Enhancing flexibility and system performance in 6G and beyond: A user-based numerology and waveform approach^✩

Abstract

A Mixed Numerology OFDM (MN-OFDM) system is essential in 6G and beyond. However, it encounters challenges due to Inter-Numerology Interference (INI). The upcoming 6G technology aims to support innovative applications with high data rates, low latency, and reliability. Therefore, effective handling of INI is crucial to meet the diverse requirements of these applications. To address INI in MN-OFDM systems, this paper proposes a User-Based Numerology and Waveform (UBNW) approach that uses various OFDM-based waveforms and their parameters to mitigate INI. By assigning a specific waveform and numerology to each user, UBNW mitigates INI, optimizes service characteristics, and addresses user demands efficiently. The required Guard Bands (GB), expressed as a ratio of user bandwidth, vary significantly across different waveforms at an SIR of 25 dB. For instance, OFDM-FOFDM needs only 2.5%, while OFDM-UFMC, OFDM-WOLA, and conventional OFDM require 7.5%, 24%, and 40%, respectively. The time-frequency efficiency also varies between the waveforms. FOFDM achieves 85.6%, UFMC achieves 81.6%, WOLA achieves 70.7%, and conventional OFDM achieves 66.8%. The simulation results demonstrate that the UBNW approach not only effectively mitigates INI but also enhances system flexibility and time-frequency efficiency while simultaneously reducing the required GB.

Keywords

6G / Artificial intelligence and machine / learning Inter-numerology interference / Mixed numerology OFDM / Multiple waveforms / User-based numerology and waveform

References

[1] S. Zhang, C. Xiang, S. Xu, 6G: connecting everything by 1000 times price reduction, IEEE Open J. Veh. Technol. 1 ( 2020) 107-115.

[2] F. Guo, F.R. Yu, H. Zhang, X. Li, H. Ji, V.C. Leung, Enabling massive IoT toward 6G: a comprehensive survey, IEEE Int. Things J. 8 (15) (2021) 11891-11915.

[3] W. Jiang, B. Han, M.A. Habibi, H.D. Schotten, The road towards 6G: a comprehensive survey, IEEE Open J. Commun. Soc. 2 (2021) 334-366.

[4] A. Yazar, H. Arslan, A waveform parameter assignment framework for 6G with the role of machine learning, IEEE Open J. Veh. Technol. 1 (2020) 156-172.

[5] Z.E. Ankarali, B. Peköz, H. Arslan,Flexible radio access beyond 5G: a future projec-tion on waveform, numerology, and frame design principles, IEEE Access 5 (2017) 18295-18309.

[6] Y.-G. Lim, T. Jung, K.S. Kim, C.-B. Chae, R.A. Valenzuela, Waveform multiplexing for new radio: numerology management and 3D evaluation, IEEE Wirel. Commun. 25 (5) (2018) 86-94.

[7] TSGR, TS 138 211-V17.3.0-5 G; NR; physical channels and modulation (3GPP TS 38.211 version 17.3.0 release 17, available online at: https://www.etsi.org/deliver/etsi_ts/138200_138299/138211/17.03.00_60/ts_138211v170300p.pdf, 2022. (Ac-cessed 27 September 2024).

[8] L. Chettri, R. Bera, A comprehensive survey on Internet of Things (IoT) toward 5G wireless systems, IEEE Int. Things J. 7 (1) (2019) 16-32.

[9] A.A. Zaidi, R. Baldemair, V. Molés-Cases, N. He, K. Werner, A. Cedergren, OFDM nu-merology design for 5G new radio to support IoT, eMBB, and MBSFN, IEEE Commun. Stand. Mag. 2 (2) (2018) 78-83.

[10] Y.L. Lee, D. Qin, L.-C. Wang, G.H. Sim, 6G massive radio access networks: key appli-cations, requirements and challenges, IEEE Open J. Veh. Technol. 2 (2020) 54-66.

[11] T.V.S. Sreedhar, N.B. Mehta, Inter-numerology interference in 5G new radio: anal-ysis and bounds for time-varying fading channels, in: ICC 2022-IEEE International Conference on Communications, IEEE, 2022, pp. 4818-4823.

[12] P. Guan, D. Wu, T. Tian, J. Zhou, X. Zhang, L. Gu, A. Benjebbour, M. Iwabuchi, Y. Kishiyama, 5G field trials: OFDM-based waveforms and mixed numerologies, IEEE J. Sel. Areas Commun. 35 (6) (2017) 1234-1243.

[13] T.V.S. Sreedhar, N.B. Mehta, Inter-numerology interference in mixed numerology OFDM systems in time-varying fading channels with phase noise, IEEE Trans. Wirel.commun. 22 (8) (2023) 5473-5485.

