CN116722360A - Stacked high-isolation full-duplex antenna based on deep learning optimization and communication equipment - Google Patents
Stacked high-isolation full-duplex antenna based on deep learning optimization and communication equipment Download PDFInfo
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- 238000002955 isolation Methods 0.000 title claims abstract description 51
- 238000013135 deep learning Methods 0.000 title claims abstract description 18
- 238000004891 communication Methods 0.000 title claims abstract description 17
- 238000005457 optimization Methods 0.000 title claims abstract description 13
- 230000003071 parasitic effect Effects 0.000 claims abstract description 65
- 230000005855 radiation Effects 0.000 claims abstract description 14
- 239000002184 metal Substances 0.000 claims abstract description 12
- 230000005284 excitation Effects 0.000 claims description 35
- 239000000523 sample Substances 0.000 claims description 20
- 230000006870 function Effects 0.000 claims description 15
- 238000003062 neural network model Methods 0.000 claims description 12
- 210000002569 neuron Anatomy 0.000 claims description 5
- 230000004044 response Effects 0.000 claims description 3
- 238000005388 cross polarization Methods 0.000 abstract description 3
- 238000013461 design Methods 0.000 description 10
- 238000010586 diagram Methods 0.000 description 8
- 238000013473 artificial intelligence Methods 0.000 description 7
- 230000008878 coupling Effects 0.000 description 5
- 238000010168 coupling process Methods 0.000 description 5
- 238000005859 coupling reaction Methods 0.000 description 5
- 230000010287 polarization Effects 0.000 description 4
- 238000013136 deep learning model Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000009977 dual effect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000007639 printing Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000003475 lamination Methods 0.000 description 1
- 238000000034 method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000007480 spreading Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 239000011800 void material Substances 0.000 description 1
Classifications
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01Q—ANTENNAS, i.e. RADIO AERIALS
- H01Q1/00—Details of, or arrangements associated with, antennas
- H01Q1/52—Means for reducing coupling between antennas; Means for reducing coupling between an antenna and another structure
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0499—Feedforward networks
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01Q—ANTENNAS, i.e. RADIO AERIALS
- H01Q1/00—Details of, or arrangements associated with, antennas
- H01Q1/12—Supports; Mounting means
- H01Q1/22—Supports; Mounting means by structural association with other equipment or articles
- H01Q1/24—Supports; Mounting means by structural association with other equipment or articles with receiving set
- H01Q1/241—Supports; Mounting means by structural association with other equipment or articles with receiving set used in mobile communications, e.g. GSM
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01Q—ANTENNAS, i.e. RADIO AERIALS
- H01Q1/00—Details of, or arrangements associated with, antennas
- H01Q1/48—Earthing means; Earth screens; Counterpoises
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01Q—ANTENNAS, i.e. RADIO AERIALS
- H01Q9/00—Electrically-short antennas having dimensions not more than twice the operating wavelength and consisting of conductive active radiating elements
- H01Q9/04—Resonant antennas
- H01Q9/0407—Substantially flat resonant element parallel to ground plane, e.g. patch antenna
- H01Q9/045—Substantially flat resonant element parallel to ground plane, e.g. patch antenna with particular feeding means
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention discloses a stacked high-isolation full-duplex antenna and communication equipment based on deep learning optimization, wherein the antenna comprises a top layer structure, a middle layer structure, a bottom layer structure and a feed structure; the top layer structure comprises a first dielectric plate, a radiation structure and a first parasitic structure, wherein the radiation structure and the first parasitic structure are arranged on the upper surface of the first dielectric plate, the radiation structure comprises a first radiator and a second radiator, the first parasitic structure is positioned between the first radiator and the second radiator, and the feed structure is used for feeding the first radiator and the second radiator; the intermediate layer structure comprises two second dielectric plates and two second parasitic structures, and the two second parasitic structures are arranged on the upper surface of the second dielectric plates; the substructure comprises a third dielectric plate and a metal floor, and the third dielectric plate is respectively stacked with the second dielectric plate and the metal floor. The antenna has the advantages of higher in-band isolation, wide frequency band, low profile and low cross polarization.
