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| Format: | Preprint |
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2026
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| Online-Zugang: | https://arxiv.org/abs/2605.08951 |
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| _version_ | 1866915997653401600 |
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| author | Farhadi, Armin Olfat, Ali |
| author_facet | Farhadi, Armin Olfat, Ali |
| contents | In modern wireless communication systems, there is a rapidly increasing demand for connectivity to wireless networks. Devices such as internet of things (IoT) devices, connected vehicles, smartphones, surveillance systems, and various other applications contribute significantly to this demand. Consequently, next-generation wireless systems must be capable of handling this enormous volume of devices and traffic. In recent years, several technologies have been introduced to address these challenges, including reconfigurable intelligent surfaces (RIS), integrated sensing and communication (ISAC), advanced antenna and intelligent surface technologies, and novel multiple access (MA) techniques. Furthermore, due to the limited resources available in communication systems, efficient resource allocation strategies are essential to support complex and high-dimensional optimization problems. In addition, modern communication systems are required to optimize resources within strict time constraints. Therefore, resource allocation solutions must be intelligent and computationally efficient. Conventional optimization techniques, such as convex optimization, are often inadequate for addressing these requirements. To overcome these limitations, novel resource allocation algorithms based on learning methods have been developed. In this paper, we comprehensively investigate advanced communication technologies alongside modern resource allocation optimization methods and algorithms based on machine learning techniques. Subsequently, current challenges of wireless networks are analyzed. Finally, open research challenges are identified. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_08951 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Comprehensive Review of Advances and Challenges in Next Generation Wireless Networks: From Novel Hardware Technologies to Learning Based Resource Allocation in 6G Farhadi, Armin Olfat, Ali Signal Processing In modern wireless communication systems, there is a rapidly increasing demand for connectivity to wireless networks. Devices such as internet of things (IoT) devices, connected vehicles, smartphones, surveillance systems, and various other applications contribute significantly to this demand. Consequently, next-generation wireless systems must be capable of handling this enormous volume of devices and traffic. In recent years, several technologies have been introduced to address these challenges, including reconfigurable intelligent surfaces (RIS), integrated sensing and communication (ISAC), advanced antenna and intelligent surface technologies, and novel multiple access (MA) techniques. Furthermore, due to the limited resources available in communication systems, efficient resource allocation strategies are essential to support complex and high-dimensional optimization problems. In addition, modern communication systems are required to optimize resources within strict time constraints. Therefore, resource allocation solutions must be intelligent and computationally efficient. Conventional optimization techniques, such as convex optimization, are often inadequate for addressing these requirements. To overcome these limitations, novel resource allocation algorithms based on learning methods have been developed. In this paper, we comprehensively investigate advanced communication technologies alongside modern resource allocation optimization methods and algorithms based on machine learning techniques. Subsequently, current challenges of wireless networks are analyzed. Finally, open research challenges are identified. |
| title | Comprehensive Review of Advances and Challenges in Next Generation Wireless Networks: From Novel Hardware Technologies to Learning Based Resource Allocation in 6G |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2605.08951 |