Abstract
5G communication systems operating above 24 GHz have promising properties for user localization and environment mapping. Existing studies have either relied on simplified abstract models of the signal propagation and the measurements, or are based on direct positioning approaches, which directly map the received waveform to a position. In this study, we consider an intermediate approach, which consists of four phases—downlink data transmission, multi-dimensional channel estimation, channel parameter clustering, and simultaneous localization and mapping (SLAM) based on a novel likelihood function. This approach can decompose the problem into simpler steps, thus leading to lower complexity. At the same time, by considering an end-to-end processing chain, we are accounting for a wide variety of practical impairments. Simulation results demonstrate the efficacy of the proposed approach.
Highlights
The standard formulation of the channel estimation problem is an ML problem, where Θk arg minΘ s log p(Ys,k Θ, Ss), (17)in which Θ contains the delays, gains, and angles of all the paths, as well as the number of paths
All projected points are close to the surface and there is a projected point very close to the deterministic reflection point. This is because the specular path of the MR has larger power than the other diffuse paths, so it is less affected by inter-path interference
We have proposed a novel method to cluster the multipath components (MPCs) by projecting the high-dimensional data into 3D points and cluster the points based on the DBSCAN algorithm, which we augmented to account for the channel gains
Summary
The standard formulation of the channel estimation problem is an ML problem, where Θk arg minΘ s log p(Ys,k Θ, Ss), (17)in which Θ contains the delays, gains, and angles of all the paths, as well as the number of paths. A cluster is usually described as a group of multipath components (MPCs) with the same parameter distribution. According to the PMBM formalism, multiple measurements per source should follow a Poisson or Bernoulli distribution. Under the standard point model, the likelihood function corresponds to a Bernoulli distribution, with [65] i xLM, s, m). Under the extended target model, the likelihood function corresponds to a Poisson point process (PPP) ([25], Equation (5)): e−γm γmZi. Zi −1 p(zi,l xLM, s, m), (37) l=0. Where γm ≥ 0 is the Poisson rate for surfaces of type m. For both (36) and (37), it has been proven that the PMBM density is a conjugate prior
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