Skip to main content

Advertisement

SpringerLink
Log in

Spectral Efficient Beamforming for mmWave MISO Systems using Deep Learning Techniques

  • Research Article-Electrical Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

The eMBB (enhanced Mobile BroadBand), URLLC (Ultra-Reliable, Low latency communication) and mMTC (massive Machine Type communication) are the drivers for 5G communication. To realize these use-cases, enhancing the throughput in available bandwidth is the fundamental requirement in the next-generation networks. If all these use-cases are satisfied without increasing the spectral efficiency, the day is not far when we start looking for even higher frequencies (probably 6G). Applying machine learning at all possible avenues in the physical layer will be a game-changer. In this paper, we propose a novel deep learning (DL) method for hybrid precoding to maximize the spectral efficiency. we consider a special case of the MIMO system with a single-output (MISO) and implement DL technique in hybrid precoding for perfect and imperfect Channel State Information (CSI). Though the blackbox method suits for massive MIMO systems with perfect CSI, we introduce a new deep learning method which directly outputs optimized beamforming even in imperfect CSI conditions. Simulation results show that the proposed DL-based beamformer improves spectrum throughput while being more robust to imperfect CSI over the traditional beamforming approaches. This work paves a way to implement machine learning in physical layer beamforming technique for 5G millimeter wave (mmWave) communications, thereby realizing cognition in wireless networks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Agyapong, Patrick Kwadwo; et al.: Design considerations for a 5G network architecture. IEEE Commun. Mag. 52, 65–75 (2014)

    Article  Google Scholar 

  2. Dahlman, Erik; et al.: 5G wireless access: requirements and realization. IEEE Commun. Mag. 52(12), 42–47 (2014)

    Article  Google Scholar 

  3. Frattasi, Simone; Rosa, Francescantonio Della: Mobile positioning and tracking: from conventional to cooperative techniques. Wiley, New York (2017)

    Book  Google Scholar 

  4. Marzetta, Thomas L.: Fundamentals of massive MIMO. Cambridge University Press, Cambridge (2016)

    Book  Google Scholar 

  5. Swindlehurst, A.Lee; et al.: Millimeter-wave massive MIMO: the next wireless revolution? IEEE Commun. Mag. 52(9), 56–62 (2014)

    Article  MathSciNet  Google Scholar 

  6. Bhushan, Naga; et al.: Network densification: the dominant theme for wireless evolution into 5G. IEEE Commun. Mag. 52(2), 82–89 (2014)

    Article  Google Scholar 

  7. Molisch, Andreas F.; et al.: Hybrid beamforming for massive MIMO: a survey. IEEE Commun. Mag. 55(9), 134–141 (2017)

    Article  Google Scholar 

  8. Han, Shuangfeng; et al.: Large-scale antenna systems with hybrid analog and digital beamforming for millimeter wave 5G. IEEE Commun. Mag. 53(1), 186–194 (2015)

    Article  Google Scholar 

  9. Zhang, Didi; et al.: Hybridly connected structure for hybrid beamforming in mmWave massive MIMO systems. IEEE Trans. Commun. 66(2), 662–674 (2017)

    Article  Google Scholar 

  10. Chen, Jung-Chieh: Hybrid beamforming with discrete phase shifters for millimeter-wave massive MIMO systems. IEEE Trans. Vehicular Technol. 66(8), 7604–7608 (2017)

    Article  Google Scholar 

  11. Song, Nuan; Yang, Tao; Sun, Huan: Overlapped subarray based hybrid beamforming for millimeter wave multiuser massive MIMO. IEEE Signal Process. Lett. 24(5), 550–554 (2017)

    Article  Google Scholar 

  12. Mumtaz, Shahid; Rodriguez, Jonathan; Dai, Linglong: MmWave massive MIMO: a paradigm for 5G. Academic Press, Cambridge (2016)

    Google Scholar 

  13. Shu, Fang, et al. 2007 A spatial multiplexing MIMO scheme with beamforming for downlink transmission. 2007 IEEE 66th Vehicular Technology Conference. IEEE

  14. Gaudes, Csar C.; et al.: Robust array beamforming with sidelobe control using support vector machines. IEEE Trans. Signal Process. 55(2), 574–584 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  15. Zhang, Hongyuan, and Rohit U. Nabar. 2012 Beamforming using predefined spatial mapping matrices”. U.S. Patent No. 8,213,870.

