CN116614826B - Coverage and capacity optimization method for simultaneous transmission and reflection surface network - Google Patents
Coverage and capacity optimization method for simultaneous transmission and reflection surface network Download PDFInfo
- Publication number
- CN116614826B CN116614826B CN202310587936.8A CN202310587936A CN116614826B CN 116614826 B CN116614826 B CN 116614826B CN 202310587936 A CN202310587936 A CN 202310587936A CN 116614826 B CN116614826 B CN 116614826B
- Authority
- CN
- China
- Prior art keywords
- transmission
- simultaneous
- reflection
- grid
- coverage
- 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.)
- Active
Links
- 230000005540 biological transmission Effects 0.000 title claims abstract description 56
- 238000000034 method Methods 0.000 title claims abstract description 32
- 238000005457 optimization Methods 0.000 title claims abstract description 16
- 230000009471 action Effects 0.000 claims abstract description 8
- 238000013459 approach Methods 0.000 claims abstract description 5
- 239000011159 matrix material Substances 0.000 claims description 13
- 230000008569 process Effects 0.000 claims description 10
- 230000006870 function Effects 0.000 claims description 9
- 238000004891 communication Methods 0.000 claims description 8
- 238000004590 computer program Methods 0.000 claims description 6
- 239000000654 additive Substances 0.000 claims description 3
- 230000000996 additive effect Effects 0.000 claims description 3
- 230000021615 conjugation Effects 0.000 claims description 3
- 230000001419 dependent effect Effects 0.000 claims description 3
- 230000010363 phase shift Effects 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 230000007704 transition Effects 0.000 claims description 3
- 238000010801 machine learning Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008094 contradictory effect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- 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)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention discloses a method for optimizing coverage and capacity of a simultaneous transmission and reflection surface network. Firstly, the coverage range and capacity performance of a simultaneous transmission and reflection surface network are analyzed, then a multi-target near-end strategy optimization algorithm based on an action value updating strategy is provided, and finally, a group of optimal solutions are obtained to approach the pareto front. The invention provides a coverage and capacity optimization method for a simultaneous transmission and reflection surface network, which has good application value.
Description
Technical Field
The invention relates to the field of wireless communication, in particular to a method for covering and optimizing capacity of a simultaneous transmission and reflection surface network.
Background
In order to support the ever-increasing heterogeneous quality of service requirements of future wireless networks, such as high data rates, low latency, high reliability, large-scale connections, etc., an emerging communication paradigm, i.e., a reconfigurable intelligent surface-controlled wireless communication environment, has been proposed. The reconfigurable intelligent surface can provide line-of-sight links for users located in obstructed areas by reflection to improve coverage and capacity of conventional wireless networks. However, conventional reconfigurable smart surfaces have a maximum 180 ° coverage, where a "dead zone" is still present on the back of the reconfigurable smart surface. To overcome this limitation, a new concept named simultaneous transmitting and reflecting surface has become attractive. The simultaneous transmitting and reflecting surfaces are capable of simultaneously transmitting and reflecting incident signals, which contributes to full spatial coverage, as compared to conventional reconfigurable smart surfaces. As a new communication paradigm, how the simultaneous transmission and reflection surfaces behave in terms of coverage and capacity is a very interesting issue. Note that coverage and capacity optimization is one of the typical operational tasks mentioned in the third generation partnership project. Since coverage and capacity have a variety of contradictory relationships, it is important to optimize them simultaneously. For example, high transmit power contributes to large coverage, but high inter-cell interference can reduce capacity performance. For this reason, a multi-objective machine learning algorithm may be a potential solution. In contrast to single-objective algorithms, multi-objective machine learning algorithms are able to handle inherent conflicts between objectives, achieving a set of optimal solutions by coordinating and compromising the requirements of the objectives. Therefore, inspired by the advantages of simultaneous transmitting and reflecting surface, simultaneous transmitting and reflecting surface wireless networks have been considered as one of the candidates for next generation wireless communication systems, and simultaneous optimizing of coverage and capacity is also a key issue in simultaneous transmitting and reflecting surface wireless networks.
Disclosure of Invention
In view of the deficiencies of the prior art, the present invention is directed to a method of coverage and capacity optimization for a simultaneous transmission and reflection surface network.