Description
Technical Field
The invention relates to a full duplex antenna, in particular to a stacked high-isolation full duplex antenna based on deep learning optimization and communication equipment, and belongs to the technical field of wireless communication.
Background
With the rapid development of 5G communication technology, the same-frequency full duplex technology plays an increasingly important role in a communication system. Simultaneous co-frequency full duplex communication means that both parties to the communication can receive and transmit in the same frequency and time period. Simultaneous co-frequency full duplex systems are generally required to achieve high isolation of 110-150 dB. Therefore, for antennas applied to full duplex systems, how to increase the isolation between the antenna transmit and receive channels is very challenging. In addition, with the rapid development of Artificial Intelligence (AI) deep learning algorithms, various industries including electromagnetics and antenna fields are rapidly spreading. The artificial intelligent deep learning algorithm model which is completed by training is similar to an electromagnetic simulation software solver, so that the model design and optimization based on electromagnetic software can be remarkably accelerated. Therefore, the artificial intelligence deep learning algorithm has great advantages in the application of the artificial intelligence deep learning algorithm to the optimal design of the antenna.
The Chinese patent document CN115810908A proposes a common-caliber broadband co-polarized dielectric patch full-duplex antenna loaded with a metal wall, the invention adopts a dielectric patch as a radiator of the antenna, and the antenna realizes that the isolation between receiving and transmitting ports is more than 22dB and the relative bandwidth is 16.8%. The Chinese patent document CN108767468A proposes an in-band full duplex antenna with adjustable frequency, the invention has the advantages of simple structure, capability of realizing rapid frequency agile communication and the like, and the isolation of the antenna is more than 25 dB in the frequency band of 2.59-2.67 GHz. The present invention in the field mostly adopts classical microstrip patch as the radiator of the antenna, and the bandwidth of the microstrip patch antenna is generally narrower or wider but the section is high and the isolation is low. An antenna with a wide bandwidth and low profile and high isolation is therefore necessarily one of the preferred features of a simultaneous co-frequency full duplex wireless communication system. At present, the optimal design of the antenna structure still depends on a traditional manual adjustment mode, and the efficiency and the precision of the optimal design of the antenna can be greatly improved by means of an AI deep learning algorithm. Thus, artificial intelligence deep learning algorithms must be the most advantageous candidates for efficient design of full duplex antennas and wireless communication devices.
At present, most of the field adopts a classical microstrip patch as a radiator of an antenna, and the bandwidth of the microstrip patch antenna is generally narrow or wide, but the section is high, the isolation is low, and a manual parameter adjustment mode is used.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings of the prior art and provides a stacked high-isolation full-duplex antenna based on deep learning optimization, which has the advantages of high in-band isolation, wide frequency band, low profile and low cross polarization.
Another object of the present invention is to provide a communication device comprising the above laminated high isolation full duplex antenna.
The aim of the invention can be achieved by adopting the following technical scheme:
a stacked high-isolation full duplex antenna based on deep learning optimization is characterized in that the antenna is optimized by adopting a radial basis function neural network model and comprises a top layer structure, a middle layer structure, a bottom layer structure and a feed structure;
the top layer structure comprises a first dielectric plate, a radiation structure and a first parasitic structure, wherein the radiation structure and the first parasitic structure are arranged on the upper surface of the first dielectric plate, the radiation structure comprises a first radiator and a second radiator, the first parasitic structure is positioned between the first radiator and the second radiator, and the feed structure is used for feeding the first radiator and the second radiator;
the intermediate layer structure comprises two second dielectric plates and two second parasitic structures, wherein the two second parasitic structures are arranged on the upper surface of the second dielectric plates, one parasitic structure is positioned at the corresponding position of the first radiator, and the other parasitic structure is positioned at the corresponding position of the second radiator;
the substructure comprises a third dielectric plate and a metal floor, wherein the third dielectric plate is stacked with the second dielectric plate and the metal floor respectively.
