SCIENCE CHINA Information Sciences, Volume 60, Issue 4: 040307(2017) https://doi.org/10.1007/s11432-017-9044-x

Quantum entropy based tabu search algorithm for energy saving in SDWN

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  • ReceivedJan 13, 2017
  • AcceptedFeb 13, 2017
  • PublishedMar 17, 2017


The energy consumption of the base station (BS) accounts for great proportion of the total wireless access network (WAN). Switching off the selected spare BSs with few network request would save a large amount of energy. It is difficult to deploy a BS energy saving strategy in existing network architecture due to the tightly coupled network devices. Therefore, we adopt the software defined wireless networks (SDWN) structure which is an sample of the wireless software defined networks (SDN). Then a novel quantum entropy based tabu search algorithm (QETS) is proposed to choose which BS to switch off, and it increases the search range and guarantee the convergence speed. The energy saving strategy can find the optimal solution with higher probabilities and can be deployed in centralized controller as a software. Theoretical analysis and simulation results show the QETS algorithm's gain over the greedy algorithm and quantum inspired tabu search algorithm (QTS) in terms of convergence.



This work was jointly supported by National High-Tech R&D Program of China (863) (Grant No. 2015AA01A705) and State Grid (Grant of ``Research and Application of Key Technologies in Smart Grid Park Energy Management and Optimization for Smart City").


[1] Marsan M A, Meo M. Network sharing and its energy benefits: a study of European mobile network operators. In: Proceedings of IEEE Global Communications Conference (GLOBECOM), Atlanta, 2013. 2561--2567. Google Scholar

[2] Wong W T, Yu Y J, Pang A C. Decentralized energy-efficient base station operation for green cellular networks. In: Proceedings of IEEE Global Communications Conference (GLOBECOM), Anaheim, 2012. 5194--5200. Google Scholar

[3] Son K, Kim H, Yi Y, et al. Base station operation and user association mechanisms for energy-delay tradeoffs in green cellular networks. IEEE J Sel Areas Commun, 2011, 29: 1525-1536 Google Scholar

[4] Yaacoub E. Achieving green LTE-A HetNets with D2D traffic offload and renewable energy powered small cell BSs. In: Proceedings of IEEE Online Conference on Green Communications (OnlineGreencomm), Tucson, 2014. 1--6. Google Scholar

[5] Zheng J C, Cai Y M, Chen X F, et al. Optimal base station sleeping in green cellular networks: a distributed cooperative framework based on game theory. IEEE Trans Wirel Commun, 2015, 14: 4391-4406 CrossRef Google Scholar

[6] Niu Z S, Guo X Y, Zhou S, et al. Characterizing energy-delay tradeoff in hyper-cellular networks with base station sleeping control. IEEE J Sel Areas Commun, 2015, 33: 641-650 CrossRef Google Scholar

[7] Han F, Safar Z, Liu K J R. Energy-efficient base-station cooperative operation with guaranteed QoS. IEEE Trans Commun, 2013, 61: 3505-3517 CrossRef Google Scholar

[8] Wu X C, Wu C M, Lin C T, et al. A multipath resource updating approach for distributed controllers in software-defined network. Sci China Inf Sci, 2016, 59: 092301-3517 CrossRef Google Scholar

[9] Hu Y N, Wang W D, Gong X Y, et al. On the feasibility and efficacy of control traffic protection in software-defined networks. Sci China Inf Sci, 2015, 58: 120104-3517 Google Scholar

[10] Karp R M. Reducibility among combinatorial problems. In: Proceedings of Symposium on the Complexity of Computer Computations, New York, 1972. 85--103. Google Scholar

[11] Chiang H P, Chou Y H, Chiu C H, et al. A quantum-inspired tabu search algorithm for solving combinatorial optimization problems. Soft Comput, 2013, 18: 1-11 Google Scholar

[12] Bernardos C J, De L O A, Serrano P, et al. An architecture for software defined wireless networking. IEEE Wirel Commun, 2014, 21: 52-61 Google Scholar

[13] Jiang X X, Du D H C. PTMAC: a prediction-based TDMA MAC protocol for reducing packet collisions in VANET. IEEE Trans Veh Technol, 2016, 65: 9209-9223 CrossRef Google Scholar

[14] Zhou Z Y, Ota K, Dong M X, et al. Energy-efficient matching for resource allocation in D2D enabled cellular networks. IEEE Trans Veh Technol, 2016, doi: 10-9223 Google Scholar

[15] Yao Y, Cheng X, Yu J, et al. Analysis and Design of a Novel Circularly Polarized Antipodal Linearly Tapered Slot Antenna. IEEE Trans Antenn Propag, 2016, 64: 4178-4187 CrossRef Google Scholar

[16] Oh E, Son K, Krishnamachari B. Dynamic base station switching-on/off strategies for green cellular networks. IEEE Trans Wirel Commun, 2013, 12: 2126-2136 CrossRef Google Scholar

[17] Hossain M F, Munasinghe K S, Jamalipour A. Distributed inter-BS cooperation aided energy efficient load balancing for cellular networks. IEEE Trans Wirel Commun, 2013, 12: 5929-5939 CrossRef Google Scholar

[18] Auer G, Giannini V, Desset C, et al. How much energy is needed to run a wireless network? IEEE Wirel Commun, 2011, 18: 40--49. Google Scholar

[19] Loss D, Divincenzo D P. Quantum computation with quantum dots. Phys Rev A, 1997, 57: 120-126 Google Scholar

[20] Glover F, Marti R. Tabu search. Gen Inform, 1998, 106: 221-225 Google Scholar

[21] Han K H, Kim J H. Quantum-inspired evolutionary algorithms with a new termination criterion, $H_\varepsilon $ gate, and two-phase scheme. IEEE Trans Evol Computat, 2004, 8: 156-169 CrossRef Google Scholar

[22] IEEE 802.16m evaluation methodology document (EMD). IEEE: Technical Report. IEEE 802.16m-08/004r5, 2009. Google Scholar

[23] Son K, Oh E, Krishnamachari B. Energy-aware hierarchical cell configuration: from deployment to operation. In: Proceedings of IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Shanghai, 2011. 289--294. Google Scholar

[24] Marsan M A, Chiaraviglio L, Ciullo D, et al. Optimal energy savings in cellular access networks. In: Proceedings of IEEE International Conference on Communications Workshops, Dresden, 2009. 1-5. Google Scholar

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