Distributed Intelligent Integrated Sensing and Communications: The 6G-DISAC Approach

This paper introduces the concept of Distributed Intelligent integrated Sensing and Communications (DISAC), which expands the capabilities of Integrated Sensing and Communications (ISAC) towards distributed architectures. Additionally, the DISAC framework integrates novel waveform design with new semantic and goal-oriented communication paradigms, enabling ISAC technologies to transition from traditional data fusion to the semantic composition of diverse sensed and shared information. This progress facilitates large-scale, energy-efficient support for high-precision spatial-temporal processing, optimizing ISAC resource utilization, and enabling effective multi-modal sensing performance. Addressing key challenges such as efficient data management and connect-compute resource utilization, 6G- DISAC stands to revolutionize applications in diverse sectors including transportation, healthcare, and industrial automation. Our study encapsulates the project vision, methodologies, and potential impact, marking a significant stride towards a more connected and intelligent world.

Empirical Validation of the Impedance-Based RIS Channel Model in an Indoor Scattering Environment

Ensuring the precision of channel modeling plays a pivotal role in the development of wireless communication systems, and this requirement remains a persistent challenge within the realm of networks supported by Reconfigurable Intelligent Surfaces (RIS). Achieving a comprehensive and reliable understanding of channel behavior in RIS-aided networks is an ongoing and complex issue that demands further exploration. In this paper, we empirically validate a recently-proposed impedance-based RIS channel model that accounts for the mutual coupling at the antenna array and precisely models the presence of scattering objects within the environment as a discrete array of loaded dipoles. To this end, we exploit real-life channel measurements collected in an office environment to demonstrate the validity of such a model and its applicability in a practical scenario. Finally, we provide numerical results demonstrating that designing the RIS configuration based upon such model leads to superior performance as compared to reference schemes.

RIS-aided Positioning Experiments based on mmWave Indoor Channel Measurements

Reconfigurable Intelligent Surfaces (RISs) are announced as a truly transformative technology, capable of smartly shaping wireless environments to optimize next-generation communication networks. Among their numerous foreseen applications, Reflective RISs (RRISs) have been shown theoretically beneficial not only to enable wireless localization through controlled multipath in situations where conventional systems would fail (e.g., with too few available base stations (BSs) and/or under radio blockages) but also to locally boost accuracy on demand (typically, in regions close to the surface). In this paper, leveraging a dedicated frequency-domain mmWave indoor channel sounding campaign, we present the first experimental evidences of such benefits, by emulating offline simple RIS-aided single-BS positioning scenarios including line-of-sight (LoS) and non-line-of-sight (NLoS), single-RIS and multi-RIS, and multiple user equipment (UE) locations, also by considering various combinations of estimated multipath parameters (e.g., delays, Angle of Departure (AoD) or gains) as inputs to basic Least Squares (LS) solvers. Despite their simplicity, these preliminary proof-of-concept validations show concretely how and when RIS-reflected paths could contribute to enhance localization performance.