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Active device detection and performance analysis of massive non-orthogonal transmissions in cellular Internet of Things

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Abstract

This paper investigates multiple access schemes for uplink and downlink transmissions in cellular networks with massive Internet of Things (IoT) devices. Recall that single-carrier frequency division multiple access and orthogonal frequency division multiple access, which are orthogonal multiple access (OMA) schemes, have been conventionally adopted for uplink and downlink transmissions in narrow-band IoT, respectively. Unlike these OMA schemes, we propose two non-orthogonal multiple access (NOMA) schemes for cellular IoT with short-packet transmissions. Especially, a generalized expectation consistent signal recovery-based algorithm is proposed to estimate active devices, channel state information and data in uplink transmission, where all of the active devices are allowed to transmit their pilots and data through the same resource block without authorization. On the other hand, the active devices estimated during uplink transmission are grouped for downlink transmission with a trade-off between performance and detection complexity. Additionally, the data error rates are analysed for both uplink and downlink transmissions with low-resolution analog-to-digital converters (ADCs), where the effects of critical parameters such as the estimation error, ADC bits, packet length, and message bits are revealed. Both simulation and analytical results are provided to demonstrate the excellent performance of the proposed NOMA schemes and algorithms, especially for active device, channel, and data estimations. More importantly, the obtained results show that the data error rate performance of downlink NOMA is superior to that of OMA when the message bits of devices in one group are selected following the proposed strategy.

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References

  1. Bockelmann C, Pratas N, Nikopour H, et al. Massive machine-type communications in 5G: physical and MAC-layer solutions. IEEE Commun Mag, 2016, 54: 59–65

    Article  Google Scholar 

  2. Jiao J, Liao S Y, Sun Y Y, et al. Fairness-improved and QoS-guaranteed resource allocation for NOMA-based S-IoT network. Sci China Inf Sci, 2021, 64: 169306

    Article  MathSciNet  Google Scholar 

  3. Gao N, Ni Q, Feng D Q, et al. Physical layer authentication under intelligent spoofing in wireless sensor networks. Signal Process, 2020, 166: 107272

    Article  Google Scholar 

  4. Ye N, Li X M, Yu H X, et al. Deep learning aided grant-free NOMA toward reliable low-latency access in tactile Internet of Things. IEEE Trans Ind Inf, 2019, 15: 2995–3005

    Article  Google Scholar 

  5. Jia R D, Chen X M, Qi Q, et al. Massive beam-division multiple access for B5G cellular Internet of Things. IEEE Int Things J, 2020, 7: 2386–2396

    Article  Google Scholar 

  6. New W K, Leow C Y, Navaie K, et al. Application of NOMA for cellular-connected UAVs: opportunities and challenges. Sci China Inf Sci, 2021, 64: 140302

    Article  Google Scholar 

  7. Ma Z, Zhang Z Q, Ding Z G, et al. Key techniques for 5G wireless communications: network architecture, physical layer, and MAC layer perspectives. Sci China Inf Sci, 2015, 58: 041301

    Article  Google Scholar 

  8. Ahn J, Shim B, Lee K B. EP-based joint active user detection and channel estimation for massive machine-type communications. IEEE Trans Commun, 2019, 67: 5178–5189

    Article  Google Scholar 

  9. Shahab M B, Abbas R, Shirvanimoghaddam M, et al. Grant-free non-orthogonal multiple access for IoT: a survey. IEEE Commun Surv Tut, 2020, 22: 1805–1838

    Article  Google Scholar 

  10. Irtaza S A, Lim S H, Choi J W. Greedy data-aided active user detection for massive machine type communications. IEEE Wirel Commun Lett, 2019, 8: 1224–1227

    Article  Google Scholar 

  11. Yu H X, Fei Z S, Zheng Z, et al. Finite-alphabet signature design for grant-free NOMA: a quantized deep learning approach. IEEE Trans Veh Technol, 2020, 69: 10975–10987

    Article  Google Scholar 

  12. Jiang S C, Yuan X J, Wang X, et al. Joint user identification, channel estimation, and signal detection for grant-free NOMA. IEEE Trans Wirel Commun, 2020, 19: 6960–6976

