution;
s4, comparing the statistic T with a decision threshold gamma:
if the test statistic T is larger than the judgment threshold gamma, the sub-band is occupied, a master user exists, and the step S5 is carried out;
if the test statistic T is smaller than the judgment threshold gamma, the sub-band is not occupied, a master user does not exist, and the cognitive user directly performs spectrum access;
s5, estimating the number of main signals
Figure BDA0002289445980000043
C, arranging the eigenvalues of the sample covariance matrix obtained in the step b from small to large, namely lambda1≥…≥λD>λD+1≥…≥λM，V＝[q1,q2,...,qM]Is a corresponding characteristic value, calculating gammak＝λk/λk+1K is 1,2, …, M-1, and an estimate of the number of primary signals is taken
Figure BDA0002289445980000044
To make gammak＝max(γ1,γ2,…,γM-1) K is 1,2, …, value of k at M-1;
s6, DOA estimation is carried out on the main signal: estimating the value according to the number of main signals
Figure BDA0002289445980000045
Structure of the device
Figure BDA0002289445980000046
Noise subspace of dimension
Figure BDA0002289445980000047
According to
Figure BDA0002289445980000048
Calculating Music space spectrum, searching Music space and finding out
Figure BDA0002289445980000049
And obtaining a main signal DOA estimated value by the peak value, and performing spectrum access on the communication direction avoiding the main user by the cognitive user through a beam forming technology.
The invention has the beneficial effects that: the spatial angle dimension information is used as a new spectrum opportunity to detect the spectrum cavity of the spatial angle dimension, and compared with the traditional dimensional spectrum sensing algorithm, the method increases the realization complexity, increases the system capacity and improves the spectrum utilization rate.
Drawings
FIG. 1 is a system diagram of a spatial-spectral sensing scheme of the present invention;
FIG. 2 is a diagram of a Uniform Circular Array (UCA) model;
FIGS. 3 and 5 are schematic diagrams of signal-to-noise ratio of detection probability VS at α ∈ [0.1,1] under Gaussian channel and Rayleigh fading channel, respectively;
fig. 4 and 6 are schematic diagrams of the DOA estimation Root Mean Square Error (RMSE) VS signal-to-noise ratio under gaussian channel and rayleigh fading channel, respectively.
Detailed Description
The technical solution of the present invention has been described in detail in the summary of the invention section, and the following description is provided to illustrate the applicability of the present invention in conjunction with a simulation example.
Suppose that there is only one master user with frequency f (D ═ 1), the transmitted signal is a QPSK signal, the number of antennas in the uniform circular array is M ═ 16, and the number of sampling points N is 10000.
First, the relationship between the signal-to-noise ratio and the detection probability of the detection scheme at different α values is comparedfaThe number of monte carlo simulations is 2000 for different signal-to-noise ratios (SNRs) — 0.01, SNR-24: 2: 4, as can be seen from fig. 3 and 5, when α e [0.1,1]The smaller the value of α, the better the detection performance of the detection scheme used in the scheme, when α is equal to 0.5 and α is equal to 1, the detection scheme used in the scheme is equivalent to the ME-GM (maximum-eigen-geometric-mean) algorithm and the MET (maximum-eigen-trace) algorithm, respectively, and as can be seen from fig. 3 and 5, when α is equal to or less than 0.4, the detection scheme used in the scheme is superior to the ME-GM algorithm and the MET algorithm.
Comparing Root Mean Square Error (RMSE) of DOA estimates at different signal-to-noise ratios, set the primary signal direction (θ, Φ) to (125 °,80.1 °), SNR to-22: 2: 4, the number of monte carlo simulations was 200 for different signal-to-noise ratios (SNRs). As can be seen from FIGS. 4 and 6, when SNR ≧ 15dB, the root mean square error RMSE of DOA estimation is <1 °, the DOA estimation scheme can more accurately estimate the arrival direction of the primary user signal, and the uniform circular array can realize 360 ° all-round estimation. After estimating the DOA of the main user signal, the cognitive user can avoid the main user access direction to perform spectrum access by using the beam forming technology, so that the spectrum utilization rate is improved, and the scheme can improve the spectrum utilization rate and increase the system capacity.

Claims (1)

