Deep learning-based spectrum sharing in next generation multi-operator cellular networks

被引:1
作者
Mughal, Danish Mehmood [1 ]
Mahboob, Tahira [2 ,3 ]
Shah, Syed Tariq [4 ]
Kim, Sang-Hyo [1 ]
Chung, Min Young [1 ]
机构
[1] Sungkyunkwan Univ, Dept Elect & Comp Engn, Suwon, South Korea
[2] Univ Glasgow, Sch Comp Sci, Glasgow, Scotland
[3] Informat Technol Punjab, Dept Elect & Comp Engn, Lahore, Pakistan
[4] Univ Essex, Sch Comp Sci & Elect Engn, Colchester, England
关键词
cellular networks; deep neural network; machine learning; resource allocation; shared spectrum; WIRELESS NETWORKS; PERFORMANCE; MODEL;
D O I
10.1002/dac.5964
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Owing to the exponential increase in wireless network services and bandwidth requirements, sharing the radio spectrum among multiple network operators seems inevitable. In wireless networks, enabling efficient spectrum sharing for resource allocation is quite challenging due to several random factors, especially in multi-operator spectrum sharing. While spectrum sensing can be useful in spectrum-sharing networks, the chance of collision exists due to the inherent unreliability of wireless networks, making operators reluctant to use sensing-based mechanisms for spectrum sharing. To circumvent these issues, we utilize an alternative approach, whereby we propose an efficient spectrum-sharing mechanism leveraging a spectrum coordinator (SC) in a multi-operator spectrum-sharing scenario assisted by deep learning (DL). In our proposed scheme, before the beginning of each timeslot, the base station of each operator transmits the number of required resources based on the number of packets in the base station's queue to SC. In addition, base stations also transmit the list of available channels to SC. After gathering information from all base stations, SC distributes this collected information to all the base stations. Each base station then utilizes the DL-based spectrum-sharing algorithm and computes the number of resources it can use based on the number of packets in its queue and the number of packets in the queues of other operators. Furthermore, by leveraging DL, each operator also computes the cost it must pay to other operators for using their resources. We evaluate the performance of the proposed network through extensive simulations. It is shown that the proposed DL-based spectrum-sharing mechanism outperforms the conventional spectrum allocation scheme, thus paving the way for more dynamic and efficient multi-operator spectrum sharing. We proposed a deep learning-assisted multi-operator spectrum sharing network leveraging spectrum coordinator. Deployed by a third-party manufacturer, the spectrum coordinator receives information related to the number of packets in the queue from each operator's base station and aggregates and relays the information to each base station. Through simulation results, we verified the superiority of the proposed spectrum-sharing network with a significant improvement in delay and throughput. image
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页数:15
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