In order to achieve the above purpose, the present invention adopts the following technical methods:
a coverage and capacity optimization method for a simultaneous transmission and reflection surface network comprises the following specific processes:
step one: analysis of coverage and capacity performance of simultaneous transmitting and reflecting surface networks:
the communication system comprises A single antenna base stations and N s A plurality of simultaneous transmitting and reflecting surfaces; wherein a single antenna base station is placed on the boundary of its service area, each simultaneous transmitting and reflecting surface containing K elements; the service area is discretized into N grids, each of which can only use a transmission or reflection pattern of simultaneous transmission and reflection surfaces; furthermore, for each simultaneous transmitting and reflecting surface, defining delta e { Tr, re } to represent the transmitting or reflecting mode, K elements will be divided into K Tr Each transmission element K Re The reflecting elements, i.e. k=k Tr +K Re The method comprises the steps of carrying out a first treatment on the surface of the Thus, the coefficients of the simultaneous transmitting and reflecting surfaces can be defined as:
wherein θ Tr And theta Re Representing a transmission surface coefficient matrix and a reflection surface coefficient matrix, respectively, wherein θ Tr And theta Re Representing a transmission surface coefficient matrix and a reflection surface coefficient matrix respectively, e and j respectively represent the 1 st transmission element amplitude, the K-th transmission element amplitude Tr Amplitude of transmission element, amplitude of reflection element 1, K Re Amplitude of each reflection element, natural logarithmic base and imaginary unit, < ->Respectively represent the 1 st transmission element phase and the K th transmission element phase Tr A transmission element phase, a 1 st reflection element phase and a K th transmission element phase Re A plurality of reflective element phases; definition of the a-th single antenna base station to the n-th s The channels of the simultaneous transmitting and reflecting surfaces are +.>The channel from the a single antenna base station to the n grid is h a,n Nth, n s The channel of the element in delta mode in the simultaneous transmitting and reflecting surface to the nth grid is +.>H represents conjugation, then the nth mesh receives the signal from the nth base station via the nth base station s The signals of the simultaneous transmitting and reflecting surfaces are:
where x represents the transmission signal and,representing zero mean and variance as delta 2 Additive white gaussian noise of (2); defining the reference signal received power as the reference signal received power from all possible sources, the nth grid reference signal received power being expressed as +.> In addition, the signal-to-interference-and-noise ratio of the nth grid may be expressed as:
assume that the reference signal received power minimum threshold of all grids is RSRP th The weighted coverage of the time step t is:
wherein,representation->Coverage weight corresponding to grid, +.>Indicating that the reference signal received power reaches a threshold RSRP th Is a grid set of (a); according to SINR n The capacity of the N grids of the entire service area can be expressed as:
wherein w is cap,n And B represents the capacity weight and bandwidth of the nth mesh, respectively; a, a * And n s * Then is dependent onA corresponding value;
step two: multi-objective near-end policy optimization algorithm based on action value update policy:
according to the Markov decision process, an action value based update strategy is introduced, under which the Markov decision process is expressed asWherein->And->Representing a preference space and a preference function, respectively, +.>In order to be a state space,is a movement space->Is a bonus space; p is a transition probability matrix indicating the probability of transitioning the current state to the next state; defining a controller as a proxy that controls both base stations to formulate a strategy from a single antenna base station to a grid, i.e. a strategy for adjustment of phase shift and transmit power, by means of simultaneous transmission and reflection surfaces; then the clip theory based loss function will be defined as:
wherein the method comprises the steps ofAnd->Representing the current policy parameter and the updated policy parameter, respectively, < >>Andrespectively representing a current policy and a new policy; />And E is the probability ratio of the dominance function and clip theory respectively; along with the training, the optimal parameters and the corresponding strategies are output until convergence.
Step three: and on the basis of the second step, a group of optimal solutions are obtained to approach the pareto front.
The present invention also provides a computer readable storage medium having stored therein a computer program which when executed by a processor implements the above method.
The invention also provides a computer device comprising a processor and a memory for storing a computer program; the processor is configured to implement the above-described method when executing the computer program.
The invention has the beneficial effects that: the invention provides a high-efficiency multi-target machine learning algorithm which can realize simultaneous optimization of coverage and capacity in a simultaneous transmission and reflection surface network and has good application value.
Drawings
FIG. 1 is a general idea of a method according to an embodiment of the invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings, wherein the present embodiment is provided with the technical method as a premise, and a detailed implementation manner and a specific operation process are provided, and the protection scope of the present invention is not limited to the present embodiment.
