Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
short-survey

Interference management in 5G and beyond networks: : A comprehensive survey

Published: 12 April 2024 Publication History
  • Get Citation Alerts
  • Abstract

    During the last decade, wireless data services have had an incredible impact on people’s lives in ways we could never have imagined. The number of mobile devices has increased exponentially and data traffic has almost doubled every year. Undoubtedly, the rate of growth will continue to be rapid with the explosive increase in demands for data rates, latency, massive connectivity, network reliability, and energy efficiency. In order to manage this level of growth and meet these requirements, the fifth-generation (5G) mobile communications network is envisioned as a revolutionary advancement combining various improvements to previous mobile generation networks and new technologies, including the use of millimeter wavebands (mm-wave), massive multiple-input multiple-output (mMIMO) multi-beam antennas, network densification, dynamic Time Division Duplex (TDD) transmission, and new waveforms with mixed numerologies. New revolutionary features including terahertz (THz) communications and the integration of Non-Terrestrial Networks (NTN) can further improve the performance and signal quality for future 6G networks. However, despite the inevitable benefits of all these key technologies, the heterogeneous and ultra-flexible structure of the 5G and beyond network brings non-orthogonality into the system and generates significant interference that needs to be handled carefully. Therefore, it is essential to design effective interference management schemes to mitigate severe and sometimes unpredictable interference in mobile networks. In this paper, we provide a comprehensive review of interference management in 5G and Beyond networks and discuss its future evolution. We start with a unified classification and a detailed explanation of the different types of interference and continue by presenting our taxonomy of existing interference management approaches. Then, after explaining interference measurement reports and signaling, we provide for each type of interference identified, an in-depth literature review and technical discussion of appropriate management schemes. We finish by discussing the main interference challenges that will be encountered in future 6G networks and by presenting insights on the suggested new interference management approaches, including useful guidelines for an AI-based solution. This review will provide a first-hand guide to the industry in determining the most relevant technology for interference management, and will also allow for consideration of future challenges and research directions.

