{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T17:53:04Z","timestamp":1775065984815,"version":"3.50.1"},"reference-count":46,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Mobile Comput."],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1109\/tmc.2024.3452986","type":"journal-article","created":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T18:02:50Z","timestamp":1725300170000},"page":"15080-15097","source":"Crossref","is-referenced-by-count":2,"title":["Dynamic Size Message Scheduling for Multi-Agent Communication Under Limited Bandwidth"],"prefix":"10.1109","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6869-3566","authenticated-orcid":false,"given":"Qingshuang","family":"Sun","sequence":"first","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University, Xi&#x2019;an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1521-8494","authenticated-orcid":false,"given":"Denis","family":"Steckelmacher","sequence":"additional","affiliation":[{"name":"AI Lab, Vrije Universiteit Brussel, Ixelles, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6509-9297","authenticated-orcid":false,"given":"Yuan","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University, Xi&#x2019;an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6346-4564","authenticated-orcid":false,"given":"Ann","family":"Now\u00e9","sequence":"additional","affiliation":[{"name":"AI Lab, Vrije Universiteit Brussel, Ixelles, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7353-4009","authenticated-orcid":false,"given":"Rapha\u00ebl","family":"Avalos","sequence":"additional","affiliation":[{"name":"AI Lab, Vrije Universiteit Brussel, Ixelles, Belgium"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1177\/0278364920916531"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1108\/IR-01-2020-0004"},{"key":"ref3","first-page":"10053","article-title":"Learning multi-agent coordination for enhancing target coverage in directional sensor networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Xu","year":"2020"},{"key":"ref4","first-page":"19","article-title":"Complexity of decentralized control: Special cases","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Allen","year":"2009"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-60990-0_12"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s10458-024-09644-x"},{"key":"ref7","first-page":"2244","article-title":"Learning multiagent communication with backpropagation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Sukhbaatar","year":"2016"},{"key":"ref8","first-page":"1538","article-title":"TARMAC: Targeted multi-agent communication","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Das","year":"2019"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TCST.2016.2599486"},{"key":"ref10","first-page":"1","article-title":"Learning to schedule communication in multi-agent reinforcement learning","volume-title":"Proc. Int. Conf. Representation Learn. Int. Conf. Representation Learn.","author":"Kim","year":"2019"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5957"},{"key":"ref12","first-page":"3212","article-title":"Efficient communication in multi-agent reinforcement learning via variance based control","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhang","year":"2019"},{"key":"ref13","first-page":"17 271","article-title":"Succinct and robust multi-agent communication with temporal message control","volume-title":"Proc. 34th Int. Conf. Neural Inf. Process. Syst.","author":"Zhang","year":"2020"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3121546"},{"key":"ref15","first-page":"6379","article-title":"Multi-agent actor-critic for mixed cooperative-competitive environments","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lowe","year":"2017"},{"key":"ref16","first-page":"22069","article-title":"Learning individually inferred communication for multi-agent cooperation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ding","year":"2020"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/IROS45743.2020.9341079"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2023.3296726"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2022.3195202"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-019-05864-5"},{"key":"ref21","first-page":"9908","article-title":"Learning efficient multi-agent communication: An information bottleneck approach","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wang","year":"2020"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2023.3339213"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA46639.2022.9812285"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3024629"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2020.2998611"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2018.2865798"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/s10846-019-01062-6"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-28929-8"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.2977374"},{"key":"ref30","article-title":"Local advantage networks for multi-agent reinforcement learning in decPOMDPs","volume-title":"Trans. Mach. Learn. Res.","author":"Avalos","year":"2023"},{"key":"ref31","article-title":"The scientist and engineer\u2019s guide to digital signal processing","author":"Smith","year":"1997"},{"key":"ref32","volume-title":"Mathematics of the Discrete Fourier Transform (DFT): With Audio Applications","author":"Smith","year":"2008"},{"key":"ref33","volume-title":"Discrete-Time Signal Processing","author":"Oppenheim","year":"1999"},{"key":"ref34","first-page":"12677","article-title":"FiLM: Frequency improved legendre memory model for long-term time series forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhou","year":"2022"},{"key":"ref35","first-page":"2449","article-title":"Spectral representations for convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Rippel","year":"2015"},{"key":"ref36","article-title":"A survey on deep learning based time series analysis with frequency transformation","volume":"abs\/2302.02173","author":"Yi","year":"2023","journal-title":"CoRR"},{"key":"ref37","first-page":"76656","article-title":"Frequency-domain MLPs are more effective learners in time series forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Yi","year":"2024"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref39","volume-title":"Pattern Recognition and Machine Learning","volume":"4","author":"Bishop","year":"2006"},{"key":"ref40","first-page":"1","article-title":"Categorical reparameterization with gumbel-softmax","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Jang","year":"2016"},{"key":"ref41","article-title":"Estimating or propagating gradients through stochastic neurons for conditional computation","author":"Bengio","year":"2013"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00181"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.tcs.2016.01.002"},{"key":"ref44","first-page":"4295","article-title":"QMIX: Monotonic value function factorisation for deep multi-agent reinforcement learning","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Rashid","year":"2018"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/584091.584093"},{"key":"ref46","volume-title":"Telecommunication System Engineering","author":"Freeman","year":"2015"}],"container-title":["IEEE Transactions on Mobile Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/7755\/10746253\/10663256.pdf?arnumber=10663256","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T15:05:36Z","timestamp":1732719936000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10663256\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":46,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tmc.2024.3452986","relation":{},"ISSN":["1536-1233","1558-0660","2161-9875"],"issn-type":[{"value":"1536-1233","type":"print"},{"value":"1558-0660","type":"electronic"},{"value":"2161-9875","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12]]}}}