The present embodiment provides a coverage and capacity optimization method for a simultaneous transmission and reflection surface network, as shown in fig. 1, in which coverage and capacity performance of the simultaneous transmission and reflection surface network are analyzed first, then a multi-objective near-end policy optimization algorithm based on an action value update policy is provided, and finally a set of optimal solutions are obtained to approach pareto fronts. The specific process is as follows:
step one: the coverage and capacity performance of the simultaneous transmitting and reflecting surface network was analyzed.
The communication system comprises A single antenna base stations and N s And simultaneously transmitting and reflecting surfaces. Wherein a single antenna base station is placed on the boundary of its service area, each simultaneous transmitting and reflecting surface contains K elements. The service area is discretized into N grids, each of which can only use a transmission or reflection pattern of simultaneous transmission and reflection surfaces. Furthermore, for each simultaneous transmitting and reflecting surface, defining delta e { Tr, re } to represent the transmitting or reflecting mode, K elements will be divided into K Tr Each transmission element K Re The reflecting elements, i.e. k=k Tr +K Re . Thus, the coefficients of the simultaneous transmitting and reflecting surfaces can be defined as:
wherein θ Tr And theta Re Representing a transmission surface coefficient matrix and a reflection surface coefficient matrix, respectively, wherein θ Tr And theta Re Representing a transmission surface coefficient matrix and a reflection surface coefficient matrix respectively, e and j respectively represent the 1 st transmission element amplitude, the K-th transmission element amplitude Tr Amplitude of transmission element, amplitude of reflection element 1, K Re Amplitude of each reflection element, natural logarithmic base and imaginary unit, < ->Respectively represent the 1 st transmission element phase and the K th transmission element phase Tr A transmission element phase, a 1 st reflection element phase and a K th transmission element phase Re A plurality of reflective element phases; definition of the a-th single antenna base station to the n-th s The channels of the simultaneous transmitting and reflecting surfaces are +.>The channel from the a single antenna base station to the n grid is h a,n Nth, n s The channel of the element in delta mode in the simultaneous transmitting and reflecting surface to the nth grid is +.>H represents conjugation, then the nth mesh receives the signal from the nth base station via the nth base station s The signals of the simultaneous transmitting and reflecting surfaces are:
where x represents the transmission signal and,representing zero mean and variance as delta 2 Additive white gaussian noise of (c). From the received signal, defining the reference signal received power as the maximum useful signal power (received power minus noise power) from all possible sources (including the base station, transmission or reflection mode of the simultaneous transmission and reflection surfaces), the reference signal received power of the nth grid can be expressed as +.>In addition, the signal-to-interference-and-noise ratio of the nth grid may be expressed as:
assume that the reference signal received power minimum threshold of all grids is RSRP th The weighted coverage of the time step t is:
wherein,representation->Coverage weight corresponding to grid, +.>Indicating that the reference signal received power reaches a threshold RSRP th Is a grid set of (a) grid sets. According to SINR n The capacity of the N grids of the entire service area can be expressed as:
wherein w is cap,n And B represents the capacity weight and bandwidth of the nth mesh, respectively; a, a * And n s * Then is dependent onCorresponding values.
Step two: multi-objective near-end policy optimization algorithm based on action value update policy:
in accordance with the Markov decision process, in a conventional multi-objective near-end policy optimization algorithm, the Markov decision process may be employedThe tuple is expressed asWherein->For the state space +.>Is a movement space->To reward space. p is a transition probability matrix indicating the probability of transitioning the current state to the next state. The controller is defined as a proxy that controls both base stations to formulate a strategy from a single antenna base station to the grid, i.e. a strategy for adjustment of phase shift and transmit power, by means of simultaneous transmission and reflection surfaces. In order to further improve the efficiency of the multi-target near-end policy optimization algorithm, an update policy based on action values is introduced in the embodiment. Under this strategy, the Markov decision process is re-represented as +.>Wherein->And->Representing the preference space and the preference function, respectively, then the clip theory based loss function will be defined as:
wherein the method comprises the steps ofAnd->Representing the current policy parameter and the updated policy parameter, respectively, < >>Andrepresenting the current policy and the new policy, respectively. />And E is the probability ratio of the dominance function and clip theory respectively. Along with the training, the optimal parameters and the corresponding strategies are output until convergence.
Step three: and on the basis of the second step, a group of optimal solutions are obtained to approach the pareto front. Various corresponding changes and modifications may be suggested to one skilled in the art in view of the foregoing teachings and all such changes and modifications are intended to be included within the scope of the appended claims.