    References

    [2]
    Shafi M., Molisch A.F., Smith P.J., Haustein T., Zhu P., De Silva P., Tufvesson F., Benjebbour A., Wunder G., 5G: A tutorial overview of standards, trials, challenges, deployment, and practice, IEEE J. Sel. Areas Commun. 35 (6) (2017) 1201–1221.
    [3]
    Dehos C., González J.L., De Domenico A., Kténas D., Dussopt L., Millimeter-wave access and backhauling: The solution to the exponential data traffic increase in 5G mobile communications systems, IEEE Commun. Mag. 52 (9) (2014) 88–95.
    [4]
    Björnson E., Larsson E.G., Marzetta T.L., Massive MIMO: Ten myths and one critical question, IEEE Commun. Mag. 54 (2) (2016) 114–123.
    [5]
    A.A. Zaidi, J. Luo, R. Gerzaguet, A. Wolfgang, R.J. Weiler, J. Vihriäla, T. Svensson, Y. Qi, H. Halbauer, Z. Zhao, P. Zetterberg, H. Miao, A Preliminary Study on Waveform Candidates for 5G Mobile Radio Communications above 6 GHz, in: IEEE 83rd Vehicular Technology Conference, 2016, pp. 1–6.
    [6]
    B. Romanous, N. Bitar, A. Imran, H. Refai, Network Densification: Challenges and Opportunities in Enabling 5G, in: IEEE International Workshop on Computer Aided Modelling and Design of Communication Links and Networks (CAMAD), 2015, pp. 129–134.
    [7]
    Ding M., López-Pérez D., Xue R., Vasilakos A.V., Chen W., On dynamic time-division-duplex transmissions for small-cell networks, IEEE Trans. Veh. Technol. 65 (11) (2016) 8933–8951.
    [8]
    Saquib N., Hossain E., Le L.B., Kim D.I., Interference management in OFDMA femtocell networks: Issues and approaches, IEEE Wirel. Commun. 19 (3) (2012) 86–95.
    [9]
    Hamza A.S., Khalifa S.S., Hamza H.S., El-Sayed K., A survey on inter-cell interference coordination techniques in OFDMA-based cellular networks, IEEE Commun. Surv. Tutor. 15 (4) (2013) 1642–1670.
    [10]
    Hossain E., Rasti M., Tabassum H., Abdelnasser A., Evolution toward 5G multi-tier cellular wireless networks: An interference management perspective, IEEE Wirel. Commun. 21 (3) (2014) 118–127.
    [11]
    Y. Zhou, L. Liu, H. Du, L. Tian, X. Wang, J. Shi, An Overview on Intercell Interference Management in Mobile Cellular Networks: From 2G to 5G, in: IEEE International Conference on Communication Systems, 2014, pp. 217–221.
    [12]
    Yassin M., AboulHassan M.A., Lahoud S., Ibrahim M., Mezher D., Cousin B., Sourour E.A., Survey of ICIC techniques in LTE networks under various mobile environment parameters, Wirel. Netw. (2015) 1–16.
    [13]
    Ali M.S., An overview on interference management in 3GPP LTE-advanced heterogeneous networks, Int. J. Future Gener. Commun. Netw. 8 (2015) 55–68.
    [14]
    Ling L., Zhou Y., Vasilakos A., Tian L., Shi J., Time-domain ICIC and optimized designs for 5G and beyond: A survey, Sci. China Inf. Sci. 62 (2019).
    [15]
    Qamar F., Hindia M., Dimyati K., Noordin K., Sadegh Amiri I., Interference management issues for the future 5G network: A review, Telecommun. Syst. 71 (2019).
    [16]
    Long Q., Chen Y., Zhang H., Lei X., Software defined 5G and 6G networks: a survey, Mob. Netw. Appl. 27 (2019).
    [17]
    Kim H., Kim J., Hong D., Dynamic TDD systems for 5G and beyond: A survey of cross-link interference mitigation, IEEE Commun. Surv. Tutor. 22 (4) (2020) 2315–2348.
    [18]
    Manap S., Dimyati K., Hindia M.N., Abu Talip M.S., Tafazolli R., Survey of radio resource management in 5G heterogeneous networks, IEEE Access 8 (2020) 131202–131223.
    [19]
    Sadjina S., Motz C., Paireder T., Huemer M., Pretl H., A survey of self-interference in LTE-advanced and 5G new radio wireless transceivers, IEEE Trans. Microw. Theory Tech. 68 (3) (2020) 1118–1131.
    [20]
    Akhtar T., Tselios C., Politis I., Radio resource management: Approaches and implementations from 4G to 5G and beyond, 2020, URL: https://doi.org/10.1007/s11276-020-02479-w.
    [21]
    Motz C., Paireder T., Pretl H., Huemer M., A survey on self-interference cancellation in mobile LTE-a/5G FDD transceivers, IEEE Trans. Circuits Syst. II 68 (3) (2021) 823–829.
    [22]
    Xu Y., Gui G., Gacanin H., Adachi F., A survey on resource allocation for 5G heterogeneous networks: Current research, future trends, and challenges, IEEE Commun. Surv. Tutor. 23 (2) (2021) 668–695.
    [23]
    Siddiqui M.U.A., Qamar F., Ahmed F., Nguyen Q.N., Hassan R., Interference management in 5G and beyond network: Requirements, challenges and future directions, IEEE Access 9 (2021) 68932–68965.
    [24]
    Kazmi S.H.A., Qamar F., Hassan R., Nisar K., Routing-based interference mitigation in SDN enabled beyond 5G communication networks: A comprehensive survey, IEEE Access 11 (2023) 4023–4041.
    [25]
    Pons M., Valenzuela E., Rodríguez B., Nolazco-Flores J.A., Del-Valle-Soto C., Utilization of 5G technologies in IoT applications: Current limitations by interference and network optimization difficulties - A review, Sensors 23 (8) (2023).
    [26]
    A. Gopalasingham, L. Roullet, N. Trabelsi, C. Chen, A. Hebbar, E. Bizouarn, Generalized Software Defined Network Platform for Radio Access Networks, in: IEEE Annual Consumer Communications & Networking Conference, 2016, pp. 626–629.
    [27]
    Rinaldi F., Raschellà A., Pizzi S., 5G NR system design: A concise survey of key features and capabilities, Wirel. Netw. 27 (2021) 5173–5188.
    [28]
    Ahmadi S., Introduction and background, in: Ahmadi S. (Ed.), 5G NR, Academic Press, 2019.
    [29]
    3GPP Technical Specification Group Radio Access Network S., TS 38.101-3/ NR; User Equipment (UE) Radio Transmission and Reception; Part 3: Range 1 and Range 2 Interworking Operation with Other Radios, fifteenth ed., 3GPP, 2018.
    [30]
    3GPP Technical Specification Group Radio Access Network S., TS 38.101-1/ NR; User Equipment (UE) Radio Transmission and Reception; Part 1: Range 1 Standalone, fifteenth ed., 3GPP, 2018.
    [31]
    Lin X., Yu D., Wiemann H., A primer on bandwidth parts in 5G new radio, 2021, pp. 357–370.
    [32]
    López-Pérez D., Güvenç I., de la Roche G., Kountouris M., Quek T.Q.S., Zhang J., Enhanced intercell interference coordination challenges in heterogeneous networks, IEEE Wirel. Commun. 18 (3) (2011) 22–30.
    [33]
    Wei L., Hu R., Qian Y., Wu G., Key elements to enable millimeter wave communications for 5G wireless systems, IEEE Wirel. Commun. 21 (2014) 136–143.
    [34]
    Chen Y., Ding C., Jia Y., Liu Y., Antenna/propagation domain self-interference cancellation (SIC) for in-band full-duplex wireless communication systems, Sensors 22 (5) (2022).
    [35]
    Hong Z., Zhang L., Li W., Wu Y., Zhu Z., Park S., Ahn S., Kwon S., Hur N., Iradier E., Montalban J., Angueira P., Frequency-domain RF self-interference cancellation for in-band full-duplex communications, IEEE Trans. Wireless Commun. (2022).
    [36]
    Demir A.F., Elkourdi M., Ibrahim M., Arslan H., Waveform design for 5G and beyond, in: 5G Networks, John Wiley & Sons, Ltd, 2018, pp. 51–76.
    [37]
    Boutiba K., Bagaa M., Ksentini A., Radio resource management in multi-numerology 5G new radio featuring network slicing, 2022, pp. 359–364.
    [38]
    Choi J., Kim B., Lee K., Hong D., A transceiver design for spectrum sharing in mixed numerology environments, IEEE Trans. Wireless Commun. 18 (5) (2019) 2707–2721.
    [39]
    Dahlman E., Parkvall S., Skold J., 5G NR: The Next Generation Wireless Access Technology, Academic Press, 2020.
    [40]
