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Spectral Efficiency Analysis for Massive MIMO System Under QoS Constraint: an Effective Capacity Perspective

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Abstract

In massive multiple-input multiple-output (MIMO) systems, dozens of mobile users can simultaneously receive signals from one base station. To obtain maximum spectral efficiency (SE), researchers have investigated the optimal number of scheduled users for one time slot. However, in practical cases, we must consider the quality of service (QoS) constraint. The probability of delay violation is an important QoS index that depends on transmission stability over a long period rather than the instantaneous transmission rate of one time slot. In this paper, we analyze the achievable effective SE of a massive MIMO system under the probability constraint of delay violation. By adopting the effective capacity (EC) theory of wireless channels, we associate the delay violation probability with the transmission rate fluctuations caused by the massive MIMO scheduling strategy used. The relationship between the effective SE, the QoS constraint, and the number of scheduled users is formulated as a continuous function. Our simulation results demonstrate how the optimal number of scheduled users changes for different QoS constraint levels. According to the changing trend, the massive MIMO system can be programmed to choose between different simple scheduling strategies for different QoS constraint levels.

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References

  1. Deng L, He Y, Zhang Y, Chen M, Li Z, Lee J, Zhang Y, Song L (2019) Device-to-device load balancing for cellular networks. IEEE Trans on Commun 67(4):3040–3054

    Article  Google Scholar 

  2. Zhao G, Chen S, Zhao L, Hanzo L (2017) Joint energy-spectral-efficiency optimization of CoMP and BS deployment in dense large-scale cellular networks. IEEE Trans Wireless Commun 16(7):4832–4847

    Article  Google Scholar 

  3. Wang F, Jiang D, Qi S (2019) An adaptive routing algorithm for integrated information networks. China Commun 7(1):196–207

    Google Scholar 

  4. Huo L, Jiang D, Lv Z (2018) Soft frequency reuse-based optimization algorithm for energy efficiency of multi-cell networks. Comput Electrical Eng 66(2):316–331

    Article  Google Scholar 

  5. Jiang D, Huo L, Lv Z, Song H, Qin W (2018) A joint multi-criteria utility-based network selection approach for vehicle-to-infrastructure networking. IEEE Trans Intell Transport Sys 19(10):3305–3319

    Article  Google Scholar 

  6. Wang F, Jiang D, Wen H, Song H (2019) Adaboost-based security level classification of mobile intelligent terminals. J Supercomput. https://doi.org/10.1007/s11227-019-02954-y

  7. Karmakar R, Chattopadhyay S, Chakraborty S (2019) Intelligent MU-MIMO user selection with dynamic link adaptation in IEEE 802.11ax. IEEE Trans Wirel Commun 18(2):1155–1165

    Article  Google Scholar 

  8. Zhu J, Song Y, Jiang D, Song H (2018) A new Deep-Q-Learning-Based transmission scheduling mechanism for the cognitive internet of things. IEEE Internet of Things Journal 5(4):2375–2385

    Article  Google Scholar 

  9. Sun M, Jiang D, Song H, Liu Y (2017) Statistical resolution limit analysis of two closely spaced signal sources using rao test. IEEE Access 5:22013–22022

    Article  Google Scholar 

  10. Tun YK, Tran NH, Ngo DT, Pandey SR, Han Z, Hong CS (2019) Wireless network slicing: generalized kelly mechanism-based resource allocation. IEEE Journal on Selected Areas in Communications 37(8):1794–1807

    Article  Google Scholar 

  11. Huo L, Jiang D (2019) Stackelberg game-based energy-efficient resource allocation for 5G cellular networks. Telecommun Sys 23(4):1–11

    Google Scholar 

  12. Jiang D, Zhang P, Lv Z, Song H (2016) Energy-efficient multi-constraint routing algorithm with load balancing for smart city applications. IEEE Internet of Things Journal 3(6):1437–1447

    Article  Google Scholar 

  13. Jiang D, Li W, Lv H (2017) An energy-efficient cooperative multicast routing in multi-hop wireless networks for smart medical applications. Neurocomputing 220(2017):160–169

    Article  Google Scholar 

  14. Larsson E, Tufvesson F, Edfors O, Marzetta T (2014) Massive MIMO for next generation wireless systems. IEEE Commun Magazine 52(2):186–195

    Article  Google Scholar 

  15. Abarghouyi H, Razavizadeh SM, Bjornson E (2018) QoE-aware beamforming design for massive MIMO heterogeneous networks. IEEE Trans Vehicular Technol 67(9):8315–8323

    Article  Google Scholar 

  16. Jiang D, Wang Y, Lv Z, Qi S, Singh S (2019) Big data analysis-based network behavior insight of cellular networks for industry 4.0 applications. IEEE Trans Industrial Inf. https://doi.org/10.1109/TII.2019.2930226

  17. Jiang D, Huo L, Li Y (2018) Fine-granularity inference and estimations to network traffic for SDN. Plos One 13(5):1–23

    Google Scholar 

  18. Huo L, Jiang D, Zhu X, Wang Y, Lv Z, Singh S (2019) An SDN-based fine-grained measurement and modeling approach to vehicular communication network traffic. Int J Commun Sys. https://doi.org/10.1002/dac.4092

  19. Huh H, Caire G, Papadopoulos H, Ramprashad S (2012) Achieving massive MIMO spectral efficiency with a not-so-large number of antennas. IEEE Trans Wireless Commun 11(9):3226–3239

    Article  Google Scholar 

  20. Ngo HQ, Larsson E, Marzetta T (2013) Energy and spectral efficiency of very large multiuser MIMO systems. IEEE Trans Commun 61(4):1436–1449

    Article  Google Scholar 

  21. Mueller R, Vehkapera M, Cottatellucci L (2013) Blind pilot decontamination, ITG Workshop on Smart Antennas (WSA), Stuttgart, Germany Mar.