1. A space domain spectrum sensing method based on multiple antennas is characterized in that for cognitive users carrying multiple antennas, the antennas are isotropic M-element uniform circular arrays, and D far-field main user signals enter the M-element uniform circular arrays from different directions in space, and the method comprises the following steps:
s1, the array antennas perform N times of sampling on the received signal, and the signal received by each array antenna of the cognitive user is represented as:
Figure FDA0002289445970000011
wherein,
Figure FDA0002289445970000012
for the phase difference caused by the propagation delay of the signal,
Figure FDA0002289445970000013
1,2, D denotes the ith primary user signal, 1,2, …, M denotes the jth receiving antenna, θiAnd
Figure FDA0002289445970000014
respectively indicates the azimuth angle and the elevation angle of the ith primary user signal, N is 0,1, …, N-1 indicates the nth sampling number, and λ indicates the wavelength,
Figure FDA0002289445970000015
Represents the carrier angular frequency; let the received data of M array antennas form an M × N dimensional matrix:
Figure FDA0002289445970000016
wherein,
Figure FDA0002289445970000017
the channel gain matrix of the receiving antenna of the main user and the cognitive user is shown,' indicates the dot product of the matrix,
Figure FDA0002289445970000018
in the form of a matrix of signals,
Figure FDA0002289445970000019
in order for the array to be popular,
Figure FDA00022894459700000110
is an additive noise matrix;
s2, calculating a sample covariance matrix
Figure FDA00022894459700000111
Figure FDA00022894459700000112
Obtaining an estimated autocorrelation function by sampling a sequence
Figure FDA00022894459700000113
Then to
Figure FDA00022894459700000114
Decomposing the eigenvalues to obtain M eigenvalues and corresponding eigenvectors thereof, thereby obtaining
Figure FDA00022894459700000115
Maximum eigenvalue of
Figure FDA00022894459700000116
Trace
Figure FDA00022894459700000117
And geometric mean of eigenvalues
Figure FDA0002289445970000021
S3, taking α E [0,1], calculating a test statistic T of the fusion detection algorithm:
Figure FDA0002289445970000022
obtaining false alarm probability P according to random matrix theoryfa：
Figure FDA0002289445970000023
Wherein,
Figure FDA0002289445970000024
σ2is the variance of white Gaussian noise w (n),
Figure FDA0002289445970000025
Figure FDA0002289445970000026
FTW(.) is a first order Tracy-Widom distribution; according to false alarm probability PfaDetermining a decision threshold gamma:
Figure FDA0002289445970000027
wherein
Figure FDA0002289445970000028
Is the inverse of the first-order Tracy-Widom distribution;
s4, comparing the statistic T with a decision threshold gamma:
if the test statistic T is larger than the judgment threshold gamma, the sub-band is occupied, a master user exists, and the step S5 is carried out;
if the test statistic T is smaller than the judgment threshold gamma, the sub-band is not occupied, a master user does not exist, and the cognitive user directly performs spectrum access;
s5, estimating the number of main signals
Figure FDA0002289445970000031
C, arranging the eigenvalues of the sample covariance matrix obtained in the step b from small to large, namely lambda1≥…≥λD>λD+1≥…≥λM，V＝[q1,q2,...,qM]Is a corresponding characteristic value, calculating gammak＝λk/λk+1K is 1,2, …, M-1, and an estimate of the number of primary signals is taken
Figure FDA0002289445970000032
To make gammak＝max(γ1,γ2,…,γM-1) K is 1,2, …, value of k at M-1;
s6, DOA estimation is carried out on the main signal: estimating the value according to the number of main signals
Figure FDA0002289445970000033
Structure of the device
Figure FDA0002289445970000034
Noise subspace of dimension
Figure FDA0002289445970000035
According to
Figure FDA0002289445970000036
Calculating Music space spectrum, searching Music space and finding out
Figure FDA0002289445970000037
Peak value, thereby obtaining the DOA estimated value of the main signal and cognizing the userAnd performing spectrum access on the direction avoiding the main user communication through a beam forming technology.
CN201911173825.2A 2019-11-26 2019-11-26 Airspace spectrum sensing method based on multiple antennas Active CN110912630B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911173825.2A CN110912630B (en) 2019-11-26 2019-11-26 Airspace spectrum sensing method based on multiple antennas

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911173825.2A CN110912630B (en) 2019-11-26 2019-11-26 Airspace spectrum sensing method based on multiple antennas

Publications (2)

Publication Number Publication Date
CN110912630A true CN110912630A (en) 2020-03-24
CN110912630B CN110912630B (en) 2021-05-14

Family

ID=69819469

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911173825.2A Active CN110912630B (en) 2019-11-26 2019-11-26 Airspace spectrum sensing method based on multiple antennas

Country Status (1)

Country Link
CN (1) CN110912630B (en)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111835392A (en) * 2020-07-13 2020-10-27 电子科技大学 Multi-antenna space-domain spectrum sensing method based on non-circular signals
CN112073131A (en) * 2020-07-29 2020-12-11 北京邮电大学 Spectrum sensing method based on phase difference distribution curve analytic expression and related equipment
CN112073130A (en) * 2020-07-29 2020-12-11 北京邮电大学 Frequency spectrum sensing method based on three-point shaping of phase difference distribution curve and related equipment
CN113037408A (en) * 2021-03-09 2021-06-25 中国人民解放军军事科学院国防科技创新研究院 Signal sensing method and device combining space arrival angle and frequency spectrum two-dimensional
WO2023213081A1 (en) * 2022-05-05 2023-11-09 中兴通讯股份有限公司 Spectrum sensing method, electronic device and computer readable storage medium

