Digital Twin (DT) technology has recently emerged as a powerful tool with the potential to revolutionize wireless systems as it enables accurate simulations, better decision-making, and tangible operational improvements. Prior studies on DT within the context of next generation wireless technologies has primarily focused on identifying potential use cases, application scenarios, and standardization challenges. However, the existing research lacks translating theoretical ideas into real-world applications. Our research, in this paper, contributes to the practical realization of DT technology in the context of 6G wireless networks, demonstrating its potential impact on network planning, performance, and user experience. In particular, we explore the construction, validation, and applications of DTs utilizing an indoor over-the-air (OTA) testbed powered by an in-house developed Next Generation Radio Access Network (NGRAN) that is fully compliant with 3rd Generation Partnership Project (3GPP) and Open RAN standards. First, we describe the steps that were followed to construct and validate a high-fidelity DT of our OTA testbed modeling both Radio Frequency (RF) environment and system components. Then, we demonstrate two pre-deployment use cases by explaining our extensive coverage estimation and network capacity planning OTA tests. Lastly, we also explore how DT enables practical machine learning solutions for post-deployment use cases and share our comprehensive OTA performance results, highlighting that our proposed mobility technique outperforms the classical approaches in terms of throughput and number of undesired handovers.

Radio signals are well suited for user localization because they are ubiquitous, can operate in the dark and maintain privacy. Many prior works learn mappings between channel state information (CSI) and position fully-supervised. However, that approach relies on position labels which are very expensive to acquire. In this work, this requirement is relaxed by using pseudo-labels during deployment, which are calculated from an inertial measurement unit (IMU). We propose practical algorithms for IMU double integration and training of the localization system. We show decimeter-level accuracy on simulated and challenging real data of 5G measurements. Our IMU-supervised method performs similarly to fully-supervised, but requires much less effort to deploy.

Intelligent integrated sensing and communication is one of key aspects of future wireless networks in which sensing can be leveraged to enhance communications and vice-versa. In this paper, we propose a novel sensing solution that can be used to represent an RF-environment. The proposed solution accounts for practical challenges such as limited time resolution due to limited bandwidth with no angle measurements while providing robustness to wireless propagation phenomena such as diffraction. Our proposed method leverages offline data collection during RF-mapping, and finds the location of virtual anchors (VAs), i.e., mirror images of a physical anchor w.r.t reflectors, through an iterative process called successive tap removal (STR). Afterwards, machine learning (ML) models are trained to predict dominant multipath components of the received wireless channel at a given location. Found VAs and their associated ML models stand for intermediate entities that represent an RF-environment. As an application, we use the developed models in the context of multipath assisted positioning to improve positioning accuracy in challenging indoor environments with heavy non-line-of-sight (NLoS) conditions. Finally, we extend our ideas to systems with multi-antenna transmitters and show that VA detection accuracy can be improved, bringing higher accuracy to the downstream positioning applications.

We present a neural network architecture for jointly learning user locations and environment mapping up to isometry, in an unsupervised way, from channel state information (CSI) values with no location information. The model is based on an encoder-decoder architecture. The encoder network maps CSI values to the user location. The decoder network models the physics of propagation by parametrizing the environment using virtual anchors. It aims at reconstructing, from the encoder output and virtual anchor location, the set of time of flights (ToFs) that are extracted from CSI using super-resolution methods. The neural network task is set prediction and is accordingly trained end-to-end. The proposed model learns an interpretable latent, i.e., user location, by just enforcing a physics-based decoder. It is shown that the proposed model achieves sub-meter accuracy on synthetic ray tracing based datasets with single anchor SISO setup while recovering the environment map up to 4cm median error in a 2D environment and 15cm in a 3D environment.

The increasing number of wireless devices operating in unlicensed spectrum motivates the development of intelligent adaptive approaches to spectrum access. We consider decentralized contention-based medium access for base stations (BSs) operating on unlicensed shared spectrum, where each BS autonomously decides whether or not to transmit on a given resource. The contention decision attempts to maximize not its own downlink throughput, but rather a network-wide objective. We formulate this problem as a decentralized partially observable Markov decision process with a novel reward structure that provides long term proportional fairness in terms of throughput. We then introduce a two-stage Markov decision process in each time slot that uses information from spectrum sensing and reception quality to make a medium access decision. Finally, we incorporate these features into a distributed reinforcement learning framework for contention-based spectrum access. Our formulation provides decentralized inference, online adaptability and also caters to partial observability of the environment through recurrent Q-learning. Empirically, we find its maximization of the proportional fairness metric to be competitive with a genie-aided adaptive energy detection threshold, while being robust to channel fading and small contention windows.