Further, the excitation input of the radial basis function neural network model is a plurality of desired performance characteristics; the hidden layer of the radial basis function neural network model has a plurality of neurons, each neuron having a gaussian function; the response output of the radial basis function neural network model is a plurality of dimensional variables of the first parasitic structure and the second parasitic structure that need to be optimized.
Further, the performance characteristics include an impedance bandwidth, a maximum isolation within the passband, a maximum gain of the first excitation port, and a maximum gain of the second excitation port.
Further, the first parasitic structure is a parasitic I-band.
Further, the second parasitic structures are parasitic E-shaped bands, the sizes of the two second parasitic structures are consistent, and the openings of the two second parasitic structures face to the same direction.
Further, the feed structure includes a first excitation port for feeding the first radiator and a second excitation port for feeding the second radiator.
Further, annular holes are formed in the first radiator and the second radiator;
the annular hole arranged on the first radiator is a first annular hole, and the first excitation port feeds the first radiator through the first annular hole;
the annular hole arranged on the second radiator is a second annular hole, and the second excitation port feeds the second radiator through the second annular hole.
Further, the first excitation port adopts a first coaxial probe, and after the first coaxial probe sequentially passes through the third dielectric plate, the second dielectric plate and the first dielectric plate, a feed piece of the first coaxial probe feeds the first radiator through the first annular hole;
the second excitation port adopts a second coaxial probe, and after the second coaxial probe sequentially passes through the third dielectric plate, the second dielectric plate and the first dielectric plate, a feed piece of the second coaxial probe feeds the second radiator through the second annular hole.
Further, the first radiator and the second radiator are rectangular patches, and the sizes of the first radiator and the second radiator are consistent.
The other object of the invention can be achieved by adopting the following technical scheme:
a communication device comprising a stacked high isolation full duplex antenna as described above.
Compared with the prior art, the invention has the following beneficial effects:
1. the working frequency band of the antenna is 3.31GHz-3.69 GHz after artificial intelligence deep learning optimization, the relative impedance bandwidth is 10.8%, the antenna works in an n78 communication frequency band of 5G, the antenna has a wider frequency band, higher in-band isolation and lower profile, and the dual polarization gains are respectively 3.62-4.87 dBi and 3.26-4.39 dBi.
2. The antenna adopts a coaxial probe coupling feed mechanism to realize horizontal polarization, so that the current coupling strength between two ports of the dual-polarized antenna is weakened; after the parasitic I-shaped band and the parasitic E-shaped band are added, a current offset phenomenon is generated on the radiator, so that the coupling strength between the transmitting port and the receiving port is greatly reduced, the isolation degree is increased, and the impedance bandwidth is further expanded.
3. According to the antenna, the artificial intelligent deep learning algorithm is adopted to conduct the optimal design of the antenna structure, a traditional manual parameter adjustment mode is replaced, and the optimal design is obtained more efficiently and accurately.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the structures shown in these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic structural diagram of a stacked high isolation full duplex antenna according to an embodiment of the present invention.
Fig. 2 is a top layer structure diagram of a stacked high isolation full duplex antenna according to an embodiment of the present invention.
Fig. 3 is a diagram showing an intermediate layer structure of a stacked high isolation full duplex antenna according to an embodiment of the present invention.
Fig. 4 is a bottom layer structure diagram of a stacked high isolation full duplex antenna according to an embodiment of the invention.
Fig. 5 is an artificial intelligence deep learning optimization model diagram of a stacked high isolation full duplex antenna according to an embodiment of the invention.
Fig. 6 is a schematic diagram of the top layer structure of a stacked high isolation full duplex antenna according to an embodiment of the invention.