    Son H., Kwon G., Park H., Park J.S., Massive MIMO precoding for interference-free multi-numerology systems, IEEE Trans. Veh. Technol. 71 (9) (2022) 9765–9780.
    [41]
    Dai L., Wang B., Ding Z., Wang Z., Chen S., Hanzo L., A survey of non-orthogonal multiple access for 5G, IEEE Commun. Surv. Tutor. 20 (3) (2018) 2294–2323.
    [42]
    Chen X., Ng D.W.K., Yu W., Larsson E.G., Al-Dhahir N., Schober R., Massive access for 5G and beyond, IEEE J. Sel. Areas Commun. 39 (3) (2021) 615–637.
    [43]
    Mao Y., Dizdar O., Clerckx B., Schober R., Popovski P., Poor H.V., Rate-splitting multiple access: Fundamentals, survey, and future research trends, IEEE Commun. Surv. Tutor. (2022).
    [44]
    Conceição F., Gomes M., Silva V., Dinis R., Silva A., Castanheira D., A survey of candidate waveforms for beyond 5G systems, Electronics 10 (1) (2021).
    [45]
    J.B. Caro, J. Ansari, A.R. Sayyed, P. de Bruin, J. Sachs, N. König, R.H. Schmitt, Empirical study on 5G NR Adjacent Channel Coexistence, in: IEEE Wireless Communications and Networking Conference (WCNC), 2023, pp. 1–6.
    [46]
    Kim R., Kim Y., Yu N.Y., Kim S.-J., Lim H., Online learning-based downlink transmission coordination in ultra-dense millimeter wave heterogeneous networks, IEEE Trans. Wireless Commun. 18 (4) (2019) 2200–2214.
    [47]
    M. Elsayed, K. Shimotakahara, M. Erol-Kantarci, Machine Learning-based Inter-Beam Inter-Cell Interference Mitigation in mmWave, in: IEEE International Conference on Communications (ICC), 2020, pp. 1–6.
    [48]
    Bechta K., Kelner J., Ziółkowski C., Nowosielski L., Inter-beam co-channel downlink and uplink interference for 5G new radio in mm-wave bands, Sensors 21 (2021) 793.
    [49]
    Busari S.A., Huq K.M.S., Mumtaz S., Dai L., Rodriguez J., Millimeter-wave massive MIMO communication for future wireless systems: A survey, IEEE Commun. Surv. Tutor. 20 (2) (2018) 836–869.
    [50]
    Kim R., Kim Y., Lim H., Inter-BS interference-aware transmission coordination for millimeter wave networks, IEEE Wireless Commun. Lett. 6 (3) (2017) 350–353.
    [51]
    Sha Z., Wang Z., Least pair-wise collision beam schedule for mmwave inter-cell interference suppression, IEEE Trans. Wireless Commun. 18 (9) (2019) 4436–4449.
    [52]
    3GPP Technical Specification Group Radio Access Network Z., TS 36.211: Evolved Universal Terrestrial Radio Access (E-UTRA); Physical Channels and Modulation; Rel.16, sixteenth ed., 3GPP, 2020.
    [53]
    Shen Z., Khoryaev A., Eriksson E., Pan X., Dynamic uplink-downlink configuration and interference management in TD-LTE, IEEE Commun. Mag. 50 (11) (2012) 51–59.
    [54]
    Dahlman E., Parkvall S., Sköld J., Chapter 21 - interference handling in TDD networks, in: Dahlman E., Parkvall S., Sköld J. (Eds.), 5G NR (Second Edition), Academic Press, 2021, pp. 433–442.
    [55]
    3GPP Technical Specification Group Radio Access Network E., TR 38.866: Study on Remote Interference Management for NR (Release 16), sixteenth ed., 3GPP, 2019.
    [56]
    Zhang H., Zhou T., Xu T., Hu H., Remote interference discrimination testbed employing AI ensemble algorithms for 6G TDD networks, Sensors 23 (4) (2023).
    [57]
    3GPP Technical Specification Group Radio Access Network H., TR 38.828: Cross Link Interference (CLI) Handling and Remote Interference Management (RIM) for NR; (Release 16), sixteenth ed., 3GPP, 2019.
    [58]
    Hong S., Brand J., Choi J.I., Jain M., Mehlman J., Katti S., Levis P., Applications of self-interference cancellation in 5G and beyond, IEEE Commun. Mag. 52 (2) (2014) 114–121.
    [59]
    Zhang J., He F., Li W., Li Y., Wang Q., Ge S., Xing J., Liu H., Li Y., Meng J., Self-interference cancellation: A comprehensive review from circuits and fields perspectives, Electronics 11 (2) (2022).
    [60]
    A. Ichkov, P. Mähönen, L. Simić, Interference-Aware User Association and Beam Pair Link Allocation in mm-Wave Cellular Networks, in: IEEE Wireless Communications and Networking Conference (WCNC), 2023, pp. 1–7.
    [61]
    E. Kim, J. Kwak, S. Chong, Exception of Dominant Interfering Beam: Low Complex Beam Scheduling in mmwave Networks, in: IEEE Wireless Communications and Networking Conference, 2020, pp. 1–6.
    [62]
    Bechta K., Ziółkowski C., Kelner J., Nowosielski L., Modeling of downlink interference in massive MIMO 5G macro-cell, Sensors 21 (2021) 597.
    [63]
    L. Afeef, H. Arslan, Beam Squint Effect in Multi-Beam mmWave Massive MIMO Systems, in: IEEE 96th Vehicular Technology Conference, 2022, pp. 1–5.
    [64]
    Hong J., Yoon P., Ahn S., Cho Y., Na J., Kwak J., Three steps toward low-complexity: Practical interference management in NOMA-based mmwave networks, IEEE Access 10 (2022) 128366–128379.
    [65]
    Zaidi A.A., Baldemair R., Tullberg H., Bjorkegren H., Sundstrom L., Medbo J., Kilinc C., Da Silva I., Waveform and numerology to support 5G services and requirements, IEEE Commun. Mag. 54 (11) (2016) 90–98.
    [66]
    Zhang X., Zhang L., Xiao P., Ma D., Wei J., Xin Y., Mixed numerologies interference analysis and inter-numerology interference cancellation for windowed OFDM systems, IEEE Trans. Veh. Technol. 67 (8) (2018) 7047–7061.
    [67]
    Marijanović L., Schwarz S., Rupp M., Multiplexing services in 5G and beyond: Optimal resource allocation based on mixed numerology and mini-slots, IEEE Access 8 (2020) 209537–209555.
    [68]
    Yazar A., Pekoz B., Arslan H., Flexible multi-numerology systems for 5G new radio, 2018.
    [69]
    J. Vihriälä, A.A. Zaidi, V. Venkatasubramanian, N. He, E. Tiirola, J. Medbo, E. Lähetkangas, K. Werner, K. Pajukoski, A. Cedergren, R. Baldemair, Numerology and Frame Structure for 5G Radio Access, in: IEEE Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 2016, pp. 1–5.
    [70]
    A.B. Kihero, M.S.J. Solaija, A. Yazar, H. Arslan, Inter-Numerology Interference Analysis for 5G and Beyond, in: IEEE Globecom Workshops (GC Wkshps), 2018, pp. 1–6.
    [71]
    Sreedhar T.V.S., Mehta N.B., Inter-numerology interference in 5G new radio: Analysis and bounds for time-varying fading channels, in: IEEE International Conference on Communications, 2022, pp. 4818–4823.
    [72]
    Kebede T., Wondie Y., Steinbrunn J., Kassa H.B., Kornegay K.T., Precoding and beamforming techniques in mmwave-massive MIMO: Performance assessment, IEEE Access 10 (2022) 16365–16387.
    [73]
    Albreem M.A., Habbash A.H.A., Abu-Hudrouss A.M., Ikki S.S., Overview of precoding techniques for massive MIMO, IEEE Access 9 (2021) 60764–60801.
    [74]
    Zhao N., Yu F.R., Jin M., Yan Q., Leung V.C.M., Interference alignment and its applications: A survey, research issues, and challenges, IEEE Commun. Surv. Tutor. 18 (3) (2016) 1779–1803.
    [75]
    Narayanasamy I., L. S J., A survey on successive interference cancellation schemes in non-orthogonal multiple access for future radio access, Wirel. Pers. Commun. 120 (2021) 1–22.
    [76]
    Manglayev T., Kizilirmak R., Kho Y.H., Comparison of parallel and successive interference cancellation for non-orthogonal multiple access, 2018, pp. 74–77.
    [77]
    Moriyama M., Kurosawa A., Matsuda T., Matsumura T., A study of parallel interference cancellation combined with successive interference cancellation for UL-NOMA systems, 2021, pp. 1–6.
    [78]
    Wei Z., Masouros C., Wong K.-K., Kang X., Multi-cell interference exploitation: Enhancing the power efficiency in cell coordination, IEEE Trans. Wireless Commun. 19 (1) (2020) 547–562.
    [79]