Precise positioning in 5G that can enable a wide variety of new use cases. We investigate the problem of accurate line-of-sight (LOS) delay estimation of an observed wireless channel using deep neural networks (NN). These delay estimates are the primary building block for deriving accurate position estimates. Our work proposes a custom super-resolution NN that exploits the properties of the wireless channel to guide the NN design. We compare against traditional algorithms used for LOS detection and show that the proposed NN shows excellent performance in the presence of weak LOS signals and dense multipath; scenarios that are challenging for traditional signal processing algorithms.

This paper presents novel data fusion methods that enable teams of vehicles to perform target search tasks without guaranteed communication. Techniques are introduced for merging estimates of a target's position from vehicles that regain contact after long periods of time, and a fully distributed team-planning algorithm is proposed, which utilizes limited shared information as it becomes available. The proposed data fusion techniques are shown to avoid overcounting information, which ensures that combining data from different vehicles will not decrease the performance of the search. Motivated by the underwater search domain, a realistic underwater acoustic communication channel is used to determine the probability of successful data transfer between two locations. The channel model is integrated into a simulation of multiple autonomous vehicles in both open water and harbor environments. The results demonstrate that the proposed distributed coordination techniques provide performance competitive with full communication.

The strategic behavior of receivers (players) in a multiple-input multiple-output Gaussian broadcast channel is investigated using the framework of non-cooperative game theory. In contrast to the non-cooperative Gaussian multiple access channel game in which each player's feasible set of actions is independent of the actions of other players, the action space of receivers in the Gaussian broadcast channel is mutually coupled, usually by a sum power or joint covariance constraint, and hence cannot be treated using traditional Nash equilibrium solution concepts. To characterize the strategic behavior of receivers in a broadcast channel game, this paper treats the broadcast channel power allocation (or covariance matrix selection) as a generalized Nash equilibrium problem with common constraints. The concept of normalized equilibrium (NoE) is used to characterize the equilibria and the existence and uniqueness of NoEs are proven for key scenarios.

This paper presents coordination and data fusion methods for teams of vehicles performing target search tasks without guaranteed communication. A fully distributed team planning algorithm is proposed that utilizes limited shared information as it becomes available, and data fusion techniques are introduced for merging estimates of the target's position from vehicles that regain contact after long periods of time. The proposed data fusion techniques are shown to avoid overcounting information, which ensures that combining data from different vehicles will not decrease the performance of the search. Motivated by the underwater search domain, a realistic underwater acoustic communication channel is used to determine the probability of successful data transfer between two locations. The channel model is integrated into a simulation of multiple autonomous vehicles in both open ocean and harbor search scenarios. The simulated experiments demonstrate that distributed coordination with limited communication significantly improves team performance versus prior techniques that continually maintain connectivity.

Cooperation between rational users has emerged as a new networking paradigm to improve the performance of wireless networks. In this paper, transmitter cooperation between wireless nodes in a Gaussian multiple access channel is studied under the framework of coalitional game theory. The stability of the grand coalition, the coalition of all users, is studied by modeling the game in partition form, in contrast to previous approaches using characteristic form games, in scenarios with infinite and finite cooperation capacity between transmitters. In both cases, irrespective of the channel gains, the grand coalition is shown to be the sum rate optimal and stable, in the sense that users do not have any incentive to leave the coalition.

Fast and accurate Carrier Frequency Offset (CFO) estimation is a problem of significance in many multi-carrier modulation based systems, especially in uplink Orthogonal Frequency Division Multiple Access (OFDMA) where the presence of multiple users exacerbates the inter-carrier interference (ICI) and results in multi-user interference (MUI). In this paper, a new technique called partial FFT demodulation is proposed. Estimators for the CFO are derived by considering an approximated matched filter for each user, implemented efficiently using several FFTs operating on sub-intervals of an OFDM block. Through simulations, the feasibility and performance of the proposed estimators are demonstrated. Associated trade-offs are discussed.

Underwater acoustic channels are well modeled by different Doppler scaling per path, a generalization of the commonly employed model with equal Doppler scaling on all paths. The path dependent Doppler shifts and wideband channel, destroy carrier orthogonality and yield severe inter-carrier interference in an Orthogonal Frequency Division Multiplexing system. Resampling is typical front-end processing for signals over a common Doppler shift channel and this work examines the choice of resampling factor for the distinct Doppler per path scenario. Two criteria are derived to evaluate the optimal resampling parameter, one using sufficient statistics and the second using a matched filtering interpretation of resampling. The filtering interpretation is then used to derive a blind estimator for the optimal resampling parameter. Simulation results show the for small to moderate Doppler spreads, the blind estimator significantly outperforms the classical packet length based Doppler scaling estimator in many operating regimes.