Wireless big data: transforming heterogeneous networks to smart networks
Tóm tắt
In HetNets (Heterogeneous Networks), each network is allocated with fixed spectrum resource and provides service to its assigned users using specific RAT (Radio Access Technology). Due to the high dynamics of load distribution among different networks, simply optimizing the performance of individual network can hardly meet the demands from the dramatically increasing access devices, the consequent upsurge of data traffic, and dynamic user QoE (Quality-of-Experience). The deployment of smart networks, which are supported by SRA (Smart Resource Allocation) among different networks and CUA (Cognitive User Access) among different users, is deemed a promising solution to these challenges. In this paper, we propose a frame-work to transform HetNets to smart networks by leveraging WBD (Wireless Big Data), CR (Cognitive Radio) and NFV (Network Function Virtualization) techniques. CR and NFV support resource slicing in spectrum, physical layers, and network layers, while WBD is used to design intelligent mechanisms for resource mapping and traffic prediction through powerful AI (Artificial Intelligence) methods. We analyze the characteristics of WBD and review possible AI methods to be utilized in smart networks. In particular, the potential of WBD is revealed through high level view on SRA, which intelligently maps radio and network resources to each network for meeting the dynamic traffic demand, as well as CUA, which allows mobile users to access the best available network with manageable cost, yet achieving target QoS (Quality-of-Service) or QoE.
Tài liệu tham khảo
J. Mitola, G. Q. Maguire. Cognitive radio: making software radios more personal [J]. IEEE personal communnications, 1999, 6(4): 13–18.
L. B. Thiagarajan, S. Attallah, Y.-C. Liang. Reconfigurable transceivers for wireless broadband access schemes [J]. IEEE wireless communications, 2007, 14(3): 48–53.
R. Mijumbi, J. Serrat, J. L. Gorricho, et al. Network function virtualization: state-of-the-art and research challenges [J]. IEEE communications surveys and tutorials, 2015, 18(1): 236–262.
C. Luo, J. Zeng, M. Yuan, et al. Telco user activity level prediction with massive mobile broadband data [J]. ACM transactions on intelligent systems and technology, 2016, 7(4): 63.
J. Mitola, G. Q. Maguire. Cognitive radio: making software radios more personal [J]. IEEE personal communications, 1999, 6(4): 13–18.
FCC Spectrum Policy Task Force. Report of the spectrum Efficiency Working Group [R]. ET Docket, 2002.
S. Haykin. Cognitive radio: brain-empowered wireless communications [J]. IEEE journal on selected areas in communications, 2005, 23(2): 201–220.
Y. C. Liang, K. C. Chen, G. Y. Li, et al. Cognitive radio networking and communications: an overview [J]. IEEE transactions on vehicular technology, 2011, 60(7): 3386–3407.
J. Lunden, V. Koivunen, H. V. Poor. Spectrum exploration and exploitation for cognitive radio: Recent advances [J]. IEEE signal processing magazine, 2015, 32(3): 123–140.
A. T. Hoang. Y. Zeng, Y. C. Liang, R. Zhang. A review on spectrum sensing for cognitive radio: challenges and solutions [J]. EURASIP journal on advances in signal processing, 2010, 2010(1): 1–15.
R. Zhang, Y. C. Liang, S. Cui. Dynamic resource allocation in cognitive radio networks [J]. IEEE signal processing magazine, 2010, 27(3): 102–114.
S. K. Sharma, T. E. Bogale, S. Chatzinotas, et al. Cognitive radio techniques under practical imperfections: a survey [J]. IEEE communications surveys and tutorials, 2015, 17(4): 1858–1884.
Q. Sun, I. Chin-Lin, S. Han, et al. Software defined air interface: a framework of 5g air interface [C]//Proceedings of IEEE Wireless Communications Network Conference, 2015, 6–11.
