In this letter, we propose a machine learning-based vision-aided beam selection (ML-VBS) for millimeter-wave indoor multi-user communications. The proposed scheme is aimed at addressing the beam selection overhead with narrow beams in a multi-user scenario. The proposed scheme relies on a base station (BS) equipped with a single camera to observe the scene and estimates the angles to the multiple users. Given the estimated angle information and the limited number of radio frequency chains at the BS, two serial deep neural network structures are employed for joint user and beam selection subject to a minimum rate constraint. The numerical evaluation shows that the proposed ML-VBS scheme achieves a good performance in terms of the multi-user angle estimation, achievable sum rate and low computational complexity compared to conventional beam selection techniques.

This paper proposes a machine learning (ML)-based joint vital signs (VS) and occupancy detection (OD) with an impulse radio-ultra wide-band (IR-UWB) sensor. Works that have been done on VS or OD development using an IR-UWB are related to how VS works. In the related experiments performed, the OD and state of individuals were not sufficiently verified, and the methods were computationally complex. Issues related to the use of ML for joint VSOD have also not been studied in the literature. Extensive experimental scenarios involving the application of an ML-based classifier for human OD and VS classification, which we extended towards three sub-scenarios, were evaluated. We formulated a solution for VS estimation, which was aligned so that each network input sequence received signal corresponding to respective VS over different scenarios. The performance of the proposal was evaluated with other competing ML-based classification algorithms. Compared to other techniques, our proposed DNN-based classifier achieved the best results, and it also offers benefits over other algorithms, such as not needing to extract features from the data.

In this paper, we present a beam tracking algorithm based on the deep Q-network (DQN) for mobile millimeter-wave (mmWave) communications. The proposed algorithm determines the receive beam angle from the received signals without knowing the channel model and dynamics. It uses the received signals of the current and previous time slots to design the state and reward of the DQN. Our goal is to maximize the signal-to-noise ratio by the actions of the designed DQN. A significant computational complexity reduction is achieved since the receiver does not need to run complicated signal processing algorithms once the DQN is properly trained. Therefore a practical implementation of mmWave beam tracking with a very large number of antennas under harsh mobile environments becomes feasible. Through the extensive simulations, we verified the performance of the proposed algorithm and demonstrated robustness to the system uncertainty and low computational complexity in comparison with particle filter and the Q-learning.

In this letter, we propose a dropout autoencoder fingerprint augmentation approach for enhanced Wi-Fi fine time measurement and received signal strength signals-based indoor localization. Due to complex indoor environment, fingerprinting techniques suffers from unrecorded measurements at some reference points, leading to incomplete fingerprint datasets. The dropout autoencoder was employed to reconstruct clean signal features for the unrecorded fingerprint measurement which can significantly affect the localization accuracy of fingerprinting systems. The localization is accomplished by utilizing deep neural networks (DNN)-based regression. We collected two datasets from experiments conducted in two indoor offices using commercial off-the-shelf devices. The performance of our proposed method was compared to existing methods, and on the respective datasets, our proposal method showed better performance with a localization accuracy of 0.3m and 0.6m for the 1-σ percentile errors and 0.66m and 1.5m for the 2-σ percentile errors.

This letter proposes a Quantum-MUSIC, the first signal processing algorithm for quantum wireless sensing of multi-user with atomic receiver. Although the atomic receiver highly enhances sensing accuracy via extremely sensitive Rydberg atoms, leveraging its potential is challenging due to its magnitude-only measurement. To fill in this gap, the proposed algorithm first retrieves the channel matrix and incorporates the multiple signal classification (MUSIC) algorithm, enabling multi-user quantum wireless sensing. Simulation results showed that the proposed algorithm outperforms the classical MUSIC algorithm, validating the theoretical potential of quantum wireless sensing.