  22. Teeti M, Sun J, Gesbert D, Liu Y (2015) The impact of physical channel on performance of subspace-based channel estimation in massive MIMO systems. IEEE Trans Wireless Commun 14(9):4743–4756

    Article  Google Scholar 

  23. Wu H, Liu Y, Wang K (2018) Analysis of DFT-based channel estimation for uplink massive MIMO systems. IEEE Commun Lett 22(2):328–331

    Article  Google Scholar 

  24. Mawatwal K, Sen D, Roy R (2017) A semi-blind channel estimation algorithm for massive MIMO systems. IEEE Wireless Commun Lett 6(1):70–73

    Article  Google Scholar 

  25. Jiang D, Wang W, Shi L, Song H (2018) A compressive sensing-based approach to end-to-end network traffic reconstruction. IEEE Trans Netw Sci Eng. https://doi.org/10.1109/TNSE.2018.2877597

  26. Chen L, Jiang D, Bao R, Xiong J, Liu F, Bei L (2017) MIMO Scheduling effectiveness analysis for bursty data service from view of QoE. Chinese J Electron 26(5):1079–1085

    Article  Google Scholar 

  27. Jiang D, Huo L, Song H (2018) Rethinking behaviors and activities of base stations in mobile cellular networks based on big data analysis. IEEE Trans Netw Sci Eng. https://doi.org/10.1109/TNSE.2018.2861388

  28. Guo C, Liang L, Li GY (2019) Resource allocation for low-latency vehicular communications: an effective capacity perspective. IEEE J Selected Areas Commun 37(4):905–917

    Article  Google Scholar 

  29. Shehab M, Alves H, Latva-aho M (2019) Effective capacity and power allocation for machine-type communication. IEEE Trans Vehicular Technol 68(4):4098–4102

    Article  Google Scholar 

  30. Cui Q, Gu Y, Ni W, Liu RP (2017) Effective capacity of licensed-assisted access in unlicensed spectrum for 5G: from theory to application. IEEE J Selected Areas Commun 35(8):1754–1767

    Article  Google Scholar 

  31. Xiao C, Zeng J, Ni W, Liu RP, Su X, Wang J (2019) Delay guarantee and effective capacity of downlink NOMA fading channels. IEEE J Selected Topics Signal Process 13(3):508–523

    Article  Google Scholar 

  32. Chen L, Jiang D, Song H, Wang P, Bao R, Zhang K, Li Y (2018) A lightweight end-side user experience data collection system for quality evaluation of multimedia communications. IEEE Access 6(1):15408–15419

    Article  Google Scholar 

  33. Shisher M, Ornee T, Hossain M (2017) QoS aware user association in massive MIMO enabled hetnets for DTU and NDTU traffic. In: 4th international conference on advances in electrical engineering (ICAEE). Dhaka, pp 363–368

  34. Chaudhari S, Cabric D (2018) QoS aware power allocation and user selection in massive MIMO underlay cognitive radio networks. IEEE Trans Cognitive Commun Netw 4(2):220–231

    Article  Google Scholar 

  35. Gao X, Edfors O, Rusek F, Tufvesson F (2015) Massive MIMO performance evaluation based on measured propagation data. IEEE Trans Wireless Commun 14(7):3899–3911

    Article  Google Scholar 

  36. Björnson E, Larsson E, Debbah M (2016) Massive MIMO for maximal spectral efficiency: How many users and pilots should be allocated?. IEEE Transactions on Wireless Communications 15(2):1293–1308

    Article  Google Scholar 

  37. Corvaja R, Armada AG (2016) Phase noise degradation in massive MIMO downlink with Zero-Forcing and maximum ratio transmission precoding. IEEE Trans Vehicular Technol 65(10):8052–8059

    Article  Google Scholar 

  38. Parfait T, Kuang Y, Jerry K (2014) Performance analysis and comparison of ZF and MRT based downlink massive MIMO systems. In: 2014 Sixth international conference on ubiquitous and future networks (ICUFN), Shanghai, 2014, pp 383–388

  39. Jin S, Liang X, Wong K, Gao X, Zhu Q (2015) Ergodic rate analysis for multipair massive MIMO Two-Way relay networks. IEEE Trans Wireless Commun 14(3):1480–1491

    Article  Google Scholar 

  40. Akhtar J, Rajawat K (2018) QoS-based antenna and user selection in large-scale fading for massive-MIMO systems. In: IEEE 19th international workshop on signal processing advances in wireless communications (SPAWC), Kalamata, pp 1–5

  41. Wu D, Negi R (2003) Effective capacity: a wireless link model for support of quality of service. IEEE Trans Wireless Commun 2(4):630–643

    Google Scholar 

  42. Hu Y, Gross J, Schmeink A (2016) On the capacity of relaying with finite blocklength. IEEE Trans Vehicular Technol 65(3):1790–1794

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported in part by the Natural Science Foundation of Jiangsu Province of China (No.BK20161165) and the applied fundamental research Foundation of Xuzhou of China (No. KC17072).

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Correspondence to Lei Chen.

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Chen, L., Zhang, L. Spectral Efficiency Analysis for Massive MIMO System Under QoS Constraint: an Effective Capacity Perspective. Mobile Netw Appl 26, 691–699 (2021). https://doi.org/10.1007/s11036-019-01414-4

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