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.

The current approach to provide cellular service is for a Mobile Network Operator (MNO) to build and operate its own network. As the density of the deployed devices increases, it becomes necessary to deploy more cells and/or utilize more bandwidth. Finding new sites for deploying new cells is getting increasingly difficult, due to various reasons, such as permitting process, deployment, and operating costs. Utilization of more bandwidth is generally associated with use of higher frequency bands, such as mmW due to the lack of available spectrum in low to mid-band range associated with good capacity coverage tradeoff. Use of mmW for large macro cell sites, may not always result in adequate coverage due to increased losses through modern building materials, tree foliage, rain etc. In this paper, to address the limitations of the current approach, we propose and study a new approach, exploiting sharing and cooperation among UEs. Compared to a typical relaying approach, UE cooperation feature increases radio capabilities of the aggregated devices and could be largely implemented in a manner transparent to the network, allowing the devices to improve Key Performance Indicators (KPIs), such as coverage and capacity, without any further major capital investment in a cellular network.

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.

5G NR is designed from the ground up to provide support for mission critical communication with ultra-reliability and low latency (URLLC) in addition to other use cases such as enhanced mobile broadband. URLLC allows 5G network performance to approach that of wired networks (e.g., Ethernet) and paves the way for new use cases. In this paper, we focus on the end-to-end design of 5G networks for industrial factory automation. Providing URLLC wireless communication in these environments is especially challenging due to highly dynamic RF variations with possible signal blockage or reflections from moving metal objects. Coordinated Multipoint (CoMP) communication, which leverages spatial diversity to improve reliability without relying on packet retransmission, is a promising approach in these environments. We present a design and performance trade-offs between different CoMP techniques and their implication to capacity, robustness, and architecture. Subsequently, we describe architecture and protocol design aspects that allow 5G to operate as part of a time sensitive network including Ethernet bridging, time synchronization, and QoS. A prototype system that validates certain aspects of the design is also presented.

A dynamic-user model for centralized wireless networks is studied, where users arrive with a certain file size and depart when the file is served by a central server. Although the exact analysis of dynamic-user systems can be complicated, it is shown that an approximate analysis can be performed in a time-scale separation regime where the file size is much larger than the time scale of service process fluctuation. A first-order approximation result is derived that shows that when file sizes are large, a complicated service process can be replaced by a simple constant-rate service process. The accuracy of the approximation is further improved through a second order approximation result that incorporates the effect of service variability. Variability in the service process is shown to reduce the effective service rate, leading to a quantification of the conventional heuristic that service variability degrades system performance

The design of hard handoff algorithms based on optimizing the tradeoff between link quality and rate of handoffs is considered. For handoff algorithms based on this criterion, adaptation is precisely defined in terms of remaining on a locus of desirable operating points as system parameters (such as mobile velocity) change. A rule based on a linear cost criterion is used to select desirable operating points. For this rule, it is shown that the optimal handoff algorithm, which is impractical, is easily adapted by fixing a single tradeoff parameter at an appropriate value. The same adaptation property is shown to hold for an easily implementable approximation to the optimal algorithm, the locally optimal (LO) handoff algorithm. This is in contrast to the poor adaptation of hysteresis based approaches which require lookup tables for adaptation. Practical estimators for all relevant system parameters based on a short window of pilot signal strength measurements are also discussed. It is shown that the LO algorithm adapts well when these simple estimators are used. A hysteresis-threshold approximation to the adaptive LO algorithm is also developed.

Previous work on estimating the performance of hard handoff algorithms has been based on Monte-Carlo simulations or asymptotic approximations. A new analysis technique for evaluating the performance of handoff algorithms is introduced. This technique may be used to accurately estimate the performance of both optimal and suboptimal handoff algorithms. Specifically, the technique allows for the calculation of an upper bound on handoff performance without the need for storing or simulating the complicated optimal solution.