A digital twin presents promising opportunities and potential benefits for various industrial use cases by enabling simulation and prediction on the virtual representation of the real-world environment. However, the implementation and maintenance costs for the digital twin are prohibitively high, restricting its widespread adoption. To address this issue, we present a framework, GT-Craft, which enables fast prototyping the geospatial-based digital twin at scale. GT-Craft automates the generation of the digital twin by using the streamed geospatial data and the semantic information extracted from deep neural network (DNN) models. As GT-Craft generates digital twins on the Unity game engine, the Unity-based simulators and game applications can seamlessly use the digital twins generated by GT-Craft. The presented framework is compatible with non-Unity-based applications and existing 3D software and simulation tools, e.g., Blender, Apple Reality Composer, and NVIDIA Omniverse, as it supports exporting the generated digital twin in the universal scene description (USD) format, which is an emerging industrial open standard for exchanging and editing 3D contents.

Coexistence between 5G cellular networks and incumbent radar systems is necessary for an increasing number of spectral bands, including highly valuable spectrum such as the C-band. This paper presents a novel coexistence framework that intelligently adjusts 5G antenna parameters to mitigate interference reaching known radar systems, while simultaneously maximizing cellular network performance. The framework leverages Gaussian process regression and differential evolution to navigate high-dimensional, non-convex spaces while effectively managing uncertainty. We propose a practical approach that utilizes user RSRP measurements to characterize communication interference on radar, addressing the non-cooperative nature of radar systems. Evaluation on AT&T Labs' high-fidelity simulator demonstrates over a 12% increase in sum-log-rate and around a 3.6 dB increase in median SINR compared to the exhaustive search with common parameter configurations across all base stations, while decreasing interference on radar to its lowest achievable level in our simulation setup.

This work investigates the joint optimization of coverage, capacity, and cell load by tuning several cell-specific antenna and cell association parameters via data-driven methods. We are particularly focused on the complexities of macrocell and small cell coexistence, and demonstrate an automated learning method whereby macrocells and small cells can strategically adapt their coverage areas. Coupled with adaptive offloading using a tunable small cell bias, we demonstrate significant throughput and coverage improvement in a realistic 5G network simulator developed by AT&T Labs. Concretely, we formulate an optimization problem to maximize network coverage and the application-layer data rate experienced by users, accounting for delays from congestion, cell loading, and packet retransmissions. We propose an algorithm that approaches the optimum via Gaussian process models and the evolutionary search: efficiently navigating the high-dimensional, nonconvex space while managing uncertainty. Our results show that the joint optimization of antenna tuning and load balancing - exemplified by load-aware cell shaping - more than doubles the cell edge throughput and increases the cell edge SINR by 8 dB, compared to bias-only optimization. Furthermore, our algorithm and overall approach appear viable for implementation.

We propose a novel framework for optimizing antenna parameter settings in a heterogeneous cellular network. We formulate an optimization problem for both coverage and capacity - in both the downlink (DL) and uplink (UL) - which configures the tilt angle, vertical half-power beamwidth (HPBW), and horizontal HPBW of each cell's antenna array across the network. The novel data-driven framework proposed for this non-convex problem, inspired by Bayesian optimization (BO) and differential evolution algorithms, is sample-efficient and converges quickly, while being scalable to large networks. By jointly optimizing DL and UL performance, we take into account the different signal power and interference characteristics of these two links, allowing a graceful trade-off between coverage and capacity in each one. Our experiments on a state-of-the-art 5G NR cellular system-level simulator developed by AT&T Labs show that the proposed algorithm consistently and significantly outperforms the 3GPP default settings, random search, and conventional BO. In one realistic setting, and compared to conventional BO, our approach increases the average sum-log-rate by over 60% while decreasing the outage probability by over 80%. Compared to the 3GPP default settings, the gains from our approach are considerably larger. The results also indicate that the practically important combination of DL throughput and UL coverage can be greatly improved by joint UL-DL optimization.

