Periodic Reporting for period 1 - 6G-SENSES (SEamless integratioN of efficient 6G wireleSs tEchnologies for communication and Sensing)
Reporting period: 2024-01-01 to 2024-12-31
6G is witnessing paradigm changes, such as the elimination of the traditional “cell-like” deployment of mobile communications infrastructure, enabled by the introduction of cell-free massive multiple-input multiple output (CF-mMIMO). This is considered a major technical asset to improve the performance of wireless networks. When all access points (APs) deployed in a system collaborate to serve a fixed number of users, as the number of APs increases, the cell boundary effect of traditional cells can be eliminated. As a result, a “cell-free” system is formed and the capacity and performance of the communication system can be significantly improved. CF-mMIMO not only increases the spectral and energy efficiency but also reduces latencies and improves the system reliability with diversity and multiplexing trade-offs, thus supporting ultrareliable low-latency transmission. Extending CF-mMIMO to high-frequency bands can increase the system capacity and effectively solve the link robustness problem.
However, the practicalities of the technology are hindering its broad implementation and deployment, which makes necessary to study the implementation architecture of the new CF-mMIMO, taking both performance and implementation complexity into account. The major challenge in the practical implementation of CF-mMIMO systems is the overhead in terms of signaling and coordination for pre- and de-coding of the transmitting and receive data, the data itself, and the required control signals for synchronization or scheduling.
Sensing is playing a key role in future 6G wireless networks, with a plethora of applications that are supposed to benefit from it. We are witnessing that the architecture of wireless systems for sensing resemble that for communications in terms of system architectures, channel characteristics, and the use of signal processing methods. Fortunately, the wide deployment of MIMO communication systems and the use of higher frequency bands for communication, have undoubtedly benefited the use of sensing functionalities in those devices initially devised for communication. In addition, recent investigations demonstrate the potential to exploit RIS technology in Integrated Sensing and Communication (ISAC) systems such as to optimize the ISACsystem in terms of accuracy and coverage. The integration of sensing functions into the communication systems is envisioned to be an integral part of the 6G and future communication systems. The early motivation of JCAS comes from the scarcity of spectrum resources . This topic is especially interesting for energy efficient resource allocation, as the resources that should be assigned to a part of a network to optimally offer the needed services can be anticipated based on the surrounding environment in combination with Artificial Intelligence (AI) / Machine Learning (ML) methods.
Moreover, RIS-assisted links further enhance the availability/coverage and sensing performance. This requires an optimization of distributed signal processing and resource allocation schemes tailored for RIS-assisted CF network architecture. On this last point, at a system level, new nodes and architectures are considered that move away from the assumption that additional capacity requires installation of new base stations. These architectural considerations will include RIS and CF architectures that could provide a better distribution of radio power around an area when compared to the centralised cellular approach.
O-RAN has emerged as a way to better organise mobile networks, and proves to be interesting for practical deployments of CF-mMIMO networks for two reasons. First, the physical layer is split between the O-RAN Distributed Units (O-DUs) and Radio Units (O-RUs). Second, extra functional blocks are introduced, enabling AI and containerized service orchestration on the network. These include the Near-Real Time RAN Intelligent Controller (Near-RT RIC), the Non-RT RIC, and the Service Management and Orchestration (SMO) framework . The new interfaces and options for network-wide control can be exploited to achieve cooperation amongst O-RUs, even beyond the borders of the O-DUs.
Within 6G-SENSES, the know-how on CF architectures provides the basis to develop JCAS services that exploit the distributed antenna and sensing environment from CF and distributed MIMO architectures. The developed CF-mMIMO ISAC system will enable the following key target outcomes of the call: cm-level accuracy localization, extremely high reliability, and extreme energy and spectrum efficiency. This is achieved by smart deployment of distributed radio units with several distributed massive antennas which allow high sensing accuracy and very high reliability (zero outages), as well as low energy consumption.
