Access Point Deployment for Localizing Accuracy and User Rate in Cell-Free Systems

Evolving next-generation mobile networks is designed to provide ubiquitous coverage and networked sensing. With utility of multi-view sensing and multi-node joint transmission, cell-free is a promising technique to realize this prospect. This paper aims to tackle the problem of access point (AP) deployment in cell-free systems to balance the sensing accuracy and user rate. By merging the D-optimality with Euclidean criterion, a novel integrated metric is proposed to be the objective function for both max-sum and max-min problems, which respectively guarantee the overall and lowest performance in multi-user communication and target tracking scenario. To solve the corresponding high dimensional non-convex multi-objective problem, the Soft actor-critic (SAC) is utilized to avoid risk of local optimal result. Numerical results demonstrate that proposed SAC-based APs deployment method achieves $20\%$ of overall performance and $120\%$ of lowest performance.

Theoretical Limit of Radar Parameter Estimation

In the field of radar parameter estimation, Cramer-Rao bound (CRB) is a commonly used theoretical limit. However, CRB is only achievable under high signal-to-noise (SNR) and does not adequately characterize performance in low and medium SNRs. In this paper, we employ the thoughts and methodologies of Shannon's information theory to study the theoretical limit of radar parameter estimation. Based on the posteriori probability density function of targets' parameters, joint range-scattering information and entropy error (EE) are defined to evaluate the performance. The closed-form approximation of EE is derived, which indicates that EE degenerates to the CRB in the high SNR region. For radar ranging, it is proved that the range information and the entropy error can be achieved by the sampling a posterior probability estimator, whose performance is entirely determined by the theoretical posteriori probability density function of the radar parameter estimation system. The range information and the entropy error are simulated with sampling a posterior probability estimator, where they are shown to outperform the CRB as they can be achieved under all SNR conditions