Performance analysis of device-to-device communications underlaying cellular networks

Wu, Wenjun; Wang, Xiang; Zhang, Yingkai; Zheng, Kan; Wang, Wenbo

doi:10.1007/s11235-014-9919-y

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…Device-to-Device (D2D) Communications [28], [29], [30], [52], Heterogeneous Networks [31], [32], [33], Vehicular Networks [53], [54] Multiple Input Multiple Output (MIMO) and Massive MIMO [55], [56], [57] Non-Orthogonal Multiple Access (NOMA) [34], [35] management. In fact, most of the game-theoretic models aim at obtaining the NE solution for the resource allocation problem.…”

Section: Topic(s) Of the Survey Related Contentmentioning

confidence: 99%

“…For instance, the resource management techniques in different communication modes of a cellular IoT network (such as the Device-to-Device (D2D) mode, HetNet mode, NOMA mode, or dynamic spectrum access-based mode) are different than those in a homogeneous cellular mode of operation. Generally the issues related to resource management in D2D systems include session setup and management [28], channel allocation and interference management in the underlay networks [29], [30]. HetNets also suffer from the same problems of co-tier and cross-tier interference [31].…”

Section: A Cellular Iot Networkmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges

Hussain

Hassan

Hussain

et al. 2020

IEEE Commun. Surv. Tutorials

280

159

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Internet-of-Things (IoT) refers to a massively heterogeneous network formed through smart devices connected to the Internet. In the wake of disruptive IoT with huge amount and variety of data, Machine Learning (ML) and Deep Learning (DL) mechanisms will play pivotal role to bring intelligence to the IoT networks. Among other aspects, ML and DL can play an essential role in addressing the challenges of resource management in large-scale IoT networks. In this article, we conduct a systematic and in-depth survey of the ML-and DL-based resource management mechanisms in cellular wireless and IoT networks. We start with the challenges of resource management in cellular IoT and low-power IoT networks, review the traditional resource management mechanisms for IoT networks, and motivate the use of ML and DL techniques for resource management in these networks. Then, we provide a comprehensive survey of the existing ML-and DL-based resource allocation techniques in wireless IoT networks and also techniques specifically designed for HetNets, MIMO and D2D communications, and NOMA networks. To this end, we also identify the future research directions in using ML and DL for resource allocation and management in IoT networks.

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