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UID:939@lincs.fr
DTSTART;TZID=Europe/Paris:20260319T140000
DTEND;TZID=Europe/Paris:20260319T150000
DTSTAMP:20260313T110322Z
URL:https://www.lincs.fr/events/coexistence-of-embb-and-mmtc-in-cf-mmimo-s
 ystems/
SUMMARY:Coexistence of eMBB and mMTC in CF-mMIMO Systems
DESCRIPTION:Future wireless networks must support heterogeneous services
 with markedly different requirements\, such as enhanced mobile broadband
 (eMBB)\, which demand high data rates\, and massive machine-type
 communications (mMTC)\, which seek long battery lifetimes. Recently\,
 cell-free massive MIMO (CF-mMIMO) has emerged as a promising paradigm for
 providing uniform service quality and flexible resource allocation in such
 scenarios.\n\nIn this talk\, we investigate uplink multiple-access schemes
 for the coexistence between these two use cases. We consider a
 non-orthogonal access strategy in which low-rate mMTC transmissions are
 spread across the time–frequency resources concurrently used by eMBB
 users\, enabling efficient resource reuse while supporting heterogeneous
 traffic. \n\nAssuming imperfect channel state information\, we derive
 closed-form expressions for the achievable uplink rates of both services\,
 based solely on statistical channel knowledge. For mMTC devices\, the
 analysis explicitly accounts for the finite blocklength (FBL) regime to
 characterize short-packet transmissions.\n\nTo accommodate heterogeneous
 service requirements\, we formulate a power-control problem that maximizes
 the minimum energy efficiency across mMTC devices while enforcing
 quality-of-service constraints for eMBB users. The resulting nonconvex
 fractional optimization is addressed via sequential fractional
 programming\, with formulations that cover both the classical Shannon and
 FBL regimes.\n\nNumerical results demonstrate that the proposed scheme
 enables effective multiplexing of eMBB and mMTC traffic while maintaining
 the desired reliability-efficiency trade-offs. As future work\, we plan to
 investigate learning-based approximations of the proposed optimization
 framework (e.g.\, using graph neural networks) to further reduce
 computational complexity and enable real-time operation.\nBIO\nSergi
 Liesegang (Member\, IEEE) received the bachelor’s degree in
 telecommunication engineering from the Universitat Politècnica de
 Catalunya (UPC) in 2015\, the joint master’s degree in telecommunication
 engineering from UPC in collaboration with the Technische Universität
 München (TUM) in 2017\, and the Ph.D. degree (cum laude) in signal theory
 and communications from UPC in 2022. From 2015 to 2018\, he was a Research
 Assistant in the Signal Theory and Communications Department at UPC. From
 March 2022 to June 2022\, he was a Visiting Researcher with the Università
 degli Studi di Cassino e del Lazio Meridionale (UNICAS). From September
 2022 to May 2023\, he was a Research Associate with UPC. From June 2023 to
 July 2025\, he was a Post-Doctoral Fellow with the Consorzio Nazionale
 Interuniversitario per le Telecomunicazioni (CNIT). Since February 2024\,
 he has been the recipient of a Marie Sk?odowska-Curie Actions (MSCA)
 Individual Grant with UNICAS and is currently on secondment with Nokia Bell
 Labs Paris-Saclay. His areas of interest include signal processing and
 wireless communication theory\, with special emphasis on beyond-5G and 6G
 systems.
CATEGORIES:Seminars
LOCATION:Amphi 3\, 19 Place Marguerite Perey\, Palaiseau\, France
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=19 Place Marguerite Perey\,
 Palaiseau\, France;X-APPLE-RADIUS=100;X-TITLE=Amphi 3:geo:0,0
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TZID:Europe/Paris
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DTSTART:20251026T020000
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