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Erschienen in: Wireless Networks 2/2023

21.10.2022 | Original Paper

Energy minimization by dynamic base station switching in heterogeneous cellular network

verfasst von: Yi Yang, Zhixin Liu, Heng Zhu, Xinping Guan, Kit Yan Chan

Erschienen in: Wireless Networks | Ausgabe 2/2023

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Abstract

5G communication technologies are expected to provide high rate and low delay services. To meet the requirements, more base stations (BS), including macrocell BS (MacBS) and microcell BS (MicBS), have to be deployed. In this dense multi-tier heterogeneous networks, the user quality of service (QoS) can be significantly improved by shortening communication distance between base stations and users. However, the network energy consumptions of base stations have been growing quickly. How to save energy consumption in these dense layered network has become a problem we have to face. In this paper, we proposed a microcell BS (MicBS) switch algorithm to reduce the network energy consumption. The BS energy consumption is associated with traffic load, which is denoted as the number of users a BS serves. Considering the time-varying traffic load, we proposed a metric named coverage ratio to characterize how many users can enjoy the services. When the coverage ratio exceeds the upper threshold, a switch off algorithm is activated. MicBSs whose energy costs are higher than their economic profit will be switched off one by one. On the contrary, if this metric is below the lower threshold, a switch on algorithm is activated. A group of inactive MicBSs surrounded by multiple unserved users will be switched on simultaneously. After the switching operations, the network coverage ratio is expected to fall between the upper and lower bounds. Simulation results show that the coverage ratio is kept with the desired level. Compared to some existing algorithms, the proposed algorithm shows more flexible switching operation and more effective energy saving.

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Fußnoten
1
Note that the maximum transmission power of MacBS shown in the table is 100W. In the simulations, we have tried several power levels, such as 20W, 40W. According to the rules of 3GPP organization, wireless base stations are divided into four categories, namely macro base station, micro base station, pico base station and femto base station. The recommended power levels of single carrier transmission for based stations are above 10W. The typical power level of outdoor base station is about 43dBm (20W). However it depends on the coverage range and carrier frequency. For instance, to extend the coverage of base station or work at the higher transmission power is needed; while in the 5G applications, with the higher carrier frequency and high transmission rate requirement, the transmission power is also increased. China Mobile, for example, requires 64 channels with a maximum transmission power of 320 Watts for its 2.6GHz RF module to support high downlink speeds.
 
