SCIENCE CHINA Information Sciences, Volume 62 , Issue 8 : 082301(2019) https://doi.org/10.1007/s11432-018-9680-9

Delay-constrained sleeping mechanism for energy saving in cache-aided ultra-dense network

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  • ReceivedJul 15, 2018
  • AcceptedNov 29, 2018
  • PublishedMay 28, 2019



This work was partially supported by National Major Project (Grant No. 2017ZX03001002-004), National Natural Science Foundation Project of China (Grant No. 61521061), and 333 Program of Jiangsu (Grant No. BRA2017366).


[1] You X H, Pan Z W, Gao X Q, et al. The 5G mobile communication: the development trends and its emerging key techniques (in Chinese). Sci Sin Inform, 2014, 44: 551--563. Google Scholar

[2] Wu J, Zhang Y, Zukerman M. Energy-Efficient Base-Stations Sleep-Mode Techniques in Green Cellular Networks: A Survey. IEEE Commun Surv Tutorials, 2015, 17: 803-826 CrossRef Google Scholar

[3] Liu C, Natarajan B, Xia H. Small Cell Base Station Sleep Strategies for Energy Efficiency. IEEE Trans Veh Technol, 2016, 65: 1652-1661 CrossRef Google Scholar

[4] Liu B, Zhao M, Zhou W, et al. Flow-level-delay constrainted small cell sleeping with macro base station cooperation for energy saving in HetNet. In: Proceedings of IEEE Vehicular Technology Conference (VTC-Fall), Boston, 2015. 1--5. Google Scholar

[5] Gamboa S, Pelov A, Maille P. Reducing the Energy Footprint of Cellular Networks with Delay-Tolerant Users. IEEE Syst J, 2017, 11: 729-739 CrossRef ADS Google Scholar

[6] Son K, Kim H, Yi Y. Base Station Operation and User Association Mechanisms for Energy-Delay Tradeoffs in Green Cellular Networks. IEEE J Sel Areas Commun, 2011, 29: 1525-1536 CrossRef Google Scholar

[7] Shi Q, Zhao L Q, Zhang Y Y. Energy-Efficiency Versus Delay Tradeoff in Wireless Networks Virtualization. IEEE Trans Veh Technol, 2018, 67: 837-841 CrossRef Google Scholar

[8] Li P, Jiang H L, Pan Z W. Energy-Delay Tradeoff in Ultra-Dense Networks Considering BS Sleeping and Cell Association. IEEE Trans Veh Technol, 2018, 67: 734-751 CrossRef Google Scholar

[9] Han T, Ansari N. Network Utility Aware Traffic Load Balancing in Backhaul-Constrained Cache-Enabled Small Cell Networks with Hybrid Power Supplies. IEEE Trans Mobile Comput, 2017, 16: 2819-2832 CrossRef Google Scholar

[10] Chen Z, Lee J, Quek T Q S. Cooperative Caching and Transmission Design in Cluster-Centric Small Cell Networks. IEEE Trans Wireless Commun, 2017, 16: 3401-3415 CrossRef Google Scholar

[11] Xu J W, Ota K, Dong M X. Saving Energy on the Edge: In-Memory Caching for Multi-Tier Heterogeneous Networks. IEEE Commun Mag, 2018, 56: 102-107 CrossRef Google Scholar

[12] MelikeE. Content caching in small cells with optimized uplink and caching power. In: Proceedings of IEEE Wireless Communications and Networking Conference (WCNC), New Orleans, 2015. 2173--2178. Google Scholar

[13] Poularakis K, Iosifidis G, Tassiulas L. Joint caching and base station activation for green heterogeneous cellular networks. In: Proceedings of IEEE International Conference on Communications (ICC), London, 2015. 3364--3369. Google Scholar

[14] Xie R C, Li Z S, Huang T. Energy-efficient joint content caching and small base station activation mechanism design in heterogeneous cellular networks. China Commun, 2017, 14: 70-83 CrossRef Google Scholar

[15] Xu D, Jin H, Zhao C L, et al. Joint Caching and Sleep-Active Scheduling for Energy-Harvesting Based Small Cells. In: Proceedings of IEEE International Conference on Wireless Communications and Signal Processing, Nanjing, 2017. 1--6. Google Scholar