  16. Gershman, Alex B 2003 Robust adaptive beamforming: an overview of recent trends and advances in the field.” 4th International Conference on Antenna Theory and Techniques (Cat. No. 03EX699). Vol. 1. IEEE

  17. Williams, Ronald J.; Zipser, David: A learning algorithm for continually running fully recurrent neural networks. Neural Comput. 1(2), 270–280 (1989)

    Article  Google Scholar 

  18. Khazankin, Grigory R., et al. 2017 System architecture for deep packet inspection in high-speed networks. 2017 Siberian Symposium on Data Science and Engineering (SSDSE). IEEE

  19. El Omar, Ayach; et al.: Spatially sparse precoding in millimeter wave MIMO systems. IEEE Trans. Wirel. Commun. 13(3), 1499–1513 (2014)

    Article  MathSciNet  Google Scholar 

  20. Björnson, Emil; Bengtsson, Mats; Ottersten, Björn: Optimal multiuser transmit beamforming: a difficult problem with a simple solution structure [lecture notes]. IEEE Signal Process. Mag. 31(4), 142–148 (2014)

    Article  Google Scholar 

  21. Shi, Qingjiang; et al.: Constrained beamforming for a MIMO multi-user downlink system: algorithms and convergence analysis. IEEE Trans. Signal Process. 64(11), 2920–2933 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  22. Shi, Qingjiang; et al.: An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel. IEEE Trans. Signal Process. 59, 4331–4340 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  23. Yoo, Taesang; Goldsmith, Andrea: On the optimality of multiantenna broadcast scheduling using zero-forcing beamforming. IEEE J. Select. Areas Commun. 24(3), 528–541 (2006)

    Article  Google Scholar 

  24. Christensen, Søren Skovgaard; et al.: Weighted sum-rate maximization using weighted MMSE for MIMO-BC beamforming design. IEEE Trans. Wirel. Commun. 7, 4792–4799 (2008t)

    Article  Google Scholar 

  25. Ghosh, Amitava; et al.: Millimeter-wave enhanced local area systems: a high-data-rate approach for future wireless networks. IEEE J. Select. Areas Commun. 32(6), 1152–1163 (2014)

    Article  Google Scholar 

  26. Sohrabi, Foad, and Wei Yu.: Hybrid beamforming with finite-resolution phase shifters for large-scale MIMO systems.” 2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2015.

  27. Gao, Xinyu; et al.: energy-efficient hybrid analog and digital precoding for mmWave MIMO systems with large antenna arrays. IEEE J. Select. Areas Commun. 34(4), 998–1009 (2016)

    Article  Google Scholar 

  28. Alkhateeb, Ahmed; Leus, Geert; Heath, Robert W.: Limited feedback hybrid precoding for multi-user millimeter wave systems. IEEE Trans. Wirel. Commun. 14(11), 6481–6494 (2015)

    Article  Google Scholar 

  29. Chen, Chiao-En; Tsai, Yu-Cheng; Yang, Chia-Hsiang: An iterative geometric mean decomposition algorithm for MIMO communications systems. IEEE Trans. Wirel. Commun. 14(1), 343–352 (2014)

    Article  Google Scholar 

  30. Jin, Juening; et al.: Hybrid precoding for millimeter wave MIMO systems: a matrix factorization approach. IEEE Trans. Wirel. Commun. 17(5), 3327–3339 (2018)

    Article  Google Scholar 

  31. Zhang, Edin, and Chiachi Huang.: 2014“On achieving optimal rate of digital precoder by RF-baseband codesign for MIMO systems.” 2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall). IEEE

  32. Wang, Gaojian, and Gerd Ascheid.: “Joint pre/post-processing design for large millimeter wave hybrid spatial processing systems.” European Wireless 2014; 20th European Wireless Conference. VDE, 2014.