[14] M.S. Sayed, H.M. Zakaria, A.M. Abdelhady, A. Zekry, Interference mitigation in mixed-numerology system using hybrid waveforms, Ain Shams Eng. J. 15 (3) (2024) 102581.

[15] B.A. Çevikgibi, A.M. Demirtaş, T. Girici, H. Arslan,Inter-numerology interference pre-equalization for 5G mixed-numerology communications, in: 2022 IEEE 95th Ve-hicular Technology Conference: (VTC2022-Spring), IEEE, 2022, pp. 1-6.

[16] H. Arslan, et al., Flexible multi-numerology systems for 5G new radio, J. Mob. Mul-timed. 14 (4) (2018) 367-394.

[17] X. Cheng, R. Zayani, H. Shaiek, D. Roviras, Inter-numerology interference analysis and cancellation for massive MIMO-OFDM downlink systems, IEEE Access 7 (2019) 177164-177176.

[18] M. Yang, Y. Chen, L. Du, Interference analysis and filter parameters optimization for uplink asynchronous F-OFDM systems, IEEE Access 7 (2019) 48356-48370.

[19] E. Memisoglu, A.B. Kihero, E. Basar, H. Arslan, Guard band reduction for 5G and beyond multiple numerologies, IEEE Commun. Lett. 24 (3) (2019) 644-647.

[20] A.B. Kihero, M.S.J. Solaija, A. Yazar, H. Arslan, Inter-numerology interference anal-ysis for 5G and beyond, in: 2018 IEEE Globecom Workshops (GC Wkshps), IEEE, 2018, pp. 1-6.

[21] A. Yazar, H. Arslan, Reliability enhancement in multi-numerology-based 5G new radio using ini-aware scheduling, EURASIP J. Wirel.commun. Netw. 2019 ( 2019) 1-14.

[22] A.F. Demir, H. Arslan, Inter-numerology interference management with adaptive guards: a cross-layer approach, IEEE Access 8 (2020) 30378-30386.

[23] B. Yang, L. Zhang, O. Onireti, P. Xiao, M.A. Imran, R. Tafazolli, Mixed-numerology signals transmission and interference cancellation for radio access network slicing, IEEE Trans. Wirel.commun. 19 (8) (2020) 5132-5147.

[24] J. Mao, L. Zhang, P. Xiao, K. Nikitopoulos, Interference analysis and power allocation in the presence of mixed numerologies, IEEE Trans. Wirel.commun. 19 (8) (2020) 5188-5203.

[25] A.B. Kihero, M.S.J. Solaija, H. Arslan, Inter-numerology interference for beyond 5G, IEEE Access 7 (2019) 146512-146523.

[26] D. Mattera, M. Tanda, Windowed OFDM for small-cell 5G uplink, Phys.commun. 39 (2020) 100993.

[27] S.-Y. Lien, S.-L. Shieh, Y. Huang, B. Su, Y.-L. Hsu, H.-Y. Wei, 5G new radio: wave-form, frame structure, multiple access, and initial access, IEEE Commun. Mag. 55 (6) (2017) 64-71.

[28] B. Farhang-Boroujeny, H. Moradi, OFDM inspired waveforms for 5G, IEEE Commun. Surv. Tutor. 18 (4) (2016) 2474-2492.

[29] H. Chen, J. Hua, F. Li, F. Chen, D. Wang, Interference analysis in the asynchronous F-OFDM systems, IEEE Trans. Commun. 67 (5) (2019) 3580-3596.

[30] D. Demmer, R. Zakaria, R. Gerzaguet, J.-B. Doré, D. Le Ruyet, Study of OFDM pre-coded filter-bank waveforms, IEEE Trans. Wirel.commun. 18 (6) (2018) 2889-2902.

[31] X. Chen, L. Wu, Z. Zhang, J. Dang, J. Wang, Adaptive modulation and filter configu-ration in universal filtered multi-carrier systems, IEEE Trans. Wirel.commun. 17 (3) (2017) 1869-1881.

[32] B. Peköz, S. Köse, H. Arslan, Extensionless adaptive transmitter and receiver win-dowing of beyond 5G frames, IEEE Trans. Veh. Technol. 69 (2) (2019) 1888-1902.

[33] J. Yli-Kaakinen, T. Levanen, A. Palin, M. Renfors, M. Valkama, Generalized fast-convolution-based filtered-OFDM: techniques and application to 5G new radio, IEEE Trans. Signal Process. 68 (2020) 1213-1228.

[34] P. Weitkemper, J. Bazzi, K. Kusume, A. Benjebbour, Y. Kishiyama, On regular re-source grid for filtered OFDM, IEEE Commun. Lett. 20 (12) (2016) 2486-2489.

[35] R. Nissel, S. Schwarz, M. Rupp, Filter bank multicarrier modulation schemes for fu-ture mobile communications, IEEE J. Sel. Areas Commun. 35 (8) (2017) 1768-1782.

