{
  "annotation_id": "652718a4-736e-4df2-9a1d-03775627b220",
  "date_created": "2026-02-17T05:53:07.837000Z",
  "date_modified": "2026-02-17T05:53:07.837000Z",
  "file_hash": "e0dc92803adca7390f2175f954ddc172a6f024e320a8ffe75365c7799510ae70",
  "private": false,
  "record": {
    "abstract": "The ongoing evolution of 5G and its enhanced version, 5G+, has significantly transformed the telecommunications landscape, driving an unprecedented demand for ultra-high-speed data transmission, ultra-low latency, and resilient connectivity. These capabilities are essential for enabling mission-critical applications such as the Internet of Things, autonomous vehicles, and smart city infrastructures. This paper investigates the important role of Artificial Intelligence (AI) in addressing the key challenges faced by 5G/5G+ networks, including interference mitigation, dynamic resource allocation, and maintaining seamless network operation. The study particularly focuses on AI-driven innovations in coding theory, which offer advanced solutions to the limitations of conventional error correction and modulation techniques. By employing deep learning, reinforcement learning, and neural network-based approaches, this research demonstrates significant advancements in error correction performance, decoding efficiency, and adaptive transmission strategies. Additionally, the integration of AI with emerging technologies, such as massive multiple-input and multiple-output, intelligent reflecting surfaces, and privacy-enhancing mechanisms, is discussed, highlighting their potential to propel the next generation of wireless networks. This paper also provides insights into the transformative impact of AI on modern wireless communication, establishing a foundation for scalable, adaptive, and more efficient network architectures.",
    "arxiv_id": "2601.06796",
    "authors": [
      "Yasir Ali",
      "Tayyab Manzoor",
      "Huan Yang",
      "Chenhang Yan",
      "Yuanqing Xia"
    ],
    "categories": [
      "eess.SP"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Artificial Intelligence Driven Channel Coding and Resource Optimization for Wireless Networks",
    "url": "https://arxiv.org/abs/2601.06796",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "8ce440d7-066c-4c03-9c7d-fbaa061902ad",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}