Efficient RRH Activation Management for 5G V2X

Jing Wen Ke, Ren Hung Hwang, Chih Yu Wang, Jian Jhih Kuo, Wei Yu Chen

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)


Vehicle-to-everything (V2X) communication is one of the key technologies of 5G New Radio to support emerging applications such as autonomous driving. Due to the high density of vehicles, Remote Radio Heads (RRHs) will be deployed as Road Side Units to support V2X. Nevertheless, activation of all RRHs during low-traffic off-peak hours may cause energy wasting. The proper activation of RRH and association between vehicles and RRHs while maintaining the required service quality are the keys to reducing energy consumption. In this work, we first formulate the problem as an Integer Linear Programming optimization problem and prove that the problem is NP-hard. Then, we propose two novel algorithms, referred to as &#x201C;Least Delete (LD)&#x201D; and &#x201C;Largest-First Rounding with Capacity Constraints (LFRCC).&#x201D; The simulation results show that the proposed algorithms can achieve significantly better performance compared with existing solutions and are competitive with the optimal solution. Specifically, the LD and LFRCC algorithms can reduce the number of activated RRHs by 86<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> and 89<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> in low-density scenarios. In high-density scenarios, the LD algorithm can reduce the number of activated RRHs by 90<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>. In addition, the solution of LFRCC is larger than that of the optimal solution within 7<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> on average.

頁(從 - 到)1-15
期刊IEEE Transactions on Mobile Computing
出版狀態Accepted/In press - 2022


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