[36] M. Nemati, H. Arslan, Low ICI symbol boundary alignment for 5G numerology de-sign, IEEE Access 6 (2017) 2356-2366.

[37] X. Liu, H.-H. Chen, X. Wang, W. Meng, Time domain precoding for OFDM/OFDMA systems without cyclic prefix, IEEE Trans. Veh. Technol. 67 (6) (2018) 5510-5514.

[38] X. Zhang, L. Zhang, P. Xiao, D. Ma, J. Wei, Y. Xin, Mixed numerologies interfer-ence analysis and inter-numerology interference cancellation for windowed OFDM systems, IEEE Trans. Veh. Technol. 67 (8) (2018) 7047-7061.

[39] M. Iwabuchi, A. Benjebbour, Y. Kishiyama, D. Wu, T. Tian, L. Gu, Y. Cui, T. Kashima, 5G field experimental trial on frequency domain multiplexing of mixed numerology, in: 2017 IEEE 85th Vehicular Technology Conference, (VTC Spring), IEEE, 2017, pp. 1-5.

[40] L. Zhang, A. Ijaz, P. Xiao, A. Quddus, R. Tafazolli, Subband filtered multi-carrier sys-tems for multi-service wireless communications, IEEE Trans. Wirel.commun. 16 (3) (2017) 1893-1907.

[41] L. Zhang, A. Ijaz, P. Xiao, M.M. Molu, R. Tafazolli, Filtered OFDM systems, algo-rithms, and performance analysis for 5G and beyond, IEEE Trans. Commun. 66 (3) (2017) 1205-1218.

[42] F. Di Stasio, M. Mondin, F. Daneshgaran, Multirate 5G downlink performance com-parison for F-OFDM and W-OFDM schemes with different numerologies, in: 2018 International Symposium on Networks, Computers and Communications (ISNCC), IEEE, 2018, pp. 1-6.

[43] X. Zhang, M. Jia, L. Chen, J. Ma, J. Qiu, Filtered-OFDM-enabler for flexible wave-form in the 5th generation cellular networks, in: 2015 IEEE Global Communications Conference (GLOBECOM), IEEE, 2015, pp. 1-6.

[44] J.F. de Valgas, D. Martín-Sacristán, J. Monserrat, 5G new radio numerologies and their impact on v2x communications, Waves, Univesitat Politecnica de Valencia, 2018, pp. 15-22.

[45] S. Pratschner, B. Tahir, L. Marijanovic, M. Mussbah, K. Kirev, R. Nissel, S. Schwarz, M. Rupp, Versatile mobile communications simulation: the Vienna 5G link level sim-ulator, EURASIP J. Wirel.commun. Netw. 2018 ( 2018) 1-17.

[46] A.F. Demir, M. Elkourdi, M. Ibrahim, H. Arslan, Waveform design for 5G and beyond, arXiv preprint, arXiv:1902.05999.

[47] B. Farhang-Boroujeny, OFDM versus filter bank multicarrier, IEEE Signal Process. Mag. 28 (3) (2011) 92-112.

[48] R. Zayani, Y. Medjahdi, H. Shaiek, D. Roviras, WOLA-OFDM: a potential candidate for asynchronous 5G, in: 2016 IEEE Globecom Workshops (GC Wkshps), IEEE, 2016, pp. 1-5.

[49] Q. Incorporated,Waveform candidates 3GPP TSG-RAN WG1 #84bR1-162199, available online at: https://www.3gpp.org/dynareport?code=TDocExMtg--R1-84b--31661.htm, 2016. (Accessed 27 September 2024).

[50] X. Zhang, L. Chen, J. Qiu, J. Abdoli, On the waveform for 5G, IEEE Commun. Mag. 54 (11) (2016) 74-80.

[51] V. Vakilian, T. Wild, F. Schaich, S. ten Brink, J.-F. Frigon, Universal-filtered multi-carrier technique for wireless systems beyond LTE, in: 2013 IEEE Globecom Work-shops (GC Wkshps), IEEE, 2013, pp. 223-228.

[52] F. Schaich, T. Wild, Y. Chen, Waveform contenders for 5G-suitability for short packet and low latency transmissions, in: 2014 IEEE 79th Vehicular Technology Conference (VTC Spring), IEEE, 2014, pp. 1-5.

[53] T. Wild, F. Schaich, Y. Chen, 5G air interface design based on universal filtered (UF-)OFDM, in: 2014 19th International Conference on Digital Signal Processing, IEEE, 2014, pp. 699-704.

[54] M.I. Al-Rayif, H.E. Seleem, A.M. Ragheb, S.A. Alshebeili, Papr reduction in UFMC for 5G cellular systems, Electronics 9 (9) (2020) 1404.