    Lavdas S., Gkonis P.K., Zinonos Z., Trakadas P., Sarakis L., Papadopoulos K., A machine learning adaptive beamforming framework for 5G millimeter wave massive MIMO multicellular networks, IEEE Access 10 (2022) 91597–91609.
    [80]
    Li A., Spano D., Krivochiza J., Domouchtsidis S., Tsinos C.G., Masouros C., Chatzinotas S., Li Y., Vucetic B., Ottersten B., A tutorial on interference exploitation via symbol-level precoding: Overview, state-of-the-art and future directions, IEEE Commun. Surv. Tutor. 22 (2) (2020) 796–839.
    [81]
    Bosisio R., Spagnolini U., Interference coordination vs. Interference randomization in multicell 3GPP LTE system, in: IEEE Wireless Communications and Networking Conference, IEEE, 2008, pp. 824–829.
    [82]
    T. Novlan, J.G. Andrews, I. Sohn, R.K. Ganti, A. Ghosh, Comparison of Fractional Frequency Reuse Approaches in the OFDMA Cellular Downlink, in: IEEE Global Telecommunications Conference, 2010, pp. 1–5.
    [83]
    Y. Lan, A. Benjebbour, A. Li, A. Harada, Efficient and Dynamic Fractional Frequency Reuse for Downlink Non-Orthogonal Multiple Access, in: IEEE Vehicular Technology Conference, 2014, pp. 1–5.
    [84]
    Sun S., Gao Q., Peng Y., Wang Y., Song L., Interference management through CoMP in 3GPP LTE-advanced networks, IEEE Wirel. Commun. 20 (1) (2013) 59–66.
    [85]
    M. Hoffmann, P. Kryszkiewicz, Radio Environment Map and Deep Q-Learning for 5G Dynamic Point Blanking, in: 2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 2022, pp. 1–3.
    [86]
    Gulia S., Ahmad A., Singh S., Gupta M., Interference management in backhaul constrained 5G HetNets through coordinated multipoint, Comput. Electr. Eng. 100 (2022).
    [87]
    Alba A.M., Janardhanan S., Kellerer W., Enabling dynamically centralized RAN architectures in 5G and beyond, IEEE Trans. Netw. Serv. Manag. 18 (3) (2021) 3509–3526.
    [88]
    Rahman M., Yanikomeroglu H., Enhancing cell-edge performance: A downlink dynamic interference avoidance scheme with inter-cell coordination, IEEE Trans. Wireless Commun. 9 (4) (2010) 1414–1425.
    [89]
    Oo T.Z., Tran N.H., Saad W., Niyato D., Han Z., Hong C.S., Offloading in HetNet: A coordination of interference mitigation, user association, and resource allocation, IEEE Trans. Mob. Comput. 16 (8) (2017) 2276–2291.
    [90]
    D.T. Ngo, L.B. Le, T. Le-Ngoc, E. Hossain, D.I. Kim, Distributed Interference Management in Femtocell Networks, in: IEEE Vehicular Technology Conference (VTC Fall), 2011, pp. 1–5.
    [91]
    Ahuja K., Xiao Y., van der Schaar M., Distributed interference management policies for heterogeneous small cell networks, IEEE J. Sel. Areas Commun. 33 (6) (2015) 1112–1126.
    [92]
    Trabelsi N., Chen C., El Azouzi R., Roullet L., Altman E., User association and resource allocation optimization in LTE cellular networks, IEEE Trans. Netw. Serv. Manag. 14 (2) (2017) 429–440.
    [93]
    Sciancalepore V., Filippini I., Mancuso V., Capone A., Banchs A., A semi-distributed mechanism for inter-cell interference coordination exploiting the ABSF paradigm, in: IEEE SECON, 2015, pp. 193–201.
    [94]
    A. Adhikary, E.A. Safadi, G. Caire, Massive MIMO and Inter-Tier Interference Coordination, in: Information Theory and Applications Workshop (ITA), 2014, pp. 1–10.
    [95]
    S. Chaudhari, H. Kwon, Machine Learning based Interference Whitening in 5G NR MIMO Receiver, in: IEEE 95th Vehicular Technology Conference, 2022, pp. 1–6.
    [96]
    S. Akoum, C.S. Chen, M. Debbah, R.W. Heath, Data sharing coordination and blind interference alignment for cellular networks, in: IEEE Global Communications Conference, 2012, pp. 4273–4277.
    [97]
    Yu L., Liu Z., Wen M., Cai D., Dang S., Wang Y., Xiao P., Sparse code multiple access for 6G wireless communication networks: Recent advances and future directions, IEEE Commun. Stand. Mag. 5 (2) (2021) 92–99.
    [98]
    Xiao J., Yang C., Anpalagan A., Ni Q., Guizani M., Joint interference management in ultra-dense small-cell networks: A multi-domain coordination perspective, IEEE Trans. Commun. 66 (11) (2018) 5470–5481.
    [99]
    Z. Lin, J. Li, Y. Zheng, N.V. Irukulapati, H. Wang, H. Sahlin, SS/PBCH Block Design in 5G New Radio (NR), in: IEEE Globecom Workshops (GC Wkshps), 2018, pp. 1–6.
    [100]
    H. Elgendi, M. Mäenpää, T. Levanen, T. Ihalainen, S. Nielsen, M. Valkama, Interference Measurement Methods in 5G NR: Principles and Performance, in: 16th International Symposium on Wireless Communication Systems (ISWCS), 2019, pp. 233–238.
    [101]
    Mishra A., Mao Y., Thomas C.K., Sanguinetti L., Clerckx B., Mitigating intra-cell pilot contamination in massive MIMO: A rate splitting approach, IEEE Trans. Wireless Commun. 22 (5) (2023) 3472–3487.
    [102]
    M. Boulouird, A. Riadi, M.M. Hassani, Pilot Contamination in Multi-Cell Massive-MIMO Systems in 5G Wireless Communications, in: International Conference on Electrical and Information Technologies (ICEIT), 2017, pp. 1–4.
    [103]
    Soret B., Domenico A.D., Bazzi S., Mahmood N.H., Pedersen K.I., Interference coordination for 5G new radio, IEEE Wirel. Commun. 25 (3) (2018) 131–137.
    [104]
    Elijah O., Leow C.Y., Rahman T.A., Nunoo S., Iliya S.Z., A comprehensive survey of pilot contamination in massive MIMO—5G system, IEEE Commun. Surv. Tutor. 18 (2) (2016) 905–923.
    [105]
    Dreifuerst R.M., Heath R.W. Jr., Massive MIMO in 5G: How beamforming, codebooks, and feedback enable larger arrays, 2023, arXiv:2301.13390.
    [106]
    Qin Z., Yin H., A review of codebooks for CSI feedback in 5G new radio and beyond, 2023, arXiv:2302.09222.
    [107]
    Liu F., Zhou X., Li G.Y., Atmospheric ducting effect in wireless communications: Challenges and opportunities, J. Commun. Inf. Netw. 6 (2) (2021) 101–109.
    [108]
    Lopez-Perez D., Chu X., Guvenc I., On the expanded region of picocells in heterogeneous networks, IEEE J. Sel. Top. Sign. Proces. 6 (3) (2012) 281–294.
    [109]
    Lopez-Perez D., Chu X., Vasilakos A.V., Claussen H., Power minimization based resource allocation for interference mitigation in OFDMA femtocell networks, IEEE J. Sel. Areas Commun. 32 (2) (2014) 333–344.
    [110]
    S. Saeidian, S. Tayamon, E. Ghadimi, Downlink Power Control in Dense 5G Radio Access Networks Through Deep Reinforcement Learning, in: IEEE International Conference on Communications (ICC), 2020, pp. 1–6.
    [111]
    Deb S., Monogioudis P., Learning-based uplink interference management in 4G LTE cellular systems, IEEE/ACM Trans. Netw. 23 (2) (2015) 398–411.
    [112]
    Altay C., Koca M., Design and analysis of energy efficient inter-tier interference coordination in heterogeneous networks, Wirel. Netw. 27 (2021) 1–16.
    [113]
    Ayala-Romero J.A., Alcaraz J.J., Zanella A., Zorzi M., Online learning for energy saving and interference coordination in HetNets, IEEE J. Sel. Areas Commun. 37 (6) (2019) 1374–1388.
    [114]
    Khan S.A., Kavak A., Aldirmaz Çolak S., Küçük K., A novel fractional frequency reuse scheme for interference management in LTE-a HetNets, IEEE Access 7 (2019) 109662–109672.
    [115]
    Q. Zeng, X. Liu, Multi-Priority Based Interference Mitigation Scheme for HetNets Uplinks: A Frequency Hopping Method, in: International Symposium on Networks, Computers and Communications (ISNCC), 2019, pp. 1–6.
    [116]
    Yan M., Yang J., Chen K., Sun Y., Feng G., Self-imitation learning-based inter-cell interference coordination in autonomous HetNets, IEEE Trans. Netw. Serv. Manag. 18 (4) (2021) 4589–4601.