Claims (3)
1. A method for simultaneous transmission and reflection surface network coverage and capacity optimization, characterized by the specific procedures:
step one: analysis of coverage and capacity performance of simultaneous transmitting and reflecting surface networks:
the communication system comprises A single antenna base stations and N s A plurality of simultaneous transmitting and reflecting surfaces; wherein a single antenna base station is placed on the boundary of its service area, each simultaneous transmitting and reflecting surface containing K elements; the service area is discretized into N grids, each of which can only use a transmission or reflection pattern of simultaneous transmission and reflection surfaces; furthermore, for each simultaneous transmitting and reflecting surface, defining delta e { Tr, re } to represent the transmitting or reflecting mode, K elements will be divided into K Tr Each transmission element K Re The reflecting elements, i.e. k=k Tr +K Re The method comprises the steps of carrying out a first treatment on the surface of the Thus, the coefficients of the simultaneous transmitting and reflecting surfaces can be defined as:
wherein θ Tr And theta Re Representing a transmission surface coefficient matrix and a reflection surface coefficient matrix respectively,e and j respectively represent the 1 st transmission element amplitude, the K-th transmission element amplitude Tr Amplitude of transmission element, amplitude of reflection element 1, K Re Amplitude of each reflection element, natural logarithmic base and imaginary unit,respectively represent the 1 st transmission element phase and the K th transmission element phase Tr A transmission element phase, a 1 st reflection element phase and a K th transmission element phase Re A plurality of reflective element phases; definition of the a-th single antenna base station to the n-th s The channels of the simultaneous transmitting and reflecting surfaces are +.>The channel from the a single antenna base station to the n grid is h a,n Nth, n s The channel of the element in delta mode in the simultaneous transmitting and reflecting surface to the nth grid is +.>H represents conjugation, then the nth mesh receives the signal from the nth base station via the nth base station s The signals of the simultaneous transmitting and reflecting surfaces are:
where x represents the transmission signal and,representing zero mean and variance as delta 2 Additive gauss of (a)White noise; defining the reference signal received power as the reference signal received power from all possible sources, the nth grid reference signal received power being expressed as +.> In addition, the signal-to-interference-and-noise ratio of the nth grid may be expressed as:
assume that the reference signal received power minimum threshold of all grids is RSRP th The weighted coverage of the time step t is:
wherein,representation->Coverage weight corresponding to grid, +.>Indicating that the reference signal received power reaches a threshold RSRP th Is a grid set of (a); according to SINR n The capacity of the N grids of the entire service area can be expressed as:
wherein w is cap,n And B respectively represents the nthCapacity weight and bandwidth of the grid; a, a * And n s * Then is dependent onA corresponding value;
step two: multi-objective near-end policy optimization algorithm based on action value update policy:
according to the Markov decision process, an action value based update strategy is introduced, under which the Markov decision process is expressed asWherein->And->Representing a preference space and a preference function, respectively, +.>For the state space +.>Is a movement space->Is a bonus space; p is a transition probability matrix indicating the probability of transitioning the current state to the next state; defining a controller as a proxy that controls both base stations to formulate a strategy from a single antenna base station to a grid, i.e. a strategy for adjustment of phase shift and transmit power, by means of simultaneous transmission and reflection surfaces; then the clip theory based loss function will be defined as:
wherein the method comprises the steps ofAnd->Representing the current policy parameter and the updated policy parameter, respectively, < >>And->Respectively representing a current policy and a new policy; />And E is the probability ratio of the dominance function and clip theory respectively; along with the training, outputting optimal parameters and corresponding strategies until convergence;
step three: and on the basis of the second step, a group of optimal solutions are obtained to approach the pareto front.
2. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a computer program which, when executed by a processor, implements the method of claim 1.
