RIS-Driven Resource Allocation Strategies for Diverse Network Environments: A Comprehensive Review
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866929660792668160 |
|---|---|
| author | Ahmed, Manzoor Xu, Fang Lyu, Yuanlin Soofi, Aized Amin Li, Yongxiao Khan, Feroz Khan, Wali Ullah Sheraz, Muhammad Chuah, Teong Chee Deng, Min |
| author_facet | Ahmed, Manzoor Xu, Fang Lyu, Yuanlin Soofi, Aized Amin Li, Yongxiao Khan, Feroz Khan, Wali Ullah Sheraz, Muhammad Chuah, Teong Chee Deng, Min |
| contents | This comprehensive survey examines how Reconfigurable Intelligent Surfaces (RIS) revolutionize resource allocation in various network frameworks. It begins by establishing a theoretical foundation with an overview of RIS technologies, including passive RIS, active RIS, and Simultaneously Transmitting and Reflecting RIS (STAR-RIS). The core of the survey focuses on RIS's role in optimizing resource allocation within Single-Input Multiple-Output (SIMO), Multiple-Input Single-Output (MISO), and Multiple-Input Multiple-Output (MIMO) systems. It further explores RIS integration in complex network environments, such as Heterogeneous Wireless Networks (HetNets) and Non-Orthogonal Multiple Access (NOMA) frameworks. Additionally, the survey investigates RIS applications in advanced communication domains like Terahertz (THz) networks, Vehicular Communication (VC), and Unmanned Aerial Vehicle (UAV) communications, highlighting the synergy between RIS and Artificial Intelligence (AI) for enhanced network efficiency. Summary tables provide comparative insights into various schemes. The survey concludes with lessons learned, future research directions, and challenges, emphasizing critical open issues. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_03075 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RIS-Driven Resource Allocation Strategies for Diverse Network Environments: A Comprehensive Review Ahmed, Manzoor Xu, Fang Lyu, Yuanlin Soofi, Aized Amin Li, Yongxiao Khan, Feroz Khan, Wali Ullah Sheraz, Muhammad Chuah, Teong Chee Deng, Min Emerging Technologies Networking and Internet Architecture This comprehensive survey examines how Reconfigurable Intelligent Surfaces (RIS) revolutionize resource allocation in various network frameworks. It begins by establishing a theoretical foundation with an overview of RIS technologies, including passive RIS, active RIS, and Simultaneously Transmitting and Reflecting RIS (STAR-RIS). The core of the survey focuses on RIS's role in optimizing resource allocation within Single-Input Multiple-Output (SIMO), Multiple-Input Single-Output (MISO), and Multiple-Input Multiple-Output (MIMO) systems. It further explores RIS integration in complex network environments, such as Heterogeneous Wireless Networks (HetNets) and Non-Orthogonal Multiple Access (NOMA) frameworks. Additionally, the survey investigates RIS applications in advanced communication domains like Terahertz (THz) networks, Vehicular Communication (VC), and Unmanned Aerial Vehicle (UAV) communications, highlighting the synergy between RIS and Artificial Intelligence (AI) for enhanced network efficiency. Summary tables provide comparative insights into various schemes. The survey concludes with lessons learned, future research directions, and challenges, emphasizing critical open issues. |
| title | RIS-Driven Resource Allocation Strategies for Diverse Network Environments: A Comprehensive Review |
| topic | Emerging Technologies Networking and Internet Architecture |
| url | https://arxiv.org/abs/2501.03075 |