Characterizing self-interference is essential to the design and evaluation of in-band full-duplex communication systems. Until now, little has been understood about this coupling in full-duplex systems operating at millimeter wave (mmWave) frequencies, and it has been shown that the highly-idealized models proposed for such do not align with practice. This work presents the first spatial and statistical model of mmWave self-interference backed by measurements, enabling engineers to draw realizations that exhibit the large-scale and small-scale spatial characteristics observed in our nearly 6.5 million measurements taken at 28 GHz. Core to our model is its use of system and model parameters having real-world meaning, which facilitates its extension to systems beyond our own phased array platform through proper parameterization. We demonstrate this by collecting nearly 13 million additional measurements to show that our model can generalize to two other system configurations. We assess our model by comparing it against actual measurements to confirm its ability to align spatially and in distribution with real-world self-interference. In addition, using both measurements and our model of self-interference, we evaluate an existing beamforming-based full-duplex mmWave solution to illustrate that our model can be reliably used to design new solutions and validate the performance improvements they may offer.

In this paper, we jointly optimize the capacity and coverage of both uplink and downlink transmissions by tuning the downtilt angle, vertical half-power beamwidth (HPBW), and horizontal HPBW of each cell's antenna array across a heterogeneous cellular network. We formulate an optimization problem and propose a novel sample-efficient algorithm to solve this non-convex problem. We evaluate our framework on a state-of-the-art cellular system-level simulator developed by AT&T Labs by comparing it with the 3GPP baseline. Example results tuned to optimize uplink coverage and downlink rate indicate that jointly optimizing the uplink and downlink directions improves uplink median and 5% outage SINR by (i) 1.6 dB and 4.5 dB, respectively, compared to downlink only-optimization and by (ii) 6.7 dB and 14.6 dB compared to the 3GPP baseline. Simultaneously, we can increase downlink median and outage SINR by comparable amounts compared to uplink-only optimization, but with larger gains in median SINR and downlink sum-rate. Our results indicate that there are significant gains to be harvested from site-specific data-driven base station parameter optimization, and they can be achieved in a scalable and automated fashion.

Modern millimeter wave (mmWave) communication systems rely on beam alignment to deliver sufficient beamforming gain to close the link between devices. We present a novel beam selection methodology for multi-panel, full-duplex mmWave systems, which we call Steer, that delivers high beamforming gain while significantly reducing the full-duplex self-interference coupled between the transmit and receive beams. Steer does not necessitate changes to conventional beam alignment methodologies nor additional over-the-air feedback, making it compatible with existing cellular standards. Instead, Steer uses conventional beam alignment to identify the general directions beams should be steered, and then it makes use of a minimal number of self-interference measurements to jointly select transmit and receive beams that deliver high gain in these directions while coupling low self-interference. We implement Steer on an industry-grade 28 GHz phased array platform and use further simulation to show that full-duplex operation with beams selected by Steer can notably outperform both half-duplex and full-duplex operation with beams chosen via conventional beam selection. For instance, Steer can reliably reduce self-interference by more than 20 dB and improve SINR by more than 10 dB, compared to conventional beam selection. Our experimental results highlight that beam alignment can be used not only to deliver high beamforming gain in full-duplex mmWave systems but also to mitigate self-interference to levels near or below the noise floor, rendering additional self-interference cancellation unnecessary with Steer.

We present measurements of the 28 GHz self-interference channel for full-duplex sectorized multi-panel millimeter wave (mmWave) systems, such as integrated access and backhaul. We measure the isolation between the input of a transmitting phased array panel and the output of a co-located receiving phased array panel, each of which is electronically steered across a number of directions in azimuth and elevation. In total, nearly 6.5 million measurements were taken in an anechoic chamber to densely inspect the directional nature of the coupling between 256-element phased arrays. We observe that highly directional mmWave beams do not necessarily offer widespread high isolation between transmitting and receiving arrays. Rather, our measurements indicate that steering the transmitter or receiver away from the other tends to offer higher isolation but even slight steering changes can lead to drastic variations in isolation. These measurements can be useful references when developing mmWave full-duplex solutions and can motivate a variety of future topics including beam/user selection and beamforming codebook design.