Fig. 7 is a schematic diagram illustrating the dimensions of an intermediate layer structure of a stacked high isolation full duplex antenna according to an embodiment of the invention.
Fig. 8 is a schematic diagram of the front dimension of a stacked high isolation full duplex antenna according to an embodiment of the invention.
Fig. 9 is a graph of feed port reflection coefficient for a stacked high isolation full duplex antenna according to an embodiment of the invention.
Fig. 10 is an isolation profile of a stacked high isolation full duplex antenna according to an embodiment of the invention.
Fig. 11 is a gain graph of a stacked high isolation full duplex antenna according to an embodiment of the invention.
Fig. 12 is a radiation pattern of a first excitation port of a stacked high isolation full duplex antenna according to an embodiment of the invention at a center frequency of 3.5 GHz.
Fig. 13 is a radiation pattern of a second excitation port of a stacked high isolation full duplex antenna according to an embodiment of the invention at a center frequency of 3.5 GHz.
Wherein, 100-radiation structure, 200-annular hole, 201-first annular hole, 202-second annular hole, 300-second parasitic structure, 301-first parasitic structure, 302-second parasitic structure, 400-first parasitic structure, 500-feed structure, 501-first excitation port, 502-second excitation port, 601-first dielectric plate, 602-second dielectric plate, 603-third dielectric plate, 700-metal floor.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by persons of ordinary skill in the art without making any inventive effort based on the embodiments of the present invention are within the scope of protection of the present invention.
Examples:
as shown in fig. 1, the present embodiment provides a stacked high-isolation full duplex antenna based on deep learning optimization, which can be applied to various communication devices, and includes a top layer structure, an intermediate layer structure, a bottom layer structure and a feeding structure 500, wherein the top layer structure includes a first dielectric plate 601, a radiating structure 100 and a first parasitic structure 400, the intermediate layer structure includes a second dielectric plate 602 and a second parasitic structure 300, the bottom layer structure includes a third dielectric plate 603 and a metal floor 700, and the first dielectric plate 601, the second dielectric plate 602 and the third dielectric plate 603 are all rectangular solids.
As shown in fig. 1 and fig. 2, the radiation structure 100 and the first parasitic structure 400 are disposed on the upper surface of the first dielectric plate 601 by printing, where the radiation structure 100 includes a first radiator 101 and a second radiator 102, the first radiator 101 and the second radiator 102 in this embodiment are rectangular patches, and have identical dimensions, the first parasitic structure 400 is located between the first radiator 101 and the second radiator 102, the first parasitic structure 400 in this embodiment is a parasitic I-band, and specifically is located in an intermediate position on the upper surface of the first dielectric plate 601, and the feeding structure 500 is used for feeding the first radiator 101 and the second radiator 102.
Further, the feed structure 500 comprises a first excitation port 501 for feeding the first radiator 101 and a second excitation port 502 for feeding the second radiator 102.
In order to make the first excitation port 501 feed the first radiator 101 and the second excitation port 502 feed the second radiator 102, annular holes 200 are provided on the first radiator 101 and the second radiator 102, the annular holes provided on the first radiator 101 are first annular holes 201, the first excitation port 501 feeds the first radiator 102 through the first annular holes 201, the annular holes provided on the second radiator 102 are second annular holes 202, and the second excitation port 502 feeds the second radiator 102 through the second annular holes 202.
Further, the first excitation port 501 is a first coaxial probe, and after the first coaxial probe passes through the third dielectric 603, the second dielectric 602 and the first dielectric 601 in sequence, the feeding sheet of the first coaxial probe feeds the first radiator 101 through the first annular hole 201; the second excitation port 502 adopts a second coaxial probe, and after the second coaxial probe sequentially passes through the third dielectric plate 603, the second dielectric plate 602 and the first dielectric plate 601, a feed piece of the second coaxial probe feeds the second radiator 102 through the second annular hole 202.
