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Integrated sensing and communications in the 3GPP New Radio: sensing limits

Santiago Fernńdez, Javier Giménez, Mari Carmen Aguayo-Torres, José A. Cortés

Abstract

Integrated Sensing and Communications (ISAC) is regarded as a key element of the beyond-fifth-generation (5G) and sixth-generation (6G) systems, raising the question of whether current 5G New Radio (NR) signal structures can meet the sensing accuracy requirements specified by the Third Generation Partnership Project (3GPP). This paper addresses this issue by analyzing the fundamental limits of range and velocity estimation through the Cramér-Rao lower bound (CRLB) for a monostatic unmanned aerial vehicle (UAV) sensing use case currently under consideration in the 3GPP standardization process. The study focuses on standardized signals and also evaluates the potential performance gains achievable with reference signals specifically designed for sensing purposes. The compact CRLB expressions derived in this work highlight the fundamental trade-offs between estimation accuracy and system parameters. The results further indicate that information from multiple slots must be exploited in the estimation process to attain the performance targets defined by the 3GPP. As a result, the 5G NR positioning reference signal (PRS), whose patterns may be suboptimal for velocity estimation when using single-slot resources, becomes suitable when multislot estimation is employed. Finally, we propose a two-step iterative range and radial-velocity estimator that attains the CRLB over a significantly wider range of distances than conventional maximum-likelihood (ML) estimators, for which the well-known threshold effect severely limits the distance range over which the accuracy requirements imposed by the 3GPP are satisfied.

Integrated sensing and communications in the 3GPP New Radio: sensing limits

Abstract

Integrated Sensing and Communications (ISAC) is regarded as a key element of the beyond-fifth-generation (5G) and sixth-generation (6G) systems, raising the question of whether current 5G New Radio (NR) signal structures can meet the sensing accuracy requirements specified by the Third Generation Partnership Project (3GPP). This paper addresses this issue by analyzing the fundamental limits of range and velocity estimation through the Cramér-Rao lower bound (CRLB) for a monostatic unmanned aerial vehicle (UAV) sensing use case currently under consideration in the 3GPP standardization process. The study focuses on standardized signals and also evaluates the potential performance gains achievable with reference signals specifically designed for sensing purposes. The compact CRLB expressions derived in this work highlight the fundamental trade-offs between estimation accuracy and system parameters. The results further indicate that information from multiple slots must be exploited in the estimation process to attain the performance targets defined by the 3GPP. As a result, the 5G NR positioning reference signal (PRS), whose patterns may be suboptimal for velocity estimation when using single-slot resources, becomes suitable when multislot estimation is employed. Finally, we propose a two-step iterative range and radial-velocity estimator that attains the CRLB over a significantly wider range of distances than conventional maximum-likelihood (ML) estimators, for which the well-known threshold effect severely limits the distance range over which the accuracy requirements imposed by the 3GPP are satisfied.

Paper Structure

This paper contains 13 sections, 37 equations, 8 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. System model
  3. Cramer-Rao lower bound of range and velocity
  4. Sensing patterns
  5. Full-slot
  6. Positioning reference signal (PRS)
  7. DDRS
  8. Range and velocity ML estimators
  9. Numerical results and discussion
  10. System parameters
  11. Sensing resolution and maximum values
  12. Accuracy assessment
  13. Conclusion

Figures (8)

  • Figure 1: Sensing use case consisting of a monostatic configuration and a single UAV.
  • Figure 2: Example of PRS patterns with the same overhead. The green resource elements are the ones for sensing, the orange ones are used for other purposes.
  • Figure 3: Patterns of the proposed DDRS signal for sensing purposes.
  • Figure 4: Maximum achievable accuracy in range and radial-velocity estimations using the full-slot sensing pattern.
  • Figure 5: Maximum achievable accuracy in range and radial-velocity estimations using different PRS sensing patterns.
  • ...and 3 more figures