The multiple-input multiple-output dual functional radar communication (MIMO-DFRC) system is a promising platform for future integrated sensing and communication applications. Ensuring reliable performance of both radar and communication functions, the beam selection is a critical technology in MIMO-DFRC systems. However, the beam selection problem is known to be NP-hard, and efficiently addressing it remains an open issue, especially in distributed systems. In this paper, we address the beam selection problem for a MIMO-DFRC system by formulating it as a semi-Markov decision process and propose a novel hierarchical reinforcement learning (HRL) algorithm. In our approach, codebook-based beam selection for transmitting and receiving BS is controlled by an agent deployed in the cloud. Inspired by the mechanism of hierarchical codebook beam training, we employ an option-based policy that enables the agent to explore different layers of the codebook and extract context information across multiple discrete time steps. We utilize an invalid action masking technique to overcome the dynamic action space problem caused by the option-based policy. Simulation results demonstrate that the HRL-based algorithm outperforms existing beam selection methods and achieves remarkable performance even under conditions of a high probability of false alarm and low signal-to-noise ratio. Furthermore, we find that the proposed algorithm exhibits promising capabilities to learn a more efficient policy beyond the full hierarchical codebook training trajectory.

Pervasive and high-accuracy positioning has become increasingly important as a fundamental enabler for intelligent connected devices in mobile networks. Nevertheless, current wireless networks heavily rely on pure model-driven techniques to achieve positioning functionality, often succumbing to performance deterioration due to hardware impairments in practical scenarios. Here, we reformulate the direction finding or angle-of-arrival (AoA) estimation problem as an image recovery task of the spatial spectrum and propose a new model-driven deep neural network (MoD-DNN) framework. The proposed MoD-DNN scheme comprises three modules: a multi-task autoencoder-based beamformer, a coarray spectrum generation module, and a model-driven deep learning-based spatial spectrum reconstruction module. Our technique enables automatic calibration of angular-dependent phase error, thereby enhancing the resilience of direction-finding precision against realistic system non-idealities. We validate the proposed scheme both using numerical simulations and field tests. The results show that the proposed MoD-DNN framework enables effective spectrum calibration and accurate AoA estimation. To the best of our knowledge, this study marks the first successful demonstration of hybrid data-and-model-driven direction finding utilizing readily available commodity 5G gNodeB.

High-accuracy positioning has become a fundamental enabler for intelligent connected devices. Nevertheless, the present wireless networks still rely on model-driven approaches to achieve positioning functionality, which are susceptible to performance degradation in practical scenarios, primarily due to hardware impairments. Integrating artificial intelligence into the positioning framework presents a promising solution to revolutionize the accuracy and robustness of location-based services. In this study, we address this challenge by reformulating the problem of angle-of-arrival (AoA) estimation into image reconstruction of spatial spectrum. To this end, we design a model-driven deep neural network (MoD-DNN), which can automatically calibrate the angular-dependent phase error. The proposed MoD-DNN approach employs an iterative optimization scheme between a convolutional neural network and a sparse conjugate gradient algorithm. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed method in enhancing spectrum calibration and AoA estimation.

In future mobile networks, the demand for real-time, accurate localization of multiple signal sources is paramount, but the facilities are often resource-constrained and the deploying environments are complex. In this context, we present a lightweight deep neural network in this work, which is tailored for multi-source angle-of-arrival (AoA) estimation under low signal-to-noise-ratio (SNR) conditions. The network employs mobile inverted bottleneck convolution (MBConv), known for its enhanced feature extraction capabilities and resilience to noise. By leveraging a scale attention mechanism, we effectively integrate the outputs of each layer without the need for neural architecture search. Trained on multi-channel data under low SNR, the network formulates angle estimation as a multi-label classification task. Experimental results confirm that, the proposed network demonstrates superior accuracy in extreme noise conditions and with limited snapshots, outperforming existing methodologies in multi-source scenarios.

The deployment of large-scale arrays in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems has enabled highly accurate localization by reaping the benefits of high angular resolution. However, the use of massive antennas in mmWave systems results in expensive hardware costs and computational burdens. This paper considers mobile user localization in quantized mmWave MIMO systems, where each base station (BS) antenna is equipped with low-resolution analog-to-digital converters. The proposed approach integrates the beamspace model with off-grid information to capture channel sparsity in the angular domain. The temporal correlation of angle-of-arrival (AoA) is characterized by a Markov process for moving users. To estimate channel gains and time-varying AoAs, while keeping the computational complexity low, we further develop generalized approximate message passing and AoA tracking methods. In dense multipath environments, determining line-of-sight (LoS) paths for precise localization poses a challenge. To address this issue, we propose a fast direct localization based on LoS identification that can also be applied when the LoS paths of some BSs are obstructed. In the final stage, the moving user locations are recovered via triangulation. Simulation results validate the effectiveness of the proposed algorithms and showcase the feasibility of implementing quantized mmWave systems for localization purposes.