The present embodiment achieves horizontal polarization using a coaxial probe coupled feed mechanism, reducing the strength of the galvanic coupling between the two ends (first excitation port 501 and second excitation port 502).
As shown in fig. 1 to 3, two second parasitic structures 300 are disposed on the upper surface of the second dielectric plate 602 by printing, the two second parasitic structures 300 in this embodiment are parasitic E-shaped strips, and have identical dimensions and openings, the two second parasitic structures 300 are respectively one of the second parasitic structures 301 and two of the second parasitic structures 302, the one of the second parasitic structures 301 is located at a corresponding position of the first radiator 101, and the two second parasitic structures 302 are located at a corresponding position of the second radiator 102.
In this embodiment, after adding the parasitic I-band and the parasitic E-band, a current cancellation phenomenon is generated on the radiator, so that the coupling strength between the transmitting port and the receiving port is greatly reduced, the isolation is increased, and the impedance bandwidth is further expanded.
As shown in fig. 1 to 4, the third dielectric plate 603 is stacked with the second dielectric plate 602 and the metal floor 700, where the third dielectric plate 603 and the metal floor 700 are stacked together by a lamination method, that is, the metal floor 700 is disposed on the back surface of the third dielectric plate 603, and the second dielectric plate 602 is disposed on the front surface of the third dielectric plate 603.
As shown in fig. 5, the antenna of the embodiment adopts an artificial intelligent deep learning model for optimization, the artificial intelligent deep learning model is an accurate radial basis function neural network model, and particularly adopts a mode of combining simulation software HFSS and Matlab software for efficient optimization design of the antenna.
Further, the excitation input of the radial basis function neural network model is 4 expected performance characteristics, namely 4 circles of the input layer, including the impedance bandwidth bw|S 11 Maximum value max|s of isolation in passband 21 I, maximum Gain max (Gain 1) of the first excitation port, and maximum Gain max (Gain 2) of the second excitation port; the hidden layer of the radial basis function neural network model has 200 neurons, namely 200 square boxes, and Gaussian function curves are arranged in the square boxes; the response output of the radial basis function neural network model is 9 dimensional variables (g) of the first parasitic structure (parasitic I-band) and the second parasitic structure (parasitic E-band) that need to be optimized 1 、L 2 、L 3 、L 4 、L 5 、W 2 、W 3 、W 4 And W is 5 ) I.e. 9 circles of the output layer.
As shown in FIGS. 6 to 8, the two radiators have the same size and the length L 1 22.1mm and width W 1 20.9 ofmm; diameter d of coaxial probe feed tab 1 3.2mm; diameter d of annular hole 2 5.8mm; the distance g between the first radiator and the second radiator is 22.7mm; length L of transverse long side of parasitic I-shaped band 2 21.7mm width W 2 2.7mm; the vertical edge of the parasitic I-shaped belt is a Z-shaped branch with the length L 3 23.6mm width W 3 4.9mm, edge length g of Z-shaped branch 1 1.0mm; for the intermediate layer structure, the two parasitic E-bands also have identical dimensions and a length L 4 20.9mm, width W 4 20.1mm and the void length on the parasitic E-band is L 5 13mm, width W 5 2.1mm; the lengths Subl of the first dielectric plate, the second dielectric plate and the third dielectric plate are all 100.0mm, the widths Subw are all 40.0mm, and the thicknesses H of the first dielectric plate and the second dielectric plate are all equal to 1 And H is 2 All 1.524mm, the thickness H of the third dielectric plate 3 0.762mm.
Fig. 9, 10, 11 and 12-13 show the reflection coefficient (|s) of the antenna according to the present embodiment 11 I and S 22 Graph of isolation (|S) 21 I) graph, gain graph, and radiation pattern. The working frequency band of the invention is 3.31GHz-3.69 GHz, the relative bandwidth is 10.8%, the antenna has the advantages of higher in-band isolation, wide frequency band, low profile, low cross polarization and the like, and the isolation degree is S 21 The I is larger than 31 dB, and the gains of the dual polarization are respectively 3.62-4.87 dBi and 3.26-4.39 dBi.
