Encyclopedia of Wireless Networks

Living Edition
| Editors: Xuemin (Sherman) Shen, Xiaodong Lin, Kuan Zhang

Big Data in 5G

  • Yue Wang
  • Zhi TianEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-32903-1_58-1



The fifth-generation wireless systems, abbreviated as 5G (Andrews et al. 2014), are proposed as the next wireless and mobile communications standards beyond the current 4G standards. 5G networks not only aim at providing higher data rate, lower latency, larger capacity, and better customer experience than 4G but also commit to fulfilling the Internet of things (IoT) with reliable and secure services at low costs (Atzori et al. 2010). To this end, 5G networks call for and rely on seamless operations of distinctive wireless technologies and solutions, including cognitive radio (CR) (Akyildiz et al. 2006), massive multiple-input multiple-output (maMIMO) (Larsson et al. 2014), millimeter wave (mmWave) communications (Rappaport et al. 2013), heterogeneous network (HetNet) architecture, cloud-based radio access, edge computing and caching (Hu et al. 2015), device and...

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  1. Akyildiz I, Lee W, Vuran M, Mohanty S (2006) NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Comput Netw 50(13):2127–2159CrossRefGoogle Scholar
  2. Alkhateeb A, El Ayach O, Leus G, Heath RW (2014) Channel estimation and hybrid precoding for millimeter wave cellular systems. IEEE J Sel Topics Signal Procss 8(5):831–846CrossRefGoogle Scholar
  3. Alpaydin E (2004) Introduction to machine learning. MIT Press, CambridgeGoogle Scholar
  4. Andrews G et al (2014) What will 5G be? IEEE J Sel Areas Commun 32(6):1065–1082CrossRefGoogle Scholar
  5. Aprem A, Murthy CR, Mehta NB (2013) Transmit power control policies for energy harvesting sensors with retransmissions. IEEE J Sel Topics Signal Process 7(5):895–906CrossRefGoogle Scholar
  6. Asadi A, Wang Q, Mancuso V (2014) A survey on device-to-device communication in cellular networks. IEEE Commun Surv Tutorials 16(4):1801–1819CrossRefGoogle Scholar
  7. Assra A, Yang J, Champagne B (2016) An EM approach for cooperative spectrum sensing in multiantenna CR networks. IEEE Trans Veh Technol 65(3):1229–1243CrossRefGoogle Scholar
  8. Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54(15):2787–2805CrossRefGoogle Scholar
  9. Bajwa WU, Haupt J, Sayeed AM, Nowak R (2010) Compressed channel sensing: a new approach to estimating sparse multipath channels. Proc IEEE 98(6):1058–1076CrossRefGoogle Scholar
  10. Bastug E, Bennis M, Debbah M (2014) Living on the edge: the role of proactive caching in 5G wireless networks. IEEE Commun Mag 52(8):82–89CrossRefGoogle Scholar
  11. Bazerque JA, Giannakis GB (2010) Distributed spectrum sensing for cognitive radio networks by exploiting sparsity. IEEE Trans Signal Process 58(3):1847–1862MathSciNetCrossRefGoogle Scholar
  12. Candes EJ, Wakin MB (2008) An introduction to compressive sampling. IEEE Signal Process Mag 25(2):21–30CrossRefGoogle Scholar
  13. Chi Y, Scharf LL, Pezeshki A, Calderbank R (2011) Sensitivity to basis mismatch in compressed sensing. IEEE Trans Signal Process 59(5):2182–2195MathSciNetCrossRefGoogle Scholar
  14. Choi KW, Hossain E (2013) Estimation of primary user parameters in cognitive radio systems via hidden Markov model. IEEE Trans Signal Process 61(3):782–795MathSciNetCrossRefGoogle Scholar
  15. Daniels RC, Caramanis CM, Heath RW (2010) Adaptation in convolutionally coded MIMO-OFDM wireless systems through supervised learning and SNR ordering. IEEE Trans Veh Technol 59(1):114–126CrossRefGoogle Scholar
  16. Donohoo BK et al (2014) Context-aware energy enhancements for smart mobile devices. IEEE Trans Mob Comput 13(8):1720–1732CrossRefGoogle Scholar
  17. Fanzi Z, Zhi T, Chen L (2010) Distributed compressive wideband spectrum sensing in cooperative multi-hop cognitive networks. In: IEEE ICC conference, Cape Town, 23–27 May 2010Google Scholar
  18. Gao Z, Hu C, Dai L, Wang Z (2016) Channel estimation for millimeter-wave massive MIMO with hybrid precoding over frequency-selective fading channels. IEEE Commun Lett 20(6):1259–1262CrossRefGoogle Scholar
  19. Gardner W (1991) Exploitation of spectral redundancy in cyclostationary signals. IEEE Signal Process Mag 8(2):14–36CrossRefGoogle Scholar
  20. Haleplidis E et al (2015) Software-defined networking (SDN): layers and architecture terminology. IRTFGoogle Scholar
  21. Hu Y et al (2015) Mobile edge computing: a key technology towards 5G, ETSI white paperGoogle Scholar
  22. Jadidi Z, Muthukkumarasamy V, Sithirasenan E, Sheikhan M (2013) Flow-based anomaly detection using neural network optimized with gsa algorithm. In: IEEE 33rd international conference on distributed computing systems workshops, Philadelphia, 8–11Google Scholar
  23. Kaelbling LP, Littman ML, Moore AW (1996) Reinforcement learning: a survey. J Artif Intell Res 4:237–285Google Scholar
  24. Larsson EG, Edfors O, Tufvesson F, Marzetta TL (2014) Massive MIMO for next generation wireless systems. IEEE Commun Mag 52(2):186–195CrossRefGoogle Scholar
