Enabling Joint Communication and Radar Sensing in Mobile Networks -- A Survey (2006.07559v4)

Abstract: Mobile network is evolving from a communication-only network towards one with joint communication and radar/radio sensing (JCAS) capabilities, that we call perceptive mobile network (PMN). In PMNs, JCAS integrates sensing into communications, sharing a majority of system modules and the same transmitted signals. The PMN is expected to provide a ubiquitous radio sensing platform and enable a vast number of novel smart applications, whilst providing non-compromised communications. In this paper, we present a broad picture of the motivation, methodologies, challenges, and research opportunities of realizing PMN, by providing a comprehensive survey for systems and technologies developed mainly in the last ten years. Beginning by reviewing the work on coexisting communication and radar systems, we highlight their limits on addressing the interference problem, and then introduce the JCAS technology. We then set up JCAS in the mobile network context and envisage its potential applications. We continue to provide a brief review of three types of JCAS systems, with particular attention to their differences in design philosophy. We then introduce a framework of PMN, including the system platform and infrastructure, three types of sensing operations, and signals usable for sensing. Subsequently, we discuss required system modifications to enable sensing on current communication-only infrastructure. Within the context of PMN, we review stimulating research problems and potential solutions, organized under nine topics: performance bounds, waveform optimization, antenna array design, clutter suppression, sensing parameter estimation, resolution of sensing ambiguity, pattern analysis, networked sensing under cellular topology, and sensing-assisted communications. We conclude the paper by listing key open research problems for the aforementioned topics and sharing some lessons that we have learned.

• The paper provides a comprehensive survey of perceptive mobile networks that integrate communication and radar sensing, detailing both system architectures and sensing operations.
• The paper outlines three sensing methods—downlink active, downlink passive, and uplink sensing—that facilitate precise tracking and environmental monitoring.
• The paper identifies key challenges such as waveform optimization, sensing parameter estimation, and networked sensing, setting the stage for future PMN advancements.

Enabling Joint Communication and Radar Sensing in Mobile Networks - A Survey

The paper provides a detailed survey of the advancement of mobile networks towards integrating joint communication and radar/radio sensing (JCAS) capabilities, creating what is termed as a Perceptive Mobile Network (PMN). The integration of sensing into mobile networks proposes a unified platform capable of offering both non-compromised communication and advanced sensing functionalities.

System Architecture and Framework

The paper proposes two potential architectures for deploying PMNs: a cloud radio access network (CRAN) and standalone base stations (BS). A CRAN integrates a central processing unit with distributed remote radio units (RRUs), employing a cooperative network architecture that leverages distributed nodes for enhanced sensing. Meanwhile, standalone BS-topologies focus on enhancing existing infrastructure individually for dual functionalities.

Sensing Operations

Three distinct sensing operations are explored:

1. Downlink Active Sensing: Utilizing reflections from a BS’s transmitted signals to sense its immediate surroundings, though this requires full-duplex capability to be efficient.
2. Downlink Passive Sensing: Involves receiving and processing signals from different BSs. This operation is robust against issues of privacy, focusing on inter-BS and environmental sensing.
3. Uplink Sensing: This approach captures uplink signals from user equipment (UE), providing insights into environmental dynamics and leveraging existing communication infrastructure with minimal changes.

Numerical Results and Research Opportunities

The survey emphasizes the strong numerical capabilities of PMNs for tracking and localizing objects with precision. Multiple research challenges are identified:

• Sensing Parameter Estimation: The need for advanced algorithms capable of deriving continuous parameters from fragmented high-mobility environments remains a challenge.
• Waveform Optimization: Optimizing waveforms to satisfy diverse radar and communication requirements is necessary for functional coherence.
• Networked Sensing: Exploiting the cellular network architecture for cooperative sensing presents significant unexplored opportunities, promising improved environmental awareness and coordinated sensing.

Practical and Theoretical Implications

The paper delineates multiple implications for both practical deployment and theoretical explorations:

• Integration of Services: Enhancing network services to supply both communication and radar data opens pathways to applications in autonomous systems, smart cities, and IoT environments.
• Spectrum Efficiency: By unifying communication and sensing, spectrum utilization can be considerably optimized, presenting a compelling advantage for operators.
• Technological Convergence: The paper underlines that resolution of existing full-duplex and synchronization challenges are pivotal in advancing the dual-functionality system from architectural theories to deployable technologies.

Future Developments and Conclusion

Addressing the technology evolution from current mobile networking into perceptive networks, the paper presents innovative considerations into network sensing and joint communications. Envisioning a future dense with IoT and smart applications, the development of PMNs could redefine wireless networking by providing adaptive sensing and communication solutions seamlessly embedded within existing technologies. The paper concludes with discussions on significant open research problems, emphasizing the combinatorial optimizations and cross-layer designs needed for realizing PMNs in high-dynamic, interference-rich environments. These findings pave the way for PMNs to eventually become the backbone of future intelligent communication networks.