3. A computer device comprising a processor and a memory, the memory for storing a computer program; the processor is configured to implement the method of claim 1 when executing the computer program.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202310587936.8A CN116614826B (en) | 2023-05-24 | 2023-05-24 | Coverage and capacity optimization method for simultaneous transmission and reflection surface network |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202310587936.8A CN116614826B (en) | 2023-05-24 | 2023-05-24 | Coverage and capacity optimization method for simultaneous transmission and reflection surface network |
Publications (2)
Publication Number | Publication Date |
---|---|
CN116614826A CN116614826A (en) | 2023-08-18 |
CN116614826B true CN116614826B (en) | 2024-01-16 |
Family
ID=87681346
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202310587936.8A Active CN116614826B (en) | 2023-05-24 | 2023-05-24 | Coverage and capacity optimization method for simultaneous transmission and reflection surface network |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN116614826B (en) |
Citations (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2015176258A1 (en) * | 2014-05-21 | 2015-11-26 | 华为技术有限公司 | Capacity expansion method and device for wireless network |
CN111181618A (en) * | 2020-01-03 | 2020-05-19 | 东南大学 | Intelligent reflection surface phase optimization method based on deep reinforcement learning |
CN113225108A (en) * | 2021-03-18 | 2021-08-06 | 北京邮电大学 | Robust beam forming method for assisting multi-cell coordinated multi-point transmission by intelligent reflector |
CN114422056A (en) * | 2021-12-03 | 2022-04-29 | 北京航空航天大学 | Air-ground non-orthogonal multiple access uplink transmission method based on intelligent reflecting surface |
CN114742231A (en) * | 2022-03-22 | 2022-07-12 | 中国人民解放军国防科技大学 | Multi-objective reinforcement learning method and device based on pareto optimization |
CN114980169A (en) * | 2022-05-16 | 2022-08-30 | 北京理工大学 | Unmanned aerial vehicle auxiliary ground communication method based on combined optimization of track and phase |
CN115052285A (en) * | 2022-05-04 | 2022-09-13 | 中国人民解放军空军工程大学 | Unmanned aerial vehicle intelligent reflecting surface safe transmission method based on deep reinforcement learning |
KR20220144679A (en) * | 2021-04-20 | 2022-10-27 | 인하대학교 산학협력단 | Method and Apparatus for Simultaneous Optimization of Transmitted Rate and The Harvested Energy in Intelligent Reflecting Surface-aided MIMO System |
CN115395993A (en) * | 2022-04-21 | 2022-11-25 | 东南大学 | Reconfigurable intelligent surface enhanced MISO-OFDM transmission method |
WO2022247685A1 (en) * | 2021-05-27 | 2022-12-01 | 中兴通讯股份有限公司 | Information transmission method, reflecting device, base station, system, electronic device, and medium |
WO2022257065A1 (en) * | 2021-06-10 | 2022-12-15 | Zte Corporation | Filtering for intelligent reflecting devices |
WO2023272418A1 (en) * | 2021-06-28 | 2023-01-05 | Qualcomm Incorporated | Cross link interference measurement resource configuration and reporting with an intelligent reflective surface for interference mitigation |
CN115915362A (en) * | 2022-09-01 | 2023-04-04 | 南京邮电大学 | STAR-RIS assisted NOMA system uplink low-power-consumption transmission method |
Family Cites Families (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101873596B (en) * | 2009-04-27 | 2014-08-13 | 中兴通讯股份有限公司 | Method and system for optimizing network coverage and capacity |
CN103384372B (en) * | 2012-05-03 | 2016-08-10 | 华为技术有限公司 | A kind of optimize network capacity and cover compromise method, Apparatus and system |
US9775068B2 (en) * | 2012-08-24 | 2017-09-26 | Actix Gmbh | Method for joint and coordinated load balancing and coverage and capacity optimization in cellular communication networks |
KR102192234B1 (en) * | 2019-10-28 | 2020-12-17 | 성균관대학교 산학협력단 | Communication method of wireless communication system including intelligent reflecting surface and an apparatus for the communication method |
CN111355520B (en) * | 2020-03-10 | 2022-03-08 | 电子科技大学 | Design method of intelligent reflection surface assisted terahertz safety communication system |
-
2023
- 2023-05-24 CN CN202310587936.8A patent/CN116614826B/en active Active
Patent Citations (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2015176258A1 (en) * | 2014-05-21 | 2015-11-26 | 华为技术有限公司 | Capacity expansion method and device for wireless network |
CN111181618A (en) * | 2020-01-03 | 2020-05-19 | 东南大学 | Intelligent reflection surface phase optimization method based on deep reinforcement learning |
CN113225108A (en) * | 2021-03-18 | 2021-08-06 | 北京邮电大学 | Robust beam forming method for assisting multi-cell coordinated multi-point transmission by intelligent reflector |
