Framework for a Perceptive Mobile Network using Joint Communication and Radar Sensing (1901.05558v1)

Abstract: In this paper, we develop a framework for a novel perceptive mobile/cellular network that integrates radar sensing function into the mobile communication network. We propose a unified system platform that enables downlink and uplink sensing, sharing the same transmitted signals with communications. We aim to tackle the fundamental sensing parameter estimation problem in perceptive mobile networks, by addressing two key challenges associated with sophisticated mobile signals and rich multipath in mobile networks. To extract sensing parameters from orthogonal frequency division multiple access (OFDMA) and spatial division multiple access (SDMA) communication signals, we propose two approaches to formulate it to problems that can be solved by compressive sensing techniques. Most sensing algorithms have limits on the number of multipath signals for their inputs. To reduce the multipath signals, as well as removing unwanted clutter signals, we propose a background subtraction method based on simple recursive computation, and provide a closed-form expression for performance characterization. The effectiveness of these methods is validated in simulations.

• The paper demonstrates a joint communication and radar sensing framework that leverages existing mobile networks for dual functionality.
• It employs direct and indirect compressive sensing methods to extract key parameters like delay, AoA, AoD, and Doppler shift amid multipath effects.
• The framework utilizes a CRAN architecture with multiuser MIMO and OFDMA to enable cooperative sensing and recursive background subtraction for clutter suppression.

Framework for a Perceptive Mobile Network using Joint Communication and Radar Sensing

The paper by Md. Lushanur Rahman et al. introduces a framework for integrating radar sensing capabilities into mobile communication networks, termed as perceptive mobile networks. This integration is achieved through Joint Communication and Radar Sensing (JCAS) technologies, enabling mobile networks to perform radar sensing without the need for additional infrastructure. The JCAS approach allows for the sharing of communication signals for simultaneous radar sensing, leveraging existing mobile infrastructure to provide dual functionality.

System Platform and Challenges

The proposed system platform is based on a Cloud-Radio Access Network (CRAN) architecture that employs multiuser MIMO and OFDMA technologies. This architecture facilitates cooperative sensing by distributing synchronized Remote Radio Units (RRUs), each equipped with multiple antennas. The paper delineates two types of sensing operations: uplink and downlink sensing. Downlink sensing involves utilizing downlink communication signals from RRUs for environmental sensing, while uplink sensing employs uplink signals from user devices.

The integration of radar sensing into mobile networks poses several challenges, particularly due to the complex structure of modern communication signals and the rich multipath environment inherent in mobile networks. Sophisticated processing techniques are required to extract sensing parameters such as delay, angle-of-arrival (AoA), angle-of-departure (AoD), and Doppler shift from these signals.

Methodology

To tackle these challenges, the authors propose two sensing parameter estimation schemes using compressive sensing techniques, which are effective in managing the sparseness of multipath signals:

1. Direct Estimation: This method utilizes the known transmitted data symbols for sensing. It formulates the signal as a multi-measurement vector (MMV) sparse problem, applying algorithms such as the fast marginalized block sparse Bayesian learning algorithm for estimation.
2. Indirect Estimation using Signal Stripping: This approach simplifies the signal input to sensing algorithms by relying on estimated data symbols and channels, akin to the decision-directed channel estimation techniques used in communications. This method significantly reduces the algorithm's complexity by separating signals from different users or RRUs.

Both methods are designed to address the fundamental challenges posed by sophisticated signal structures and multipath environments in mobile networks, and have been validated through a series of simulations.

Background Subtraction for Clutter Suppression

The paper also presents a low-complexity background subtraction method for clutter reduction. This method involves recursive computation to estimate and subtract clutter signals, defined as echoes with near-zero Doppler frequencies, from the sensing input. The recursive algorithm gradually refines the clutter estimation, providing a cleaner input for sensing parameter estimation.

Implications and Future Directions

The proposed framework and methodologies demonstrate how mobile networks can evolve into perceptive systems that provide simultaneous communication and sensing capabilities. The research opens avenues for various applications, including object detection and collision avoidance in vehicular networks, enhanced situational awareness in smart cities, and improved cellular services.

Further research could explore optimization of the proposed algorithms for complexity and performance, as well as the development of novel sensing applications leveraging the perceptive mobile network. Integrating machine learning techniques for extended sensing applications could also enhance the network's sensing capabilities without explicitly requiring parameter estimation.

In conclusion, this paper provides a robust foundation for integrating radar sensing into mobile networks, facilitating new applications that can benefit both communication and sensing domains. The integration model described holds promise for the development of ubiquitous sensing technologies in future mobile network deployments.