    [117]
    Vu K., Bennis M., Samarakoon S., Debbah m., Latva-aho M., Joint load balancing and interference mitigation in 5G heterogeneous networks, IEEE Trans. Wireless Commun. 16 (2017) 6032–6046.
    [118]
    K. Park, H. Kim, D. Kwon, H. Kim, H. Kang, M.-H. Shin, J. Kim, W. Hur, The Reinforcement Learning based Interference Whitening Scheme for 5G, in: IEEE Vehicular Technology Conference, 2021, pp. 1–5.
    [119]
    Y. Li, X. Lei, P. Fan, D. Chen, An SCMA-based Uplink Inter-Cell Interference Cancellation Technique for 5G Wireless Systems, in: 2015 International Conference on Wireless Communications & Signal Processing (WCSP), 2015, pp. 1–5.
    [120]
    Kaneko M., Nakano T., Hayashi K., Kamenosono T., Sakai H., Distributed resource allocation with local CSI overhearing and scheduling prediction for OFDMA heterogeneous networks, IEEE Trans. Veh. Technol. 66 (2) (2017) 1186–1199.
    [121]
    Liu Y., Chen C.S., Sung C.W., Singh C., A game theoretic distributed algorithm for FeICIC optimization in LTE-A HetNets, IEEE/ACM Trans. Netw. 25 (6) (2017) 3500–3513.
    [122]
    Osama M., El Ramly S., Abdelhamid B., Interference mitigation and power minimization in 5G heterogeneous networks, Electronics 10 (14) (2021) 1723.
    [123]
    Costa Neto F.H., Araújo D.C., Mota M.P., Maciel T.F., de Almeida A.L.F., Uplink power control framework based on reinforcement learning for 5g networks, IEEE Trans. Veh. Technol. 70 (6) (2021) 5734–5748.
    [124]
    Mismar F., Evans B., Alkhateeb A., Deep reinforcement learning for 5G networks: Joint beamforming, power control, and interference coordination, 2019.
    [125]
    Y. Song, S. Xu, Beam Management Based Multi-cell Interference Suppression for Millimeter Wave Communications, in: IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), 2021, pp. 1–5.
    [126]
    Sha Z., Wang Z., Chen S., Hanzo L., Graph theory based beam scheduling for inter-cell interference avoidance in MmWave cellular networks, IEEE Trans. Veh. Technol. 69 (4) (2020) 3929–3942.
    [127]
    Maeng S.J., Park S.H., Cho Y.S., Hybrid beamforming for reduction of inter-beam interference in millimeter-wave cellular systems, Sensors 18 (2) (2018).
    [128]
    S.J. Maeng, S.H. Park, S.H. Moon, Y.S. Cho, Inter-Beam Interference Reduction Technique for Millimeter-Wave Cellular Systems Using Hybrid Beamforming, in: IEEE 88th Vehicular Technology Conference (VTC-Fall), 2018, pp. 1–5.
    [129]
    Zhang Y., Osman T., Alkhateeb A., Online beam learning with interference nulling for millimeter wave MIMO systems, 2022, arXiv:2209.04509.
    [130]
    Zhang Y., Osman T., Alkhateeb A., A digital twin assisted framework for interference nulling in millimeter wave MIMO systems, 2023, arXiv:2301.13311.
    [131]
    S. Li, M. Derakhshani, C. Chen, S. Lambotharan, Outage Probability Analysis for Two-Antennas MISO-NOMA Downlink with Statistical CSI, in: IEEE Global Communications Conference, 2019, pp. 1–6.
    [132]
    Q. Chen, H. Zhao, L. Li, H. Long, J. Wang, X. Hou, A Closed-Loop UL Power Control Scheme for Interference Mitigation in Dynamic TD-LTE Systems, in: IEEE 81st Vehicular Technology Conference, 2015, pp. 1–5.
    [133]
    K. Hiltunen, M. Matinmikko-Blue, Interference Control Mechanism for 5G Indoor Micro Operators Utilizing Dynamic TDD, in: IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2018, pp. 1–7.
    [134]
    H. Takahashi, K. Yokomakura, K. Imamura, A Transmit Power Control Based Interference Mitigation Scheme for Small Cell Networks Using Dynamic TDD in LTE-Advanced Systems, in: IEEE 79th Vehicular Technology Conference, 2014, pp. 1–5.
    [135]
    Y. Wang, M. Tao, Dynamic Uplink/Downlink Configuration Using Q-Learning in Femtocell Networks, in: IEEE/CIC International Conference on Communications in China (ICCC), 2014, pp. 53–58.
    [136]
    Tang F., Zhou Y., Kato N., Deep reinforcement learning for dynamic uplink/downlink resource allocation in high mobility 5G HetNet, IEEE J. Sel. Areas Commun. 38 (12) (2020) 2773–2782.
    [137]
    Ardah K., Fodor G., Silva Y.C.B., Freitas W.C., Cavalcanti F.R.P., A novel cell reconfiguration technique for dynamic TDD wireless networks, IEEE Wireless Commun. Lett. 7 (3) (2018) 320–323.
    [138]
    Lee K., Park Y., Na M., Wang H., Hong D., Aligned reverse frame structure for interference mitigation in dynamic TDD systems, IEEE Trans. Wireless Commun. 16 (10) (2017) 6967–6978.
    [139]
    F. Sun, Y. Zhao, H. Sun, Centralized Cell Cluster Interference Mitigation for Dynamic TDD DL/UL Configuration with Traffic Adaptation for HTN Networks, in: IEEE 82nd Vehicular Technology Conference, 2015, pp. 1–5.
    [140]
    J. Nasreddine, S. El Hajj Hassan, Interference Mitigation and Traffic Adaptation Using Cell Clustering for LTE-TDD systems, in: IEEE International Multidisciplinary Conference on Engineering Technology (IMCET), 2016, pp. 155–159.
    [141]
    Long Y., Chen Z., Interference-cancelled asymmetric traffic cellular networks: Dynamic TDD meets massive MIMO, IEEE Trans. Veh. Technol. 67 (10) (2018) 9785–9800.
    [142]
    Guimarães F.R.V., Fodor G., Freitas W.C., Silva Y.C.B., Pricing-based distributed beamforming for dynamic time division duplexing systems, IEEE Trans. Veh. Technol. 67 (4) (2018) 3145–3157.
    [143]
    Tibhirt A., Slock D., Yuan-Wu Y., Transceiver design in dynamic TDD with reduced-rank MIMO interference channels, in: WTS, 22nd Annual Wireless Telecommunications Symposium, IEEE, Boston, United States, 2023.
    [144]
    Guo S., Hou X., Wang H., Dynamic TDD and interference management towards 5G, 2018, pp. 1–6,.
    [145]
    A.A. Esswie, K.I. Pedersen, Cross-Link Interference Suppression by Orthogonal Projector for 5G Dynamic TDD URLLC Systems, in: IEEE Wireless Communications and Networking Conference (WCNC), 2020, pp. 1–6.
    [146]
    Tan J.-S., Yang S., Meng K., Zhang J., Tang Y., Bu Y., Wang G., Lightweight machine learning for digital cross-link interference cancellation with RF chain characteristics in flexible duplex MIMO systems, IEEE Wireless Commun. Lett. (2023).
    [147]
    H. Zhang, T. Zhou, T. Xu, Y. Wang, H. Hu, FNN-Based Prediction of Wireless Channel with Atmospheric Duct, in: IEEE International Conference on Communications, 2021, pp. 1–6.
    [148]
    Yang K., Guo X., Wu Z.-S., Wu J., Wu T., Zhao K., Qu T., Linghu L., Using multi-source real landform data to predict and analyze intercity remote interference of 5G communication with ducting and troposcatter effects, Remote Sens. 14 (2022) 4515.
    [149]
    Zhou T., Sun T., Hu H., Xu H., Yang Y., Harjula I., Koucheryavy Y., Analysis and prediction of 100 km-scale atmospheric duct interference in TD-LTE networks, J. Commun. Inf. Netw. 2 (1) (2017) 66–80.
    [150]
    T. Sun, T. Zhou, H. Xu, Y. Yang, A Random Forest-Based Prediction Method of Atmospheric Duct Interference in TD-LTE Networks, in: IEEE Globecom Workshops (GC Wkshps), 2017, pp. 1–6.
    [151]
    J.-H. Shen, J.-X. Liu, J.-L. Zuo, W.-B. Ding, A. Shen, Y. Fang, Y. Zhang, X.-D. Wang, F. Luo, Recognition and Optimization of Atmospheric Duct in TD-LTE System Based on Convolutional Neural Network, in: IEEE Intl Conf on Parallel Distributed Processing with Applications, Big Data Cloud Computing, Sustainable Computing Communications, Social Computing Networking, 2020, pp. 1389–1393.
    [152]
    Peralta E., Levanen T., Mäenpää M., Yuk Y., Pedersen K., Nielsen S., Valkama M., Remote interference management in 5G new radio: Methods and performance, EURASIP J. Wireless Commun. Networking (45) (2021).
    [153]
    Y. Wang, Y. Chen, T. Zhou, H. Hu, A Traceable Approach to Remote Interference Management for New Radio, in: IEEE International Conference on Communications Workshops (ICC Workshops), 2019, pp. 1–6.
    [154]
    S. Ku, K. Lee, C. Lee, Interference Mitigation between Remote Base Stations, in: International Conference on Electronics, Information, and Communication (ICEIC), 2023, pp. 1–4.
    [155]
    Boyd S., Parikh N., Chu E., Peleato B., Eckstein J., Distributed optimization and statistical learning via the alternating direction method of multipliers, Found. Trends Mach. Learn. 3 (2011) 1–122.
    [156]
    Shen A., Zhang Y., Guo B., Wang G., Gao Y., Liu J., Liu D., Liu Y., Hu X., Xie T., Monitoring and avoidance of atmospheric duct on interference optimization in TD-LTE system, in: Sun S., Chen N., Tian T. (Eds.), Signal and Information Processing, Networking and Computers, Springer Singapore, Singapore, 2018, pp. 36–45.
    [157]
    Guo C., Liu F., Chen S., Feng C., Zeng Z., Advances on exploiting polarization in wireless communications: Channels, technologies, and applications, IEEE Commun. Surv. Tutor. 19 (1) (2017) 125–166.
    [158]
    Askar R., Chung J., Guo Z., Ko H., Keusgen W., Haustein T., Interference handling challenges toward full duplex evolution in 5G and beyond cellular networks, IEEE Wirel. Commun. 28 (1) (2021) 51–59.
    [159]
    Mori S., Mizutani K., Harada H., A digital self-interference cancellation scheme for in-band full-duplex-applied 5G system and its software-defined radio implementation, IEEE Open J. Veh. Technol. (2023) 1–13.
    [160]
    Luo H., Holm M., Ratnarajah T., On the performance of active analog self-interference cancellation techniques for beyond 5G systems, China Commun. 18 (10) (2021) 158–168.
    [161]
    Ahmed E., Eltawil A.M., All-digital self-interference cancellation technique for full-duplex systems, IEEE Trans. Wireless Commun. 14 (7) (2015) 3519–3532.
    [162]
    S. Mori, K. Mizutani, H. Harada, Digital Self-Interference Cancellation Scheme for Full-Duplex Cellular System in 5G, in: IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2022, pp. 1165–1170.
    [163]
    Panse V., Jain T.K., Sharma P.K., Kothari A., Digital self-interference cancellation in the era of machine learning: A comprehensive review, Phys. Commun. 50 (2022).
    [164]
    Hong Z.H., Zhang L., Li W., Wu Y., Zhu Z., Park S.-I., Ahn S., Kwon S., Hur N., Iradier E., Montalban J., Angueira P., Frequency-domain RF self-interference cancellation for in-band full-duplex communications, IEEE Trans. Wireless Commun. 22 (4) (2023) 2352–2363.
    [165]
    E. Iradier, I. Bilbao, J. Montalban, Y. Wu, L. Zhang, W. Li, Z. Hong, Analog Cancellation in ATSC 3.0 for Enabling Inter-Tower Communications Network, in: IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2021, pp. 1–6.
    [166]
    Chen T., Garikapati S., Nagulu A., Gaonkar A., Kohli M., Kadota I., Krishnaswamy H., Zussman G., A survey and quantitative evaluation of integrated circuit-based antenna interfaces and self-interference cancellers for full-duplex, IEEE Open J. Commun. Soc. 2 (2021) 1753–1776.
    [167]
    S. Garikapati, A. Gaonkar, A. Nagulu, T. Chen, G. Zussman, H. Krishnaswamy, Performance Comparison of Time-Domain and Frequency-Domain RF Self-Interference Cancellation in Full-Duplex Wireless Systems, in: Asilomar Conference on Signals, Systems, and Computers, 2020, pp. 1574–1578.
    [168]
    H. Luo, M. Holm, T. Ratnarajah, Wideband Active Analog Self-Interference Cancellation for 5G and Beyond Full-Duplex Systems, in: Asilomar Conference on Signals, Systems, and Computers, 2020, pp. 868–872.
    [169]
    Sun J.J., Chang M.P., Prucnal P.R., Demonstration of over-the-air RF self-interference cancellation using an optical system, IEEE Photonics Technol. Lett. 29 (4) (2017) 397–400.
    [170]
    Luo H., Bishnu A., Ratnarajah T., Design and analysis of in-band full-duplex private 5G networks using FR2 band, IEEE Access 9 (2021) 166886–166905.
    [171]
    Y. Cao, X. Cao, H. Seo, J. Zhou, An Integrated Full-Duplex/FDD Duplexer and Receiver Achieving 100 MHz Bandwidth 58 dB/48 dB Self-Interference Suppression Using Hybrid-Analog-Digital Autonomous Adaptation Loops, in: IEEE/MTT-S International Microwave Symposium (IMS), 2020, pp. 1203–1206.
    [172]
    Islam S.M.R., Avazov N., Dobre O.A., Kwak K.-s., Power-domain non-orthogonal multiple access (NOMA) in 5G systems: Potentials and challenges, IEEE Commun. Surv. Tutor. 19 (2) (2017) 721–742.
    [173]
    Boviz D., Chen C., Yang S., Effective design of multi-user reception and fronthaul rate allocation in 5G cloud RAN, IEEE J. Sel. Areas Commun. 35 (8) (2017) 1825–1836.
    [174]
    H. Tabassum, M.S. Ali, E. Hossain, M.J. Hossain, D.I. Kim, Uplink Vs. Downlink NOMA in Cellular Networks: Challenges and Research Directions, in: IEEE 85th Vehicular Technology Conference, 2017, pp. 1–7.
    [175]
    Fu Y., Salaün L., Sung C., Chen C., Subcarrier and power allocation for the downlink of multicarrier NOMA systems, IEEE Trans. Veh. Technol. 67 (12) (2018) 11833–11847.
    [176]
    L. Salaün, C. Chen, M. Coupechoux, Optimal Joint Subcarrier and Power Allocation in NOMA is Strongly NP-Hard, in: IEEE International Conference on Communications, 2018, pp. 1–7.
    [177]
    X. Zhang, J. Wang, J. Wang, J. Song, A Novel User Pairing in Downlink Non-Orthogonal Multiple Access, in: IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2018, pp. 1–5.
    [178]
    Mouni N.S., M. P.R., Kumar A., Upadhyay P.K., Adaptive multi-user clustering and power allocation for NOMA systems with imperfect SIC, 2022, arXiv:2203.15828.
    [179]
    N.S. Mouni, M.P. Reddy, A. Kumar, P.K. Upadhyay, Enhanced User Pairing and Power Allocation Strategies for Downlink NOMA Systems with Imperfections in SIC, in: 15th International Conference on COMmunication Systems & NETworkS (COMSNETS), 2023, pp. 457–461.
    [180]
    Santos Y.P., Silveira L.F.Q., Adaptive clustering of users in power domain NOMA, Sensors 23 (11) (2023).
    [181]
    Islam S.M.R., Zeng M., Dobre O.A., Kwak K.-S., Resource allocation for downlink NOMA systems: Key techniques and open issues, IEEE Wirel. Commun. 25 (2) (2018) 40–47.
    [182]
    Gao X., Dai L., Chen Z., Wang Z., Zhang Z., Near-optimal beam selection for beamspace MmWave massive MIMO systems, IEEE Commun. Lett. 20 (5) (2016) 1054–1057.
    [183]
    Choi J., Beam selection in mm-wave multiuser MIMO systems using compressive sensing, IEEE Trans. Commun. 63 (8) (2015) 2936–2947.
    [184]
    Feng Z., Clerckx B., Deep reinforcement learning for multi-user massive MIMO with channel aging, 2023, arXiv:2302.06853.
    [185]
    S.S. Nair, S. Bhashyam, Robust Nonlinear Precoding in MU-MIMO using Partial Interfering Beam Feedback, in: IEEE Wireless Communications and Networking Conference, 2023, pp. 1–6.
    [186]
    M. Elsayed, M. Erol-Kantarci, Radio Resource and Beam Management in 5G mmWave Using Clustering and Deep Reinforcement Learning, in: IEEE Global Communications Conference, 2020, pp. 1–6.
    [187]
    R. Takahashi, H. Matsuo, F. Adachi, Joint Multilayered User Clustering and Scheduling for 5G Advanced Ultra-dense RAN, in: IEEE 92nd Vehicular Technology Conference, 2020, pp. 1–5.
    [188]
    Wang J., Zhu H., Gomes N.J., Wang J., Frequency reuse of beam allocation for multiuser massive MIMO systems, IEEE Trans. Wireless Commun. 17 (4) (2018) 2346–2359.
    [189]
    M.Y. Javed, N. Tervo, A. Pärssinen, Inter-beam Interference Reduction in Hybrid mmW Beamforming Transceivers, in: IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2018, pp. 220–224.
    [190]
    Javed M.Y., Tervo N., Leinonen M.E., Pärssinen A., Wideband inter-beam interference cancellation for mmw/sub-THz phased arrays with squint, IEEE Trans. Veh. Technol. (2023) 1–13.
    [191]
    Akbar R., Klumperink E.A.M., Tervo N., Stadius K., Rahkonen T., Pärssinen A., A wideband IF receiver chip for flexibly scalable mmwave subarray combining and interference rejection, IEEE Trans. Microw. Theory Tech. (2023) 1–16.
    [192]
    Chiu Y.-T., Liu K.-H., Beam selection and power allocation for massive connectivity in millimeter wave NOMA systems, IEEE Access 8 (2020) 53868–53882.
    [193]
    Elsayed M., Erol-Kantarci M., Yanikomeroglu H., Transfer reinforcement learning for 5G new radio mmwave networks, IEEE Trans. Wireless Commun. 20 (5) (2021) 2838–2849.
    [194]
    Wang S., Long Y., Ruby R., Fu X., Clustering and power optimization in mmwave massive MIMO–NOMA systems, Phys. Commun. 49 (2021).
    [195]
    Tang S., Ma Z., Xiao M., Hao L., Hybrid transceiver design for beamspace MIMO-NOMA in code-domain for MmWave communication using lens antenna array, IEEE J. Sel. Areas Commun. 38 (9) (2020) 2118–2127.
    [196]
    Liu Y., Zhang S., Mu X., Ding Z., Schober R., Al-Dhahir N., Hossain E., Shen X., Evolution of NOMA toward next generation multiple access (NGMA) for 6G, IEEE J. Sel. Areas Commun. 40 (4) (2022) 1037–1071.
    [197]
    Mao J., Zhang L., McWade S., Chen H., Xiao P., Characterizing inter-numerology interference in mixed-numerology OFDM systems, 2020, arXiv:2009.13348.
    [198]
    Mao J., Zhang L., Xiao P., Nikitopoulos K., Interference analysis and power allocation in the presence of mixed numerologies, IEEE Trans. Wireless Commun. 19 (8) (2020) 5188–5203.
    [199]
    Sreedhar T.V.S., Mehta N.B., Inter-numerology interference in mixed numerology OFDM systems in time-varying fading channels with phase noise, IEEE Trans. Wireless Commun. (2023).
    [200]
    Wu F., Tan R., Zhang C., Fan W., Chen X., Niyato D., Liu Y., Mixed numerology interference recognition approach for 5G NR, IEEE Wireless Commun. Lett. 10 (10) (2021) 2135–2139.
    [201]
    Demir A.F., Arslan H., Inter-numerology interference management with adaptive guards: A cross-layer approach, IEEE Access 8 (2020) 30378–30386.
    [202]
    Memisoglu E., Kihero A.B., Basar E., Arslan H., Guard band reduction for 5G and beyond multiple numerologies, IEEE Commun. Lett. 24 (3) (2020) 644–647.
    [203]
    Yang B., Zhang L., Onireti O., Xiao P., Imran M.A., Tafazolli R., Mixed-numerology signals transmission and interference cancellation for radio access network slicing, IEEE Trans. Wireless Commun. 19 (8) (2020) 5132–5147.
    [204]
    B.A. Çevikgibi, A.M. Demirtaş, T. Girici, H. Arslan, Inter-Numerology Interference Pre-Equalization for 5G Mixed-Numerology Communications, in: IEEE 95th Vehicular Technology Conference, 2022, pp. 1–6.
    [205]
    Zhang L., Ijaz A., Xiao P., Quddus A., Tafazolli R., Subband filtered multi-carrier systems for multi-service wireless communications, IEEE Trans. Wireless Commun. 16 (3) (2017) 1893–1907.
    [206]
    Cheng X., Zayani R., Shaiek H., Roviras D., Inter-numerology interference analysis and cancellation for massive MIMO-OFDM downlink systems, IEEE Access 7 (2019) 177164–177176.
    [207]
    Cheng X., Zayani R., Shaiek H., Roviras D., Analysis and cancellation of mixed-numerologies interference for massive MIMO-OFDM UL, IEEE Wireless Commun. Lett. 9 (4) (2020) 470–474.
    [208]
    Yazar A., Arslan H., Reliability enhancement in multi-numerology-based 5G new radio using INI-aware scheduling, EURASIP J. Wireless Commun. Networking 2019 (2019).
    [209]
    M. Zambianco, G. Verticale, Spectrum Allocation for Network Slices with Inter-Numerology Interference using Deep Reinforcement Learning, in: IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, 2020, pp. 1–7.
    [210]
    Zambianco M., Verticale G., Intelligent multi-branch allocation of spectrum slices for inter-numerology interference minimization, Comput. Netw. 196 (2021).
    [211]
    M. Zambianco, G. Verticale, Mixed-Numerology Interference-Aware Spectrum Allocation for eMBB and URLLC Network Slices, in: IEEE Mediterranean Communication and Computer Networking Conference (MedComNet), 2021, pp. 1–8.
    [212]
    Esmaeily A., Mendis H.V.K., Mahmoodi T., Kralevska K., Beyond 5G resource slicing with mixed-numerologies for mission critical URLLC and eMBB coexistence, IEEE Open J. Commun. Soc. 4 (2023) 727–747.
    [213]
    Kihero A.B., Solaija M.S.J., Arslan H., Inter-numerology interference for beyond 5G, IEEE Access 7 (2019) 146512–146523.
    [214]
    Yazar A., Tusha S.D., Arslan H., 6G vision: An ultra-flexible perspective, ITU J. Future Evol. Technol. 1 (1) (2020) 121–140.
    [215]
    Tataria H., Shafi M., Molisch A.F., Dohler M., Sjöland H., Tufvesson F., 6G wireless systems: Vision, requirements, challenges, insights, and opportunities, Proc. IEEE 109 (7) (2021) 1166–1199.
    [216]
    Bariah L., Mohjazi L., Muhaidat S., Sofotasios P.C., Kurt G.K., Yanikomeroglu H., Dobre O.A., A prospective look: Key enabling technologies, applications and open research topics in 6G networks, IEEE Access 8 (2020) 174792–174820.
    [217]
    Bernardos C., Uusitalo M., Anton C., Artuñedo D., Demestichas P., Fettweis G., Frascolla V., Kaloxylos A., Koumaras H., Magen J., Merino P., Norp T., Rugeland P., Tomkos I., Aguiar R., Chico J., Bourdoux A., Ayed D., Azcorra A., Zou Y., European vision for the 6G network ecosystem, 2021.
    [218]
    Chowdhury M.Z., Shahjalal M., Ahmed S., Jang Y.M., 6G wireless communication systems: Applications, requirements, technologies, challenges, and research directions, IEEE Open J. Commun. Soc. 1 (2020) 957–975.
    [219]
    Shafie A., Yang G.N., Han C., Jornet J.M., Juntti M., Kurner T., Terahertz communications for 6G and beyond wireless networks: Challenges, key advancements, and opportunities, IEEE Netw. (2022) 1–8.
    [220]
    Saad W., Bennis M., Chen M., A vision of 6G wireless systems: Applications, trends, technologies, and open research problems, IEEE Netw. 34 (3) (2020) 134–142.
    [221]
    Dogra A., Jha R.K., Jain S., A survey on beyond 5G network with the advent of 6G: Architecture and emerging technologies, IEEE Access 9 (2021) 67512–67547.
    [222]
    Jiang W., Han B., Habibi M.A., Schotten H.D., The road towards 6G: A comprehensive survey, IEEE Open J. Commun. Soc. 2 (2021) 334–366.
    [223]
    Tariq F., Khandaker M.R.A., Wong K.-K., Imran M.A., Bennis M., Debbah M., A speculative study on 6G, IEEE Wirel. Commun. 27 (4) (2020) 118–125.
    [224]
    Hassan B., Baig S., Asif M., Key technologies for ultra-reliable and low-latency communication in 6G, IEEE Commun. Stand. Mag. 5 (2) (2021) 106–113.
    [225]
    Rajatheva N., Atzeni I., Bjornson E., Bourdoux A., Buzzi S., Dore J.-B., Erkucuk S., Fuentes M., Guan K., Hu Y., Huang X., Hulkkonen J., Jornet J.M., Katz M., Nilsson R., Panayirci E., Rabie K., Rajapaksha N., Salehi M., Sarieddeen H., Svensson T., Tervo O., Tolli A., Wu Q., Xu W., White paper on broadband connectivity in 6G, 2020, URL: https://arxiv.org/abs/2004.14247.