Current wireless networks employ sophisticated multi-user transmission techniques to fully utilize the physical layer resources for data transmission. At the MAC layer, these techniques rely on a semi-static map that translates the channel quality of users to the potential transmission rate (more precisely, a map from the Channel Quality Index to the Modulation and Coding Scheme) for user selection and scheduling decisions. However, such a static map does not adapt to the actual deployment scenario and can lead to large performance losses. Furthermore, adaptively learning this map can be inefficient, particularly when there are a large number of users. In this work, we make this learning efficient by clustering users. Specifically, we develop an online learning approach that jointly clusters users and channel-states, and learns the associated rate regions of each cluster. This approach generates a scenario-specific map that replaces the static map that is currently used in practice. Furthermore, we show that our learning algorithm achieves sub-linear regret when compared to an omniscient genie. Next, we develop a user selection algorithm for multi-user scheduling using the learned user-clusters and associated rate regions. Our algorithms are validated on the WiNGS simulator from AT&T Labs, that implements the PHY/MAC stack and simulates the channel. We show that our algorithm can efficiently learn user clusters and the rate regions associated with the user sets for any observed channel state. Moreover, our simulations show that a deployment-scenario-specific map significantly outperforms the current static map approach for resource allocation at the MAC layer.

We present measurements and analysis of self-interference in multi-panel millimeter wave (mmWave) full-duplex communication systems at 28 GHz. In an anechoic chamber, we measure the self-interference power between the input of a transmitting phased array and the output of a colocated receiving phased array, each of which is electronically steered across a number of directions in azimuth and elevation. These self-interference power measurements shed light on the potential for a full-duplex communication system to successfully receive a desired signal while transmitting in-band. Our nearly 6.5 million measurements illustrate that more self-interference tends to be coupled when the transmitting and receiving phased arrays steer their beams toward one another but that slight shifts in steering direction (on the order of one degree) can lead to significant fluctuations in self-interference power. We analyze these measurements to characterize the spatial variability of self-interference to better quantify and statistically model this sensitivity. Our analyses and statistical results can be useful references when developing and evaluating mmWave full-duplex systems and motivate a variety of future topics including beam selection, beamforming codebook design, and self-interference channel modeling.

We propose an online algorithm for clustering channel-states and learning the associated achievable multiuser rates. Our motivation stems from the complexity of multiuser scheduling. For instance, MU-MIMO scheduling involves the selection of a user subset and associated rate selection each time-slot for varying channel states (the vector of quantized channels matrices for each of the users) — a complex integer optimization problem that is different for each channel state. Instead, our algorithm clusters the collection of channel states to a much lower dimension, and for each cluster provides achievable multiuser capacity trade-offs, which can be used for user and rate selection. Our algorithm uses a bandit approach, where it learns both the unknown partitions of the channel-state space (channel-state clustering) as well as the rate region for each cluster along a pre-specified set of directions, by observing the success/failure of the scheduling decisions (e.g. through packet loss). We propose an epoch-greedy learning algorithm that achieves a sub-linear regret, given access to a class of classifying functions over the channel-state space. We empirically validate our approach on a high-fidelity 5G New Radio (NR) wireless simulator developed within AT&T Labs. We show that our epoch-greedy bandit algorithm learns the channel-state clusters and the associated rate regions. Further, adaptive scheduling using this learned rate-region model (map from channel-state to the set of feasible rates) outperforms the corresponding hand-tuned static maps in multiple settings. Thus, we believe that auto-tuning cellular systems through learning-assisted scheduling algorithms can significantly improve performance in real deployments.

Finding an optimum configuration of base station (BS) antenna parameters is a challenging, non-convex problem for cellular networks. The chosen configuration has major implications for coverage and throughput in real-world systems, as it effects signal strength differently throughout the cell, as well as dictating the interference caused to other cells. In this paper, we propose a novel and sample-efficient data-driven methodology for optimizing antenna downtilt angles. Our approach combines Bayesian optimization (BO) with Differential Evolution (DE): BO decreases the computational burden of DE, while DE helps BO avoid the curse of dimensionality. We evaluate the performance on a realistic state-of-the-art cellular system simulator developed by AT&T Labs, that includes all layers of the protocol stack and sophisticated channel models. Our results show that the proposed algorithm outperforms Bayesian optimization, random selection, and the baseline settings adopted in 3GPP by nontrivial amounts in terms of both capacity and coverage. Also, our approach is notably more time-efficient than DE alone.

Network-assisted device discovery includes initiating the D2D network-assisted device discovery of a receiving UE to enable a target UE and the receiving UE to establish a D2D communication. A discovery feasibility measurement is performed to determine whether a D2D communication between the target UE and the receiving UE is feasible. A discovery setup message is transmitted and a discovery report is received from the target UE.

The present application provides a method for performing uplink carrier handover by a User Equipment (UE) in a wireless communication system, the method comprising: receiving information associated with uplink carrier handover from a base station; and based on uplink The link carrier switching associated information performs uplink carrier switching for transmitting the sounding reference signal SRS.

An apparatus for user equipment (UE). The UE comprises a transceiver configured to receive an indication of a partial subframe configuration over an unlicensed spectrum in a licensed assisted access (LAA) cell. The UE further includes at least one processor configured to determine a resource element (RE) mapping rule based on the indication of the partial subframe configuration; and identify an RE position of at least one reference signal to be received from the eNB based on the RE mapping rule.

A method for efficient data transmission in a wireless communication system includes dynamically configuring at least one period cycle (P-CYCLE) pattern comprising a period-on (P-ON) duration and a period-off (P-OFF) duration that are adjusted in accordance with a number of transmissions from user equipments (UEs) operating in a shared band spectrum, wherein the number of transmissions comprises a number of successful transmissions or a number of unsuccessful transmissions received from the UEs. The method further includes transmitting the P-CYCLE pattern including the P-ON duration and the P-OFF duration to the UEs using a downlink channel over the shared spectrum band, wherein the downlink channel comprises a higher layer signal or a physical layer signal.

Initial access is the process which allows a mobile user to first connect to a cellular network. It consists of two main steps: cell search (CS) on the downlink and random access (RA) on the uplink. Millimeter wave (mm-wave) cellular systems typically must rely on directional beamforming (BF) in order to create a viable connection. The BF direction must, therefore, be learned—as well as used—in the initial access process for mm-wave cellular networks. This paper considers four simple but representative initial access protocols that use various combinations of directional BF and omnidirectional transmission and reception at the mobile and the BS, during the CS and RA phases. We provide a system-level analysis of the success probability for CS and RA for each one, as well as of the initial access delay and user-perceived downlink throughput (UPT). For a baseline exhaustive search protocol, we find the optimal BS beamwidth and observe that in terms of initial access delay it is decreasing as blockage becomes more severe, but is relatively constant (about $\pi /12$ ) for UPT. Of the considered protocols, the best tradeoff between initial access delay and UPT is achieved under a fast CS protocol.

This paper investigates the effectiveness of multipath-decorrelating antenna motion in reducing the initialization time of global navigation satellite system (GNSS) receivers employing low-cost single-frequency antennas for carrier-phase differential GNSS (CDGNSS) positioning. Fast initialization times with low-cost antennas will encourage the expansion of CDGNSS into the mass market, bringing the benefits of globally referenced centimeter-accurate positioning to many consumer applications, such as augmented reality and autonomous vehicles, that have so far been hampered by the several-meter-level errors of traditional GNSS positioning. Poor multipath suppression common to low-cost antennas results in large and strongly time-correlated phase errors when a receiver is static. Such errors can result in the CDGNSS initialization time, the so-called time to ambiguity resolution (TAR), extending to hundreds of seconds—many times longer than for higher cost survey-grade antennas, which have substantially better multipath suppression. This paper demonstrates that TAR can be significantly reduced through antenna motion, particularly gentle wavelength-scale random antenna motion. Such motion acts to decrease the correlation time of the multipath-induced phase errors. A priori knowledge of the motion profile is shown to further reduce TAR, with the reduction shown to be more pronounced as the initialization scenario is more challenging.

A user equipment (UE) in a wireless communication system. The UE comprises at least one processor configured to determine a first subcarrier spacing and a transceiver configured to transmit, to a base station (BS), random access signals generated with the first subcarrier spacing and receive a downlink control signaling comprising a physical (PHY) resource configuration that includes a second subcarrier spacing. The UE further comprises at least one processor configured to set the PHY resource configuration for at least one of uplink transmission or downlink reception.