arXiv subjects

Joonyoung Cho

Publications and source records attributed to Joonyoung Cho.

8 recordsLinked to original sources

arXiv2026

DFT-p-FDMA Based Chirp Transmission in CP-OFDM for Unified ISAC Waveform Design

We propose an integrated sensing and communications (ISAC) framework that supports chirp signal transmission in CP-OFDM-based multiple access communication systems, enabling efficient coexistence of communication and sensing capabilities. Our framework employs the discrete Fourier transform phase rotated and permuted frequency division multiple access (DFT-p-FDMA) waveform to transmit chirp signals using a portion of the frequency resources, while ensuring interference-free concurrent CP-OFDM data transmissions on other bands. We analyze the effective channel behavior under the DFT-p-FDMA waveform, characterizing how delays and Doppler shifts impact radar target echoes. We also show how processing multiple received symbols improves Doppler resolution in practical scenarios. Our framework allows flexible adjustment of range-Doppler resolution through optimized time-frequency resource allocation, offering a versatile solution for ISAC applications. Simulation results validate the framework's performance in delay and Doppler estimation, highlighting its potential to support ISAC in next-generation wireless networks.

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arXiv2026

Residual Diffusion Models for Variable-Rate Joint Source Channel Coding of MIMO CSI

Despite significant advancements in deep learning based CSI compression, some key limitations remain unaddressed. Current approaches predominantly treat CSI compression as a source-coding problem, thereby neglecting transmission errors. Conventional separate source and channel coding suffers from the cliff effect, leading to significant deterioration in reconstruction performance under challenging channel conditions. While existing autoencoder-based compression schemes can be readily extended to support joint source-channel coding, they struggle to capture complex channel distributions and exhibit poor scalability with increasing parameter count. To overcome these inherent limitations of autoencoder-based approaches, we propose Residual-Diffusion Joint Source-Channel Coding (RD- JSCC), a novel framework that integrates a lightweight autoencoder with a residual diffusion module to iteratively refine CSI reconstruction. Our flexible decoding strategy balances computational efficiency and performance by dynamically switching between low-complexity autoencoder decoding and sophisticated diffusion-based refinement based on channel conditions. Comprehensive simulations demonstrate that RD-JSCC significantly outperforms existing autoencoder-based approaches in challenging wireless environments. Furthermore, RD-JSCC offers several practical features, including a low-latency 2-step diffusion during inference, support for multiple compression rates with a single model, robustness to fixed-bit quantization, and adaptability to imperfect channel estimation.

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arXiv2024

Learned Pulse Shaping Design for PAPR Reduction in DFT-s-OFDM

High peak-to-average power ratio (PAPR) is one of the main factors limiting cell coverage for cellular systems, especially in the uplink direction. Discrete Fourier transform spread orthogonal frequency-domain multiplexing (DFT-s-OFDM) with spectrally-extended frequency-domain spectrum shaping (FDSS) is one of the efficient techniques deployed to lower the PAPR of the uplink waveforms. In this work, we propose a machine learning-based framework to determine the FDSS filter, optimizing a tradeoff between the symbol error rate (SER), the PAPR, and the spectral flatness requirements. Our end-to-end optimization framework considers multiple important design constraints, including the Nyquist zero-ISI (inter-symbol interference) condition. The numerical results show that learned FDSS filters lower the PAPR compared to conventional baselines, with minimal SER degradation. Tuning the parameters of the optimization also helps us understand the fundamental limitations and characteristics of the FDSS filters for PAPR reduction.

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arXiv2024

Nested Construction of Polar Codes via Transformers

Tailoring polar code construction for decoding algorithms beyond successive cancellation has remained a topic of significant interest in the field. However, despite the inherent nested structure of polar codes, the use of sequence models in polar code construction is understudied. In this work, we propose using a sequence modeling framework to iteratively construct a polar code for any given length and rate under various channel conditions. Simulations show that polar codes designed via sequential modeling using transformers outperform both 5G-NR sequence and Density Evolution based approaches for both AWGN and Rayleigh fading channels.

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arXiv2022

Predicting Future CSI Feedback For Highly-Mobile Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) system is promising in providing unprecedentedly high data rate. To achieve its full potential, the transceiver needs complete channel state information (CSI) to perform transmit/receive precoding/combining. This requirement, however, is challenging in the practical systems due to the unavoidable processing and feedback delays, which oftentimes degrades the performance to a great extent, especially in the high mobility scenarios. In this paper, we develop a deep learning based channel prediction framework that proactively predicts the downlink channel state information based on the past observed channel sequence. In its core, the model adopts a 3-D convolutional neural network (CNN) based architecture to efficiently learn the temporal, spatial and frequency correlations of downlink channel samples, based on which accurate channel prediction can be performed. Simulation results highlight the potential of the developed learning model in extracting information and predicting future downlink channels directly from the observed past channel sequence, which significantly improves the performance compared to the sample-and-hold approach, and mitigates the impact of the dynamic communication environment.

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arXiv2022

PolarDenseNet: A Deep Learning Model for CSI Feedback in MIMO Systems

In multiple-input multiple-output (MIMO) systems, the high-resolution channel information (CSI) is required at the base station (BS) to ensure optimal performance, especially in the case of multi-user MIMO (MU-MIMO) systems. In the absence of channel reciprocity in frequency division duplex (FDD) systems, the user needs to send the CSI to the BS. Often the large overhead associated with this CSI feedback in FDD systems becomes the bottleneck in improving the system performance. In this paper, we propose an AI-based CSI feedback based on an auto-encoder architecture that encodes the CSI at UE into a low-dimensional latent space and decodes it back at the BS by effectively reducing the feedback overhead while minimizing the loss during recovery. Our simulation results show that the AI-based proposed architecture outperforms the state-of-the-art high-resolution linear combination codebook using the DFT basis adopted in the 5G New Radio (NR) system.

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arXiv2021

Turbo Autoencoder with a Trainable Interleaver

A critical aspect of reliable communication involves the design of codes that allow transmissions to be robustly and computationally efficiently decoded under noisy conditions. Advances in the design of reliable codes have been driven by coding theory and have been sporadic. Recently, it is shown that channel codes that are comparable to modern codes can be learned solely via deep learning. In particular, Turbo Autoencoder (TURBOAE), introduced by Jiang et al., is shown to achieve the reliability of Turbo codes for Additive White Gaussian Noise channels. In this paper, we focus on applying the idea of TURBOAE to various practical channels, such as fading channels and chirp noise channels. We introduce TURBOAE-TI, a novel neural architecture that combines TURBOAE with a trainable interleaver design. We develop a carefully-designed training procedure and a novel interleaver penalty function that are crucial in learning the interleaver and TURBOAE jointly. We demonstrate that TURBOAE-TI outperforms TURBOAE and LTE Turbo codes for several channels of interest. We also provide interpretation analysis to better understand TURBOAE-TI.

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arXiv2018

Physical Uplink Control Channel Design for 5G New Radio

The next generation wireless communication system, 5G, or New Radio (NR) will provide access to information and sharing of data anywhere, anytime by various users and applications with diverse multi-dimensional requirements. