ZTE Communications ›› 2021, Vol. 19 ›› Issue (1): 20-29.DOI: 10.12142/ZTECOM.202101004
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LIU Zhuang1,2(), GAO Yin1,2, LI Dapeng1, CHEN Jiajun1, HAN Jiren1
Received:
2020-12-10
Online:
2021-03-25
Published:
2021-04-09
About author:
LIU Zhuang (LIU Zhuang, GAO Yin, LI Dapeng, CHEN Jiajun, HAN Jiren. Enabling Energy Efficiency in 5G Network[J]. ZTE Communications, 2021, 19(1): 20-29.
5G Intra-System ES Scenario | Coverage Provider | Capacity Booster Provider | Description |
---|---|---|---|
1 | gNB connected with 5GC | gNB connected with 5GC | intra-RAT ES |
2 | ng-eNB connected with 5GC | ng-eNB connected with 5GC | |
3 | gNB connected with 5GC | ng-eNB connected with 5GC | inter-RAT ES |
4 | ng-eNB connected with 5GC | gNB connected with 5GC |
Table 1. 5 G intra-system energy saving scenarios (only connected with 5GC)
5G Intra-System ES Scenario | Coverage Provider | Capacity Booster Provider | Description |
---|---|---|---|
1 | gNB connected with 5GC | gNB connected with 5GC | intra-RAT ES |
2 | ng-eNB connected with 5GC | ng-eNB connected with 5GC | |
3 | gNB connected with 5GC | ng-eNB connected with 5GC | inter-RAT ES |
4 | ng-eNB connected with 5GC | gNB connected with 5GC |
Scenario | Coverage Provider | Capacity Booster Provider |
---|---|---|
1 | eNB connected with EPC | gNB connected with 5GC |
2 | eNB connected with EPC | ng-eNB connected with 5GC |
Table 2 The inter-system ES scenarios of 4G and 5G systems (involving EPC and 5GC)
Scenario | Coverage Provider | Capacity Booster Provider |
---|---|---|
1 | eNB connected with EPC | gNB connected with 5GC |
2 | eNB connected with EPC | ng-eNB connected with 5GC |
Figure 11 Next-generation radio access network (NG-RAN) node connected with 5GC informs cell status to Long Term Evolution (LTE) eNB connected with evolved packet core (EPC) network
Model | Accuracy | Speed | Complexity |
---|---|---|---|
ARIMA | Medium | Fast | Low |
Prophet | Medium | Fast | Low |
LSTM | High | Slow | High |
RF | High | Slow | High |
Ensemble | High | Extremely slow | High |
Table 3 Comparison and analysis of the machine learning models
Model | Accuracy | Speed | Complexity |
---|---|---|---|
ARIMA | Medium | Fast | Low |
Prophet | Medium | Fast | Low |
LSTM | High | Slow | High |
RF | High | Slow | High |
Ensemble | High | Extremely slow | High |
Switch-off Strategy | Number of Measured Cells | Power Consumption of Measured Cells (kWh/Week) | Electricity Charge Saving of Measured Cells (CNY/Week) | ||||
---|---|---|---|---|---|---|---|
No ES | Conventional ES | AI ES | Conventional ES | AI ES | Increase | ||
Carrier | 8 | 382 | 377 | 364 | 5 | 18 | 13 |
Carrier+symbol | 7 | 366 | 344 | 305 | 22 | 61 | 39 |
Channel | 633 | 16 853 | 16 265 | 15 872 | 588 | 981 | 393 |
Channel+symbol | 327 | 8 387 | 7 541 | 6 555 | 846 | 1 832 | 986 |
Total | 975 | 25 988 | 24 527 | 22 304 | 1 461 | 3 684 | 2 223 |
Table 4 Comparison of power consumption and electricity charge saving with/without ES methods
Switch-off Strategy | Number of Measured Cells | Power Consumption of Measured Cells (kWh/Week) | Electricity Charge Saving of Measured Cells (CNY/Week) | ||||
---|---|---|---|---|---|---|---|
No ES | Conventional ES | AI ES | Conventional ES | AI ES | Increase | ||
Carrier | 8 | 382 | 377 | 364 | 5 | 18 | 13 |
Carrier+symbol | 7 | 366 | 344 | 305 | 22 | 61 | 39 |
Channel | 633 | 16 853 | 16 265 | 15 872 | 588 | 981 | 393 |
Channel+symbol | 327 | 8 387 | 7 541 | 6 555 | 846 | 1 832 | 986 |
Total | 975 | 25 988 | 24 527 | 22 304 | 1 461 | 3 684 | 2 223 |
1 | 3GPP. Technical specification—energy efficiency of 5G release 16: TS28.310 V16.2.0 [S]. 2020 |
2 | ZTE. Consideration on inter⁃RAT energy saving: 3GPP R3⁃191453 [R]. 2019 |
3 | Qualcomm. Inter⁃system inter⁃RAT energy saving: 3GPP R3⁃204802 [R]. 2020 |
4 | 3GPP. Technical specification—S1 application protocol release 16: TS36.413 V16.2.0 [S]. 2020 |
5 | 3GPP. Technical specification—NG application protocol release 16: TS38.413 V16.3.0 [S]. 2020 |
6 | 3GPP. Technical specification—5G end to end key performance indicators release 16: TS 28.554 16.5.0 [S]. 2020 |
7 | GAO Y, CHEN J, LIU Z, et al. Machine learning based energy saving scheme in wireless access networks [C]//16th International Wireless Communications and Mobile Computing Conference (IWCMC). Limassol, Cyprus: IEEE, 2020: 1573–1578 |
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