Second edition CENTRIC Newsletter

CENTRIC Project is proud to share the second edition of its 2023 newsletter presenting the project's progress and results after almost one year of the project lifetime. In this newsletter edition, we present selected project achievements on particular aspects of designing the novel CENTRIC AI-supported air interface, followed by a presentation of recent project dissemination activities.


Channel State Information (CSI) acquisition at the Base Station (BS) received from each User Equipment (UE) is critical to sweep its beam towards the corresponding UE accurately. However, transmitting uncompressed CSI in limited-rate feedback channels is impossible due to the significant signalling overhead in the uplink channel. This motivates us to exploit the potential of employing Artificial Intelligence/Machine Learning (AIML) for CSI compression and reconstruction.  An AI encoder at UE has been investigated to compress and quantize the CSI to a codeword in bits before sending it over the air to the network (NW), which contains an AI decoder to reconstruct CSI. Conventionally, the AI encoder and decoder are trained jointly in the same training session. However, it enforces the disclosure of the proprietary AI encoder or AI decoder details among UE and NW vendors. However, [...] 


AIML-based Channel State Information (CSI) compression that uses autoencoders to lower the overhead of feedback on the MIMO channel information from the UE to the NW has been an active study in 3GPP RAN1 Release-18. One of the discussion topics for the CSI compression is on the quantization of the compressed CSI at the UE-side and dequantization at the NW-side. The two main options considered are quantization-aware models, where the trained model includes quantization, and quantization-non-aware models, where quantization is handled separately.
We focus on improving the performance of quantization-non-aware models by introducing adaptive non-uniform quantization of the CSI compression (i.e., for the output of CSI encoder).  [...]


One of the objectives of the CENTRIC project is to develop a user-centric communication stack that can be tailored to user-specific needs. For this, we introduce a neural network (NN)-based multiuser multiple-input multiple-output (MU-MIMO) receiver with 5G New Radio (5G NR) physical uplink shared channel (PUSCH) compatibility. To showcase the practicability of the approach, we have presented a hardware-in-the-loop demonstrator at the Brooklyn 6G Summit 2023. A specific focus of the neural network architecture is on flexibility concerning a varying number of users and a configurable number of subcarriers. This flexibility is a key enabler for practical deployment. The proposed architecture does neither require any retraining if additional users join or leave the network, nor if the number of allocated subcarriers or physical resource blocks (PRBs) change. The neural receiver and our experiments are implemented using NVIDIA Sionna library for link-level simulations. [...]


The control of dynamical systems is the backbone of modern technologies, ranging from industrial processes to autonomous vehicles. In many of these scenarios, systems must be controlled while satisfying a set of safety and reliability constraints concerning the unknown evolution of a target process.

In our recent work[1], we proposed Probabilistic Time Series-Conformal Risk Prediction (PTS-CRC), a novel calibration procedure that enables reliable modelling of uncertainty regarding future system states. PTS-CRC predictions can be used to solve model predictive control problems under reliability, safety, and performance constraints. [...]


• Cluster-then-Match: Efficient Management of Human-Centric, Cell-Less 6G Networks.  IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), 04 Jun 203

• Cooperative Navigation via Relational Graphs and State Abstraction. IEEE Networking Letters, 06 Jun 2023

• Bayesian and Multi-Armed Contextual Meta-Optimization for Efficient Wireless Radio Resource Management. IEEE Transactions on Cognitive Communications and Networking, 19 Jun 2023

• The Role of AI on 6G MAC. The Role of AI on 6G MAC, 18 Jul 2023

• Method and Signaling for Encoding the Downlink Control Information (DCI) Message in 6G Radio Qccess Networks WO. PCT Application, 25 Sep 2023

• 4MERGE: Meta Reinforcement Learning for Tunable RL Agents at the Edge. IEEE Globecom 2023, 04 Dec 2023

• Emergent Communication Protocol Learning for Task Offloading in Industrial Internet of Things2023. IEEE Global Communications Conference: IoT and Sensor Networks, 04 Dec 2023


Anastasius Gavras from Eurescom and member of the CENTRIC management team, participated at the FUSECO Forum on 14-15 September 2023 in Berlin.

In the session “Beyond 5G and 6G Network Technologies and Enablers: RAN, Core Network, Edge Computing, Network Management and AI”, he presented a brief overview of the CENTRIC project and an outlook on the expected results of it. [...]


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CENTRIC-SNS project has received funding from the European Horizon Europe Programme for research, technological development and demonstration under grant agreement 101096379.


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