36 research outputs found
Analysis and design of multirate synchronous sampling schemes for sparse multiband signals
We consider the problem of developing efficient sampling schemes for multiband sparse signals. Previous results on multicoset sampling implementations that lead to universal sampling patterns (which guarantee perfect reconstruction), are based on a set of appropriate interleaved analog to digital converters, all of them operating at the same sampling frequency. In this paper we propose an alternative multirate synchronous implementation of multicoset codes, that is, all the analog to digital converters in the sampling scheme operate at different sampling frequencies, without need of introducing any delay. The interleaving is achieved through the usage of different rates, whose sum is significantly lower than the Nyquist rate of the multiband signal. To obtain universal patterns the sampling matrix is formulated and analyzed. Appropriate choices of the parameters, that is the block length and the sampling rates, are also proposed
Clock and Orientation-Robust Simultaneous Radio Localization and Mapping at Millimeter Wave Bands
This paper proposes a radio simultaneous location and mapping (radio-SLAM)
scheme based on sparse multipath channel estimation. By leveraging sparse
channel estimation schemes at millimeter wave bands, namely high resolution
estimates of the multipath angle of arrival (AoA), time difference of arrival
(TDoA), and angle of departure (AoD), we develop a radio-SLAM algorithm that
operates without any requirements of clock synchronization, receiver
orientation knowledge, multiple anchor points, or two-way protocols. Thanks to
the AoD information obtained via compressed sensing (CS) of the channel, the
proposed scheme can estimate the receiver clock offset and orientation from a
single anchor transmission, achieving sub-meter accuracy in a realistic typical
channel simulation.Comment: This is the author's pre-print version of a paper accepted for
presentation in IEEE WCNC 2023, Glasgow, Scotlan
Autoregressive Attention Neural Networks for Non-Line-of-Sight User Tracking with Dynamic Metasurface Antennas
User localization and tracking in the upcoming generation of wireless
networks have the potential to be revolutionized by technologies such as the
Dynamic Metasurface Antennas (DMAs). Commonly proposed algorithmic approaches
rely on assumptions about relatively dominant Line-of-Sight (LoS) paths, or
require pilot transmission sequences whose length is comparable to the number
of DMA elements, thus, leading to limited effectiveness and considerable
measurement overheads in blocked LoS and dynamic multipath environments. In
this paper, we present a two-stage machine-learning-based approach for user
tracking, specifically designed for non-LoS multipath settings. A newly
proposed attention-based Neural Network (NN) is first trained to map noisy
channel responses to potential user positions, regardless of user mobility
patterns. This architecture constitutes a modification of the prominent vision
transformer, specifically modified for extracting information from
high-dimensional frequency response signals. As a second stage, the NN's
predictions for the past user positions are passed through a learnable
autoregressive model to exploit the time-correlated channel information and
obtain the final position predictions. The channel estimation procedure
leverages a DMA receive architecture with partially-connected radio frequency
chains, which results to reduced numbers of pilots. The numerical evaluation
over an outdoor ray-tracing scenario illustrates that despite LoS blockage,
this methodology is capable of achieving high position accuracy across various
multipath settings.Comment: 5 pages, 3 figures, accepted for presentation by 2023 IEEE
International Workshop on Computational Advances in Multi-Sensor Adaptive
Processing (CAMSAP 2023