Distributed Beam Alignment in Sub-THz Indoor D2D Networks

Devices in a device-to-device (D2D) network operating in sub-THz frequencies require knowledge of the spatial channel that connects them to their peers. Acquiring such high dimensional channel state information entails large overhead, which drastically increases with the number of network devices. In this paper, we propose an accelerated method to achieve network-wide beam alignment in an efficient way. To this aim, we consider compressed sensing (CS) estimation enabled by a novel design of pilot sequences. Our designed pilots have constant envelope to alleviate hardware requirements at the transmitters, while they exhibit a “comb-like” spectrum that flexibly allocates energy only on certain frequencies. This design enables multiple devices to transmit their pilots concurrently while remaining orthogonal in frequency, achieving simultaneous alignment of multiple devices. Furthermore, we present a sequential partitioning strategy into transmitters and receivers that results in logarithmic scaling of the overhead with the number of devices, as opposed to the conventional linear scaling. Finally, we show via accurate modeling of the indoor propagation environment and ray tracing simulations that the resulting sub-THz channels after successful beamforming are approximately frequency flat, therefore suitable for efficient single carrier transmission without equalization. We compare our results against an ”802.11ad inspired” baseline and show that our method is capable to greatly reduce the number of pilots required to achieve network-wide alignment.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Wireless Communications
Published
2025-10-09
DOI
https://doi.org/10.1109/twc.2025.3617168
Primary Topic
Millimeter-Wave Propagation and Modeling
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Distributed Beam Alignment in Sub-THz Indoor D2D Networks

Fabian Jaensch, Jan Christian Hauffen, Shuangyang Li, Giuseppe Caire et al.
IEEE Transactions on Wireless Communications
Millimeter-Wave Propagation and Modeling
article

Distributed Beam Alignment in Sub-THz Indoor D2D Networks

Fabian Jaensch, Jan Christian Hauffen, Shuangyang Li, Giuseppe Caire, Fernando Pedraza
article en

Abstract

Devices in a device-to-device (D2D) network operating in sub-THz frequencies require knowledge of the spatial channel that connects them to their peers. Acquiring such high dimensional channel state information entails large overhead, which drastically increases with the number of network devices. In this paper, we propose an accelerated method to achieve network-wide beam alignment in an efficient way. To this aim, we consider compressed sensing (CS) estimation enabled by a novel design of pilot sequences. Our designed pilots have constant envelope to alleviate hardware requirements at the transmitters, while they exhibit a “comb-like” spectrum that flexibly allocates energy only on certain frequencies. This design enables multiple devices to transmit their pilots concurrently while remaining orthogonal in frequency, achieving simultaneous alignment of multiple devices. Furthermore, we present a sequential partitioning strategy into transmitters and receivers that results in logarithmic scaling of the overhead with the number of devices, as opposed to the conventional linear scaling. Finally, we show via accurate modeling of the indoor propagation environment and ray tracing simulations that the resulting sub-THz channels after successful beamforming are approximately frequency flat, therefore suitable for efficient single carrier transmission without equalization. We compare our results against an ”802.11ad inspired” baseline and show that our method is capable to greatly reduce the number of pilots required to achieve network-wide alignment.

IEEE Transactions on Wireless CommunicationsVol. 25
Technische Universität Berlin (DE)
Openalex Percentile: Top 72%
Millimeter-Wave Propagation and Modeling
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.