This paper presents a new method for user localization in single-site massive Multiple-Input-Multiple-Output (MIMO) systems, which circumvents the need for large labeled datasets typically required for training data-driven models. Instead, the proposed model utilizes a limited set of geo-tagged Channel State Information (CSI) samples for training. The approach combines a Fully-Connected Auto-Encoder (FC-AE) with a Gaussian Process Regression (GPR) model. The GPR model is efficient, as it requires only a minimal amount of labeled data for training, although it presents challenges in computational complexity. To address this complexity, the FC-AE is introduced, which encodes the Angle-Delay Profile (ADP) transformation of the CSI data. The training dataset for the FC-AE is crafted by employing data augmentation techniques on a small collection of unlabeled data. The simulation results demonstrate that FC-AE is scenario-independent and adaptable to new scenarios with similar ADP characteristics. Additionally, our FC-AE-GPR model surpasses the performance of the Convolutional Neural Network model and the non-parametric grid search method when provided with limited labeled data, applicable in both indoor and outdoor settings.

Deep neural networks (DNNs) in the wireless communication domain have been shown to be hardly generalizable to scenarios where the train and test datasets follow a different distribution. This lack of generalization poses a significant hurdle to the practical utilization of DNNs in wireless communication. In this paper, we propose a generalizable deep learning approach for millimeter wave (mmWave) beam selection using sub-6 GHz channel state information (CSI) measurements, referred to as PARAMOUNT. First, we provide a detailed discussion on physical aspects of the electromagnetic wave scattering in the mmWave and sub-6 GHz bands. Based on this discussion, we develop the augmented discrete angle delay profile (ADADP) which is a novel linear transformation for the sub-6 GHz CSI that extracts the angle-delay attributes and provides a semantic visual representation of the multi-path clusters. Next, we introduce a convolutional neural network (CNN) structure that can learn the signatures of the path clusters in the sub-6 GHz ADADP representation and transform it to mmWave band beam indices. We demonstrate by extensive simulations on several different datasets that PARAMOUNT can generalize beyond the training dataset which is mainly due to transfer learning principles that allow transferring information from previously learned tasks to the learning of new unseen tasks.

This paper presents a novel antenna configuration to measure directions of multiple signal sources at the receiver in a THz mobile network via a single channel measurement. Directional communication is an intrinsic attribute of THz wireless networks and the knowledge of direction should be harvested continuously to maintain link quality. Direction discovery can potentially impose an immense burden on the network that limits its communication capacity exceedingly. To utterly mitigate direction discovery overhead, we propose a novel technique called spectrum shaping capable of measuring direction, power, and relative distance of propagation paths via a single measurement. We demonstrate that the proposed technique is also able to measure the transmitter antenna orientation. We evaluate the performance of the proposed design in several scenarios and show that the introduced technique performs similar to a large array of antennas while attaining a much simpler hardware architecture. Results show that the spectrum shaping with only two antennas placed 0.5 mm, 5 mm, and 1 cm apart performs direction of arrival estimation similar to a much more complex uniform linear array equipped with 7, 60, and 120 antennas, respectively.

This paper presents a new map-assisted localization approach utilizing Chanel State Information (CSI) in Massive Multiple-Input Multiple-Output (MIMO) systems. Map-assisted localization is an environment-aware approach in which the communication system has information regarding the surrounding environment. By combining radio frequency ray tracing parameters of the multipath components (MPC) with the environment map, it is possible to accomplish localization. Unfortunately, in real-world scenarios, ray tracing parameters are typically not explicitly available. Thus, additional complexity is added at a base station to obtain this information. On the other hand, CSI is a common communication parameter, usually estimated for any communication channel. In this work, we leverage the already available CSI data to propose a novel map-assisted CSI localization approach, referred to as MAP-CSI. We show that Angle-of-Departure (AoD) and Time-of-Arrival (ToA) can be extracted from CSI and then be used in combination with the environment map to localize the user. We perform simulations on a public MIMO dataset and show that our method works for both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. We compare our method to the state-of-the-art (SoA) method that uses the ray tracing data. Using MAP-CSI, we accomplish an average localization error of 1.8 m in LOS and 2.8 m in mixed (combination of LOS and NLOS samples) scenarios. On the other hand, SoA ray tracing has an average error of 1.0 m and 2.2 m, respectively, but requires explicit AoD and ToA information to perform the localization task.

This paper presents a data-driven localization framework with high precision in time-varying complex multi-path environments, such as dense urban areas and indoors, where GPS and model-based localization techniques come short. We consider the angle-delay profile (ADP), a linear transformation of channel state information (CSI), in massive MIMO systems and show that ADPs preserve users' motion when stacked temporally. We discuss that given a static environment, future frames of ADP time-series are predictable employing a video frame prediction algorithm. We express that a deep convolutional neural network (DCNN) can be employed to learn the background static scattering environment. To detect foreground changes in the environment, corresponding to path blockage or addition, we introduce an algorithm taking advantage of the trained DCNN. Furthermore, we present DyLoc, a data-driven framework to recover distorted ADPs due to foreground changes and to obtain precise location estimations. We evaluate the performance of DyLoc in several dynamic scenarios employing DeepMIMO dataset [1] to generate geo-tagged CSI datasets for indoor and outdoor environments. We show that previous DCNN-based techniques fail to perform with desirable accuracy in dynamic environments, while DyLoc pursues localization precisely. Moreover, simulations show that as the environment gets richer in terms of the number of multipath, DyLoc gets more robust to foreground changes.