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Artificial Intelligence Aided D-band Network for 5G Long Term Evolution

Periodic Reporting for period 1 - ARIADNE (Artificial Intelligence Aided D-band Network for 5G Long Term Evolution)

Reporting period: 2019-11-01 to 2021-04-30

With the aspiration to transform the current (5G) wireless thinking from focusing on "local" network improvements (e.g. isolating the radio access level or the resources management level etc.), to realizing a longer term vision of pervasive mobile virtual services, through a network managing computing and connectivity functions in an integrated way, ARIADNE envisions to bring together a novel high frequency advanced radio architecture and an Artificial Intelligence (AI) network processing and management approach in a unified system beyond 5G concept.
The vision of ARIADNE is to investigate, theoretically analyze, design, develop, and showcase in a proof of concept demonstrator, an innovative wireless communications concept addressing networks beyond 5G, in which ultra-high spectral efficient and reliable communications in the bandwidth-rich D-band can be dynamically established and reconfigured by Machine Learning (ML)-based design and intelligent network management, in both “Line of Sight” (LOS) and “Non-Line of Sight” (NLOS) environments (Figure 1).
In this respect, the ARIADNE is working towards identifying and assessing the critical technology gaps and inventing, optimizing and demonstrating the appropriate enablers, expected to catalyze the road to beyond 5G. In particular, the ARIADNE approach is established, developed and evaluated based on its three pillars:
• PILLAR I: D-band for 100 Gbit/s reliable wireless connectivity,
• PILLAR II: Communications beyond the Shannon paradigm, by means of metasurfaces for NLOS/obstructed LOS connectivity, in order to guarantee connectivity reliability, by making the environment itself reconfigurable
• PILLAR III: Artificial Intelligence based wireless system concept, by means of ML approaches to optimize the architecture, the signal and data processing and all network management functions.
These three ARIADNE pillars represent the main building to successfully address the following 7 major Key Performance indicators (KPIs), listed below, along the project objectives presented in (Figure 2):
• Aggregate throughput of wireless access for any traffic load/pattern (100 Gbps)
• E2E throughput in all relevant usage scenarios, backhaul/fronthaul, ad hoc backhaul, NLOS/obstructed (100 Gbps)
• E2E latency minimization (‘zero’ latency)
• Coverage of the D-band link (100 m outdoors)
• Connectivity Reliability for massive number of nodes (‘always’ available)
• Energy efficiency (energy consumption reduction by 10x compared to 5G)
• Complexity reduction (10x compared to 5G)
Already during first six months on its life time, the ARIADNE project consortium defined a reference system model as basis for future investigations, which will be carried out within the project scope. The ARIADNE system concept includes three general use cases and corresponding scenarios, allowing detailed system modelling and design as well as further planned research activities:
• Use case 1 - Outdoor backhaul/fronthaul fixed topology network
o Long-range LOS rooftop point-point backhauling – a simple scenario without using RIS and
o Street-level point-point & point-multipoint backhauling/fronthauling – also a static scenario, but enabling NLOS communications via RIS
• Use case 2 - RIS based NLOS connectivity
o Advanced indoor connectivity – establishing alternative LOS hops via RIS,
o Data kiosk scenario – where a large amount of data is beamed to slowly moving users/devices during a limited time period,
• Use case 3 - Ad-hoc connectivity in moving network topology
o Dynamic front/backhauling for 5G and beyond access nodes & repeaters - e.g. where one of the antennas or base stations are replaced by a drone (equipped with antenna / base station),
o V2V and V2X connectivity – Vehicle to Vehicle and Vehicle to everything connectivity, where the moving network topology emerges from current tra¬ffic conditions.
After one year on its lifetime, the ARIADNE project specified its system model as basis for future investigations which will be carried out within the project scope. An analysis of the D-band directional link, including consideration of suitable channel modelling approaches, possible ways forward in performance evaluation, and preliminary studies on appropriate application of machine learning techniques in this context have been concluded. Furthermore, bases for application of Reconfigurable Intelligent Surfaces and reconfigurable antennas for D-Band have been laid down.
Important highlights of these advances beyond the state of the art with respect to the D-band wireless connectivity pillar (I) include
• ARIADNE beyond 5G critical use cases and system requirements specifications,
• D- band channel measurements and characterization w.r.t. small scale fading rain attenuation, LOS and long-distance links,
• A/D and D/A converter board for interfacing with the RF front-end was designed and is being manufactured,
• An XPIC algorithm was developed, including an initial implementation,
• The design of a broadband transceiver chip set at D-band.
• The development of an LC reflect-array lab demonstrator at W-band, with high-gain, direct addressing and full 3D steering,
• The design of metasurface topologies for multi-frequency anomalous reflectors and beam splitters at D-band.
Furthermore, important highlights of advances beyond the state of the art with respect to the Communications beyond the Shannon pillar (II) include
• capacity-achieving scheme for RIS-assisted communications based on jointly encoding information in the RIS reflection pattern as well as in the transmitted signal,
• scaling laws of RIS-assisted communications in the near-field and far-field regimes,
• optimal RIS placement scheme,
• performance limits quantitative assessment in the presence of transceiver hardware, imperfections in both point-to-point and RIS-assisted communications,
• prediction-based tracking algorithm proposed, which reduces the tracking overhead, while ensuring the desired levels of accuracy, and
• quantitative analysis of network interference generated by RIS.
Finally, important highlights of advances beyond the state of the art with respect to the Artificial Intelligence based wireless system concept pillar (III) include
• a low-complexity algorithm design based on the stochastic block gradient descent method for optimum beamforming in the presence of random blockage,
• an Intelligent Spectrum Learning algorithm design exploiting Convolutional Neural networks for interference management in RIS-aided networks,
• the design and evaluation of a protocol for minimizing Initial Access delay in D-band wireless systems and RIS-empowered D-band wireless systems, and
• formulation and solution of the cell-user assignment problem using the proposed AI/ML hybrid framework combining Metaheuristics and Machine Learning algorithms
ARIADNE System Concept
Application of Reconfigurable Intelligent Surfaces (RIS)
Project objectives