B. Han, V. Gopalakrishnan, L. Ji, et al. Network function virtualization: challenges and opportunities for innovations [J]. IEEE communications magazine, 2015, 53(2): 90–97.
M. Chiosi, D. Clarke, P. Willis. et al. Network functions virtualisation: an introduction, benefits, enablers, challenges and call for action [R]. Darmstadt-Germany: SDN and OpenFlow World Congress, 2012.
G. ETSI. Network functions virtualisation (nfv): architectural framework [J], ETSI GS NFV, 2013, 2(2): V1.
C. Liang, F. R. Yu, X. Zhang. Information-centric network function virtualization over 5g mobile wireless networks[J]. IEEE network, 2015, 29(3):68–74, 2015.
R. Kokku, R. Mahindra, H. Zhang, et al. Nvs: a substrate for virtualizing wireless resources in cellular networks [J]. IEEE/ACM transactions on networking, 2012, 20(5): 1333–1346.
C. Liang, F. R. Yu. Wireless virtualization for next generation mobile cellular networks [J]. IEEE wireless communications, 2015, 22(1): 61–69.
V. Yazici, U. C. Kozat, M. O. Sunay. A new control plane for 5g network architecture with a case study on unified handoff, mobility, and routing management [J]. IEEE communications magazine, 2014, 52(11): 76–85.
K. P. Murphy. Machine learning: a probabilistic perspective [M]. USA: MIT press, 2012.
D. D. Clark, C. Partridge, J. C. Ramming, et al. A knowledge plane for the Internet [C]//Proceedings of Special Interest Group on Data Communication, 2003, 3–10.
T. G. Dietterich, P. Langley. Cognitive networks: towards self-aware networks [M]. USA: Wiley, 2007.
R. W. Thomas, D. H. Friend, L. A. Dasilva, et al. Cognitive networks: adaptation and learning to achieve end-to-end performance objectives [J]. IEEE communications magazine, 2006, 44(12): 51–57.
N. Abbas, Y. Nasser, K. El Ahmad. Recent advances on artificial intelligence and learning techniques in cognitive radio networks [J]. EURASIP journal on wireless communications and network, 2015, 2015(1): 1.
S. Chouvardas, S. Valentin, M. Draief, et al. A method to reconstruct coverage loss maps based on matrix completion and adaptive sampling [C]//Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, 2016, 6390–6394.
Z. Lu, G. De Veciana. Optimizing stored video delivery for mobile networks: the value of knowing the future [C]//Proceedings of IEEE Conference on Computer Communications, 2013, 2706–2714.
N. Marz, J. Warren. Big data: principles and best practices of scalable realtime data systems [M]. USA: O Reilly Media, 2015.
Y. Zheng, L. Capra, O. Wolfson, et al. Urban computing: concepts, methodologies, and applications [J]. ACM transactions on intelligent systems and technology, 2014, 5(3): 1–38.
M. F. Balcan, A. Blum, S. Fine, et al. Distributed learning, communication complexity and privacy [C]//Proceedings of Conference on Learning Theory, 2012, 23: 26.1-26.22.
E. J. Cands, B. Recht. Exact matrix completion via convex optimization [J]. Foundations of computational mathematics, 2009, 9(6): 717–772.
Y. Zheng. Methodologies for cross-domain data fusion: An overview [J]. IEEE transactions on big data, 2015, 1(1): 16–34.
E. E. Papalexakis, C. Faloutsos, N. D. Sidiropoulos. Tensors for data mining and data fusion: models, applications, and scalable algorithms [J]. ACM transactions on intelligent systems and technology, 2016, 8(2): 16:116:44.
C. Xu, D. Tao, C. Xu. A survey on multi-view learning [J]. Preprint arXiv:1304.5634, 2013.
K. Crammer, M. Kearns, J. Wortman. Learning from multiple sources [J]. Journal of machine learning research, 2008, 9(12): 1757–1774.