Literatur
1.
Zurück zum Zitat Zhang, H., Liu, H., Cheng, J., & Leung, V. C. M. (2018). Downlink energy efficiency of power allocation and wireless backhaul bandwidth allocation in heterogeneous small cell networks. IEEE Transactions on Communications, 66(4), 1705–1716.CrossRef Zhang, H., Liu, H., Cheng, J., & Leung, V. C. M. (2018). Downlink energy efficiency of power allocation and wireless backhaul bandwidth allocation in heterogeneous small cell networks. IEEE Transactions on Communications, 66(4), 1705–1716.CrossRef
2.
Zurück zum Zitat Liu, Z., Gao, L., Liu, Y., Guan, X., Ma, K., & Wang, Y. (2020). Efficient QoS support for robust resource allocation in blockchain-based femtocell networks. IEEE Transactions on Industrial Informatics, 16(11), 7070–7080.CrossRef Liu, Z., Gao, L., Liu, Y., Guan, X., Ma, K., & Wang, Y. (2020). Efficient QoS support for robust resource allocation in blockchain-based femtocell networks. IEEE Transactions on Industrial Informatics, 16(11), 7070–7080.CrossRef
3.
Zurück zum Zitat Auer, G., Blume, O., Giannini, V., Godor, I., Imran, M., Jading, Y., Katranaras, E., Olsson, M., Sabella, D., & Skillermark , P. et al. (2010). D2.3: energy efficiency analysis of the reference systems, areas of improvements and target breakdown. INFSO-ICTB247733 EARTH (Energy Aware Radio Network Technoledge), pp. 39–49. Auer, G., Blume, O., Giannini, V., Godor, I., Imran, M., Jading, Y., Katranaras, E., Olsson, M., Sabella, D., & Skillermark , P. et al. (2010). D2.3: energy efficiency analysis of the reference systems, areas of improvements and target breakdown. INFSO-ICTB247733 EARTH (Energy Aware Radio Network Technoledge), pp. 39–49.
4.
Zurück zum Zitat Xu, Y., Xie, H., Wu, Q., Huang, C., & Yuen, C. (2022). Robust max-min energy efficiency for RIS-aided hetnets with distortion noises. IEEE Transactions on Communications, 70(2), 1457–1471.CrossRef Xu, Y., Xie, H., Wu, Q., Huang, C., & Yuen, C. (2022). Robust max-min energy efficiency for RIS-aided hetnets with distortion noises. IEEE Transactions on Communications, 70(2), 1457–1471.CrossRef
5.
Zurück zum Zitat Xu, Y., Gui, G., Gacanin, H., & Adachi, F. (2021). A survey on resource allocation for 5G heterogeneous networks: Current research, future trends, and challenges. IEEE Communications Surveys and Tutorials, 23(2), 668–695.CrossRef Xu, Y., Gui, G., Gacanin, H., & Adachi, F. (2021). A survey on resource allocation for 5G heterogeneous networks: Current research, future trends, and challenges. IEEE Communications Surveys and Tutorials, 23(2), 668–695.CrossRef
6.
Zurück zum Zitat Liu, Z., Liang, C., Yuan, Y., Chan, K., & Guan, X. (2021). Resource allocation based on user pairing and subcarrier matching for downlink non-orthogonal multiple access networks. IEEE/CAA Journal of Automatic Sinica, 8(3), 670–680.CrossRef Liu, Z., Liang, C., Yuan, Y., Chan, K., & Guan, X. (2021). Resource allocation based on user pairing and subcarrier matching for downlink non-orthogonal multiple access networks. IEEE/CAA Journal of Automatic Sinica, 8(3), 670–680.CrossRef
7.
Zurück zum Zitat Zhao, C., Han, J., Ding, X., & Yang, F. (2020) A novel approach of dynamic base station switching strategy based on markov decision process for interference alignment in VANETs, Wireless Networks, pp. 1–18. Zhao, C., Han, J., Ding, X., & Yang, F. (2020) A novel approach of dynamic base station switching strategy based on markov decision process for interference alignment in VANETs, Wireless Networks, pp. 1–18.
8.
Zurück zum Zitat Lassila, P., Gebrehiwot, E. M., & Aalto, S. (2019). Optimal energy-aware load balancing and base station switch-off control in 5G hetnets. Computer Networks, 159, 10–22.CrossRef Lassila, P., Gebrehiwot, E. M., & Aalto, S. (2019). Optimal energy-aware load balancing and base station switch-off control in 5G hetnets. Computer Networks, 159, 10–22.CrossRef
9.
Zurück zum Zitat Xu, Y., Xie, H., Liang, C., & Yu, F. R. (2021). Robust secure energy efficiency optimization in SWIPT-aided heterogeneous networks with a non-linear energy harvesting model. IEEE Internet of Things Journal, 8(19), 14 908-14 919.CrossRef Xu, Y., Xie, H., Liang, C., & Yu, F. R. (2021). Robust secure energy efficiency optimization in SWIPT-aided heterogeneous networks with a non-linear energy harvesting model. IEEE Internet of Things Journal, 8(19), 14 908-14 919.CrossRef
10.
Zurück zum Zitat Wu, J., Zhang, Y., Zukerman, M., & Yung, E. K. (2015). Energy-efficient base-stations sleep-mode techniques in green cellular networks: A survey. IEEE Communications Surveys Tutorials, 17(2), 803–826.CrossRef Wu, J., Zhang, Y., Zukerman, M., & Yung, E. K. (2015). Energy-efficient base-stations sleep-mode techniques in green cellular networks: A survey. IEEE Communications Surveys Tutorials, 17(2), 803–826.CrossRef
11.
Zurück zum Zitat Peng, J., Hong, P., & Xue, K. (2014). Stochastic analysis of optimal base station energy saving in cellular networks with sleep mode. IEEE Communications Letters, 18(4), 612–615.CrossRef Peng, J., Hong, P., & Xue, K. (2014). Stochastic analysis of optimal base station energy saving in cellular networks with sleep mode. IEEE Communications Letters, 18(4), 612–615.CrossRef
12.
Zurück zum Zitat Liu, Z., Zhu, H., Yuan, Y., Yang, Y., & Chan, K. Y. (2020). Optimization of base station density and user transmission power in multi-tier heterogeneous cellular systems. Computer Communications, 161, 334–343.CrossRef Liu, Z., Zhu, H., Yuan, Y., Yang, Y., & Chan, K. Y. (2020). Optimization of base station density and user transmission power in multi-tier heterogeneous cellular systems. Computer Communications, 161, 334–343.CrossRef
13.
Zurück zum Zitat Lee, S. H., & Sohn, I. (2015). Affinity propagation for energy-efficient BS operations in green cellular networks. IEEE Transactions on Wireless Communications, 14(8), 4534–4545.CrossRef Lee, S. H., & Sohn, I. (2015). Affinity propagation for energy-efficient BS operations in green cellular networks. IEEE Transactions on Wireless Communications, 14(8), 4534–4545.CrossRef
14.
Zurück zum Zitat Ben Rached, N., Ghazzai, H., Kadri, A., & Alouini, M. (2018). A time-varied probabilistic on/off switching algorithm for cellular networks. IEEE Communications Letters, 22(3), 634–637.CrossRef Ben Rached, N., Ghazzai, H., Kadri, A., & Alouini, M. (2018). A time-varied probabilistic on/off switching algorithm for cellular networks. IEEE Communications Letters, 22(3), 634–637.CrossRef
15.
Zurück zum Zitat Kim, J., Lee, H., & Chong, S. (2018). Traffic-aware energy-saving base station sleeping and clustering in cooperative networks. IEEE Transactions on Wireless Communications, 17(2), 1173–1186.CrossRef Kim, J., Lee, H., & Chong, S. (2018). Traffic-aware energy-saving base station sleeping and clustering in cooperative networks. IEEE Transactions on Wireless Communications, 17(2), 1173–1186.CrossRef
16.
Zurück zum Zitat Beitelmal, T., Szyszkowicz, S. S., Gonzlez, D. G., & Yanikomeroglu, H. (2018). Sector and site switch-off regular patterns for energy saving in cellular networks. IEEE Transactions on Wireless Communications, 17(5), 2932–2945.CrossRef Beitelmal, T., Szyszkowicz, S. S., Gonzlez, D. G., & Yanikomeroglu, H. (2018). Sector and site switch-off regular patterns for energy saving in cellular networks. IEEE Transactions on Wireless Communications, 17(5), 2932–2945.CrossRef
17.
Zurück zum Zitat Panahi, F. H., Panahi, F. H., Hattab, G., Ohtsuki, T., & Cabric, D. (2018). Green heterogeneous networks via an intelligent sleep/wake-up mechanism and D2D communications. IEEE Transactions on Green Communications and Networking, 2(4), 915–931.CrossRef Panahi, F. H., Panahi, F. H., Hattab, G., Ohtsuki, T., & Cabric, D. (2018). Green heterogeneous networks via an intelligent sleep/wake-up mechanism and D2D communications. IEEE Transactions on Green Communications and Networking, 2(4), 915–931.CrossRef
18.
Zurück zum Zitat Yu, N., Miao, Y., Mu, L., Du, H., Huang, H., & Jia, X. (2016). Minimizing energy cost by dynamic switching on/off base stations in cellular networks. IEEE Transactions on Wireless Communications, 15(11), 7457–7469.CrossRef Yu, N., Miao, Y., Mu, L., Du, H., Huang, H., & Jia, X. (2016). Minimizing energy cost by dynamic switching on/off base stations in cellular networks. IEEE Transactions on Wireless Communications, 15(11), 7457–7469.CrossRef
19.
Zurück zum Zitat Amine, A.E., Chaiban, J.P., Hassan, H.A.H., Dini, P., Nuaymi, L., & Achkar, R. (2022). Energy optimization with multi-sleeping control in 5g heterogeneous networks using reinforcement learning, IEEE Transactions on Network and Service Management, early access, https://doi.org/10.1109/TNSM.2022.3157650. Amine, A.E., Chaiban, J.P., Hassan, H.A.H., Dini, P., Nuaymi, L., & Achkar, R. (2022). Energy optimization with multi-sleeping control in 5g heterogeneous networks using reinforcement learning, IEEE Transactions on Network and Service Management, early access, https://​doi.​org/​10.​1109/​TNSM.​2022.​3157650.​
20.
