융합정보논문지 (Journal of Convergence for Information Technology)

중소기업융합학회 (Convergence Society for SMB)
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MIMO 기반의 IoT 통신 잡음을 최소화하기 위해서 딥러닝을 활용한 비밀키 차원 분배 메커니즘

Secret Key-Dimensional Distribution Mechanism Using Deep Learning to Minimize IoT Communication Noise Based on MIMO

  • 조성남 (한국과학기술정보연구원 학술정보공유센터) ;
  • 정윤수 (목원대학교 정보통신융합공학부)
  • Cho, Sung-Nam (Korea Institute of Science and Technology Information) ;
  • Jeong, Yoon-Su (Department of information Communication Convergence Engineering, Mokwon University)
  • 투고 : 2020.09.29
  • 심사 : 2020.11.20
  • 발행 : 2020.11.28
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초록

IoT 장치가 기하급수적으로 증가하면서 다중 안테나를 통해 IoT 정보를 송·수신하기 위한 MIMO 간섭 최소화 및 전송 용량 증대는 가장 큰 이슈로 남아있는 상황이다. 본 논문에서는 MIMO 기반의 IoT 통신 잡음을 최소화하기 위해서 딥러닝을 활용한 비밀키 차원 분배 메커니즘을 제안한다. 제안 메커니즘은 다중의 안테나를 통해 송·수신되는 IoT 정보를 딥러닝을 사용하여 일괄적으로 분산 처리함으로써 송·수신 과정 중에 발생하는 자원 손실을 최소화하고 있다. 또한, 제안 메커니즘은 AP들간의 직접적인 간섭이 없는 기지국의 다중 안테나 다중 스트림 전송을 통해 용량을 최대로 증대시킬 수 있도록 다차원 키 분배 처리 과정을 적용하였다. 또한, 제안 메커니즘은 다중 안테나 기술을 최대한 활용하기 위해서 IoT 정보의 주파수 채널 수에 따라 비밀키를 차원 분배하는 방식을 적용함으로써 IoT 정보수에 따른 비밀키 사용 빈도수를 딥러닝하여 IoT 정보를 서로 동기화하고 있다.

As IoT devices increase exponentially, minimizing MIMO interference and increasing transmission capacity for sending and receiving IoT information through multiple antennas remain the biggest issues. In this paper, secret key-level distribution mechanism using deep learning is proposed to minimize MIMO-based IoT communication noise. The proposed mechanism minimizes resource loss during transmission and reception process by dispersing IoT information sent and received through multiple antennas in batches using deep learning. In addition, the proposed mechanism applied a multidimensional key distribution processing process to maximize capacity through multiple antenna multiple stream transmission at base stations without direct interference between the APs. In addition, the proposed mechanism synchronizes IoT information by deep learning the frequency of use of secret keys according to the number of IoT information by applying the method of distributing secret keys in dimension according to the number of frequency channels of IoT information in order to make the most of the multiple antenna technology.

키워드

참고문헌

  1. P. Popovski, K. F. Trillingsgaard, O. Simeone & G. Durisi. (2018). 5g wireless network slicing for embb, urllc, and mmtc: A communicationtheoretic view. IEEE Access, 6, 55765-55779. https://doi.org/10.1109/ACCESS.2018.2872781
  2. H. Han, X. Guo & Y. Li. (2017). A High Throughput Pilot Allocation for M2M Communication in Crowded Massive MIMO Systems. IEEE Transactions on Vehicular Technology, 66(10), 9572-9576. DOI : 10.1109/TVT.2017.2702604
  3. L. Liu & W. Yu. (2018). Massive Connectivity With Massive MIMO-Part I:Device Activity Detection and Channel Estimation. IEEE Transactions on Signal Processing, 66(11), 2933-2946. DOI : 10.1109/TSP.2018.2818082
  4. F. Boccardi, R. W. Heath, A. Lozano, T. L. Marzetta & P. Popovski. (2014). Five disruptive technology directions for 5G. IEEE Communications Magazine, 52(2), 1-11. DOI : 10.1109/MCOM.2014.6736746
  5. T. L. Marzetta (2010). Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas. IEEE Transactions on Wireless Communications, 9(11), 3590-3600. DOI : 10.1109/TWC.2010.092810.091092
  6. E. Bjornson, J. Hoydis & L. Sanguinetti. (2017). Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency. Foundations and Trends in Signal Processing, 11(3-4), 516. DOI : 10.1561/2000000093
  7. E. Bjrnson, E. de Carvalho, J. H. Sorensen, E. G. Larsson & P. Popovski. (2017). A Random Access Protocol for Pilot Allocation in Crowded Massive MIMO Systems. IEEE Transactions on Wireless Communications, 16(4), 2220-2234. DOI : 10.1109/TWC.2017.2660489
  8. H. Q. Ngo, E. G. Larsson & T. L. Marzetta. (2014). Aspects of favorable propagation in Massive MIMO. In 2014 22nd European Signal Processing Conference (EUSIPCO) (pp. 76-80). IEEE.
  9. E. Bjornson, E. G. Larsson & M. Debbah. (2016). Massive MIMO for Maximal Spectral Efficiency: How Many Users and Pilots Should Be Allocated. IEEE Transactions on Wireless Communications, 15(2), 1293-1308. DOI : 10.1109/TWC.2015.2488634
  10. J. Hoydis, S. ten Brink & M. Debbah. (2013). Massive MIMO in the UL/DL of Cellular Networks: How Many Antennas Do We Need?. IEEE Journal on Selected Areas in Communications, 31(2), 160-171. DOI : 10.1109/JSAC.2013.130205
  11. T. Marzetta, E. Larsson, H. Yang & H. Ngo. (2016). Fundamentals of Massive MIMO. New York, NY, USA: Cambridge University Press, 2016.
  12. A. Khansefid & H. Minn. (2015). Achievable downlink rates of mrc and zf precoders in massive MIMO with uplink and downlink pilot contamination. IEEE Trans. Commun., 63(12), 4849-4864. DOI : 10.1109/TCOMM.2015.2482965
  13. K. Upadhya, S. A. Vorobyov & M. Vehkapera. (2017). Superimposed pilots are superior for mitigating pilot contamination in massive MIMO. IEEE Transactions on Signal Processing, 65(11), 2917-2932. DOI : 10.1109/TSP.2017.2675859
  14. D. Verenzuela, E. Bjornson & L. Sanguinetti. (2018). Spectral and energy efficiency of superimposed pilots in uplink massive MIMO. IEEE Transactions on Wireless Communications, 17(11), 7099-7115. DOI : 10.1109/TWC.2018.2860939
  15. E. Bjornson, J. Hoydis & L. Sanguinetti. (2018). Massive MIMO has unlimited capacity. IEEE Transactions on Wireless Communications, 17(1), 574-590. DOI : 10.1109/TWC.2017.2768423
  16. K. Wang & Z. Ding. (2016). FEC Code Anchored Robust Design of Massive MIMO Receivers. IEEE Transactions on Wireless Communications, 16(12), 8223-8235. DOI : 10.1109/TWC.2016.2613516
  17. S. R. Panigrahi, N. Bjorsell, & M. Bengtsson. (2017). Feasibility of Large Antenna Arrays towards Low Latency Ultra Reliable Communication. In 2017 IEEE International Conference on Industrial Technology (ICIT) (pp. 1289-1294). IEEE. DOI : 10.1109/ICIT.2017.7915549