O. Kwon, N. Lee, B. Shin. Data quality management, data usage experience and acquisition intention of big data analytics [J]. International journal of information management, 2014, 34(3): 387–394.
H. Jagadish, J. Gehrke, A. Labrinidis, et al. Big data and its technical challenges [J]. Communications of the ACM, 2014, 57(7): 86–94.
J. Lundn, S. R. Kulkarni, V. Koivunen, et al. Multiagent reinforcement learning based spectrum sensing policies for cognitive radio networks [J]. IEEE journal of selected topics in signal processing, 2013, 7(5): 858–868.
B. Wang, Y. Wu, K. R. Liu, et al. Clancy. An antijamming stochastic game for cognitive radio networks [J]. IEEE journal on selected areas in communications, 2011, 29(4): 877–889.
K. Liu, Q. Zhao. Distributed learning in multi-armed bandit with multiple players [J]. IEEE transactions on signal processing, 2010, 58(11): 5667–5681.
R. Kitchin. The real-time city? big data and smart urbanism [J]. GeoJournal, 2014, 79(1): 1–14.
S. J. Pan, Q. Yang. A survey on transfer learning [J]. IEEE transactions on knowledge and data engineering, 2010, 22(10): 1345–1359.
D. A. Ross, J. Lim, R.-S. Lin, et al. Incremental learning for robust visual tracking [J]. International journal of computer vision, 2008, 77(1-3): 125–141.
K. W. Choi, E. Hossain. Estimation of primary user parameters in cognitive radio systems via hidden markov model [J]. IEEE transactions on signal processing, 2013, 61(3): 782–795.
T. C. Fu. A review on time series data mining [J]. Engineering applications of artificial intelligence, 2011, 24(1): 164–181.
N. I. Sapankevych, R. Sankar. Time series prediction using support vector machines: a survey [J]. IEEE computational intelligence magazine, 2009, 4(2): 24–38.
S. Papadimitriou, J. Sun, C. Faloutso. Streaming pattern discovery in multiple time-series [C]//Proceedings of International Conference on Very Large Data Bases, 2005: 697–708.
J. Sun, D. Tao, S. Papadimitriou, et al. Incremental tensor analysis: theory and applications[J]. ACM transactions on knowledge discovery from data, 2008, 2(3): 1–37.
S. Yu, M. Liu, W. Dou, et al. Networking for big data: A survey [J]. IEEE communications surveys and tutorials, 2016, 19(1): 531–549.
V. Vapnik, R. Izmailov. Learning using privileged information: similarity control and knowledge transfer [J]. Journal of machine learning research, 2015, 16: 2023–2049.
S. Han, Y. C. Liang, B. H. Soong. Spectrum refarming: a new paradigm of spectrum sharing for cellular networks [J]. IEEE transactions on communications, 2015, 63(5): 1895–1906.
O. Mammela, P. Mannersalo. Cognitive wireless access selection at client side: performance study of a qlearning approach [C]//Proceedings of IEEE Network Operations and Management Symposium, 2014: 1–4.
H. Tabrizi, G. Farhadi, J. Cioffi. A learning-based network selection method in heterogeneous wireless systems [C]//Proceedings of IEEE Global Communications Conference, 2011: 1–5.
H. Tabrizi, G. Farhadi, J. Cio. Dynamic handoffdecision in heterogeneous wireless systems: Q-learning approach [C]//Proceedings of IEEE International Conference on Communications, 2012: 3217–3222.
M. Jordan, T. Mitchell. Machine learning: trends, perspectives, and prospects[J]. Science, 2015, 349(6245): 255–260.
L. Sorber, M. Van Barel, L. De Lathauwer. Structured data fusion [J]. IEEE journal of selected topics in signal processing, 2015, 9(4): 586–600.
S. Yu. Big privacy: challenges and opportunities of privacy study in the age of big data [J]. IEEE access, 2016, 4: 2751–2763.