6G-SENSES proposes the integration of novel 6G RAN technologies such as CF-mMIMO and ISAC to support the 6G vision that is sustained by the current (and future) architectural framework based on 3GPP and O-RAN. The project considers a multi-technology RAN ecosystem with technologies that are able to offer sensing functionalities (3GPP and non-3GPP). These technologies are Sub-6, Wi-Fi, mmWave and 5G NR, which will coexist in a ISAC framework whose goal is to obtain a faithful representation of the surrounding environment. This framework will make use of new physical layer technologies to increase the cooperation between technologies and their inherent capabilities to improve the precision/acccuracy of the sensing capabilities. To further strengthen communication and sensing functionalities, the project will leverage RIS and will work on the design, optimization and modelling of the surfaces. Sensing information stemming from these technologies will be pushed to the O-RAN framework for optimization purposes and to build up a so-called network twin.
However, the practicalities of the technology are hindering its broad implementation and deployment, which makes necessary to study the implementation architecture of the new CF-mMIMO, taking both performance and implementation complexity into account. The major challenge in the practical implementation of CF-mMIMO systems is the overhead in terms of signaling and coordination for pre- and de-coding of the transmitting and receive data, the data itself, and the required control signals for synchronization or scheduling.
Sensing is playing a key role in future 6G wireless networks, with a plethora of applications that are supposed to benefit from it. We are witnessing that the architecture of wireless systems for sensing resemble that for communications in terms of system architectures, channel characteristics, and the use of signal processing methods. Fortunately, the wide deployment of MIMO communication systems and the use of higher frequency bands for communication, have undoubtedly benefited the use of sensing functionalities in those devices initially devised for communication. In addition, recent investigations demonstrate the potential to exploit RIS technology in Integrated Sensing and Communication (ISAC) systems such as to optimize the ISACsystem in terms of accuracy and coverage. The integration of sensing functions into the communication systems is envisioned to be an integral part of the 6G and future communication systems. The early motivation of JCAS comes from the scarcity of spectrum resources . This topic is especially interesting for energy efficient resource allocation, as the resources that should be assigned to a part of a network to optimally offer the needed services can be anticipated based on the surrounding environment in combination with Artificial Intelligence (AI) / Machine Learning (ML) methods.
Moreover, RIS-assisted links further enhance the availability/coverage and sensing performance. This requires an optimization of distributed signal processing and resource allocation schemes tailored for RIS-assisted CF network architecture. On this last point, at a system level, new nodes and architectures are considered that move away from the assumption that additional capacity requires installation of new base stations. These architectural considerations will include RIS and CF architectures that could provide a better distribution of radio power around an area when compared to the centralised cellular approach.
O-RAN has emerged as a way to better organise mobile networks, and proves to be interesting for practical deployments of CF-mMIMO networks for two reasons. First, the physical layer is split between the O-RAN Distributed Units (O-DUs) and Radio Units (O-RUs). Second, extra functional blocks are introduced, enabling AI and containerized service orchestration on the network. These include the Near-Real Time RAN Intelligent Controller (Near-RT RIC), the Non-RT RIC, and the Service Management and Orchestration (SMO) framework . The new interfaces and options for network-wide control can be exploited to achieve cooperation amongst O-RUs, even beyond the borders of the O-DUs.
Within 6G-SENSES, the know-how on CF architectures provides the basis to develop JCAS services that exploit the distributed antenna and sensing environment from CF and distributed MIMO architectures. The developed CF-mMIMO ISAC system will enable the following key target outcomes of the call: cm-level accuracy localization, extremely high reliability, and extreme energy and spectrum efficiency. This is achieved by smart deployment of distributed radio units with several distributed massive antennas which allow high sensing accuracy and very high reliability (zero outages), as well as low energy consumption.
6G-SENSES proposes the integration of novel 6G RAN technologies such as CF-mMIMO and ISAC to support the 6G vision that is sustained by the current (and future) architectural framework based on 3GPP and O-RAN. The project considers a multi-technology RAN ecosystem with technologies that are able to offer sensing functionalities (3GPP and non-3GPP). These technologies are Sub-6, Wi-Fi, mmWave and 5G NR, which will coexist in a ISAC framework whose goal is to obtain a faithful representation of the surrounding environment. This framework will make use of new physical layer technologies to increase the cooperation between technologies and their inherent capabilities to improve the precision/acccuracy of the sensing capabilities. To further strengthen communication and sensing functionalities, the project will leverage RIS and will work on the design, optimization and modelling of the surfaces. Sensing information stemming from these technologies will be pushed to the O-RAN framework for optimization purposes and to build up a so-called network twin.
(to be included by Period 1 Review)