Book contents
- Frontmatter
- Contents
- List of contributors
- Preface
- Part I Mathematical foundations
- Part II Big data over cyber networks
- 5 Big data analytics systems
- 6 Distributed big data storage in optical wireless networks
- 7 Big data aware wireless communication: challenges and opportunities
- 8 Big data processing for smart grid security
- Part III Big data over social networks
- Part IV Big data over biological networks
- Index
- References
7 - Big data aware wireless communication: challenges and opportunities
from Part II - Big data over cyber networks
Published online by Cambridge University Press: 18 December 2015
Book contents
- Frontmatter
- Contents
- List of contributors
- Preface
- Part I Mathematical foundations
- Part II Big data over cyber networks
- 5 Big data analytics systems
- 6 Distributed big data storage in optical wireless networks
- 7 Big data aware wireless communication: challenges and opportunities
- 8 Big data processing for smart grid security
- Part III Big data over social networks
- Part IV Big data over biological networks
- Index
- References
Summary
The fast-growing wireless data service is pushing our communication network's processing power to its limit. The ever-increasing data traffic poses imminent challenges to all aspects of the wireless system design, such as spectrum efficiency, computing capabilities, and backhaul link capacity, etc. At the same time, the massive amount of mobile data traffic may also lead to potential system performance gain that is otherwise not achievable with conventional wireless signal processing models. In this chapter, we investigate the challenges and opportunities in the design of scalable wireless systems to embrace such a “big data” era.We review state-of-the-art techniques in wireless big data processing and study the potential implementations of key technologies in the future wireless systems. We show that proper wireless system designs could harness, and in fact take advantages of the mobile big data traffic.
Introduction
After decades of rapid growth in data services, modern society has entered the so-called “big data” era, where the mobile network is a major contributor. As of the year 2013, the global penetration of mobile subscribers had reached 92%, producing staggeringly 6800 PetaBytes (6.8 × 1018) of mobile data worldwide [1]. The surge of mobile data traffic in recent years is mainly attributed to the popularity of smartphones, mobile tablets, and other smart mobile devices. Mobile broadband applications such as web surfing, social networking, and online videos are now ubiquitously accessible by these mobile devices, without limitations from time and location. The recent survey shows that the number of smartphone users currently accounts for merely 25%–30% of the entire mobile subscribers. However, the figure will double in the next three years and continue to grow given the considerable room in the smartphone market for further uptake.With a compound annual growth rate of 45%, it is expected that the mobile data traffic will increase by ten times from 2013 to 2019.
In addition to the vast amount of wireless data, wireless signal processing often amplifies the system big data effect in the pursuit of higher performance gain. To combat the fading channel, diversity schemes, especially the MIMO antenna technologies, are extensively used in both mobile terminals (MTs) and base stations (BSs). Numerous schemes with co-located and distributed antennas have been proposed over the years to increase the data rate or extend the cellular coverage.
- Type
- Chapter
- Information
- Big Data over Networks , pp. 180 - 216Publisher: Cambridge University PressPrint publication year: 2016
References
[1] Ericsson Mobility Report, http://www.ericsson.com/res/docs/2013/ericsson-mobilityreport- november-2013.pdf, November 2013.
[2] , , , and , “Massivemimo for next generation wireless systems,” IEEE Communications Magazine, vol. 52, no. 2, pp. 186–195, February 2014.Google Scholar
[3] , “Sevenways that hetnets are a cellular paradigm shift,” IEEE Communications Magazine, vol. 51, no. 3, pp. 136–144, March 2013.Google Scholar
[4] , , , , , , , , and , “Coordinated multipoint: concepts, performance, and field trial results,” IEEE Communications Magazine, vol. 49, no. 2, pp. 102–111, February 2011.Google Scholar
[5] China Mobile, “C-RAN: the road towards green RAN,” White Paper, ver 2 (2011).
[6] , , , and , “Understanding traffic dynamics in cellular data networks,” in Proceedings IEEE International Conference on Computer Communications (INFOCOM), Orlando, FL, USA, June 2011.Google Scholar
[7] , , , et al., “Comparison of caching strategies in modern cellular backhaul networks,” in Proceedings ACM International Conference on Mobile Systems, Applications, and Services (MobiSys), Taipei, Taiwan, June 2013.Google Scholar
[8] and , “Context-aware communities and their impact on information influence in mobile social networks,” in Proceedings IEEE International Conference on Pervasive Computing and Communications (PERCOM) Workshops, Lugano, Switzerland, March 2012.Google Scholar
[9] Open Networking Foundation, “Software-defined networking: the new norm for networks,” white paper, 2012.