KR20220144679A (en) * | 2021-04-20 | 2022-10-27 | 인하대학교 산학협력단 | Method and Apparatus for Simultaneous Optimization of Transmitted Rate and The Harvested Energy in Intelligent Reflecting Surface-aided MIMO System |
WO2022247685A1 (en) * | 2021-05-27 | 2022-12-01 | 中兴通讯股份有限公司 | Information transmission method, reflecting device, base station, system, electronic device, and medium |
WO2022257065A1 (en) * | 2021-06-10 | 2022-12-15 | Zte Corporation | Filtering for intelligent reflecting devices |
WO2023272418A1 (en) * | 2021-06-28 | 2023-01-05 | Qualcomm Incorporated | Cross link interference measurement resource configuration and reporting with an intelligent reflective surface for interference mitigation |
CN114422056A (en) * | 2021-12-03 | 2022-04-29 | 北京航空航天大学 | Air-ground non-orthogonal multiple access uplink transmission method based on intelligent reflecting surface |
CN114742231A (en) * | 2022-03-22 | 2022-07-12 | 中国人民解放军国防科技大学 | Multi-objective reinforcement learning method and device based on pareto optimization |
CN115395993A (en) * | 2022-04-21 | 2022-11-25 | 东南大学 | Reconfigurable intelligent surface enhanced MISO-OFDM transmission method |
CN115052285A (en) * | 2022-05-04 | 2022-09-13 | 中国人民解放军空军工程大学 | Unmanned aerial vehicle intelligent reflecting surface safe transmission method based on deep reinforcement learning |
CN114980169A (en) * | 2022-05-16 | 2022-08-30 | 北京理工大学 | Unmanned aerial vehicle auxiliary ground communication method based on combined optimization of track and phase |
CN115915362A (en) * | 2022-09-01 | 2023-04-04 | 南京邮电大学 | STAR-RIS assisted NOMA system uplink low-power-consumption transmission method |
Non-Patent Citations (2)
Title |
---|
基于强化学习的定向无线通信网络抗干扰资源调度算法;谢添;高士顺;赵海涛;林沂;熊俊;;电波科学学报(04);全文 * |
谢添 ; 高士顺 ; 赵海涛 ; 林沂 ; 熊俊 ; .基于强化学习的定向无线通信网络抗干扰资源调度算法.电波科学学报.2020,(04),全文. * |
Also Published As
Publication number | Publication date |
---|---|
CN116614826A (en) | 2023-08-18 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Maksymyuk et al. | Deep learning based massive MIMO beamforming for 5G mobile network | |
CN113873622B (en) | Communication network energy saving method based on reconfigurable intelligent surface | |
CN113225794B (en) | Full-duplex cognitive communication power control method based on deep reinforcement learning | |
CN112272418A (en) | RIS-assisted D2D communication transmission mode selection method | |
Xu et al. | Weighted sum rate maximization in IRS-BackCom enabled downlink multi-cell MISO network | |
CN111277308A (en) | Wave width control method based on machine learning | |
CN117081636B (en) | Transmitting power optimization method and device for reconfigurable intelligent surface auxiliary active interference | |
Chen et al. | Deep reinforcement learning for resource allocation in massive MIMO | |
CN116614826B (en) | Coverage and capacity optimization method for simultaneous transmission and reflection surface network | |
CN115379478B (en) | Robust energy consumption optimization method based on RIS auxiliary digital energy simultaneous transmission network | |
CN113595609B (en) | Collaborative signal transmission method of cellular mobile communication system based on reinforcement learning | |
CN114980156B (en) | AP switch switching method of honeycomb millimeter wave-free large-scale MIMO system | |
CN114051251B (en) | Dynamic switching method for implementing base station with assistance of intelligent reflecting surface | |
CN114222310B (en) | Method for optimizing reflection of combined 3D wave beam forming and intelligent reflecting surface | |
CN116015503A (en) | Multi-reconfigurable intelligent surface selection method in wireless communication system considering aggregated interference | |
CN115243295A (en) | IRS-assisted SWIPT-D2D system resource allocation method based on deep reinforcement learning | |
CN115412936A (en) | IRS (intelligent resource management) assisted D2D (device-to-device) system resource allocation method based on multi-agent DQN (differential Quadrature reference network) | |
CN114364034A (en) | RIS assisted user centralized de-cellular system resource management semi-parallel method based on DRL | |
CN113395757A (en) | Deep reinforcement learning cognitive network power control method based on improved return function | |
Akbarpour-Kasgari et al. | Deep Reinforcement Learning in mmW-NOMA: Joint Power Allocation and Hybrid Beamforming | |
Bian et al. | On the Outage Probability of Multi-RIS Assisted Two-Way Communication System with RIS Selection | |
CN114389656B (en) | Antenna selection and symbol-level precoding joint design method for ultra-large-scale MIMO | |
Liu et al. | Active IRS-Assisted Resource Allocation for MISO System | |
CN114158123B (en) | Intelligent reflecting surface Massive MIMO system resource allocation method | |
CN114501428B (en) | Safe Massive MIMO system resource allocation method for intelligent reflecting surface |
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 |