  33. Andrews, Jeffrey G.; et al.: Modeling and analyzing millimeter wave cellular systems. IEEE Trans. Commun. 65(1), 403–430 (2016)

    Google Scholar 

  34. Sohrabi, Foad; Wei, Yu: Hybrid digital and analog beamforming design for large-scale antenna arrays. IEEE J. Select. Topic. Signal Process. 10(3), 501–513 (2016)

    Article  Google Scholar 

  35. Bogale, Tadilo Endeshaw, and Long Bao Le.: “Beamforming for multiuser massive MIMO systems: Digital versus hybrid analog-digital.” 2014 IEEE Global Communications Conference. IEEE, 2014.

  36. Lee, Junho; Gil, Gye-Tae; Lee, Yong H.: Channel estimation via orthogonal matching pursuit for hybrid MIMO systems in millimeter wave communications. IEEE Trans. Commun. 64(6), 2370–2386 (2016)

    Article  Google Scholar 

  37. Kwon, Yongjin, Jihoon Chung, and Youngchul Sung.: “Hybrid beamformer design for mmwave wideband multi-user MIMO-OFDM systems.” 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2017.

  38. Yu, Xianghao; et al.: Alternating minimization algorithms for hybrid precoding in millimeter wave MIMO systems. IEEE J. Select. Topic. Signal Process. 10(3), 485–500 (2016)

    Article  Google Scholar 

  39. Chen, Chiang-Hen; et al.: Compressive sensing (CS) assisted low-complexity beamspace hybrid precoding for millimeter-wave MIMO systems. IEEE Trans. Signal Process. 65(6), 1412–1424 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  40. Parish, Eric J.; Duraisamy, Karthik: A paradigm for data-driven predictive modeling using field inversion and machine learning. J. Comput. Phys. 305, 758–774 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  41. Huang, Hongji; et al.: Deep learning for super-resolution channel estimation and DOA estimation based massive MIMO system. IEEE Trans. Vehicular Technol. 67(9), 8549–8560 (2018)

    Article  Google Scholar 

  42. Ding, Zhiguo; Vincent Poor, H.: Design of massive-MIMO-NOMA with limited feedback. IEEE Signal Process. Lett. 23(5), 629–633 (2016)

    Article  Google Scholar 

  43. Tao, Jiyun, et al.: “Constrained Deep Neural Network Based Hybrid Beamforming for Millimeter Wave Massive MIMO Systems.” ICC 2019-2019 IEEE International Conference on Communications (ICC). IEEE, 2019.

  44. Klautau, Aldebaro, et al.: “5G MIMO data for machine learning: Application to beam-selection using deep learning.” 2018 Information Theory and Applications Workshop (ITA). IEEE, 2018.

  45. Kato, Nei; et al.: The deep learning vision for heterogeneous network traffic control: proposal, challenges, and future perspective”. IEEE Wirel. Commun. 24(3), 146–153 (2016)

    Article  Google Scholar 

  46. Huang, Hongji; et al.: Deep-learning-based millimeter-wave massive MIMO for hybrid precoding. IEEE Trans. Vehicular Technol. 68(3), 3027–3032 (2019)

    Article  Google Scholar 

  47. Dörner, Sebastian; et al.: Deep learning based communication over the air. IEEE J. Select. Topic. Signal Process. 12(1), 132–143 (2017)

    Article  Google Scholar 

  48. Wen, Chao-Kai; Shih, Wan-Ting; Jin, Shi: Deep learning for massive MIMO CSI feedback. IEEE Wirel. Commun. Lett. 7(5), 748–751 (2018)

    Article  Google Scholar 

  49. He, Hengtao; et al.: Deep learning-based channel estimation for beamspace mmWave massive MIMO systems. IEEE Wirel. Commun. Lett. 7(5), 852–855 (2018)

    Article  Google Scholar 

  50. Alkhateeb, Ahmed; et al.: Deep learning coordinated beamforming for highly-mobile millimeter wave systems. IEEE Access 6, 37328–37348 (2018)

    Article  Google Scholar 

  51. Alkhateeb, Ahmed, Robert W. Heath, and Geert Leus.: “Achievable rates of multi-user millimeter wave systems with hybrid precoding.” 2015 IEEE International Conference on Communication Workshop (ICCW). IEEE, 2015.

