Deep Learning Based Radio Resource Management in NOMA Networks: User Association, Subchannel and Power Allocation

Zhang, Haijun; Zhang, Haisen; Long, Keping; Karagiannidis, George K.

doi:10.1109/tnse.2020.3004333

Cited by 93 publications

(46 citation statements)

References 28 publications

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“…Similarly, Q-learning was employed in (Amiri and Mehrpouyan, 2018) to develop a self-organized power allocation strategy in mmW networks. Finally, in (Zhang et al, 2020b), Zhang et al presented a semi-supervised learning and DNN for subchannel and power allocation in directional NOMA wireless THz networks. The algorithm requires as inputs the set of users and their channel vectors as well as a predetermined number of clusters.…”

Section: Mac and Rrm Layermentioning

confidence: 99%

Machine Learning: A Catalyst for THz Wireless Networks

Boulogeorgos

Yaqub²,

Renzo

et al. 2021

Front. Comms. Net.

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With the vision to transform the current wireless network into a cyber-physical intelligent platform capable of supporting bandwidth-hungry and latency-constrained applications, both academia and industry turned their attention to the development of artificial intelligence (AI) enabled terahertz (THz) wireless networks. In this article, we list the applications of THz wireless systems in the beyond fifth generation era and discuss their enabling technologies and fundamental challenges that can be formulated as AI problems. These problems are related to physical, medium/multiple access control, radio resource management, network and transport layer. For each of them, we report the AI approaches, which have been recognized as possible solutions in the technical literature, emphasizing their principles and limitations. Finally, we provide an insightful discussion concerning research gaps and possible future directions.

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Section: Mac and Rrm Layermentioning

confidence: 99%

Machine Learning: A Catalyst for THz Wireless Networks

Boulogeorgos

Yaqub²,

Renzo

et al. 2021

Front. Comms. Net.

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“…In (4), C1 indicates that each device is required to achieve its minimum required throughput, while C2 limits the transmission power to the maximum transmission power of each device. If more than two devices are assigned to the same channel, the performance of the SIC process is dramatically reduced [20]- [22]. To this end, C3 limits the number of assigned devices per channel to two devices.…”

Section: A Problem Formulationmentioning

confidence: 99%

Semi-Grant-Free Non-Orthogonal Multiple Access for Tactile Internet of Things

Pliatsios

Boulogeorgos

Λάγκας

et al. 2021

2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

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Ultra-low latency connections for a massive number of devices are one of the main requirements of the nextgeneration tactile Internet-of-Things (TIoT). Grant-free nonorthogonal multiple access (GF-NOMA) is a novel paradigm that leverages the advantages of grant-free access and non-orthogonal transmissions, to deliver ultra-low latency connectivity. In this work, we present a joint channel assignment and power allocation solution for semi-GF-NOMA systems, which provides access to both grant-based (GB) and grant-free (GF) devices, maximizes the network throughput, and is capable of ensuring each device's throughput requirements. In this direction, we provide the mathematical formulation of the aforementioned problem. After explaining that it is not convex, we propose a solution strategy based on the Lagrange multipliers and subgradient method. To evaluate the performance of our solution, we carry out system-level Monte Carlo simulations. The simulation results indicate that the proposed solution can optimize the total system throughput and achieve a high association rate, while taking into account the minimum throughput requirements of both GB and GF devices.

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“…Furthermore, it can improve the ability of CR networks to cope with time-varying environments by modeling spectrum sensing and channel access with the help of RL or DRL (DQN). However, the current researches on clustered CUAV mainly focus on the optimal design of transmission strategies, including access protocol mechanisms, channel allocation and cluster management [22]- [25], etc. How to apply RL to model and design the joint cooperative spectrum sensing and channel access for the clustered CUAV system, and to further enhance the robustness of clustered CUAV communication networks in time-varying channel environments has not been discussed yet.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Reinforcement Learning Based Cooperative Content Caching for Mobile Edge Networks

et al. 2019

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Designing clustered unmanned aerial vehicle (UAV) communication networks based on cognitive radio (CR) and reinforcement learning can significantly improve the intelligence level of clustered UAV communication networks and the robustness of the system in a time-varying environment. Among them, designing smarter systems for spectrum sensing and access is a key research issue in CR. Therefore, we focus on the dynamic cooperative spectrum sensing and channel access in clustered cognitive UAV (CUAV) communication networks. Due to the lack of prior statistical information on the primary user (PU) channel occupancy state, we propose to use multiagent reinforcement learning (MARL) to model CUAV spectrum competition and cooperative decision-making problem in this dynamic scenario, and a return function based on the weighted compound of sensing-transmission cost and utility is introduced to characterize the real-time rewards of multi-agent game. On this basis, a time slot multi-round revisit exhaustive search algorithm based on virtual controller (VC-EXH), a Q-learning algorithm based on independent learner (IL-Q) and a deep Q-learning algorithm based on independent learner (IL-DQN) are respectively proposed. Further, the information exchange overhead, execution complexity and convergence of the three algorithms are briefly analyzed. Through the numerical simulation analysis, all three algorithms can converge quickly, significantly improve system performance and increase the utilization of idle spectrum resources.

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Deep Learning Based Radio Resource Management in NOMA Networks: User Association, Subchannel and Power Allocation

Cited by 93 publications

References 28 publications

Machine Learning: A Catalyst for THz Wireless Networks

Machine Learning: A Catalyst for THz Wireless Networks

Semi-Grant-Free Non-Orthogonal Multiple Access for Tactile Internet of Things

Multi-Agent Reinforcement Learning Based Cooperative Content Caching for Mobile Edge Networks

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