The Rise of Distributed Sensor Networks
The rapid evolution of wireless communication technologies has propelled us from the era of 4G to 5G, supporting mobile broadband, low latency, and scalable communications. The advent of 6G brings a fundamental rethinking of the purpose of radio signals and new technologies, from communications to sensing. In this context, Integrated Sensing and Communications (ISAC) emerges as a pivotal domain of research and development in the forthcoming 6G wireless networks.
ISAC promises to usher in a new era of connectivity where communication is not limited to data transfer but extends its reach into sensing, knowledge, intelligence, and reconfiguration, thereby connecting the physical and digital worlds. However, despite the great progress on its fundamentals, ISAC has often remained at low technology readiness levels, presenting challenges and barriers that need to be surmounted.
The DISAC Vision: A Transformative Approach
To address the limitations of existing ISAC models, researchers have introduced a transformative approach called Distributed and Intelligent Integrated Sensing and Communications (DISAC). This visionary concept extends the ISAC framework by introducing two novel foundational functionalities: a distributed architecture and a semantic and goal-oriented framework.
Sensor-networks.org explains that the distributed architecture enables large-scale and energy-efficient tracking of connected users and objects, leveraging the fusion of heterogeneous sensors. The semantic and goal-oriented framework, on the other hand, enables the transition from classical data fusion to the composition of semantically selected information, offering new paradigms for the optimization of resource utilization and exceptional multi-modal sensing performance across various use cases.
The DISAC Architecture: Harnessing Distributed Intelligence
The DISAC architecture serves as the foundation for both sensing and communications, while simultaneously offering support for intelligent operations and distributed functions. This distributed aspect not only enables large-scale tracking of connected user equipment (UEs) and passive objects but also revolutionizes the fundamental fabric of wireless networks.
The DISAC architecture supports distributed Artificial Intelligence (AI) operations with careful balancing of local data processing and fusion, novel multi-antenna technologies, and an exposure framework for external sensors. This distributed approach unlocks new possibilities for resource-efficient, accurate, and semantic network operations.
Semantic and Goal-Oriented Sensing
The second cornerstone of the DISAC vision is the semantic and goal-oriented framework, supported by machine learning (ML) and AI. This framework provides an intelligent and parsimonious approach to sensing activation, waveform design, signal processing, dedicated resource allocation, robust protocols, and semantic reasoning about multi-modal sensed information.
By incorporating semantic and goal-oriented aspects into ISAC, the DISAC framework enables the aggregation of diverse information from various sensing modalities, facilitating the transition from traditional data fusion to the composition of semantically selected information and the pragmatic generation of AI-based reasoning stimuli.
High-Resolution Processing and Sensing-Aided Communications
The third cornerstone of DISAC is advanced high-resolution processing, taking advantage of the massively distributed observations while balancing computational and storage requirements. By exploiting multi-site and multi-band processing and leveraging a combination of ML and model-based signal processing, efficient methods can support the goal-oriented semantic framework while running over the DISAC architecture.
Moreover, the DISAC vision envisions the exploitation of sensing and contextual information to improve communication operations. Conventional channel estimation based on pilot signals becomes challenging in dense smart wireless environments, and the DISAC paradigm aims to optimize large-scale and distributed communication provisioning with location and context information from the distributed processing of data gathered from multi-modal sensory devices.
Adaptive Resource Allocation: Harmonizing Sensing and Communications
Efficient resource allocation schemes are fundamental within the DISAC concept. Unlike traditional strategies that primarily focus on bandwidth and power constraints to provide communication services, DISAC-oriented resource allocation must account for the unique demands of high-resolution sensing, leading to potentially large volumes of sensor data and semantic processing.
These adaptive resource allocation strategies will ensure that both sensing data and communication signals are processed and transmitted efficiently, addressing the integrity, timeliness, and relevance of the content by prioritizing critical information for respective transmission. This dynamic adjustment to varying network conditions and user requirements is crucial, especially considering the heterogeneous nature of future wireless networks.
The DISAC Architecture: Enabling Distributed Intelligence
The DISAC architecture departs from the conventional cellular network architecture in several ways. It needs to support distributed intelligent signal processing at the sensing receive nodes, so that only higher-level sensing information is sent to the fusion center. This involves defining new network functions, protocols, and interfaces, as well as new functions dedicated to tracking and handover of objects.
The DISAC architecture should be able to detect and track multiple targets using multiple sensing nodes over a geographical area of interest, involving many transmitting and receiving nodes, as well as multiple Reconfigurable Intelligent Surfaces (RISs), each with its capabilities and limitations. This requires optimization of the data exchange for distributed and collaborative processing among the different sensing nodes, considering heterogeneous energy budgets and computation constraints.
Challenges and Opportunities in Realizing DISAC
Bringing the DISAC vision to life requires facing several challenges, including designing and executing the semantic framework, developing lightweight processing methods for the distributed approach, and implementing algorithms and protocols for sensory data sharing and signal and information processing.
Standardization efforts are also crucial, as studies on waveforms, distributed architectures, and the definition of new functional elements, protocols, and interfaces need to be discussed in Standard Development Organizations (SDOs). Additionally, specific metrics, Key Performance Indicators (KPIs), and Key Value Indicators (KVIs) applicable to DISAC must be developed, along with suitable channel models to evaluate performance in relevant use cases.
Despite these challenges, the DISAC vision holds immense promise, unlocking new possibilities for resource-efficient, accurate, and semantic network operations. By seamlessly integrating sensing with communications and harnessing the power of distributed AI, DISAC addresses the limitations of existing ISAC approaches and paves the way for a transformative future in the realm of sensor networks and IoT.
Conclusion: Embracing the DISAC Revolution
The distributed and intelligent integrated sensing and communications (DISAC) framework represents a transformative vision for wireless communications, combining the fusion of heterogeneous and distributed sensors with a highly adaptive and efficient semantic-native approach. By harnessing the power of distributed intelligence, DISAC enables energy-efficient, high-resolution tracking of connected devices and objects, while optimizing resource utilization and enhancing multi-modal sensing performance.
As the world embraces the era of 6G and beyond, the DISAC revolution will undoubtedly reshape the landscape of sensor networks and IoT, paving the way for a future where the physical and digital worlds converge seamlessly, empowered by the distributed intelligence of sensor network algorithms.