Zurück zum Zitat Oh, E., Son, K., & Krishnamachari, B. (2013). Dynamic base station switching-on/off strategies for green cellular networks. IEEE Transactions on Wireless Communications, 12(5), 2126–2136.CrossRef Oh, E., Son, K., & Krishnamachari, B. (2013). Dynamic base station switching-on/off strategies for green cellular networks. IEEE Transactions on Wireless Communications, 12(5), 2126–2136.CrossRef
21.
Zurück zum Zitat Ajmone Marsan, M., Chiaraviglio, L., Ciullo, D., & Meo, M. (2009) Optimal energy savings in cellular access networks, in 2009 IEEE International Conference on Communications Workshops, pp. 1–5. Ajmone Marsan, M., Chiaraviglio, L., Ciullo, D., & Meo, M. (2009) Optimal energy savings in cellular access networks, in 2009 IEEE International Conference on Communications Workshops, pp. 1–5.
22.
Zurück zum Zitat Dahal, M. S., Shrestha, J. N., & Shakya, S. R. (2018). Energy saving technique and measurement in green wireless communication. Energy, 159, 21–31.CrossRef Dahal, M. S., Shrestha, J. N., & Shakya, S. R. (2018). Energy saving technique and measurement in green wireless communication. Energy, 159, 21–31.CrossRef
23.
Zurück zum Zitat Tabassum, H., Siddique, U., Hossain, E., & Hossain, M. J. (2014). Downlink performance of cellular systems with base station sleeping, user association, and scheduling. IEEE Transactions on Wireless Communications, 13(10), 5752–5767.CrossRef Tabassum, H., Siddique, U., Hossain, E., & Hossain, M. J. (2014). Downlink performance of cellular systems with base station sleeping, user association, and scheduling. IEEE Transactions on Wireless Communications, 13(10), 5752–5767.CrossRef
24.
Zurück zum Zitat Liu, Z., Xie, Y., Chan, K., & Guan, X. (2019). Chance-constrained optimization in d2d-based vehicular communication network. IEEE Transactions on Vehicular Technology, 68(5), 5045–5058.CrossRef Liu, Z., Xie, Y., Chan, K., & Guan, X. (2019). Chance-constrained optimization in d2d-based vehicular communication network. IEEE Transactions on Vehicular Technology, 68(5), 5045–5058.CrossRef
25.
Zurück zum Zitat Ignacio, G., Bo, Y., Shuai, Z., & Yu, C. (2021). Optimization of base station on-off switching with a machine learning approach, in ICC 2021-IEEE International Conference on Communications, pp. 1–6. Ignacio, G., Bo, Y., Shuai, Z., & Yu, C. (2021). Optimization of base station on-off switching with a machine learning approach, in ICC 2021-IEEE International Conference on Communications, pp. 1–6.
26.
Zurück zum Zitat Marcin, H., Adrian, K., Pawel, K., & Koudouridis, G. P. (2020). A reinforcement learning approach for base station on/off switching in heterogeneous m-mimo networks, in 2020 IEEE 21st International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 170–172. Marcin, H., Adrian, K., Pawel, K., & Koudouridis, G. P. (2020). A reinforcement learning approach for base station on/off switching in heterogeneous m-mimo networks, in 2020 IEEE 21st International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 170–172.
27.
Zurück zum Zitat Haenggi, M. (2012). Stochastic geometry for wireless networks. Cambridge: Cambridge Press.CrossRefMATH Haenggi, M. (2012). Stochastic geometry for wireless networks. Cambridge: Cambridge Press.CrossRefMATH
28.
Zurück zum Zitat Yuanai, X., Zhixin, L., Yan, C. K., & Xinping, G. (2020). Energy-spectral efficiency optimization in vehicular communications: Joint clustering and pricing-based robust power control approach. IEEE Transactions on Vehicular Technology, 69(11), 13 673-13 685.CrossRef Yuanai, X., Zhixin, L., Yan, C. K., & Xinping, G. (2020). Energy-spectral efficiency optimization in vehicular communications: Joint clustering and pricing-based robust power control approach. IEEE Transactions on Vehicular Technology, 69(11), 13 673-13 685.CrossRef
29.
Zurück zum Zitat Andrews, J. G., Baccelli, F., & Ganti, R. K. (2011). A tractable approach to coverage and rate in cellular networks. IEEE Transactions on Communications, 59(11), 3122–3134.CrossRef Andrews, J. G., Baccelli, F., & Ganti, R. K. (2011). A tractable approach to coverage and rate in cellular networks. IEEE Transactions on Communications, 59(11), 3122–3134.CrossRef
Metadaten
Titel
Energy minimization by dynamic base station switching in heterogeneous cellular network
verfasst von
Yi Yang
Zhixin Liu
Heng Zhu
Xinping Guan
Kit Yan Chan
Publikationsdatum
21.10.2022
Verlag
Springer US
Erschienen in
Wireless Networks / Ausgabe 2/2023
Print ISSN: 1022-0038
Elektronische ISSN: 1572-8196
DOI
https://doi.org/10.1007/s11276-022-03167-7

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