[16] Pappas N, Chen Z, Dimitriou I. Throughput and Delay Analysis of Wireless Caching Helper Systems With Random Availability. IEEE Access, 2018, 6: 9667-9678 CrossRef Google Scholar

[17] Doan K N, van Nguyen T, Quek T Q S. Content-Aware Proactive Caching for Backhaul Offloading in Cellular Network. IEEE Trans Wireless Commun, 2018, 17: 3128-3140 CrossRef Google Scholar

[18] Gao S, Li P, Pan Z W, et al. Machine Learning based Small Cell Cache Strategy for Ultra Dense Networks. In: Proceedings of IEEE International Conference on Wireless Communications and Signal Processing (WCSP), Nanjing, 2017. 1--5. Google Scholar

[19] Haenggi M, Andrews J G, Baccelli F. Stochastic geometry and random graphs for the analysis and design of wireless networks. IEEE J Sel Areas Commun, 2009, 27: 1029-1046 CrossRef Google Scholar

[20] Takagi H. Queueing Analysis: A Foundation of Performance Evaluation, Volume I: Vacation and Priority Systems. 1st ed. Amsterdam: Elsevier, 1991. 30--55. Google Scholar

[21] Yang J, Yang Q H, Kwak K S. Power-Delay Tradeoff in Wireless Powered Communication Networks. IEEE Trans Veh Technol, 2017, 66: 3280-3292 CrossRef Google Scholar

[22] Zhang G Z, Quek T, Huang A, et al. Backhaul-aware Base Station Association in Two-tier Heterogeneous Cellular Networks. In: Proceedings of IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Stockholm, 2015. 390--394. Google Scholar

[23] Li P, Shen Y, Sahito F, et al. BS sleeping strategy for energy-delay tradeoff in wireless-backhauling UDN. Science China Information Sciences, 2018. doi: 10.1007/s11432-018-9494-9. Google Scholar

[24] Zhu W X, Xu P P, Bui T O. Energy-efficient cell-association bias adjustment algorithm for ultra-dense networks. Sci China Inf Sci, 2018, 61: 022306 CrossRef Google Scholar

[25] Huang S, Liang B, Li J D. Distributed Interference and Delay Aware Design for D2D Communication in Large Wireless Networks With Adaptive Interference Estimation. IEEE Trans Wireless Commun, 2017, 16: 3924-3939 CrossRef Google Scholar

[26] Mo Y, Peng M, Xiang H Y. Resource Allocation in Cloud Radio Access Networks With Device-to-Device Communications. IEEE Access, 2017, 5: 1250-1262 CrossRef Google Scholar

[27] Neely M J. Stochastic network optimization with application to communication and queueing systems. In: Synthesis Lectures on Communication Networks. San Rafael: Morgan and Claypool, 2010. 1--211. Google Scholar

[28] Wang C W, Mei W Y, Qin X Y. Quantum entropy based tabu search algorithm for energy saving in SDWN. Sci China Inf Sci, 2017, 60: 040307 CrossRef Google Scholar

[29] Jo H S, Xia P, Andrews J G. Open, closed, and shared access femtocells in the downlink. J Wireless Com Network, 2012, 2012: 363-378 CrossRef Google Scholar

[30] Li K, Yang C, Chen Z. Optimization and Analysis of Probabilistic Caching in $N$ -Tier Heterogeneous Networks. IEEE Trans Wireless Commun, 2018, 17: 1283-1297 CrossRef Google Scholar

[31] Cui Q M, Cui Z Y, Zheng W. Energy-aware deployment of dense heterogeneous cellular networks with QoS constraints. Sci China Inf Sci, 2017, 60: 042303 CrossRef Google Scholar

  • Figure 1

    (Color online) System model for a cache-aided UDN.

  • Figure 2

    (Color online) Time-averaged energy consumption vs. weighting factor $V$, calculated by Algorithm 1.

  • Figure 3

    (Color online) Time-averaged queue length vs. weighting factor $V$, obtained in the simulation study.

  • Figure 4

    (Color online) Time-averaged delays in file and video contents vs. weighting factor $V$, obtained in the simulation.

