Joint throughput maximization, interference cancellation, and power efficiency for multi-IRS-empowered UAV communications
Contemporary mobile communication systems encounter formidable challenges in urban environments and dead zones, manifesting as issues in reliability, coverage, and quality of service (QoS). Addressing these challenges, the integration of intelligent reflecting surface (IRS)-assisted unmanned aerial...
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Veröffentlicht in: | Signal, image and video processing image and video processing, 2024-07, Vol.18 (5), p.4029-4043 |
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description | Contemporary mobile communication systems encounter formidable challenges in urban environments and dead zones, manifesting as issues in reliability, coverage, and quality of service (QoS). Addressing these challenges, the integration of intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communications emerges as a transformative solution. This paradigm not only offloads ground base stations but also ensures reliability and QoS adherence in a cost-effective manner. Additionally, the synergistic deployment of reconfigurable intelligent surfaces (RIS) with non-orthogonal multiple access (NOMA) holds significant promise for elevating the performance of mobile networks. This paper delves into the integration of multi-IRS configurations to augment UAV communication within intelligent mobile networks. The overarching objective is to minimize average total power consumption while simultaneously maximizing network-wide throughput and reliability. This is achieved through the joint optimization of resource allocation, UAV trajectory, velocity, and IRS phase shift design. The focal point is to establish reliable and efficient connectivity for mobile users and Internet of Things (IoT) nodes across diverse scenarios, thereby ensuring QoS benchmarks and unlocking new commercial opportunities. Given the non-convex and NP-hard nature of the optimization problem, we first introduce a deep deterministic policy gradient algorithm to optimize multi-IRS multi-user associations, paving the way for efficient convergence. Subsequently, a sequential convex approximation (SCA) algorithm and a Dinkelbach-based approach are employed to optimize UAV trajectory, followed by the fine-tuning of successive interference cancellation (SIC) decoding order scheduling and power allocation. Theoretical analysis and performance evaluations substantiate the profound advantages of the proposed algorithm, showcasing commendable convergence rates and enhanced energy efficiency. |
doi_str_mv | 10.1007/s11760-024-03015-5 |
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Addressing these challenges, the integration of intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communications emerges as a transformative solution. This paradigm not only offloads ground base stations but also ensures reliability and QoS adherence in a cost-effective manner. Additionally, the synergistic deployment of reconfigurable intelligent surfaces (RIS) with non-orthogonal multiple access (NOMA) holds significant promise for elevating the performance of mobile networks. This paper delves into the integration of multi-IRS configurations to augment UAV communication within intelligent mobile networks. The overarching objective is to minimize average total power consumption while simultaneously maximizing network-wide throughput and reliability. This is achieved through the joint optimization of resource allocation, UAV trajectory, velocity, and IRS phase shift design. The focal point is to establish reliable and efficient connectivity for mobile users and Internet of Things (IoT) nodes across diverse scenarios, thereby ensuring QoS benchmarks and unlocking new commercial opportunities. Given the non-convex and NP-hard nature of the optimization problem, we first introduce a deep deterministic policy gradient algorithm to optimize multi-IRS multi-user associations, paving the way for efficient convergence. Subsequently, a sequential convex approximation (SCA) algorithm and a Dinkelbach-based approach are employed to optimize UAV trajectory, followed by the fine-tuning of successive interference cancellation (SIC) decoding order scheduling and power allocation. 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Addressing these challenges, the integration of intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communications emerges as a transformative solution. This paradigm not only offloads ground base stations but also ensures reliability and QoS adherence in a cost-effective manner. Additionally, the synergistic deployment of reconfigurable intelligent surfaces (RIS) with non-orthogonal multiple access (NOMA) holds significant promise for elevating the performance of mobile networks. This paper delves into the integration of multi-IRS configurations to augment UAV communication within intelligent mobile networks. The overarching objective is to minimize average total power consumption while simultaneously maximizing network-wide throughput and reliability. This is achieved through the joint optimization of resource allocation, UAV trajectory, velocity, and IRS phase shift design. 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Theoretical analysis and performance evaluations substantiate the profound advantages of the proposed algorithm, showcasing commendable convergence rates and enhanced energy efficiency.