  25. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444CrossRefGoogle Scholar
  26. Liu K, Zhao Q (2010) Distributed learning in cognitive radio networks: multi-armed bandit with distributed multiple players. In: IEEE ICASSP conference, Dallas, 14–19 Mar 2010Google Scholar
  27. Maghsudi S, Stanczak S (2015) Channel selection for network-assisted D2D communication via no-regret bandit learning with calibrated forecasting. IEEE Trans Wirel Commun 14(3):1309–1322CrossRefGoogle Scholar
  28. Otterlo M, Wiering M (2012) Reinforcement learning and Markov decision processes. In: Reinforcement learning. Springer, Berlin/Heidelberg, pp 3–42CrossRefGoogle Scholar
  29. Polo Y, Wang Y, Pandharipande A, Leus G (2009) Compressive wide-band spectrum sensing. In: IEEE ICASSP conference, Taipei, 19–24 Apr 2009Google Scholar
  30. Qiu RC et al (2011) Cognitive radio network for the smart grid: experimental system architecture, control algorithms, security, and microgrid testbed. IEEE Trans Smart Grid 2(4):724–740CrossRefGoogle Scholar
  31. Rappaport TS et al (2013) Millimeter wave mobile communications for 5G cellular: it will work. IEEE Access 1(1):335–349MathSciNetCrossRefGoogle Scholar
  32. Romero D, Ariananda D, Tian Z, Leus G (2016) Compressive covariance sensing: structure-based compressive sensing beyond sparsity. IEEE Signal Process Mag 33(1):78–93CrossRefGoogle Scholar
  33. Sanchez-Fernandez M, de-Prado-Cumplido M, Arenas-Garcia J, Perez-Cruz F (2004) SVM multiregression for nonlinear channel estimation in multiple-input multiple-output systems. IEEE Trans Signal Process 52(8):2298–2307MathSciNetCrossRefGoogle Scholar
  34. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117CrossRefGoogle Scholar
  35. Schniter P, Sayeed AM (2014) Channel estimation and precoder design for millimeter-wave communications: the sparse way. In: Asilomar conference on signals, systems, and computers, Pacific Grove, 2–5 Nov 2014Google Scholar
  36. Tian Z (2008) Compressed wideband sensing in cooperative cognitive radio networks. In: IEEE GLOBECOM conference, New Orleans, 30 Nov–4 Dec 2008Google Scholar
  37. Tian Z (2011) Cyclic feature based wideband spectrum sensing using compressive sampling. In: IEEE ICC conference, Kyoto, 5–9 June 2011Google Scholar
  38. Tian Z, Giannakis GB (2007) Compressed sensing for wideband cognitive radios. In: IEEE ICASSP conference, Honolulu, 15–20 Apr 2007Google Scholar
  39. Tian Z, Tafesse Y, Sadler BM (2012) Cyclic feature detection from sub-Nyquist samples for wideband spectrum sensing. IEEE J Sel Topics Signal Process 6(1):58–69CrossRefGoogle Scholar
  40. Tian Z, Zhang Z, Wang Y (2017) Low-complexity optimization for two dimensional direction-of-arrival estimation via decoupled atomic norm minimization. In: IEEE ICASSP conference, New Orleans, 5–9 Mar 2017Google Scholar
  41. Wang Y, Tian Z, Feng C (2010) A two-step compressed spectrum sensing scheme for wideband cognitive radios. In: IEEE GLOBECOM conference, Miami, 6–10 Dec 2010Google Scholar
  42. Wang Y, Tian Z, Feng C (2011) Cooperative spectrum sensing based on matrix rank minimization. In: IEEE ICASSP conference, Prague, 22–27 May 2011Google Scholar
  43. Wang Y, Tian Z, Feng C (2012a) Sparsity order estimation and its application in compressed spectrum sensing for cognitive radios. IEEE Trans Wirel Commun 11(6):2116–2125CrossRefGoogle Scholar
  44. Wang Y, Tian Z, Feng C (2012b) Collecting detection diversity and complexity gain in cooperative spectrum sensing. IEEE Trans Wirel Commun 11(8):2876–2883Google Scholar
  45. Wang X et al (2014) Cache in the air: exploiting content caching and delivery techniques for 5G systems. IEEE Commun Mag 52(2):131–139CrossRefGoogle Scholar
  46. Wang Y, Tian Z, Feng S, Zhang P (2016a) Efficient channel statistics estimation for millimeter-wave MIMO systems. In: IEEE ICASSP conference, Shanghai, 20–25 Mar 2016Google Scholar
  47. Wang Y, Tian Z, Feng S, Zhang P (2016b) A fast channel estimation approach for millimeter-wave massive MIMO systems. In: IEEE GlobalSIP conference, Washington, 7–9 Dec 2016Google Scholar
  48. Wang Y, Xu P, Tian Z (2017) Efficient channel estimation for massive MIMO systems via truncated two-dimensional atomic norm minimization. IEEE ICC Conf, Paris, 21–25 May 2017Google Scholar
  49. Wen C et al (2015) Channel estimation for massive MIMO using Gaussian-mixture Bayesian learning. IEEE Trans Wirel Commun 14(3):1356–1368CrossRefGoogle Scholar
  50. Zeng YH, Liang YC, Hoang AT, Zhang R (2010) A review on spectrum sensing for cognitive radio: challenges and solutions. EURASIP J Adv Signal Process 2010:1–15CrossRefGoogle Scholar
  51. Zeng F, Li C, Tian Z (2011) Distributed compressive spectrum sensing in cooperative multi-hop wideband cognitive networks. IEEE J Sel Topics Signal Process 5(1):37–48CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.ECE DepartmentGeorge Mason UniversityFairfaxUSA

Section editors and affiliations

  • Rahim Tafazolli
  • Rose Hu

There are no affiliations available