[11] , , and , “From context awareness to socially aware computing,” IEEE Pervasive Computing, vol. 11, no. 1, pp. 32–14, January–March 2012.Google Scholar
[12] , , and , “Clock synchronization for wireless sensor networks: a survey,” Ad Hoc Networks, vol. 3, no. 3, pp. 281–323, December 2003.Google Scholar
[13] and A, “Transmission of wireless mimo-type signals over a single optical fiber without wdm,” IEEE Transactions on Microwave Theory and Techniques, vol. 58, no. 11, pp. 3094–3102, November 2010.Google Scholar
[15] , , , and , “Uplink macro diversity of limited backhaul cellular network,” IEEE Trans. Information Theory, vol. 55, no. 8, pp. 3457–3478, August 2009.Google Scholar
[16] and , “Distributed compression for mimo coordinated networks with a backhaul constraint,” IEEE Transactions on Communications, vol. 8, no. 9, pp. 4698–4709, September 2009.Google Scholar
[17] and , “Optimized backhaul compression for uplink cloud radio access network,” IEEE Journal on Selected Areas in Communications, vol. 32, no. 6, pp. 1295–1307, June 2014.Google Scholar
[18] , , , and , “Joint decompression and decoding for cloud radio access networks,” IEEE Signal Processing Letters, vol. 20, no. 5, pp. 503–506, May 2013.Google Scholar
[19] , , , and S, “Robust and efficient distributed compression for cloud radio access networks,” IEEE Transactions on Vehicular Technology, vol. 62, no. 2, pp. 692–703, February 2013.Google Scholar
[20] and , “Uplink multicell processing with limited backhaul via per-base-station successive interference cancellation,” IEEE Journal on Selected Areas in Communications, vol. 30, no. 10, pp. 1981–1993, October 2013.Google Scholar
[21] , , and , “Downlink multicell processing with limitedbackhaul capacity,” EURASIP Journal on Advances in Signal Processing, pp. 1–10, May 2009.Google Scholar
[22] , , , and , “Joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks,” IEEE Transactions on Signal Processing, vol. 61, no. 22, pp. 5646–5658, November 2013.Google Scholar
[23] and , “A framework for optimizing the uplink performance of distributed antenna systems under a constrained backhaul,” in Proceedings IEEE International Conference on Communications (ICC), Glasgow, Scotland, June 2007.Google Scholar
[24] and , “Exploiting opportunistic multiuser detection in decentralized multiuser mimo systems,” IEEE Transactions on Wireless Communications, vol. 10, no. 8, pp. 2474–2485, August 2011.Google Scholar
[25] , , and , “Before/after precoded massive mimo in cloud radio access networks,” in Proceedings International Conference on Communications (ICC) Workshop, Budapest, Hungary, June 2013.Google Scholar
[26] P and , “Sparse precoding in multicell mimo systems,” in Proceedings IEEE Wireless Communications and Networking Conference (WCNC), Paris, France, April 2012.Google Scholar
[27] , , , and , “Joint base station clustering and beamformer design for partial coordinated transmission in heterogeneous networks,” IEEE Journal on Selected Areas in Communications, vol. 31, no. 2, pp. 226–240, February 2013.Google Scholar
[28] and , “Backhaul limited asymmetric cooperations for mimo cellular networks via semidefinite relaxation,” IEEE Transactions on Signal Processing, vol. 62, no. 3, pp. 684–693, February 2014.Google Scholar
[29] , , and , “Coordinated multipoint transmission with limited backhaul data transfer,” IEEE Transactions on Wireless Communications, vol. 12, no. 6, pp. 2762– 2775, June 2013.Google Scholar
[30] and , “Optimized data sharing in multicell mimo with finite backhaul capacity,” IEEE Transactions on Signal Processing, vol. 59, no. 12, pp. 6102–6111, December 2011.Google Scholar
[31] , , and , “Downlink base station cooperative transmission under limited-capacity backhaul,” IEEE Transactions on Wireless Communications, vol. 12, no. 8, pp. 3746–3759, August 2013.Google Scholar
[32] , , and , “Cooperative wireless multicell ofdma network with backhaul capacity constraints,” in Proceedings IEEE International Conference on Communications (ICC), Kyoto, Japan, June 2011.Google Scholar
