Phase-Only Positioning: Overcoming Integer Ambiguity Challenge through Deep Learning

This paper investigates uplink carrier phase positioning (CPP) in cell-free (CF) or distributed antenna system context, assuming a challenging case where only phase measurements are utilized as observations. In general, CPP can achieve sub-meter to centimeter-level accuracy but is challenged by the integer ambiguity problem. In this work, we propose two deep learning approaches for phase-only positioning, overcoming the integer ambiguity challenge. The first one directly uses phase measurements, while the second one first estimates integer ambiguities and then integrates them with phase measurements for improved accuracy. Our numerical results demonstrate that an inference complexity reduction of two to three orders of magnitude is achieved, compared to maximum likelihood baseline solution, depending on the approach and parameter configuration. This emphasizes the potential of the developed deep learning solutions for efficient and precise positioning in future CF 6G systems.

Pilot-Based End-to-End Radio Positioning and Mapping for ISAC: Beyond Point-Based Landmarks

Integrated sensing and communication enables simultaneous communication and sensing tasks, including precise radio positioning and mapping, essential for future 6G networks. Current methods typically model environmental landmarks as isolated incidence points or small reflection areas, lacking detailed attributes essential for advanced environmental interpretation. This paper addresses these limitations by developing an end-to-end cooperative uplink framework involving multiple base stations and users. Our method uniquely estimates extended landmark objects and incorporates obstruction-based outlier removal to mitigate multi-bounce signal effects. Validation using realistic ray-tracing data demonstrates substantial improvements in the richness of the estimated environmental map.

UNILoc: Unified Localization Combining Model-Based Geometry and Unsupervised Learning

Accurate mobile device localization is critical for emerging 5G/6G applications such as autonomous vehicles and augmented reality. In this paper, we propose a unified localization method that integrates model-based and machine learning (ML)-based methods to reap their respective advantages by exploiting available map information. In order to avoid supervised learning, we generate training labels automatically via optimal transport (OT) by fusing geometric estimates with building layouts. Ray-tracing based simulations are carried out to demonstrate that the proposed method significantly improves positioning accuracy for both line-of-sight (LoS) users (compared to ML-based methods) and non-line-of-sight (NLoS) users (compared to model-based methods). Remarkably, the unified method is able to achieve competitive overall performance with the fully-supervised fingerprinting, while eliminating the need for cumbersome labeled data measurement and collection.

Target Handover in Distributed Integrated Sensing and Communication

The concept of 6G distributed integrated sensing and communications (DISAC) builds upon the functionality of integrated sensing and communications (ISAC) by integrating distributed architectures, significantly enhancing both sensing and communication coverage and performance. In 6G DISAC systems, tracking target trajectories requires base stations (BSs) to hand over their tracked targets to neighboring BSs. Determining what information to share, where, how, and when is critical to effective handover. This paper addresses the target handover challenge in DISAC systems and introduces a method enabling BSs to share essential target trajectory information at appropriate time steps, facilitating seamless handovers to other BSs. The target tracking problem is tackled using the standard trajectory Poisson multi-Bernoulli mixture (TPMBM) filter, enhanced with the proposed handover algorithm. Simulation results confirm the effectiveness of the implemented tracking solution.

Batch SLAM with PMBM Data Association Sampling and Graph-Based Optimization

Simultaneous localization and mapping (SLAM) methods need to both solve the data association (DA) problem and the joint estimation of the sensor trajectory and the map, conditioned on a DA. In this paper, we propose a novel integrated approach to solve both the DA problem and the batch SLAM problem simultaneously, combining random finite set (RFS) theory and the graph-based SLAM approach. A sampling method based on the Poisson multi-Bernoulli mixture (PMBM) density is designed for dealing with the DA uncertainty, and a graph-based SLAM solver is applied for the conditional SLAM problem. In the end, a post-processing approach is applied to merge SLAM results from different iterations. Using synthetic data, it is demonstrated that the proposed SLAM approach achieves performance close to the posterior Cram\'er-Rao bound, and outperforms state-of-the-art RFS-based SLAM filters in high clutter and high process noise scenarios.