    Article  Google Scholar 

  13. Choi J. NOMA-based compressive random access using gaussian spreading. IEEE Trans Commun, 2019, 67: 5167–5177

    Article  Google Scholar 

  14. Tropp J A, Gilbert A C. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inform Theory, 2007, 53: 4655–4666

    Article  MathSciNet  MATH  Google Scholar 

  15. Schepker H F, Bockelmann C, Dekorsy A. Exploiting sparsity in channel and data estimation for sporadic multi-user communication. In: Proceedings of International Symposium on Wireless Communication Systems, Ilmenau, 2013. 1–5

  16. Majumdar A, Ward R K. Fast group sparse classification. In: Proceedings of IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, 2009. 11–16

  17. Donoho D L, Maleki A, Montanari A. Message-passing algorithms for compressed sensing. Proc Natl Acad Sci, 2009, 106: 18914–18919

    Article  Google Scholar 

  18. Liu L, Yu W. Massive connectivity with massive MIMO-part I: device activity detection and channel estimation. IEEE Trans Signal Process, 2018, 66: 2933–2946

    Article  MathSciNet  MATH  Google Scholar 

  19. Huang N H, Chiueh T D. Sequence design and user activity detection for uplink grant-free NOMA in mMTC networks. IEEE Open J Commun Soc, 2021, 2: 384–395

    Article  Google Scholar 

  20. Zou Q Y, Zhang H C, Wen C K, et al. Concise derivation for generalized approximate message passing using expectation propagation. IEEE Signal Process Lett, 2018, 25: 1835–1839

    Article  Google Scholar 

  21. Zou Q Y, Zhang H C, Cai D H, et al. Message passing based joint channel and user activity estimation for uplink grant-free massive MIMO systems with low-precision ADCs. IEEE Signal Process Lett, 2020, 27: 506–510

    Article  Google Scholar 

  22. Zou Q Y, Zhang H C, Cai D H, et al. A low-complexity joint user activity, channel and data estimation for grant-free massive MIMO systems. IEEE Signal Process Lett, 2020, 27: 1290–1294

    Article  Google Scholar 

  23. Fu J W, Wu G, Zhang Y Z, et al. Active user identification based on asynchronous sparse bayesian learning with SVM. IEEE Access, 2019, 7: 108116

    Article  Google Scholar 

  24. Wen C K, Wang C J, Jin S, et al. Bayes-optimal joint channel-and-data estimation for massive MIMO with low-precision ADCs. IEEE Trans Signal Process, 2016, 64: 2541–2556

    Article  MathSciNet  MATH  Google Scholar 

  25. Shao X D, Chen X M, Zhong C J, et al. A unified design of massive access for cellular Internet of Things. IEEE Internet Things J, 2019, 6: 3934–3947

    Article  Google Scholar 

  26. Ren H, Pan C H, Deng Y S, et al. Joint power and blocklength optimization for URLLC in a factory automation scenario. IEEE Trans Wirel Commun, 2020, 19: 1786–1801

    Article  Google Scholar 

  27. Hu Y L, Gursoy M C, Schmeink A. Efficient transmission schemes for low-latency networks: NOMA vs. relaying. In: Proceedings of IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications, Montreal, 2017. 1–6

  28. Zhang Q Q, Zhang L, Liang Y C, et al. Backscatter-NOMA: a symbiotic system of cellular and Internet-of-Things networks. IEEE Access, 2019, 7: 20000–20013

    Article  Google Scholar 

  29. Ma Z, Xiao M, Xiao Y, et al. High-reliability and low-latency wireless communication for Internet of Things: challenges, fundamentals, and enabling technologies. IEEE Int Things J, 2019, 6: 7946–7970

    Article  Google Scholar 

  30. Yu Y H, Chen H, Li Y H, et al. On the performance of non-orthogonal multiple access in short-packet communications. IEEE Commun Lett, 2018, 22: 590–593