[33] , , , , and , “Femtocaching: wireless video content delivery through distributed caching helpers,” in Proceedings International Conference on Computer Communications (INFOCOM), Orlando, FL, USA, March 2012.Google Scholar
[34] and , “Cache-enabled opportunistic cooperative mimo for video streaming in wireless systems,” IEEE Transactions on Signal Processing, vol. 62, no. 2, pp. 390–402, January 2014.Google Scholar
[35] , , and , “Wireless device-to-device communications with distributed caching,” in IEEE International Symposium on Information Theory Proceedings (ISIT), Cambridge, MA, USA, July 2012.Google Scholar
[36] , , and , “Optimal throughput-outage trade-off in wireless one-hop caching networks,” in IEEE International Symposium on Information Theory Proceedings (ISIT), Istanbul, Turkey, July 2012.Google Scholar
[37] and , “Spatial traffic estimation and characterization for mobile communication network design,” IEEE Journal on Selected Areas in Communications, vol. 16, no. 5, pp. 804–811, June 1998.Google Scholar
[38] , , , , and , “Tube: time-dependent pricing for mobile data,” ACM SIGCOMM Computer Communication Review, vol. 42, no. 4, pp. 247–258, August 2012.Google Scholar
[39] and , “Mobility-based predictive call admission control and bandwidth reservation in wireless cellular networks,” Computer Networks, vol. 38, no. 5, pp. 577–589, October 2002.Google Scholar
[40] , , and , “Context-aware mobile computing: learning context-dependent personal preferences from a wearable sensor array,” IEEE Transactions on Mobile Computing, vol. 5, no. 2, pp. 113–127, February 2006.Google Scholar
[41] and , “Mining call and mobility data to improve paging efficiency in cellular networks,” in Proceedings ACM Mobile Computing and Networking (MobiCom), Montreal, Canada, September 2007.Google Scholar
[42] and , “A class of mobile motion prediction algorithms for wireless mobile computing and communication,” Mobile Networks and Applications, vol. 1, no. 2, pp. 113–121, October 1996.Google Scholar
[43] , , and , “Mobility modeling, location tracking, and trajectory prediction in wireless atm networks,” IEEE Journal on Selected Areas in Communications, vol. 6, no. 6, pp. 922–936, August 1998.Google Scholar
[44] and , “User mobility modeling and characterization of mobility patterns,” IEEE Journal on Selected Areas in Communications, vol. 15, no. 7, pp. 1239–1252, September 1997.Google Scholar
[45] and , “Movement prediction in wireless networks using mobility traces,” in Proceedings IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, USA, January 2010.Google Scholar
[46] , , and , “Mobility modelling and trajectory prediction for cellular networks with mobile base stations,” in Proceedings ACM International Symposium on Mobile ad hoc Networking and Computing (MobiHoc), Annapolis, MD, USA, June 2003.Google Scholar
[47] , , , et al., “User mobility modeling and characterization of mobility patterns,” IEEE Journal on Selected Areas in Communications, vol. 19, no. 10, pp. 1931–1941, October 2001.Google Scholar
[48] and , “The predictive user mobility profile framework for wireless multimedia networks,” IEEE/ACM Trans. Networking, vol. 12, no. 6, pp. 1021–1035, December 2004.Google Scholar
[49] and , “Mining user moving patterns for personal data allocation in a mobile computing system,” in Proceedings IEEE International Conference on Parallel Processing (ICPP), Toronto, Canada, August 2000.Google Scholar
[50] and , “Mobility-based predictive call admission control and bandwidth reservation in wireless cellular networks,” Computer Networks, vol. 38, no. 5, pp. 577–589, April 2002.Google Scholar
[51] , , , et al., “Sensing meets mobile social networks: the design, implementation and evaluation of the cenceme application,” in Proceedings ACMconference on Embedded Network Sensor Systems (Sensys), Raleigh, NC, USA, November 2008.Google Scholar
[52] and , “Traffic prediction from wireless environment sensing,” in Proceedings IEEE Wireless Communications and Networking Conference (WCNC), Budapest, Hungary, April 2009.