    [226]
    Yazar A., Arslan H., A waveform parameter assignment framework for 6G with the role of machine learning, IEEE Open J. Veh. Technol. 1 (2020) 156–172.
    [227]
    Lee Y.L., Qin D., Wang L.-C., Sim G.H., 6G massive radio access networks: Key applications, requirements and challenges, IEEE Open J. Veh. Technol. 2 (2021) 54–66.
    [228]
    Berardinelli G., Mogensen P., Adeogun R.O., 6G subnetworks for life-critical communication, in: 6G Wireless Summit (6G SUMMIT), 2020, pp. 1–5.
    [229]
    Du X., Wang T., Feng Q., Ye C., Tao T., Wang L., Shi Y., Chen M., Multi-agent reinforcement learning for dynamic resource management in 6G in-x subnetworks, IEEE Trans. Wireless Commun. (2022).
    [230]
    Adeogun R., Berardinelli G., Mogensen P.E., Enhanced interference management for 6G in-x subnetworks, IEEE Access 10 (2022) 45784–45798.
    [231]
    Berardinelli G., Baracca P., Adeogun R.O., Khosravirad S.R., Schaich F., Upadhya K., Li D., Tao T., Viswanathan H., Mogensen P., Extreme communication in 6G: Vision and challenges for ‘in-X’ subnetworks, IEEE Open J. Commun. Soc. 2 (2021) 2516–2535.
    [232]
    Mu X., Wang Z., Liu Y., NOMA for integrating sensing and communications towards 6G: A multiple access perspective, 2022,. URL: https://arxiv.org/abs/2206.00377.
    [233]
    Liu F., Cui Y., Masouros C., Xu J., Han T.X., Eldar Y.C., Buzzi S., Integrated sensing and communications: Toward dual-functional wireless networks for 6G and beyond, IEEE J. Sel. Areas Commun. 40 (6) (2022) 1728–1767.
    [234]
    D.K. Pin Tan, J. He, Y. Li, A. Bayesteh, Y. Chen, P. Zhu, W. Tong, Integrated Sensing and Communication in 6G: Motivations, Use Cases, Requirements, Challenges and Future Directions, in: 2021 1st IEEE International Online Symposium on Joint Communications & Sensing (JC&S), 2021, pp. 1–6.
    [235]
    Wei Z., Qu H., Wang Y., Yuan X., Wu H., Du Y., Han K., Zhang N., Feng Z., Integrated sensing and communication signals towards 5G-a and 6G: A survey, IEEE Internet Things J. (2023).
    [236]
    Yan S., Cao X., Liu Z., Liu X., Interference management in 6G space and terrestrial integrated networks: Challenges and approaches, Intell. Converg. Netw. 1 (3) (2020) 271–280.
    [237]
    Zhu X., Jiang C., Integrated satellite-terrestrial networks toward 6G: Architectures, applications, and challenges, IEEE Internet Things J. 9 (1) (2022) 437–461.
    [238]
    Shahjalal M., Kim W., Khalid W., Moon S., Khan M., Liu S., Lim S., Kim E., Yun D.-W., Lee J., Lee W., Hwang S.-H., Kim D., Lee J.-W., Yu H., Sung Y., Jang Y.M., Enabling technologies for AI empowered 6G massive radio access networks, ICT Express (2022).
    [239]
    Peng D., He D., Li Y., Wang Z., Integrating terrestrial and satellite multibeam systems toward 6G: Techniques and challenges for interference mitigation, IEEE Wirel. Commun. 29 (1) (2022) 24–31.
    [240]
    Alidadi Shamsabadi A., Yadav A., Abbasi O., Yanikomeroglu H., Handling interference in integrated HAPS-terrestrial networks through radio resource management, IEEE Wireless Commun. Lett. 11 (12) (2022) 2585–2589.
    [241]
    Geraci G., Garcia-Rodriguez A., Azari M.M., Lozano A., Mezzavilla M., Chatzinotas S., Chen Y., Rangan S., Renzo M.D., What will the future of UAV cellular communications be? A flight from 5G to 6G, IEEE Commun. Surv. Tutor. 24 (3) (2022) 1304–1335.
    [242]
    Liu X., Lam K.-Y., Li F., Zhao J., Wang L., Durrani T.S., Spectrum sharing for 6G integrated satellite-terrestrial communication networks based on NOMA and CR, IEEE Netw. 35 (4) (2021) 28–34.
    [243]
    Chen N., Liu C., Jia H., Okada M., Intelligent reflecting surface aided network under interference toward 6G applications, IEEE Netw. 36 (4) (2022) 18–27.
    [244]
    Demir O.T., Björnson E., Sanguinetti L., Foundations of user-centric cell-free massive MIMO, 2021.
    [245]
    Chen S., Zhang J., Zhang J., Emil B., Ai B., A survey on user-centric cell-free massive MIMO systems, Digit. Commun. Netw. 8 (5) (2022) 695–719.
    [246]
    Elhoushy S., Ibrahim M., Hamouda W., Cell-free massive MIMO: A survey, IEEE Commun. Surv. Tutor. 24 (1) (2022) 492–523.
    [247]
    Jang H.S., Jung B.C., Quek T.Q.S., Sung D.K., Resource-hopping-based grant-free multiple access for 6G-enabled massive IoT networks, IEEE Internet Things J. 8 (20) (2021) 15349–15360.
    [248]
    S. Mishra, L. Salaun, J.-M. Gorce, C. Chen, Connection Throughput Maximization for Grant Based Massive IoT with Graph Matching, in: IEEE Global Communications Conference, 2023.
    [249]
    L. Salaun, H. Yang, S. Mishra, C. Chen, A GNN Approach for Cell-Free Massive MIMO, in: IEEE Global Communications Conference, 2022, pp. 3053–3058.
    [250]
    Alwarafy A., Abdallah M., Çiftler B.S., Al-Fuqaha A., Hamdi M., The frontiers of deep reinforcement learning for resource management in future wireless HetNets: Techniques, challenges, and research directions, IEEE Open J. Commun. Soc. 3 (2022) 322–365.
    [251]
    Lin M., Zhao Y., Artificial intelligence-empowered resource management for future wireless communications: A survey, China Commun. 17 (3) (2020) 58–77.
    [252]
    Kato N., Mao B., Tang F., Kawamoto Y., Liu J., Ten challenges in advancing machine learning technologies toward 6G, IEEE Wirel. Commun. 27 (3) (2020) 96–103.
    [253]
    Bariah L., Sari H., Debbah M., Digital twin-empowered communications: A new frontier of wireless networks, 2023, arXiv:2307.00973.
    [254]
    Lowe R., Wu Y.I., Tamar A., Harb J., Pieter Abbeel O., Mordatch I., Multi-agent actor-critic for mixed cooperative-competitive environments, Adv. Neural Inf. Process. Syst. 30 (2017).
    [255]
    Yu C., Velu A., Vinitsky E., Gao J., Wang Y., Bayen A., Wu Y., The surprising effectiveness of PPO in cooperative multi-agent games, Adv. Neural Inf. Process. Syst. 35 (2022) 24611–24624.
    [256]
    Kuba J.G., Chen R., Wen M., Wen Y., Sun F., Wang J., Yang Y., Trust region policy optimisation in multi-agent reinforcement learning, in: The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022, OpenReview.net, 2022, URL: https://openreview.net/forum?id=EcGGFkNTxdJ.

    Cited By

    View all

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image Computer Networks: The International Journal of Computer and Telecommunications Networking
    Computer Networks: The International Journal of Computer and Telecommunications Networking  Volume 239, Issue C
    Feb 2024
    299 pages

    Publisher

    Elsevier North-Holland, Inc.

    United States

    Publication History

    Published: 12 April 2024

    Author Tags

    1. 5G and beyond mobile networks
    2. Interference management
    3. Survey
    4. Inter-cell interference
    5. Multi-user interference
    6. Inter-beam interference
    7. Cross-link interference
    8. Remote interference
    9. Inter-numerology interference
    10. Self interference

    Qualifiers

    • Short-survey

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 0
      Total Downloads
    • Downloads (Last 12 months)0
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 27 Jul 2024

    Other Metrics

    Citations

    Cited By

    View all

    View Options

    View options

    Get Access

    Login options

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media