Wireless Communication Technologies and Signal Processing – Standardisation and Follow-up/PoCs

The scope of this topic focuses on the following areas:

• Physical layer technologies for enhanced spectral efficiency which includes energy-efficient new waveform design (featuring e.g. low PAPR, low complexity processing), backwards compatibility with existing CP-OFDM waveforms, or self-synchronizing modulation and waveform. Modulation and coding aspects are also in scope, including source-channel codes for short packet transmission, coding and caching for over-the-air computing in the network edge, or energy-efficient implementation of key physical-layer algorithms such as FEC or channel estimation. Asynchronous non-orthogonal multiple access schemes (e.g. NOMA, RSMA), advanced interference cancellation, advanced (full) duplexing strategies, or UE relaying can be considered, as well. Where relevant, RAN coordination (e.g. multi-TRP), improved lower layer signalling in 6G Air Interface, or physical-layer security aspects can also be addressed.
• Extreme exploitation of MIMO technologies, this including advanced massive MIMO technologies and extremely large antenna arrays (XL-MIMO) for increased network capacity and spectral efficiency and/or enhanced indoor coverage/localization; holographic beamforming and novel beam management schemes in massive MIMO settings leveraging on hybrid analog-digital front-end architectures; scalable, robust and low-complexity/overhead CSI acquisition strategies; massively distributed and cell-free MIMO technologies and architectures for improved coverage, reliability and mobility support and related synchronization, calibration and coordination aspects.
• AI/ML & semantic communications targeted at providing a native AI framework for RAN networks by using AI/ML for the lower layers of the protocol stack (e.g. including PHY, MAC and resource optimization), including AI-assisted multi-user and massive-MIMO systems channel coding aspects, the reduction of power consumption in the RAN or multi-modal model training (e.g. vision-assisted) for improved radio/network efficiency. Protocol learning, automated generation of lean, customizable neural radio protocol stacks, and learning networks, as well as related aspects such as distributed and centralized learning trade-offs, or conflict and anomaly detection and resolution are also in scope. This focus area notably includes semantic communications, or trustworthy/safe/explainable AI and the exploitation of generative AI for RAN optimization.
• Spectrum sharing and RAN co-existence focuses on aspects such as dynamic spectrum sharing between 5G and 6G, spectrum sharing and coexistence mechanisms to enable 6G in 7-15 GHz band, spectrum sharing and re-use for sustainability; overcoming limitations of variable numerology and, where deemed relevant, spectrum sharing with satellite, radar, or other terrestrial networks.
• Automation and disaggregation in the RAN segment: leveraging on open RAN architectures this area includes AI/ML powered automation and optimisation, micro-orchestration of RAN functions, programmable networks and API native, and exposure of network capabilities.
• Agile use of function accelerators at the 6G RAN/compute continuum which includes the integration of multi-processor SoC/accelerators, flexible and modular hardware/software architectures for communication-compute-control co-design, heterogeneous resource management, while meeting the new requirements and expectations on confidential computing solutions.

Note: Applicants may address a subset of the above priorities/areas. To facilitate the evaluation applicants should clearly identify the areas they are comprehensively addressing.