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UID:867@lincs.fr
DTSTART;TZID=Europe/Paris:20241218T020000
DTEND;TZID=Europe/Paris:20241218T050000
DTSTAMP:20241217T124147Z
URL:https://www.lincs.fr/events/a-stochastic-geometry-framework-for-ris-as
 sisted-cellular-networks/
SUMMARY:A Stochastic Geometry Framework for RIS-Assisted Cellular Networks
DESCRIPTION:This thesis is jointly supervised by Prof. François Baccelli\,
 Dr. Luis Uzeda Garcia\, and Dr. Stefano Paris.\n\n\n\n\nThis thesis
 investigates the impact of deploying reconfigurable intelligent surfaces
 (RIS)\, which can engineer spatial diversity in complex cellular networks\,
 at a system level. We develop a framework to characterize the performance
 of RIS-assisted cellular networks\, focusing on downlink coverage
 probability and ergodic rate\, where we consider that multiple RISs can
 serve one UE simultaneously. To account for the inherent randomness in the
 spatial deployments of base stations (BSs) and RISs\, we model the
 placements of the RISs as point processes (PPs) conditioned on the
 associated BSs\, which are modeled by a Poisson point process (PPP). These
 RIS PPs can be adapted based on the deployment strategy. We focus on
 modeling the RISs as a Matérn cluster process (MCP)\, where each RIS
 cluster is a finite PPP with support of a disc centered on the association
 BS. We assume that the system uses the orthogonal frequency division
 multiplexing (OFDM) technique to exploit the multipath diversity provided
 by RISs. The coverage probability and the ergodic rate can be evaluated
 when RISs operate as batched powerless beamformers. The resulting
 analytical expressions provide a general methodology to evaluate the impact
 of key RIS-related parameters\, such as the density of RISs\, on
 system-level performance. To demonstrate the framework’s broad
 applicability\, we also analyze a RIS placement variant where RISs are
 deployed around coverage holes. Furthermore\, the proposed framework
 enables techno-economic analysis of RIS-assisted networks. We introduce a
 relative cost model considering the total cost of ownership (TCO) of
 deploying both BSs and RISs\, along with a return on investment (ROI) model
 that is proportional to spectral efficiency. This approach gives operators
 quantitative insights to develop investment strategies regarding whether to
 invest in RISs based on current BS and RIS densities. To assess performance
 and conduct techno-economic analysis\, the analytical expressions involving
 multiple integrals are computationally complex. We address this challenge
 by developing a novel numerical solver based on the Genz-Malik rule and
 parallel computing\, allowing efficient evaluation within acceptable time
 consumption and improving the practical applicability of the proposed
 framework. Based on this solver\, numerical evaluations of the analytical
 expressions and Monte-Carlosimulations jointly validate the proposed
 analytical approach and provide valuable insights into the design of future
 RIS-assisted cellular networks.\n
CATEGORIES:PhD Defense
LOCATION:Inria Paris\, 48 rue Barrault\, Paris\, 75013\, France
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=48 rue Barrault\, Paris\,
 75013\, France;X-APPLE-RADIUS=100;X-TITLE=Inria Paris:geo:0,0
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TZID:Europe/Paris
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DTSTART:20241027T020000
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