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101808334A (en) * 2010-03-15 2010-08-18 北京科技大学 Spectrum perception method for detecting angle of arrival of authorized user in cognitive radio
CN102291186A (en) * 2011-07-06 2011-12-21 电子科技大学 Frequency spectrum perceiving method based on estimation of signal arrival direction
JP2015087132A (en) * 2013-10-28 2015-05-07 株式会社東芝 Signal detection device and signal detection method
CN109600181A (en) * 2018-12-17 2019-04-09 电子科技大学 A kind of frequency spectrum sensing method for multiple antennas

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101808334A (en) * 2010-03-15 2010-08-18 北京科技大学 Spectrum perception method for detecting angle of arrival of authorized user in cognitive radio
CN102291186A (en) * 2011-07-06 2011-12-21 电子科技大学 Frequency spectrum perceiving method based on estimation of signal arrival direction
JP2015087132A (en) * 2013-10-28 2015-05-07 株式会社東芝 Signal detection device and signal detection method
CN109600181A (en) * 2018-12-17 2019-04-09 电子科技大学 A kind of frequency spectrum sensing method for multiple antennas

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
M. G. POROZANTZIDOU等: "Azimuth and elevation angles estimation using 2-D MUSIC algorithm with an L-shape antenna", 《2010 IEEE ANTENNAS AND PROPAGATION SOCIETY INTERNATIONAL SYMPOSIUM》 *
赵文静等: "基于特征值的频谱感知融合算法", 《通信学报》 *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111835392A (en) * 2020-07-13 2020-10-27 电子科技大学 Multi-antenna space-domain spectrum sensing method based on non-circular signals
CN111835392B (en) * 2020-07-13 2023-04-28 电子科技大学 Multi-antenna airspace frequency spectrum sensing method based on non-circular signals
CN112073131A (en) * 2020-07-29 2020-12-11 北京邮电大学 Spectrum sensing method based on phase difference distribution curve analytic expression and related equipment
CN112073130A (en) * 2020-07-29 2020-12-11 北京邮电大学 Frequency spectrum sensing method based on three-point shaping of phase difference distribution curve and related equipment
CN113037408A (en) * 2021-03-09 2021-06-25 中国人民解放军军事科学院国防科技创新研究院 Signal sensing method and device combining space arrival angle and frequency spectrum two-dimensional
CN113037408B (en) * 2021-03-09 2022-04-08 中国人民解放军军事科学院国防科技创新研究院 Signal sensing method and device combining space arrival angle and frequency spectrum two-dimensional
WO2023213081A1 (en) * 2022-05-05 2023-11-09 中兴通讯股份有限公司 Spectrum sensing method, electronic device and computer readable storage medium

Also Published As

Publication number Publication date
CN110912630B (en) 2021-05-14

Similar Documents

Publication Publication Date Title
CN110912630B (en) Airspace spectrum sensing method based on multiple antennas
Guo et al. Indoor localization by fusing a group of fingerprints based on random forests
Xiong et al. Towards fine-grained radio-based indoor location
CN103747455B (en) Channel modeling method and characteristic parameter matching method based on the distribution of nonisotropic scattering body
CN108646213B (en) Direct wave AOA (automatic optical inspection) judgment method in indoor multipath environment
CN114144977B (en) Beam forming method, device, wireless access network equipment and readable storage medium
CN112469119B (en) Positioning method, positioning device, computer equipment and storage medium
CN111405657B (en) CSI-based single access point positioning method based on arrival angle and arrival time difference
Adam et al. Perfomance study of direction of arrival (DOA) estimation algorithms for linear array antenna
CN111835392B (en) Multi-antenna airspace frequency spectrum sensing method based on non-circular signals
US9407339B2 (en) Monotonic optimization method for achieving the maximum weighted sum-rate in multicell downlink MISO systems
CN104506256B (en) Performance evaluation method for MIMO (Multiple Input Multiple Output) multi-antenna system and multi-antenna system
CN110149134A (en) A kind of more feed satellite interference suppressing methods based on frequency spectrum perception
Widianto et al. Performance evaluation of an IoT device using a cognitive radio in GLRT approach
CN105847196B (en) A kind of channel estimation methods of the multiple cell Massive mimo system based on DOA
CN112752339B (en) Fingerprint database updating method based on large-scale MIMO single-station system
Wang et al. DOA estimation of smart antenna signal based on MUSIC algorithm
WO2017152504A1 (en) Beam search method and apparatus
JP2004061468A (en) Method for estimating arrival direction of multiplex wave using spatial characteristic and receiving beam formation device using the same
Nxumalo et al. Direction of arrival (DOA) estimation for smart antennas in weather impacted environments
Xue et al. A novel clustering method based on density peaks and its validation by channel measurement
TATEGAMI et al. 2D-DOA estimation by compressed sensing using sub-arrays in an actual urban street cell environment
Claudien et al. Performance analysis of 2D-ESPRIT algorithm for URA in estimation of 2D-DOA in massive-mimo systems
Ding et al. A Method of Spectrum Sensing Based on Multiple Antennas
Hu et al. The effects of the rotating step on analyzing the virtual multi-antenna measurement results at 28 GHz

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant