CoChat
Sign Up
Cited by 45Multi-Agent Systems and NegotiationSoftware-Defined Networks and 5GIoT and Edge/Fog Computing

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

A novel Distributed AI framework with ML for D2D communication in 5G/6G networks

Ioannou, Iacovos, Christophorou, Christophoros, Vassiliou, Vasos, Pitsillides, Andreas·2022·Computer Networks
DOI 10.1016/j.comnet.2022.108987
Open this paper in CoChat — free account

Overview

Integrity signals

Registry

Crossref · 10.1016/j.comnet.2022.108987

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 1 references

Research works printed but not linked: 73

  1. Akyildiz, 2020, 6G and beyond: The future of wireless communications systems, IEEE Access, 8, 133995, 10.1109/ACCESS.2020.3010896 · DOI
  2. Akyildiz, 2016, 5G roadmap: 10 key enabling technologies, Comput. Netw., 106, 17, 10.1016/j.comnet.2016.06.010 · DOI
  3. Ioannou, 2020, Distributed artificial intelligence solution for D2D communication in 5G networks, IEEE Syst. J., 14, 4232, 10.1109/JSYST.2020.2979044 · DOI
  4. Letaief, 2019, The roadmap to 6G: AI empowered wireless networks, IEEE Commun. Mag., 57, 84, 10.1109/MCOM.2019.1900271 · DOI · Paper page
  5. David, 2018, 6G vision and requirements: Is there any need for beyond 5g?, IEEE Veh. Technol. Mag., 13, 72, 10.1109/MVT.2018.2848498 · DOI · Paper page
  6. Giordani, 2020, Toward 6G networks: Use cases and technologies, IEEE Commun. Mag., 58, 55, 10.1109/MCOM.001.1900411 · DOI · Paper page
  7. S. Yrjola, Decentralized 6G Business Models, in: 6G Wireless Summit, Finland, 2019, pp. 1–2, 2019. · DOI
  8. Li, 2020, The collective advantage for advancing communications and intelligence, IEEE Wirel. Commun., 27, 96, 10.1109/MWC.001.1900417 · DOI · Paper page
  9. Rao, 1995, BDI Agents: From theory to practice, Icmas, 95, 312
  10. Lu, 2019, Artificial agent: The fusion of artificial intelligence and a mobile agent for energy-efficient traffic control in wireless sensor networks, Future Gener. Comput. Syst., 95, 45, 10.1016/j.future.2018.12.024 · DOI
  11. Lee, 2014, Two-stage semi-distributed resource management for device-to-device communication in cellular networks, IEEE Trans. Wireless Commun., 13, 1908, 10.1109/TWC.2014.022014.130480 · DOI
  12. Wu, 2015, Dynamic distributed resource sharing for mobile D2D communications, IEEE Trans. Wireless Commun., 14, 5417, 10.1109/TWC.2015.2438292 · DOI
  13. Librino, 2018, Distributed mode and power selection for non-orthogonal D2D communications: A stochastic approach, IEEE Trans. Cogn. Commun. Netw., 4, 232, 10.1109/TCCN.2018.2809721 · DOI
  14. Nguyen, 2016, Distributed resource allocation for D2D communications underlay cellular networks, IEEE Commun. Lett., 20, 942, 10.1109/LCOMM.2015.2498925 · DOI
  15. Yin, 2013, Distributed resource allocation for D2D communication underlaying cellular networks, 2013 IEEE International Conference on Communications Workshops, ICC 2013, 138, 10.1109/ICCW.2013.6649216 · DOI
  16. Fornara, 2003, Among autonomous agents in multiagent systems, Sci.-N. Y., 1
  17. Srivastava, 1999, Applications of intelligent agents, Electron. Inf. Plan., 26, 273
  18. Mikolajczak, 2005, Designing and building parallel programs: Concepts and tools for parallel software engineering [book review], IEEE Concurr., 5, 88, 10.1109/MCC.1997.588301 · DOI
  19. Alam, 2009, Agent Computing and Multi-Agent Systems, 298, 10.1007/978-3-642-01639-4_26 · DOI
  20. Fodor, 2012, Design aspects of network assisted device-to-device communications, IEEE Commun. Mag., 50, 170, 10.1109/MCOM.2012.6163598 · DOI
  21. Gandotra, 2016, Device-to-device communication in cellular networks: A survey, J. Netw. Comput. Appl., 71, 1801
  22. Ahmad, 2017, Resource management in D2D communication: An optimization perspective, J. Netw. Comput. Appl., 93, 51, 10.1016/j.jnca.2017.03.017 · DOI
  23. Song, 2014, Game-theoretic resource allocation methods for device-to-device communication, IEEE Wirel. Commun., 21, 136, 10.1109/MWC.2014.6845058 · DOI
  24. Noura, 2016, A survey on interference management for device-to-device (D2D) communication and its challenges in 5G networks, J. Netw. Comput. Appl., 71, 130, 10.1016/j.jnca.2016.04.021 · DOI
  25. Hossain, 2015, 5G cellular: key enabling technologies and research challenges, IEEE Instrum. Meas. Mag., 18, 11, 10.1109/MIM.2015.7108393 · DOI · Paper page
  26. Ge, 2016, 5G ultra-dense cellular networks, IEEE Wirel. Commun., 23, 72, 10.1109/MWC.2016.7422408 · DOI
  27. Valiveti, 2017, EHSD: AN exemplary handover scheme during D2D communication based on decentralization of SDN, Wirel. Pers. Commun., 94, 2393, 10.1007/s11277-016-3490-7 · DOI
  28. Liu, 2015, Device-to-device communications for enhancing quality of experience in software defined multi-tier LTE-A networks, IEEE Netw., 29, 46
  29. Sadik, 2018, SDN-Based handover scheme for multi-tier LTE/Femto and D2D networks, Comput. Netw., 142, 142, 10.1016/j.comnet.2018.06.004 · DOI
  30. Kang, 2014, Mobile device-to-device (D2D) content delivery networking: A design and optimization framework, J. Commun. Netw., 16, 568, 10.1109/JCN.2014.000095 · DOI
  31. Ometov, 2016, Implementing secure network-assisted D2D framework in live 3GPP LTE deployment, 2016 IEEE International Conference on Communications Workshops, ICC 2016, 749, 10.1109/ICCW.2016.7503877 · DOI
  32. Bai, 2016, Caching based socially-aware D2D communications in wireless content delivery networks: a hypergraph framework, IEEE Wirel. Commun., 23, 74, 10.1109/MWC.2016.7553029 · DOI
  33. Pu, 2016, D2D fogging: An energy-efficient and incentive-aware task offloading framework via network-assisted D2D collaboration, IEEE J. Sel. Areas Commun., 34, 3887, 10.1109/JSAC.2016.2624118 · DOI
  34. Yu, 2016, A D2D-multicast based computation offloading framework for interactive applications, 2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings, 16
  35. Doumiati, 2017, A framework for clustering LTE devices for implementing group D2D communication and multicast capability, 2017 8th International Conference on Information and Communication Systems, ICICS 2017, 216
  36. Asadi, 2017, Dore: An experimental framework to enable outband D2D relay in cellular networks, ArXiv, 25, 2930
  37. Kumar, 2018, A novel distributed Q-learning based resource reservation framework for facilitating D2D content access requests in LTE-A networks, IEEE Trans. Netw. Serv. Manag., 15, 718, 10.1109/TNSM.2018.2807594 · DOI
  38. Doumiati, 2019, A framework of topological interference management and clustering for D2D networks, IEEE Trans. Commun., 67, 7856, 10.1109/TCOMM.2019.2931319 · DOI
  39. Yi, 2020, EDirect: Energy-efficient D2D-assisted relaying framework for cellular signaling reduction, IEEE/ACM Trans. Netw., 28, 860, 10.1109/TNET.2020.2973540 · DOI
  40. Thomas, 2019, FINDER: A D2D based critical communications framework for disaster management in 5G, Peer-To-Peer Netw. Appl., 12, 912, 10.1007/s12083-018-0689-2 · DOI
  41. K. Doppler, C.H. Yu, C.B. Ribeiro, P. Jänis, Mode selection for device-to-device communication underlaying an LTE-advanced network, in: IEEE Wireless Communications and Networking Conference, WCNC, 2010, http://dx.doi.org/10.1109/WCNC.2010.5506248. · DOI
  42. Pang, 2013, Joint mode selection and resource allocation using evolutionary algorithm for device-to-device communication underlaying cellular networks, J. Commun., 8, 751, 10.12720/jcm.8.11.751-757 · DOI
  43. Liu, 2016, Optimal mode selection in D2D-enabled multibase station systems, IEEE Commun. Lett., 20, 470, 10.1109/LCOMM.2015.2514100 · DOI
  44. Xu, 2017, Joint relay selection and resource allocation for energy-efficient D2D cooperative communications using matching theory, Appl. Sci. (Switzerland), 7, 1
  45. Feng, 2015, On the tradeoff between optimal relay selection and protocol design in hybrid D2D networks, 2015 IEEE International Conference on Communication Workshop, ICCW 2015, 705, 10.1109/ICCW.2015.7247264 · DOI
  46. Kim, 2014, An iterative hungarian method to joint relay selection and resource allocation for D2D communications, IEEE Wirel. Commun. Lett., 3, 625, 10.1109/LWC.2014.2338318 · DOI
  47. Kazmi, 2017, Mode selection and resource allocation in device-to-device communications: A matching game approach, IEEE Trans. Mob. Comput., 16, 3126, 10.1109/TMC.2017.2689768 · DOI
  48. Gui, 2018, Multi-hop relay-aided underlay D2D communications for improving cellular coverage quality, IEEE Access, 6, 14318, 10.1109/ACCESS.2018.2796247 · DOI
  49. Schweidtmann, 2019, Deterministic global optimization with artificial neural networks embedded, J. Optim. Theory Appl., 180, 925, 10.1007/s10957-018-1396-0 · DOI
  50. Seok, 2020, Secure D2D communication for 5G IoT network based on lightweight cryptography, Appl. Sci., 10, 10.3390/app10010217 · DOI
