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Found 23 papers, 0 papers with code
HyperCLOVA X Technical Report
We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding.
GI-Free Pilot-Aided Channel Estimation for Affine Frequency Division Multiplexing Systems
The recently developed affine frequency division multiplexing (AFDM) can achieve full diversity in doubly selective channels, providing a comprehensive sparse representation of the delay-Doppler domain channel.
Frame Structure and Protocol Design for Sensing-Assisted NR-V2X Communications
In this study, we propose novel frame structures that incorporate ISAC signals for three crucial stages in the NR-V2X system: initial access, connected mode, and beam failure and recovery.
Optimal BER Minimum Precoder Design for OTFS-Based ISAC Systems
This paper investigates the bit error rate (BER) minimum pre-coder design for an orthogonal time frequency space (OTFS)-based integrated sensing and communications (ISAC) system, which is considered as a promising technique for enabling future wireless networks.
From OTFS to DD-ISAC: Integrating Sensing and Communications in the Delay Doppler Domain
The recently proposed Orthogonal Time Frequency Space (OTFS) modulation, which exploits various advantages of Delay Doppler (DD) channels, has been shown to support reliable communication in high-mobility scenarios.
Probabilistic Constellation Shaping for OFDM-Based ISAC Signaling
Integrated Sensing and Communications (ISAC) has garnered significant attention as a promising technology for the upcoming sixth-generation wireless communication systems (6G).
When UAVs Meet ISAC: Real-Time Trajectory Design for Secure Communications
The real-time unmanned aerial vehicle (UAV) trajectory design of secure integrated sensing and communication (ISAC) is optimized.
Radar Sensing via OTFS Signaling
By multiplexing information symbols in the delay-Doppler (DD) domain, orthogonal time frequency space (OTFS) is a promising candidate for future wireless communication in high-mobility scenarios.
Waveform Design for Communication-Assisted Sensing in 6G Perceptive Networks
In particular, a pair of transmission schemes, namely, separated S&C and dual-functional waveform designs, are proposed to optimize the sensing QoS under the constraints of the rate-distortion and power budget.
Towards ISAC-Empowered Vehicular Networks: Framework, Advances, and Opportunities
Connected and autonomous vehicle (CAV) networks face several challenges, such as low throughput, high latency, and poor localization accuracy.
Joint Beam Scheduling and Power Allocation for SWIPT in Mixed Near- and Far-Field Channels
Extremely large-scale array (XL-array) has emerged as a promising technology to enhance the spectrum efficiency and spatial resolution in future wireless networks, leading to a fundamental paradigm shift from conventional far-field communications towards the new near-field communications.
ISAC-Enabled V2I Networks Based on 5G NR: How Much Can the Overhead Be Reduced?
The emergence of the fifth-generation (5G) New Radio (NR) brings additional possibilities to vehicle-to-everything (V2X) network with improved quality of services.
Radar Sensing via OTFS Signaling: A Delay Doppler Signal Processing Perspective
The recently proposed orthogonal time frequency space (OTFS) modulation multiplexes data symbols in the delay-Doppler (DD) domain.
Predictive Precoder Design for OTFS-Enabled URLLC: A Deep Learning Approach
This paper investigates the orthogonal time frequency space (OTFS) transmission for enabling ultra-reliable low-latency communications (URLLC).
Deep CLSTM for Predictive Beamforming in Integrated Sensing and Communication-enabled Vehicular Networks
Predictive beamforming design is an essential task in realizing high-mobility integrated sensing and communication (ISAC), which highly depends on the accuracy of the channel prediction (CP), i. e., predicting the angular parameters of users.
Vehicular Connectivity on Complex Trajectories: Roadway-Geometry Aware ISAC Beam-tracking
In this paper, we propose sensing-assisted beamforming designs for vehicles on arbitrarily shaped roads by relying on integrated sensing and communication (ISAC) signalling. Specifically, we aim to address the limitations of conventional ISAC beam-tracking schemes that do not apply to complex road geometries.
Integrated Sensing and Communications for V2I Networks: Dynamic Predictive Beamforming for Extended Vehicle Targets
The first stage is exploited for ISAC transmission, where a wide beam is adopted for both communication and sensing.
Learning-based Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networks
Specifically, the convolution and LSTM modules are successively adopted in the proposed HCL-Net to exploit the spatial and temporal dependencies of communication channels to further improve the learning performance.
Integrated Sensing and Communication-assisted Orthogonal Time Frequency Space Transmission for Vehicular Networks
Benefiting from the OTFS-ISAC signals, the roadside unit (RSU) is capable of simultaneously transmitting downlink information to the vehicles and estimating the sensing parameters of vehicles, e. g., locations and speeds, based on the reflected echoes.
Off-grid Channel Estimation with Sparse Bayesian Learning for OTFS Systems
OTFS channel estimation is first formulated as a one-dimensional (1D) off-grid sparse signal recovery (SSR) problem based on a virtual sampling grid defined in the DD space, where the on-grid and off-grid components of the delay and Doppler shifts are separated for estimation.
Information Theory Information Theory
Cross Domain Iterative Detection for Orthogonal Time Frequency Space Modulation
Different from conventional OTFS detection methods, the proposed algorithm applies basic estimation/detection approaches to both the time domain and delay-Doppler (DD) domain and iteratively updates the extrinsic information from two domains with the unitary transformation.
Information Theory Information Theory
Location-aware Predictive Beamforming for UAV Communications: A Deep Learning Approach
Unmanned aerial vehicle (UAV)-assisted communication becomes a promising technique to realize the beyond fifth generation (5G) wireless networks, due to the high mobility and maneuverability of UAVs which can adapt to heterogeneous requirements of different applications.
Bayesian Predictive Beamforming for Vehicular Networks: A Low-overhead Joint Radar-Communication Approach
To accurately estimate the motion parameters of vehicles in real-time, we propose a novel message passing algorithm based on factor graph, which yields near optimal solution to the maximum a posteriori estimation.
