Complexity Reduction in Massive-MIMO-NOMA SIC Receiver in Presence of Imperfect CSI
محورهای موضوعی : Wireless Network
Nilufar Tutunchi
1
( Science and Research university)
Afrooz Haghbin
2
(Science and Research university)
Behrad Mahboobi
3
(Science and Research university)
کلید واژه: Massive MIMO , NOMA , Complexity , SIC , Neumann, Gauss - Seidel,
چکیده مقاله :
One of the main reasons for switching to the next generation of communication systems is the demand of increasing capacity and network connections. This goal can be achieved using massive multiple input - multiple output (massive-MIMO) systems in combination with Non-orthogonal multiple access (NOMA) technique. NOMA technology uses the successive interference cancellation (SIC) receiver to detect user’s signals which imposes an additional complexity on the system. In this paper, we proposed two methods to reduce the system complexity. The proposed method despite imperfect channel state information (CSI) in the receiver, there is not significantly reduction in the system performance. Since the computation of matrices inverse has a high computational complexity, we used the Neumann series approximation method and the Gauss-Seidel decomposition method to compute matrices inverse in the SIC receiver. Simulation results are provided at the end of the paper in terms of bit error rate (BER) at the receiver which show, these methods have lower computational complexity in comparison with the traditional methods while they cause a slight performance reduction in the SIC receiver. Also, we examined the increasing and decreasing value of imperfect channel state information in the system performance which shows the increasing value of imperfect channel state information, cause a slight performance reduction in SIC receiver.
One of the main reasons for switching to the next generation of communication systems is the demand of increasing capacity and network connections. This goal can be achieved using massive multiple input - multiple output (massive-MIMO) systems in combination with Non-orthogonal multiple access (NOMA) technique. NOMA technology uses the successive interference cancellation (SIC) receiver to detect user’s signals which imposes an additional complexity on the system. In this paper, we proposed two methods to reduce the system complexity. The proposed method despite imperfect channel state information (CSI) in the receiver, there is not significantly reduction in the system performance. Since the computation of matrices inverse has a high computational complexity, we used the Neumann series approximation method and the Gauss-Seidel decomposition method to compute matrices inverse in the SIC receiver. Simulation results are provided at the end of the paper in terms of bit error rate (BER) at the receiver which show, these methods have lower computational complexity in comparison with the traditional methods while they cause a slight performance reduction in the SIC receiver. Also, we examined the increasing and decreasing value of imperfect channel state information in the system performance which shows the increasing value of imperfect channel state information, cause a slight performance reduction in SIC receiver.
[1] J. G. Andrews, S. Buzzi, W. Choi, S. V. Hanly, A. Lozano, A. C. Soong, et al., "What will 5G be?," IEEE Journal on selected areas in communications, vol. 32, pp. 1065-1082, 2014.
[2] L. Dai, B. Wang, Y. Yuan, S. Han, I. Chih-Lin, and Z. Wang, "Non-orthogonal multiple access for 5G: solutions, challenges, opportunities, and future research trends," IEEE Communications Magazine, vol. 53, pp. 74-81, 2015.
[3] Z. Ding, Z. Yang, P. Fan, and H. V. Poor, "On the performance of non-orthogonal multiple access in 5G systems with randomly deployed users," IEEE signal processing letters, vol. 21, pp. 1501-1505, 2014.
[4] Q. Sun, S. Han, I. Chin-Lin, and Z. Pan, "On the ergodic capacity of MIMO NOMA systems," IEEE Wireless Communications Letters, vol. 4, pp. 405-408, 2015.
[5] Z. Ding, F. Adachi, and H. V. Poor, "The application of MIMO to non-orthogonal multiple access," IEEE Transactions on Wireless Communications, vol. 15, pp. 537-552, 2015.
[6] A. Benjebbour, K. Saito, A. Li, Y. Kishiyama, and T. Nakamura, "Non-orthogonal multiple access (NOMA): Concept and design," Signal Processing for 5G, 2016.
[7] Z. Ding and H. V. Poor, "Design of massive-MIMO-NOMA with limited feedback," IEEE Signal Processing Letters, vol. 23, pp. 629-633, 2016.
[8] X. Chen, A. Beiijebbour, A. Li, H. Jiang, and H. Kayama, "Consideration on successive interference canceller (SIC) receiver at cell-edge users for non-orthogonal multiple access (NOMA) with SU-MIMO," in 2015 IEEE 26th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 2015, pp. 522-526.
[9] J. Kim, J. Koh, J. Kang, K. Lee, and J. Kang, "Design of user clustering and precoding for downlink non-orthogonal multiple access (NOMA)," in MILCOM 2015-2015 IEEE Military Communications Conference, 2015, pp. 1170-1175.
[10] S. Liu, C. Zhang, and G. Lyu, "User selection and power schedule for downlink non-orthogonal multiple access (NOMA) system," in 2015 IEEE International Conference on Communication Workshop (ICCW), 2015, pp. 2561-2565.
[11] F. Rezaei, A. R. Heidarpour, C. Tellambura, and A. Tadaion, "Underlaid Spectrum Sharing for Cell-Free Massive MIMO-NOMA," IEEE Communications Letters, 2020.
[12] O. Gustafsson, E. Bertilsson, J. Klasson, and C. Ingemarsson, "Approximate Neumann series or exact matrix inversion for massive MIMO?," in 2017 IEEE 24th Symposium on Computer Arithmetic (ARITH), 2017, pp. 62-63.
[13] M. Wu, B. Yin, G. Wang, C. Dick, J. R. Cavallaro, and C. Studer, "Large-scale MIMO detection for 3GPP LTE: Algorithms and FPGA implementations," IEEE Journal of Selected Topics in Signal Processing, vol. 8, pp. 916-929, 2014.
[14] C. H. Rycroft, "Iterative methods for linear systems," School of Engineering and Applied Sciences, Harvard University, Cambridge, 2007.