  51. Zhang, 2016, SeDS: SEcure data sharing strategy for D2D communication in LTE-advanced networks, IEEE Trans. Veh. Technol., 65, 2659, 10.1109/TVT.2015.2416002 · DOI
  52. Amaral, 2018, Embedding multi-agent system frameworks: A benchmarking, An. Comput. Beach, 939
  53. Babu Pamula, 2016, Multi-agent system by using BDI in artificial intelligence, Int. J. Dev. Comput. Sci. Technol. (IJDCST), 5585, 2321
  54. Caillou, 2017, A simple-to-use BDI architecture for agent-based modeling and simulation, Adv. Intell. Syst. Comput., 528, 15, 10.1007/978-3-319-47253-9_2 · DOI
  55. V.M. Le, B. Gaudou, P. Taillandier, D.A. Vo, A New BDI Architecture To Formalize Cognitive Agent Behaviors Into Simulations, in: 7th Conference KES on Agent and Multi-Agent System - Technologies and Applications(KES-AMSTA 2013), Vol. 252, Hue City, Vietnam, 2013, pp. 395–403, URL.
  56. Jager, 2017, Advances in social simulation 2015, 460, 10.1007/978-3-319-47253-9 · DOI
  57. Haerder, 1983, Principles of transaction-oriented database recovery, ACM Comput. Surv., 15, 287, 10.1145/289.291 · DOI
  58. Kruse, 2008, Fuzzy neural network, Scholarpedia, 3, 6043, 10.4249/scholarpedia.6043 · DOI
  59. Zhang, 2011, Weighted sum-rate maximization in multi-cell networks via coordinated scheduling and discrete power control, IEEE J. Sel. Areas Commun., 29, 1214, 10.1109/JSAC.2011.110609 · DOI
  60. 2013, LTE Direct; the case for device-to-device proximate discovery, Qualcomm
  61. 2020, Wi-Fi direct ® specification, WiFi Alliance
  62. Goh, 1995, Back-propagation neural networks for modeling complex systems, Artif. Intell. Eng., 9, 143, 10.1016/0954-1810(94)00011-S · DOI
  63. Frank, 1998, Comparative analysis of fuzzy ART and ART-2A network clustering performance, IEEE Trans. Neural Netw., 9, 544, 10.1109/72.668896 · DOI
  64. Aydin Keskin, 2010, The fuzzy ART algorithm: A categorization method for supplier evaluation and selection, Expert Syst. Appl., 37, 1235, 10.1016/j.eswa.2009.06.004 · DOI
  65. Carpenter, 1991, Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system, Neural Netw., 4, 759, 10.1016/0893-6080(91)90056-B · DOI
  66. Li, 2016, Research and improvement of DBSCAN cluster algorithm, Proceedings - 2015 7th International Conference on Information Technology in Medicine and Education, ITME 2015, 537
  67. Smiti, 2014, WCOID-DG: AN approach for case base maintenance based on weighting, clustering, outliers, internal detection and dbsan-gmeans, J. Comput. System Sci., 80, 27, 10.1016/j.jcss.2013.03.006 · DOI
  68. Li, 2020, A systematic review of unsupervised learning techniques for software defect prediction, Inf. Softw. Technol., 122, 10.1016/j.infsof.2020.106287 · DOI
  69. Li, 2004, Minimum entropy clustering and applications to gene expression analysis, Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004., 142
  70. Shams, 2013, Basics of coalitional games with applications to communications and networking, Eurasip J. Wirel. Commun. Netw., 2013, 201, 10.1186/1687-1499-2013-201 · DOI
  71. Saad, 2009, Coalitional game theory for communication networks, IEEE Signal Process. Mag., 26, 77, 10.1109/MSP.2009.000000 · DOI
  72. Shams, 2013, Basics of coalitional games with applications to communications and networking, Eurasip J. Wirel. Commun. Netw., 2013, 1, 10.1186/1687-1499-2013-201 · DOI
  73. Peters, 2008, Cooperative game theory models, Game Theory, 133, 10.1007/978-3-540-69291-1_10 · DOI

Cited by

Sorted by citations

Metadata

Authors
Ioannou, I., Christophorou, C., Vassiliou, V., & Pitsillides, A.
Affiliations
  • University of Cyprus (CY)
  • University of Johannesburg (ZA)
Corresponding author
Ioannou, Iacovos
ORCID
Venue
Computer Networks
Publisher
Elsevier BV
Published
2022-07
Volume
211
Pages
108987
ISSN
1389-1286 (ISSN-L)
DOI
10.1016/j.comnet.2022.108987
OpenAlex
W4224938538
Cited by
45
References
1
Citations by year
2026: 10 · 2025: 11 · 2024: 12 · 2023: 9 · 2022: 3
FWCI
2.88
Type
journal article
Language
en
Open access
closed
Licence
Locations
Computer Networks (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
  • Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
Keywords
distributed artificial intelligence, device-to-device communication, 5G/6G networks, BDI agents, multi-agent systems, fuzzy logic, mobile devices, transmission mode selection, spectral efficiency, power consumption, cluster formation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review