  52. Xie, Tian; et al.: Geometric mean decomposition based hybrid precoding for millimeter-wave massive MIMO. China Commun. 15(5), 229–238 (2018)

    Article  Google Scholar 

  53. Gao, Zhen; et al.: MmWave massive-MIMO-based wireless backhaul for the 5G ultra-dense network. IEEE Wirel. Commun. 22(5), 13–21 (2015)

    Article  Google Scholar 

  54. Ye, Hao; Li, Geoffrey Ye; Juang, Biing-Hwang: Power of deep learning for channel estimation and signal detection in OFDM systems. IEEE Wirel. Commun. Lett. 7(1), 114–117 (2017)

    Article  Google Scholar 

  55. Xia, Wenchao, et al.: “A deep learning framework for optimization of MISO downlink beamforming.” arXiv preprint arXiv:1901.00354 2019.

  56. Lin, Tian, and Yu Zhu.: “Beamforming Design for Large-Scale Antenna Arrays Using Deep Learning.” arXiv preprint arXiv:1904.03657 2019.

  57. Baek, Myung-Sun, et al.: “Implementation methodologies of deep learning-based signal detection for conventional MIMO transmitters.” IEEE Transactions on Broadcasting 65.3 : 636-642, 2019

  58. Xu, Jie; Liu, Liang; Zhang, Rui: Multiuser MISO beamforming for simultaneous wireless information and power transfer. IEEE Trans. Signal Process. 62(18), 4798–4810 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  59. Liu, Liang; Zhang, Rui; Chua, Kee-Chaing: Secrecy wireless information and power transfer with MISO beamforming. IEEE Trans. Signal Process. 62(7), 1850–1863 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  60. Tuan, Pham Viet; Koo, Insoo: Optimal multiuser MISO beamforming for power-splitting SWIPT cognitive radio networks. IEEE Access 5, 14141–14153 (2014)

    Article  Google Scholar 

  61. Zhang, Rui; Cui, Shuguang: Cooperative interference management with MISO beamforming. IEEE Trans. Signal Process. 58(10), 5450–5458 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  62. Ma, Hui; et al.: Robust MISO beamforming with cooperative jamming for secure transmission from perspectives of QoS and secrecy rate. IEEE Trans. Commun. 66(2), 767–780 (2017)

    Article  Google Scholar 

  63. Timotheou, Stelios; et al.: Beamforming for MISO interference channels with QoS and RF energy transfer. IEEE Trans. Wirel. Commun. 13(5), 2646–2658 (2014)

    Article  Google Scholar 

  64. Huang, Hao; et al.: Unsupervised learning-based fast beamforming design for downlink MIMO. IEEE Access 7, 7599–7605 (2018)

    Article  Google Scholar 

  65. Elbir, Ahmet M.: A deep learning framework for hybrid beamforming without instantaneous CSI feedback. IEEE Trans. Vehicular Technol. 69(10), 11743–11755 (2020)

    Article  Google Scholar 

  66. Huang, Shaocheng; Ye, Yu; Xiao, Ming: Hybrid beamforming for millimeter wave multi-user MIMO systems using learning machine. IEEE Wireless Communications Letters 9(11), 1914–1918 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electronics and Communication Engineering, REVA University, Bengaluru, India

    Abdul Haq Nalband, Mrinal Sarvagya & Mohammed Riyaz Ahmed

Authors
  1. Abdul Haq Nalband
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mrinal Sarvagya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammed Riyaz Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdul Haq Nalband.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nalband, A.H., Sarvagya, M. & Ahmed, M.R. Spectral Efficient Beamforming for mmWave MISO Systems using Deep Learning Techniques. Arab J Sci Eng 46, 9783–9795 (2021). https://doi.org/10.1007/s13369-021-05552-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-021-05552-4

Keywords

Search

Navigation