    Article  Google Scholar 

  31. Ding Z G, Fan P Z, Poor H V. Impact of user pairing on 5G nonorthogonal multiple-access downlink transmissions. IEEE Trans Veh Technol, 2016, 65: 6010–6023

    Article  Google Scholar 

  32. Nikopour H, Yi E, Bayesteh A, et al. SCMA for downlink multiple access of 5G wireless networks. In: Proccessing of IEEE Global Communications Conference, Austin, 2014. 3940–3945

  33. He H T, Wen C K, Jin S. Bayesian optimal data detector for hybrid mmWave MIMO-OFDM systems with low-resolution ADCs. IEEE J Sel Top Signal Process, 2018, 12: 469–483

    Article  Google Scholar 

  34. Rangan S, Schniter P, Fletcher A K, et al. On the convergence of approximate message passing with arbitrary matrices. IEEE Trans Inform Theory, 2019, 65: 5339–5351

    Article  MathSciNet  MATH  Google Scholar 

  35. Fletcher A K, Rangan S, Goyal V K, et al. Robust predictive quantization: analysis and design via convex optimization. IEEE J Sel Top Signal Process, 2007, 1: 618–632

    Article  Google Scholar 

  36. Jacobsson S, Durisi G, Coldrey M, et al. Throughput analysis of massive MIMO uplink with low-resolution ADCs. IEEE Trans Wirel Commun, 2017, 16: 4038–4051

    Article  Google Scholar 

  37. Zhang J Y, Dai L L, He Z Y, et al. Performance analysis of mixed-ADC massive MIMO systems over rician fading channels. IEEE J Sel Areas Commun, 2017, 35: 1327–1338

    Article  Google Scholar 

  38. Mo J, Alkhateeb A, Abu-Surra S, et al. Hybrid architectures with few-bit ADC receivers: achievable rates and energy-rate tradeoffs. IEEE Trans Wirel Commun, 2017, 16: 2274–2287

    Article  Google Scholar 

  39. Rangan S. Generalized approximate message passing for estimation with random linear mixing. In: Proceedings of IEEE International Symposium on Information Theory Proceedings, Petersburg, 2011. 2168–2172

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Acknowledgements

This work of Donghong CAI was supported by National Natural Science Foundation of China (Grant No. 62001190) and China Postdoctoral Science Foundation (Grant No. 2021M691249). This work of Pingzhi FAN was supported by National Natural Science Foundation of China (Grant No. 62020106001) and 111 Project (Grant No. 111-2-14). This work of Yanqing XU was supported by China Postdoctoral Science Foundation (Grant No. 2021M693100). This work of Zhiquan LIU was supported by National Natural Science Foundation of China (Grant No. 61802146) and Guangdong Basic and Applied Basic Research Foundation (Grant No. 2019A1515011017).

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Authors and Affiliations

  1. College of Cyber Security, Jinan University, Guangzhou, 510632, China

    Donghong Cai & Zhiquan Liu

  2. Institute of Mobile Communications, Southwest Jiaotong University, Chengdu, 610031, China

    Donghong Cai & Pingzhi Fan

  3. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Qiuyun Zou

  4. School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China

    Yanqing Xu

  5. The University of Science and Technology of China, Hefei, 230026, China

    Yanqing Xu

  6. School of Electrical and Electronic Engineering, The University of Manchester, Manchester, M13 9PL, UK

    Zhiguo Ding

Authors
  1. Donghong Cai
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  2. Pingzhi Fan
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  3. Qiuyun Zou
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  4. Yanqing Xu
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  6. Zhiquan Liu
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Corresponding author

Correspondence to Qiuyun Zou.

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Appendixes A–D. The supporting information is available online at www.info.scichina.com and www.link.springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.

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Cai, D., Fan, P., Zou, Q. et al. Active device detection and performance analysis of massive non-orthogonal transmissions in cellular Internet of Things. Sci. China Inf. Sci. 65, 182301 (2022). https://doi.org/10.1007/s11432-021-3328-y

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  • DOI: https://doi.org/10.1007/s11432-021-3328-y

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