The Integrated Sensing and Communication Revolution for 6G: Vision, Techniques, and Applications

Future wireless networks will integrate sensing, learning and communication to provide new services beyond communication and to become more resilient. Sensors at the network infrastructure, sensors on the user equipment, and the sensing capability of the communication signal itself provide a new source of data that connects the physical and radio frequency environments. A wireless network that harnesses all these sensing data can not only enable additional sensing services, but also become more resilient to channel-dependent effects like blockage and better support adaptation in dynamic environments as networks reconfigure. In this paper, we provide a vision for integrated sensing and communication (ISAC) networks and an overview of how signal processing, optimization and machine learning techniques can be leveraged to make them a reality in the context of 6G. We also include some examples of the performance of several of these strategies when evaluated using a simulation framework based on a combination of ray tracing measurements and mathematical models that mix the digital and physical worlds.

Robust Snapshot Radio SLAM

The intrinsic geometric connections between millimeter-wave (mmWave) signals and the propagation environment can be leveraged for simultaneous localization and mapping (SLAM) in 5G and beyond networks. However, estimated channel parameters that are mismatched to the utilized geometric model can cause the SLAM solution to degrade. In this paper, we propose a robust snapshot radio SLAM algorithm for mixed line-of-sight (LoS) and non-line-of-sight (NLoS) environments that can estimate the unknown user equipment (UE) state, map of the environment as well as the presence of the LoS path. The proposed method can accurately detect outliers and the LoS path, enabling robust estimation in both LoS and NLoS conditions. The proposed method is validated using 60 GHz experimental data, indicating superior performance compared to the state-of-the-art.

Millimeter-wave Radio SLAM: End-to-End Processing Methods and Experimental Validation

In this article, we address the timely topic of cellular bistatic simultaneous localization and mapping (SLAM) with specific focus on complete processing solutions from raw I/Q samples to user equipment (UE) and landmark location information in millimeter-wave (mmWave) networks. Firstly, we propose a new multipath channel parameter estimation solution which operates directly with beam reference signal received power (BRSRP) measurements, alleviating the need to know the true antenna beampatterns or the underlying beamforming weights. Additionally, the method has built-in robustness against unavoidable antenna sidelobes. Secondly, we propose new snapshot SLAM algorithms that have increased robustness and identifiability compared to prior-art, in practical built environments with complex clutter and multi-bounce propagation scenarios. The performance of the proposed methods is assessed at the 60 GHz mmWave band, via both realistic ray-tracing evaluations as well as true experimental measurements, in an indoor environment. Wide set of offered results clearly demonstrate the improved performance, compared to the relevant prior-art, in terms of the channel parameter estimation as well as the end-to-end SLAM performance. Finally, the article provides the measured 60 GHz data openly available for the research community, facilitating results reproducibility as well as further algorithm development.

MmWave Mapping and SLAM for 5G and Beyond

Device localization and radar-like mapping are at the heart of integrated sensing and communication, enabling not only new services and applications, but can also improve communication quality with reduced overheads. These forms of sensing are however susceptible to data association problems, due to the unknown relation between measurements and detected objects or targets. In this chapter, we provide an overview of the fundamental tools used to solve mapping, tracking, and simultaneous localization and mapping (SLAM) problems. We distinguish the different types of sensing problems and then focus on mapping and SLAM as running examples. Starting from the applicable models and definitions, we describe the different algorithmic approaches, with a particular focus on how to deal with data association problems. In particular, methods based on random finite set theory and Bayesian graphical models are introduced in detail. A numerical study with synthetic and experimental data is then used to compare these approaches in a variety of scenarios.

Doppler Exploitation in Bistatic mmWave Radio SLAM

Networks in 5G and beyond utilize millimeter wave (mmWave) radio signals, large bandwidths, and large antenna arrays, which bring opportunities in jointly localizing the user equipment and mapping the propagation environment, termed as simultaneous localization and mapping (SLAM). Existing approaches mainly rely on delays and angles, and ignore the Doppler, although it contains geometric information. In this paper, we study the benefits of exploiting Doppler in SLAM through deriving the posterior Cram\'er-Rao bounds (PCRBs) and formulating the extended Kalman-Poisson multi-Bernoulli sequential filtering solution with Doppler as one of the involved measurements. Both theoretical PCRB analysis and simulation results demonstrate the efficacy of utilizing Doppler.