In summary, the antenna adopts the artificial intelligent deep learning algorithm to perform the optimal design of the antenna structure, replaces the traditional manual parameter adjustment mode, and obtains the optimal design more efficiently and accurately.
The foregoing is only illustrative of the present invention, and the embodiments of the present invention are not limited to the above-described embodiments, but any other changes, modifications, substitutions, combinations, and simplifications that do not depart from the spirit and principles of the present invention should be made in the equivalent manner and are included in the scope of the present invention.
Claims (10)
1. The stacked high-isolation full-duplex antenna based on deep learning optimization is characterized by being optimized by adopting a radial basis function neural network model and comprising a top layer structure, a middle layer structure, a bottom layer structure and a feed structure;
the top layer structure comprises a first dielectric plate, a radiation structure and a first parasitic structure, wherein the radiation structure and the first parasitic structure are arranged on the upper surface of the first dielectric plate, the radiation structure comprises a first radiator and a second radiator, the first parasitic structure is positioned between the first radiator and the second radiator, and the feed structure is used for feeding the first radiator and the second radiator;
the intermediate layer structure comprises two second dielectric plates and two second parasitic structures, wherein the two second parasitic structures are arranged on the upper surface of the second dielectric plates, one parasitic structure is positioned at the corresponding position of the first radiator, and the other parasitic structure is positioned at the corresponding position of the second radiator;
the substructure comprises a third dielectric plate and a metal floor, wherein the third dielectric plate is stacked with the second dielectric plate and the metal floor respectively.
2. The stacked high isolation full duplex antenna according to claim 1, wherein excitation inputs of the radial basis function neural network model are a plurality of desired performance characteristics; the hidden layer of the radial basis function neural network model has a plurality of neurons, each neuron having a gaussian function; the response output of the radial basis function neural network model is a plurality of dimensional variables of the first parasitic structure and the second parasitic structure that need to be optimized.
3. The stacked high isolation full duplex antenna of claim 2, wherein the performance characteristics include an impedance bandwidth, a maximum in-passband isolation, a maximum gain of the first excitation port, and a maximum gain of the second excitation port.
4. A stacked high isolation full duplex antenna according to any of claims 1-3, wherein said first parasitic structure is a parasitic I-band.
5. A stacked high isolation full duplex antenna according to any of claims 1-3, wherein said second parasitic structures are parasitic E-strips, the dimensions of the two second parasitic structures are identical and the openings of the two second parasitic structures are oriented identical.
6. A stacked high isolation full duplex antenna according to any of claims 1-3, wherein the feed structure comprises a first excitation port for feeding a first radiator and a second excitation port for feeding a second radiator.
7. The stacked, high isolation, full duplex antenna of claim 6, wherein the first radiator and the second radiator are each provided with an annular aperture;
the annular hole arranged on the first radiator is a first annular hole, and the first excitation port feeds the first radiator through the first annular hole;
the annular hole arranged on the second radiator is a second annular hole, and the second excitation port feeds the second radiator through the second annular hole.
8. The stacked, high isolation, full duplex antenna of claim 7, wherein the first excitation port employs a first coaxial probe, the first coaxial probe feeding the first radiator through the first annular aperture after sequentially passing through the third dielectric plate, the second dielectric plate, and the first dielectric plate;
the second excitation port adopts a second coaxial probe, and after the second coaxial probe sequentially passes through the third dielectric plate, the second dielectric plate and the first dielectric plate, a feed piece of the second coaxial probe feeds the second radiator through the second annular hole.
9. A stacked, high isolation, full duplex antenna according to any of claims 1-3, wherein said first and second radiators are rectangular patches, the first and second radiators being of uniform size.
10. A communication device comprising a stacked high isolation full duplex antenna according to any of claims 1-9.
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| CN116722360B (en) | 2023-10-31 |
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