  • Figure 5

    (Color online) Weighting factor $V$ vs. the time-averaged energy consumption and the time-averaged delay of file contents at $C=5$.

  • Figure 6

    (Color online) Time-averaged energy consumption vs. weighting factor for different sleeping schemes and caching capacities.

  • Figure 7

    (Color online) Time-averaged delay vs. weighting factor for different sleeping schemes and caching capacities.

  • Table 1   System parameter
    Parameter Value Parameter Value
    ${\lambda~_m}$ $5\times10^{-6}$ ${\lambda~_s}$ $2.5\times10^{-5}$
    ${\lambda~_V}$ $0.5~~{\rm~s}^{-1}$ ${\lambda~_F}$ $1~~{\rm~s}^{-1}$
    $\Delta~p_{m}$ $10$ $\Delta~p_{s}$ $8$
    ${P_{s0}}$ $4.8$ W ${P_{m0}}$ $10$ W
    ${P_S}$ 2.4 W ${P_{mb}}$$8$ W
    ${P_{mt}}$ $46~$ dBm ${P_{st}}$$30$ dBm
    ${L_V}$ 10 MB $L_F$5 MB
    ${W_m}$ 10 MHz $W_s$ 10 MHz
    ${E_{S}}$ $1.5$ W ${{\omega~_{{{cs}}}}}$ $2\times10^{-9}$ J/byte

    Algorithm 1 A delay-constrained sleeping scheme

    01: Initialize: $\!{Q}(0)\!\!=\!0$, $\!{G}(0)\!\!=\!0$, $\!\rho(0)\!\!=\!0$, $\!\xi(0)\!=\!0$, $\!{\cal{B}}_{\rm~on}$, $\!{\cal{B}}_{\rm~off}$. 02: Repeat 03: Calculate $R_{k}(t)$, $r_{j}(t)$, $\rho_{k}(t)$, $\xi~_{j}(t)$ and ${{p}}_k^{F}$. 04: Compute $A_{k}(t)$ and $M_{j}(t)$ according to (6) and (15). 05: Update $Q_{k}(t)$ and $G_{j}(t)$ according to (10) and (17). 06: Calculate $P(t)$ according to (5). 07: $t=t+1$, 08: Update $\bar~\rho_{k}$ and $\bar~\xi~_{j}$ according to (14) and (21). 09: Update $\bar~Q_{k}$, $\bar~G_{j}$ and $\bar~P$, according to (13), (20) and (30). 10: End Repeat when $t=T$, $T$ is total number of time slots. 11: While $\Psi~>~1$, 12: Repeat 13: Calculate ${\Gamma~_{\rm~on}}\!\left(~k~\right)~\!=\!~F\!\left(~{{\cal~B}_{\rm~on}}\!~\cup~\!~\left\{~{k}~\right\}~\right)\!~-\!~F\!\left({\cal~B}_{\rm~on}~\right)~\!+~\!{E_s}$, $\forall~k~\!~\in~\!~{\cal~{B}}_{\rm~off}$. 14: If ${k^*}~\!=\!~\mathop~{\arg~\min~}\limits_{k~\in~{{\cal~B}_{{{\rm~on}}}}}~{\Gamma~_{\rm~on}}\left(~k~\right)$, Then ${{\cal~B}_{{{\rm~on}}}}\!~\leftarrow~\!~{{\cal~B}_{{{\rm~on}}}}~\cup~\{~{k^*}\}~$. 15: End Repeat when $0<\Psi~<~1$. 16: End While 17: While $0<\Psi<~1$, 18: Calculate ${\Gamma~_{\rm~off}}\!\left(~k~\right)~\!=\!~F\!\left({\cal~B}_{\rm~on}~\right)~\!-\!~F\!\left(~{{\cal~B}_{\rm~on}}\!-\!~\left\{~{k}~\right\}\!~\right)\!~-~\!{E_s}$, $\forall~k~\!~\in~\!~{\cal~{B}}_{\rm~on}$. 19: If ${\Gamma~_{\rm~off}}\!\left(~k~\right)~\!~>0$, Then ${{\cal~B}_{{{\rm~off}}}}~\leftarrow~{{\cal~B}_{{{\rm~off}}}}~\cup~\{~{k}\}~$. 20: End While