[53] and , “Reality mining: sensing complex social systems,” Personal and Ubiquitous Computing, vol. 10, no. 4, pp. 255–268, May 2006.Google Scholar
[54] and , “Location patterns of mobile users a large-scale study,” in Proceedings IEEE International Conference on Computer Communications (INFOCOM), Turin, Italy, April 2013.Google Scholar
[55] and , “Probability distribution of channel holding time in cellular telephony systems,” in Proceedings IEEE Vehicular Technology Conference (VTC), Atlanta, GA, USA, April 1996.Google Scholar
[56] and , “What did you do today?: discovering daily routines from large-scale mobile data,” in Proceedings ACM International Conference on Multimedia, Vancouver, BC, Canada, October 2008.Google Scholar
[57] and , “Analysis of a location-based social network,” in Proceedings IEEE International Conference on Computational Science and Engineering (CSE), Vancouver, BC, Canada, August 2009.Google Scholar
[58] , , and , “Reinforcement learning for context awareness and intelligence in wireless networks: Review, new features and open issues,” Journal of Network and Computer Applications, vol. 35, no. 1, pp. 253–267, January 2012.Google Scholar
[59] and , “A tutorial on decomposition methods fornetwork utility maximization,” IEEE Journal on Selected Areas in Communications, vol. 24, no. 8, pp. 1439–1451, August 2006.Google Scholar
[60] , , , , and , “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends in Machine Learning, vol. 3, no. 1, pp. 1–122, January 2011.Google Scholar
[61] and , “Robust PCA as bilinear decompositionwith outlier-sparsity regularization,” IEEE Transactions on Signal Processing, vol. 60, no. 10, pp. 5176–5190, October 2012.Google Scholar
[62] , , and , “Blind PARAFAC receivers for DS-CDMA systems,” IEEE Transactions on Signal Processing, vol. 48, no. 3, pp. 810–823, March 2000.Google Scholar
[63] and , “Unsupervised multiway data analysis: A literature survey,” IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 1, pp. 6–20, January 2009.Google Scholar
[64] and , “Multi-way compressed sensing for sparse low-rank tensors,” IEEE Signal Processing Letters, vol. 19, no. 11, pp. 757–760, October 2012.Google Scholar
[65] , “Active learning literature survey,” Technical Report, University of Wisconsin, Madison, vol. 52, pp. 55–66, January 2010.Google Scholar
[66] , , and , “A fast learning algorithm for deep belief nets,” Neural Computation, vol. 18, no. 7, pp. 1527–1554, July 2006.Google Scholar
[67] and , “Learning-based optimization of cache content in a small cell base station,” in IEEE International Conference on Communications (ICC) 2014, 2014, accepted and available at http://arxiv.org/abs/1402.3247.
[68] , “Online learning and online convex optimization,” Foundations and Trends in Machine Learning, vol. 4, no. 2, pp. 107–194, February 2012.Google Scholar
[69] , “Mobile computing: the next decade,” ACM SIGMOBILE Mobile Computing and Communications Review, vol. 15, no. 2, pp. 2–10, January 2011.Google Scholar
[70] , “Hyrax: cloud computing on mobile devices using mapreduce,” Masters Thesis, Carnegie Mellon University, 2009.
[71] and , “A virtual cloud computing provider for mobile devices,” in Proceedings ACM Workshop on Mobile Cloud Computing and Services: Social Networks and Beyond (MCS), San Francisco, CA, USA, June 2010.Google Scholar
[72] , , , and , “The case for vm-based cloudlets in mobile computing,” IEEE Pervasive Computing, vol. 8, no. 4, pp. 14–23, October– December 2009.Google Scholar
[73] , , , , and , “Future evolution in wireless network architectures: towards a 'cloud of antennas’,” in Proceedings IEEE Vehicular Technology Conference (VTC), Quebec City, Canada, September 2012.Google Scholar
[74] , , and , “Volare: context-aware adaptive cloud service discovery for mobile systems,” in Proceedings ACM International Workshop on Adaptive and Reflective Middleware, Bangalore, India, November 2010.Google Scholar
[75] , , , and , “Context-aware battery management for mobile phones,” in Pervasive Computing and Communications (PerCom), Hong Kong, China, March 2008.Google Scholar
[76] , , , et al., “Context-awareness inwireless and mobile computing revisited to embrace social networking,” IEEE Communications Magazine, vol. 50, no. 6, pp. 74–81, June 2012.Google Scholar
[77] and , “Improving network management with software defined networking,” IEEE Communications Magazine, vol. 51, no. 2, pp. 114–119, February 2013.Google Scholar
[78] , , , et al., “Openflow: enabling innovation in campus networks,” ACM SIGCOMM Computer Communication Review, vol. 38, no. 2, pp. 69–74, March 2008.Google Scholar
[79] , , , et al., “Nox: towards an operating system for networks,” ACM SIGCOMM Computer Communication Review, vol. 38, no. 3, pp. 105–110, July 2008.Google Scholar
[80] , Software Defined Radio: Origins, Drivers, and International Perspectives, West Sussex, England: John Wiley & Sons, 2002.
[81] , , , et al., “Blueprint for introducing innovation into wireless mobile networks,” in Proceedings ACM SIGCOMM Workshop, New Delhi, India, August 2010.Google Scholar
[82] , , , and , “Softran: software defined radio access network,” in Proceedings ACM SIGCOMM Workshop, Hong Kong, China, August 2013.Google Scholar
[83] , , and , “Softcell: scalable and flexible cellular core network architecture,” in Proceedings ACM Conference on Emerging Networking Experiments and Technologies (CoNEXT), Santa Barbara, CA, USA, December 2013.Google Scholar
[84] , , , et al., “Min flow rate maximization for software defined radio access networks,” http://arxiv.org/abs/1312.5345, 2013.
- 3
- Cited by