DoCoMo Communications Laboratories Europe GmbH

In mobile communication systems, the increasing densification of radio access networks is creating unprecedented computational stress for baseband processing, threatening the industry's sustainability, and new computing paradigms are urgently needed to improve the efficiency of wireless processors. Quantum computing promises to revolutionize many computing-intensive tasks across diverse fields and therefore may be the key to realizing ultra-dense next-generation mobile systems that remain economically and environmentally viable. This paper investigates the potential of Quantum computing to accelerate Forward Error Correction (FEC), the most compute-heavy component of wireless processors. We first propose Qu4Fec, a novel solution for decoding Low-Density Parity Check (LDPC) codes on Quantum Processing Units (QPUs), which we show to outperform state-of-the-art approaches, by reducing the Block Error Rate (BLER) by nearly an order of magnitude in simulation. We then implement Qu4Fec on a real-world QPU platform to study its practical viability and performance. Our experiments reveal that current cutting-edge QPU architectures curb the capabilities of FEC and expose the underlying factors, including long qubit chains, scaling, and quantization. Based on these insights, we suggest original blueprints for future QPUs that can better support Quantum-based wireless processors. Overall, this paper provides a reliable reality check for the feasibility of wireless processing on Quantum annealers: as QPUs start to be considered part of a possible 6G landscape, our work may open new research paths towards the design of FEC methods for Quantum-powered wireless processors.

Smart factories enhance production efficiency and sustainability, but emergencies like human errors, machinery failures and natural disasters pose significant risks. In critical situations, such as fires or earthquakes, collaborative robots can assist first-responders by entering damaged buildings and locating missing persons, mitigating potential losses. Unlike previous solutions that overlook the critical aspect of energy management, in this paper we propose REACT, a smart energy-aware orchestrator that optimizes the exploration phase, ensuring prolonged operational time and effective area coverage. Our solution leverages a fleet of collaborative robots equipped with advanced sensors and communication capabilities to explore and navigate unknown indoor environments, such as smart factories affected by fires or earthquakes, with high density of obstacles. By leveraging real-time data exchange and cooperative algorithms, the robots dynamically adjust their paths, minimize redundant movements and reduce energy consumption. Extensive simulations confirm that our approach significantly improves the efficiency and reliability of search and rescue missions in complex indoor environments, improving the exploration rate by 10% over existing methods and reaching a map coverage of 97% under time critical operations, up to nearly 100% under relaxed time constraint.

This article introduces a field verification of automated driving using the Cooperative Infrastructure Platform being developed by NTT laboratories.The Cooperative Infrastructure Platform aims to ensure stable communication in remote monitoring and improve the safety of automated driving.This platform is also being used as the communication function of the remote control system for automated driving developed by NTT DOCOMO, and field tests are being conducted in collaboration with NTT DOCOMO.

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's vision, methodologies, and potential impact, marking a significant stride towards a more connected and intelligent world.

We attempted to construct a digital twin of Kobe City center, from the aspect of pedestrian traffic simulation. Policy evaluation was conducted using traffic demand based on mobile phone population statistics provided by NTT DOCOMO, INC., CrowdWalk, an agent-based pedestrian simulator, and manually edited map obtained from Open Street Map to implement pedestrian signals. Background pedestrian population was estimated to be 6509, and 10,000 evacuees are assumed in the Shinko area. All of them are assumed to evacuate into three stations, JR Sannomiya, Hankyu Sannomiya, and Motomachi. The evacuation time was originally simulated to be 25,685 s without any aided policies. As a policy, splitting the evacuation route reduced the evacuation time into 17,780 s which is 69% of the original. In addition, removing signals reduced the time furthermore into 9550 s, 37% in total. Only removing the signal resulted 12,475 s, which is a reduction to 52% of the original.

This paper presents millimeter-wave band coverage extension when the front-end module can operate at cryogenic temperatures. It is very important to attain sufficient coverage area for servicing millimeter-wave band communications. However, in general, the coverage area becomes smaller than that of operating cellular bands such as 2 GHz. The cryogenically cooled front-end module provides to reduce noise figure (NF) by several dB from the room temperature. This feature means that the coverage area can expand by the reduced NF at cryogenic temperatures. The calculation results confirm that the coverage can expand 2.75 times when the cryogenically cooled front-end module operates at 100 K. These results lead to reduce the number of millimeter-wave base stations.

This white paper summarizes the main findings from the European research landscape on the vision of the 6G architecture. Such a design vision is derived from around 45 projects starting from October 2020 in all relevant areas of 5G while paving the way towards 6G, within the 5G Public Private-Partnership (5G PPP) in the scope of the European Framework for Research and Innovation (the list of contributing projects can be obtained from the 5G PPP website at https://5g-ppp.eu/5g-ppp-phase-3-projects/.). At present, the European networking research community has started a new program along with 33 projects on the Smart Networks and Service (SNS) programme that will focus on 5G advanced and 6G. The 5G/B5G Architecture Working Group (WG), as part of the 5G PPP Initiative, is identifying and capturing novel trends and key technological enablers for the realization of the 5G and 6G architecture. The main findings and results of the Architecture WG are now captured in this white paper, which presents a consolidated view from European perspective on the technical directions for the architecture design in the 6G era.<br> The white paper starts with an analysis of the societal, economic, regulatory and technological trends on the design and development of future 6G networks towards 2030 and beyond. Infused by emerging and disruptive digital technologies on the horizon, wireless networks are and will be the keystone for enabling a transformation of the global economy and the evolution of human society. The evolution journey will carry on in the years ahead, driving a large scale of adoption of 5G and 5G-Advanced use cases with significantly decreased deployment and operation costs, and enabling new and innovative use-case-driven solutions towards the 6G with higher economic and societal values.<br> To realize aforementioned trends, this white paper highlights the related research work and presents all the key elements and key architecture enablers and solutions of future 6G network design; a design that is deeply rooted in real needs and can profoundly benefit humanity in the mid-to-long term. Specifically, a high-level view of the 6G End-to-End architecture as well as a functional view of the 6G reference architecture are introduced in this white paper, taking into consideration of new stakeholders in the mobile network ecosystem and how the architectural work is taking into account their requirements in all the domains of the network. The key architecture enablers, that will form the backbone of future 6G network architecture, includes all the related technological solutions for building intelligent, flexible, sustainable, secure, programmable networks and enabling versatile radio technologies, localization and sensing in the 6G networks.

In the COVID-19 epidemic, balancing a trade-off between preventing the spread of infection and maintaining economic activity is a global challenge. Based on the idea that avoiding crowds leads to the prevention of the spread of infection, we propose to leverage a dynamic pricing method to level out congestion with an aim to balance the trade-off between preventing the spread of infection and economic activity. In our method, reward points are provided according to the degree of congestion in stores to encourage customers to visit stores at less crowded times to avoid crowds. Since store congestion is greatly affected by movement restrictions such as a state of emergency, we propose a demand prediction model that takes into account the biases of the data acquisition circumstances. In an offline evaluation, we validated the effectiveness of the proposed unbiased demand prediction model based on the data from an actual campaign conducted for more than 7 months in Kyushu University. The evaluation results showed that our unbiased model reduced the prediction error by up to relatively 25.0% compared with the model that does not consider biases. Our system has been deployed in our closed service since December, 2021. Online evaluation result showed that our application improved conversion rate by 12.0% and reduced cost per acquisition by up to 11.6%.