Joint Communication and Localization in Millimeter Wave Networks

Kwon, Girim; Conti, Andrea; Park, Hyuncheol; Win, Moe Z.

doi:10.1109/jstsp.2021.3113115

Cited by 38 publications

(22 citation statements)

References 87 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…. , δ M (ψ)] T , and accordingly, the estimate of R UE is given by RUE = R( ψ), with R(ψ) characterized as in (19).…”

Section: A Step 1: Estimation Of Ue Rotation Matrixmentioning

confidence: 99%

MmWave 6D Radio Localization with a Snapshot Observation from a Single BS

Nazari¹,

Seco‐Granados²,

Johannisson³

et al. 2022

Preprint

View full text Add to dashboard Cite

Accurate and ubiquitous localization is crucial for a variety of applications such as logistics, navigation, intelligent transport, monitoring, and control. Exploiting mmWave signals in 5G and Beyond 5G systems can provide accurate localization with limited infrastructure. We consider the single base station localization problem and extend it to 3D position and 3D orientation estimation of an unsynchronized multi-antenna user, using downlink MIMO-OFDM signals. Through a Fisher information analysis, we show that the problem is often identifiable, provided that there is at least one additional multipath component, even if the position of corresponding incidence point is a priori unknown. Subsequently, we pose a maximum likelihood (ML) estimation problem, to jointly estimate the 3D position and 3D orientation of the user as well as several nuisance parameters (the user clock offset and the positions of incidence points corresponding to the multipath). The ML problem is a highdimensional non-convex optimization problem over a product of Euclidean and Riemannian manifolds. To avoid complex exhaustive search procedures, we propose a geometric initial estimate of all parameters, which reduces the problem to a 1dimensional search over a finite interval. Numerical results show the efficiency of the proposed ad-hoc estimation, whose gap to the Cramér-Rao bound (CRB) is tightened using ML estimation.

show abstract

“…. , δ M (ψ)] T , and accordingly, the estimate of R UE is given by RUE = R( ψ), with R(ψ) characterized as in (19).…”

Section: A Step 1: Estimation Of Ue Rotation Matrixmentioning

confidence: 99%

MmWave 6D Radio Localization with a Snapshot Observation from a Single BS

Nazari¹,

Seco‐Granados²,

Johannisson³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The idea is to use the communication signaling to sense the user's position as a simultaneous functionality. Once the user's position is obtained, the base station system can better serve the user's communication [8]. The sensing can also be divided into farfield sensing and near-field sensing, depending on the distance between BS and the user.…”

Section: Introductionmentioning

confidence: 99%

Beam Squint Assisted User Localization in Near-Field Communications Systems

Luo¹,

Gao²

2022

Preprint

View full text Add to dashboard Cite

The beam squint phenomenon in massive multiinput and multi-output wideband communications has been widely concerned recently, which generally deteriorates the beamforming performance. In this paper, we find that with the aid of the time-delay lines (TDs), the range and trajectory of the beam squint of a near-field communications system can be freely controlled, and hence it is possible to reversely utilize the beam squint for user localization. We derive the trajectory equation for near-field beam squint points and design a way to control the trajectory of these beam squint points. With the proposed design, beamforming from different subcarriers would purposely point to different angles and different distances such that users from different positions would receive the maximum power at different subcarriers. Hence, one can simply find the different users' position from the beam squint effect. Simulation results demonstrate the effectiveness of the proposed scheme.

show abstract

“…The grey dotted line is the trajectory of the agent, and we draw two positions of an agent at t = 1 and t = t . design, power allocation [36], and intercarrier interference utilization [37]. Our main contributions are presented in detail as follows:…”

Section: Introductionmentioning

confidence: 99%

Hybrid Active and Passive Sensing for SLAM in Wireless Communication Systems

Yang,

Wen,

Jin

2022

Preprint

View full text Add to dashboard Cite

Integrating sensing functions into future mobile equipment has become an important trend. Realizing different types of sensing and achieving mutual enhancement under the existing communication hardware architecture is a crucial challenge in realizing the deep integration of sensing and communication.In the 5G New Radio context, active sensing can be performed through uplink beam sweeping on the user equipment (UE) side to observe the surrounding environment. In addition, the UE can perform passive sensing through downlink channel estimation to measure the multipath component (MPC) information. This study is the first to develop a hybrid simultaneous localization and mapping (SLAM) mechanism that combines active and passive sensing, in which mutual enhancement between the two sensing modes is realized in communication systems. Specifically, we first establish a common feature associated with the reflective surface to bridge active and passive sensing, thus enabling information fusion. Based on the common feature, we can attain physical anchor initialization through MPC with the assistance of active sensing. Then, we extend the classic probabilistic data association SLAM mechanism to achieve UE localization and continuously refine the physical anchor and target reflections through the subsequent passive sensing. Numerical results show that the proposed hybrid active and passive sensing-based SLAM mechanism can work successfully in tricky scenarios without any prior information on the floor plan, anchors, or agents. Moreover, the proposed algorithm demonstrates significant performance gains compared with active or passive sensing only mechanisms.

show abstract

Joint Communication and Localization in Millimeter Wave Networks

Cited by 38 publications

References 87 publications

MmWave 6D Radio Localization with a Snapshot Observation from a Single BS

MmWave 6D Radio Localization with a Snapshot Observation from a Single BS

Beam Squint Assisted User Localization in Near-Field Communications Systems

Hybrid Active and Passive Sensing for SLAM in Wireless Communication Systems

Contact Info

Product

Resources

About