Robust phase-based positioning using massive MIMO with limited bandwidth

Abstract

This paper presents a robust phase-based positioning framework using a massive MIMO system. The phase-based distance estimates of MPCs together with other parameters are tracked with an EKF, the state dimension of which varies with the birth-death processes of paths. The RIMAX and the modeling of dense multipath component in the framework further enhance the quality of parameter tracking by providing an accurate initial state and the underlying noise covariance. The tracked MPCs are fed into a time-of-arrival self-calibration positioning algorithm for simultaneous trajectory and environment estimation. Throughout the positioning process, no prior knowledge of the surrounding environment and base station position is needed. The performance is evaluated with the measurement of a 2D complex movement, which was performed in a sports hall with an antenna array with 128 ports as base station using a standard cellular bandwidth of 40 MHz. The positioning result shows that the mean deviation of the estimated user equipment trajectory from the ground truth is 13 cm. In summary, the proposed framework is a promising high-resolution radio-based positioning solution for current and next generation cellular systems.

Deep convolutional neural networks for massive MIMO fingerprint-based positioning

“…For example, the approach proposed in [4] detects a terminal's position (from a grid of candidate positions) using line-of-sight (LOS) based triangulation from a terminal to several distributed massive MIMO BSs. In [5], positioning is performed using the phases of multipath components estimated from massive MIMO channels. Another positioning approach was proposed in [6], where received signal strength (RSS) based fingerprinting from one single-antenna terminal to N BS M -antenna BSs is employed.…”

Massive MIMO-Based Localization and Mapping Exploiting Phase Information of Multipath Components

“…whereμ k ,n = [d î , ϕ s,n a θ s,n e ] T , and a f,i is the ith column of A f in (35). ϕ s,n a and θ s,n e are the azimuth and elevation angles after interpolation, with n a = 1, · · · , N a and n e = 1, · · · , N e .…”

“…The vector b f ∈ C Nf×1 accounts for the system frequency response by using the complex shifting matrix defined in (35). The detailed formulation of the matrix valued function (38) can be found in [34].…”

“…• We use the estimated MPC distances from real channel measurements to show that radio-based localization in harsh multipath environments is possible even using only low signal bandwidth by exploiting a massive MIMO system. Parts of this paper were published in [35], where the feasibility of the phase-based localization using standard cellular bandwidths was demonstrated. This paper presents more insights into the framework as well as more in-depth analysis of the channel estimation results with both synthetic and real measurements.…”

Full Downlink Channel Reconstruction Using Incomplete Uplink Channel Measurements in Massive MIMO Networks

“…This approach has been proposed to eliminate or significantly reduce the overhead caused by DL channel estimation feedback [7], [8]. Using the parameters of the propagation paths [9]- [12], the DL channel can be inferred using UL channel measurements. However, such an approach becomes inapplicable for receive-only antennas since the BS cannot directly obtain the information about the propagation parameters for the receive-only antennas.…”