[55] F. Schaich, T. Wild, Waveform contenders for 5G—OFDM vs. FBMC vs. UFMC, in: 2014 6th International Symposium on Communications, Control and Signal Process-ing (ISCCSP), IEEE, 2014, pp. 457-460.

[56] S. Venkatesan, R.A. Valenzuela, OFDM for 5G: cyclic prefix versus zero postfix, and filtering versus windowing, in: 2016 IEEE International Conference on Communica-tions (ICC), IEEE, 2016, pp. 1-5.

[57] A. Hammoodi, L. Audah, M.A. Taher,Green coexistence for 5G waveform candidates: a review, IEEE Access 7 (2019) 10103-10126.

[58] R.-A. Pitaval, B.M. Popović, Filtered-prefix OFDM, IEEE Commun. Lett. 23 (1) (2018) 28-31.

[59] J. Abdoli, M. Jia, J. Ma, Filtered OFDM: a new waveform for future wireless sys-tems, in: 2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), IEEE, 2015, pp. 66-70.

[60] Huawei, HiSilicon, F-OFDM scheme and filter design, 3GPP TSG RAN WG 1 meet-ing #85 R1-165425, available online at: https://www.3gpp.org/dynareport?code=TDocExMtg--R1-85--31662.htm, 2016. (Accessed 27 September 2024).

[61] L. Wang, S. Tao, L. Zhao, D. Zhou, Z. Liu, Y. Sun, Resource scheduling in URLLC and mMBB coexistence based on dynamic selection numerology, Wirel.commun. Mob.comput. 2024 (1) (2024) 9480388.

[62] E. Memisoglu, A.E. Duranay, H. Arslan, Numerology scheduling for papr reduction in mixed numerologies, IEEE Wirel. Commun. Lett. 10 (6) (2021) 1197-1201.

[63] A.F. Demir, H. Arslan, The impact of adaptive guards for 5G and beyond, in: 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), IEEE, 2017, pp. 1-5.

[64] Y.-G. Lim, T. Jung, K.S. Kim, C.-B. Chae, Waveform multiplexing for 5G: a concept and 3D evaluation, in: 2017 European Conference on Networks and Communications (EuCNC), IEEE, 2017, pp. 1-5.

[65] A.A. Zaidi, R. Baldemair, H. Tullberg, H. Bjorkegren, L. Sundstrom, J. Medbo, C. Kil- inc, I. Da Silva, Waveform and numerology to support 5G services and requirements, IEEE Commun. Mag. 54 (11) (2016) 90-98.

[66] B. Yang, X. Zhang, L. Zhang, A. Farhang, P. Xiao, M. Ali Imran, Windowed OFDM for mixed-numerology 5G and beyond systems, in: Radio Access Network Slicing and Virtualization for 5G Vertical Industries, 2021, pp. 43-61.

[67] M. Yang, Y. Chen, Y. Liu, L. Du, Z. Liu, CP optimization for multi-numerologies F-OFDM in MIMO system, in: 2019 28th Wireless and Optical Communications Con-ference (WOCC), IEEE, 2019, pp. 1-5.

[68] L. Miuccio, D. Panno, P. Pollara, S. Riolo, Implementation of a simulation environ-ment for multi-numerology scenarios in 5G Vienna link level simulator, in: 2021 IEEE International Conference on Computing (ICOCO), IEEE, 2021, pp. 134-139.

[69] H. Yang, A. Alphones, Z. Xiong, D. Niyato, J. Zhao, K. Wu, Artificial-intelligence-enabled intelligent 6G networks, IEEE Netw. 34 (6) (2020) 272-280.

[70] L.-H. Shen, K.-T. Feng, L. Hanzo, Five facets of 6G: research challenges and oppor-tunities, ACM Comput. Surv. 55 (11) (2023) 1-39.

[71] A. Yazar, H. Arslan, Selection of waveform parameters using machine learning for 5G and beyond, in: 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), IEEE, 2019, pp. 1-6.

[72] X. Liu, J. Zhang, J. Wei, Numerology selection for OFDM systems based on deep neural networks, arXiv preprint, arXiv:2011.04247.

[73] T. Oyedare, V.K. Shah, D.J. Jakubisin, J.H. Reed, Interference suppression using deep learning: current approaches and open challenges, IEEE Access 10 (2022) 66238-66266.

[74] H.-S. Lee, D.-E. Lee, Resource allocation in wireless networks with federated learn-ing: network adaptability and learning acceleration, ICT Express 8 (1) (2022) 31-36.

[75] A.M.H. Alibraheemi, M.N. Hindia, K. Dimyati, T. F.T.M.N. Izam, J. Yahaya, F. Qamar, Z.H. Abdullah, A survey of resource management in D2D communication for B5G networks, IEEE Access 11 (2023) 7892-7923.