Outdoor and indoor path loss modeling at the sub-THz band

In this letter, we present new measurement results to model large-scale path loss at the sub-THz (141-145 GHz) band, for both indoor and outdoor scenarios. Extensive measurement campaigns have been carried out, taking into account both line-of-sight (LoS) and non line-of-sight (NLoS) propagation. For all considered propagation scenarios, existing omni-directional and directional path loss model have been developed, based on the so-called close-in (CI) free-space reference distance model. Moreover, path loss modeling is applied for the 2nd and 3rd strongest multipath components (MPCs). Thus, path loss exponent and large-scale shadow fading estimates are provided. Moreover, power angular spread analysis is depicted, using power angular information up to the 3rd strongest MP

Autonomous reconfigurable intelligent surfaces through wireless energy harvesting

In this paper, we examine the potential for a reconfigurable intelligent surface (RIS) to be powered by energy harvested from information signals. This feature might be key to reap the benefits of RIS technology's lower power consumption compared to active relays. We first identify the main RIS power-consuming components and then propose an energy harvesting and power consumption model. Furthermore, we formulate and solve the problem of the optimal RIS placement together with the amplitude and phase response adjustment of its elements in order to maximize the signal-to-noise ratio (SNR) while harvesting sufficient energy for its operation. Finally, numerical results validate the autonomous operation potential and reveal the range of power consumption values that enables it.This work was supported by the European Commission’s Horizon 2020 research and innovation programme ARIADNE (No. 871464), the Luxembourg National Research Fund (FNR) under the CORE project RISOTTI (ref. 14773976), and the Digital Futures Center.Peer ReviewedPostprint (author's final draft

Wireless energy harvesting for autonomous reconfigurable intelligent surfaces

In the current contribution, we examine the feasibility of fully-energy-autonomous operation of reconfigurable intelligent surfaces (RIS) through wireless energy harvesting (EH) from incident information signals. Towards this, we first identify the main RIS energy-consuming components and present a suitable and accurate energy-consumption model that is based on the recently proposed integrated controller architecture and includes the energy consumption needed for channel estimation. Building on this model, we introduce a novel RIS architecture that enables EH through RIS unit-cell (UC) splitting. Subsequently, we introduce an EH policy, where a subset of the UCs is used for beamsteering, while the remaining UCs absorb energy. In particular, we formulate a subset allocation optimization problem that aims at maximizing the signal-to-noise ratio (SNR) at the receiver without violating the RIS’s energy consumption demands. As a problem solution, we present low-complexity heuristic algorithms. The presented numerical results reveal the feasibility of the proposed architecture and the efficiency of the presented algorithms with respect to both the optimal and very high-complexity brute-force approach and the one corresponding to random subset selection. Furthermore, the results reveal how important the placement of the RIS as close to the transmitter as possible is, for increasing the harvesting effectiveness.This work was supported by the Luxembourg National Research Fund (FNR) under the CORE project RISOTTI (ref. 14773976), the European Commission’s Horizon 2020 research and innovation programme (ARIADNE) under grant agreement No. 871464, and the Digital Futures center.Peer ReviewedPostprint (published version

Autonomous Reconfigurable Intelligent Surfaces Through Wireless Energy Harvesting

In this paper, we examine the potential for a reconfigurable intelligent surface (RIS) to be powered by energy harvested from information signals. This feature might be key to reap the benefits of RIS technology's lower power consumption compared to active relays. We first identify the main RIS power-consuming components and then propose an energy harvesting and power consumption model. Furthermore, we formulate and solve the problem of the optimal RIS placement together with the amplitude and phase response adjustment of its elements in order to maximize the signal-to-noise ratio (SNR) while harvesting sufficient energy for its operation. Finally, numerical results validate the autonomous operation potential and reveal the range of power consumption values that enables it.This work was supported by the European Commission’s Horizon 2020 research and innovation programme ARIADNE (No. 871464), the Luxembourg National Research Fund (FNR) under the CORE project RISOTTI (ref. 14773976), and the Digital Futures Center.Peer ReviewedPostprint (author's final draft

D4.2 Intelligent D-Band wireless systems and networks initial designs

This deliverable gives the results of the ARIADNE project's Task 4.2: Machine Learning based network intelligence. It presents the work conducted on various aspects of network management to deliver system level, qualitative solutions that leverage diverse machine learning techniques. The different chapters present system level, simulation and algorithmic models based on multi-agent reinforcement learning, deep reinforcement learning, learning automata for complex event forecasting, system level model for proactive handovers and resource allocation, model-driven deep learning-based channel estimation and feedbacks as well as strategies for deployment of machine learning based solutions. In short, the D4.2 provides results on promising AI and ML based methods along with their limitations and potentials that have been investigated in the ARIADNE project

On the Rate and Energy Efficiency Comparison of Reconfigurable Intelligent Surfaces with Relays

International audienceIn this paper, we perform a performance comparison, in terms of achievable rate and energy efficiency, of a reconfigurable intelligent surface (RIS)-aided nextgeneration fronthaul network operating in the D band with its relay-aided counterpart. For their energy efficiency comparison, the insertion losses and power consumption of the electronic components related with the deployed nodes is taken into account. Numerical results show that the RIS-aided network outperforms the relay-aided one, both in terms of rate and energy efficiency, only for adequately large RISs

Initial Investigation of D-band Small-Scale Fading Statistics

| openaire: EC/H2020/871464/EU//ARIADNEThis work investigates small-scale fading statistics of D-band channels, based on recent measurements performed in a shopping mall. The measurements include both line-of-sight and obstructed-line-of-sight links. From a limited number of measured data sets of plane waves we synthesize many small-scale fading fading realizations by applying uniformly distributed phases to each plane wave. The results show that small-scale fading of the line-of-sight and obstructed line-of-sight channels in the studied environment is statistically described by Weibull and Nakagami-m distributions, respectively.Peer reviewe

Interoperability Criteria and mechanisms for seamless inter-working between UMTS-HSDPA and HIPERLAN/2 networks enhanced with MIMO Techniques

Abstract. The concept 4G wireless communication systems is to provide a user with a rich range of services across different radio access technologies, while maintaining the service's minimum QoS requirement, independently on the coverage area, mobility conditions, and using a single mobile terminal. To achieve this goal the need for interoperable heterogeneous wireless networks emerges. In this article we identify the challenges that arise when enhancing interoperability functionality to two different radio access networks. Specifically we will focus on inter-working UTRA FDD HSDPA and WLANs networks, as two strong candidates for composing the 4G environment. We also propose an interoperable architecture to achieve seamless inter-working between the aforementioned networks. The article also introduces two interoperable criteria that triggers interoperability based on the use of Cost functions. Namely, these triggers are the initial user assignment to the optimal network and inter-system handover. Each of the two triggers initiates a respective interoperability algorithm. Finally, In order to characterize the inter-operability mechanisms behaviour and address an accurate QoS performance analysis, a software simulation platform has been developed. The platform is enhanced with MIMO transceivers and takes into consideration: network configuration, propagation conditions, fast fading, and service requirements