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 … Show more
“…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.…”
This paper provides an initial investigation on the application of convolutional neural networks (CNNs) for fingerprint-based positioning using measured massive MIMO channels. When represented in appropriate domains, massive MIMO channels have a sparse structure which can be efficiently learned by CNNs for positioning purposes. We evaluate the positioning accuracy of state-of-the-art CNNs with channel fingerprints generated from a channel model with a rich clustered structure: the COST 2100 channel model. We find that moderately deep CNNs can achieve fractional-wavelength positioning accuracies, provided that an enough representative data set is available for training.
“…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.…”
This paper provides an initial investigation on the application of convolutional neural networks (CNNs) for fingerprint-based positioning using measured massive MIMO channels. When represented in appropriate domains, massive MIMO channels have a sparse structure which can be efficiently learned by CNNs for positioning purposes. We evaluate the positioning accuracy of state-of-the-art CNNs with channel fingerprints generated from a channel model with a rich clustered structure: the COST 2100 channel model. We find that moderately deep CNNs can achieve fractional-wavelength positioning accuracies, provided that an enough representative data set is available for training.
“…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 .…”
Section: Mpc Parameters and Noise Parameters Initialization With Rmentioning
confidence: 99%
“…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].…”
Section: (34) the Vector B Rxmentioning
confidence: 99%
“…• 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.…”
Section: B Contributions and Organization Of The Papermentioning
In this paper, we present a robust multipath-based localization and mapping framework that exploits the phases of specular multipath components (MPCs) using a massive multipleinput multiple-output (MIMO) array at the base station. Utilizing the phase information related to the propagation distances of the MPCs enables the possibility of localization with extraordinary accuracy even with limited bandwidth. The specular MPC parameters along with the parameters of the noise and the dense multipath component (DMC) are tracked using an extended Kalman filter (EKF), which enables to preserve the distancerelated phase changes of the MPC complex amplitudes. The DMC comprises all non-resolvable MPCs, which occur due to finite measurement aperture. The estimation of the DMC parameters enhances the estimation quality of the specular MPCs and therefore also the quality of localization and mapping. The estimated MPC propagation distances are subsequently used as input to a distance-based localization and mapping algorithm. This algorithm does not need prior knowledge about the surrounding environment and base station position. The performance is demonstrated with real radio-channel measurements using an antenna array with 128 ports at the base station side and a standard cellular signal bandwidth of 40 MHz. The results show that high accuracy localization is possible even with such a low bandwidth.Index Terms-Parametric channel estimation, extended Kalman filter, massive MIMO radio channel, localization and mapping.
“…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.…”
While more and more antennas are integrated into a single mobile user equipment to increase communication quality and throughput, the number of antennas used for transmission is commonly restricted due to the concerns on hardware complexity and energy consumption, making it impossible to achieve the maximum channel capacity. This paper investigates the problem of reconstructing the full downlink channel from incomplete uplink channel measurements in Massive MIMO systems. We present ARDI, a scheme that builds a bridge between radio channel and physical signal propagation environment to link spatial information about the non-transmitting antennas with their radio channels. By inferring locations and orientations of the non-transmitting antennas from an incomplete set of uplink channels, ARDI can reconstruct the downlink channels for non-transmitting antennas. We derive closed-form solution to reconstruct antenna orientation in both single-path and multi-path propagation environments. The performance of ARDI is evaluated using simulations with realistic human movement. The results demonstrate that ARDI is capable of accurately reconstructing full downlink channels when the signal-to-noise ratio is higher than 15dB, thereby expanding the channel capacity of Massive MIMO networks.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.