</description><subject>Algorithms</subject><subject>Computer Imaging</subject><subject>Computer Science</subject><subject>Convergence</subject><subject>Ground stations</subject><subject>Image Processing and Computer Vision</subject><subject>Interference</subject><subject>Internet of Things</subject><subject>Maximization</subject><subject>Mobile communication systems</subject><subject>Multimedia Information Systems</subject><subject>Nonorthogonal multiple access</subject><subject>Optimization</subject><subject>Original Paper</subject><subject>Pattern Recognition and Graphics</subject><subject>Performance evaluation</subject><subject>Power consumption</subject><subject>Quality of service</subject><subject>Reconfigurable intelligent surfaces</subject><subject>Reliability</subject><subject>Resource allocation</subject><subject>Signal,Image and Speech Processing</subject><subject>Unmanned aerial vehicles</subject><subject>Urban environments</subject><subject>Vision</subject><issn>1863-1703</issn><issn>1863-1711</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kF9LwzAUxYMoOOa-gE8BX43eNG2TPo7hn8lAUOdr6Npky2ibmrTo_PRm69A385BcOL9zbjgIXVK4oQD81lPKUyAQxQQY0IQkJ2hERcoI5ZSe_s7AztHE-y2EwyIuUjFCuydrmg53G2f79abtO1znX6Y233lnbHONg6icVk41hcJFHu6qOkp5U-LWfiqHldamMAHZYW0drvuqM2T-8kpUfQBUiZfTd1zYuu4bUxz8_gKd6bzyanJ8x2h5f_c2eySL54f5bLogBaNZR3SUxKBZASWD8GUFMU-FKLWKRKxXWS6ymMOqhCTmOi6pzmheZoniQq80zyBhY3Q15LbOfvTKd3Jre9eElZJBkrFMJJAGKhqowlnvndKydabO3U5SkPuW5dCyDC3LQ8tyH80Gkw9ws1buL_of1w_IeIFD</recordid><startdate>20240701</startdate><enddate>20240701</enddate><creator>Gu, LiFen</creator><creator>Mohajer, Amin</creator><general>Springer London</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20240701</creationdate><title>Joint throughput maximization, interference cancellation, and power efficiency for multi-IRS-empowered UAV communications</title><author>Gu, LiFen ; Mohajer, Amin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-f2540f3c0d30786e047688dfe284fb9a89470bd0547f4d1f91ad95e78fbf79053</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Computer Imaging</topic><topic>Computer Science</topic><topic>Convergence</topic><topic>Ground stations</topic><topic>Image Processing and Computer Vision</topic><topic>Interference</topic><topic>Internet of Things</topic><topic>Maximization</topic><topic>Mobile communication systems</topic><topic>Multimedia Information Systems</topic><topic>Nonorthogonal multiple access</topic><topic>Optimization</topic><topic>Original Paper</topic><topic>Pattern Recognition and Graphics</topic><topic>Performance evaluation</topic><topic>Power consumption</topic><topic>Quality of service</topic><topic>Reconfigurable intelligent surfaces</topic><topic>Reliability</topic><topic>Resource allocation</topic><topic>Signal,Image and Speech Processing</topic><topic>Unmanned aerial vehicles</topic><topic>Urban environments</topic><topic>Vision</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gu, LiFen</creatorcontrib><creatorcontrib>Mohajer, Amin</creatorcontrib><collection>CrossRef</collection><jtitle>Signal, image and video processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gu, LiFen</au><au>Mohajer, Amin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Joint throughput maximization, interference cancellation, and power efficiency for multi-IRS-empowered UAV communications</atitle><jtitle>Signal, image and video processing</jtitle><stitle>SIViP</stitle><date>2024-07-01</date><risdate>2024</risdate><volume>18</volume><issue>5</issue><spage>4029</spage><epage>4043</epage><pages>4029-4043</pages><issn>1863-1703</issn><eissn>1863-1711</eissn><abstract>Contemporary mobile communication systems encounter formidable challenges in urban environments and dead zones, manifesting as issues in reliability, coverage, and quality of service (QoS). Addressing these challenges, the integration of intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communications emerges as a transformative solution. This paradigm not only offloads ground base stations but also ensures reliability and QoS adherence in a cost-effective manner. Additionally, the synergistic deployment of reconfigurable intelligent surfaces (RIS) with non-orthogonal multiple access (NOMA) holds significant promise for elevating the performance of mobile networks. This paper delves into the integration of multi-IRS configurations to augment UAV communication within intelligent mobile networks. The overarching objective is to minimize average total power consumption while simultaneously maximizing network-wide throughput and reliability. This is achieved through the joint optimization of resource allocation, UAV trajectory, velocity, and IRS phase shift design. The focal point is to establish reliable and efficient connectivity for mobile users and Internet of Things (IoT) nodes across diverse scenarios, thereby ensuring QoS benchmarks and unlocking new commercial opportunities. Given the non-convex and NP-hard nature of the optimization problem, we first introduce a deep deterministic policy gradient algorithm to optimize multi-IRS multi-user associations, paving the way for efficient convergence. Subsequently, a sequential convex approximation (SCA) algorithm and a Dinkelbach-based approach are employed to optimize UAV trajectory, followed by the fine-tuning of successive interference cancellation (SIC) decoding order scheduling and power allocation. 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subjects | Algorithms Computer Imaging Computer Science Convergence Ground stations Image Processing and Computer Vision Interference Internet of Things Maximization Mobile communication systems Multimedia Information Systems Nonorthogonal multiple access Optimization Original Paper Pattern Recognition and Graphics Performance evaluation Power consumption Quality of service Reconfigurable intelligent surfaces Reliability Resource allocation Signal,Image and Speech Processing Unmanned aerial vehicles Urban environments Vision |
title | Joint throughput maximization, interference cancellation, and power efficiency for multi-IRS-empowered UAV communications |
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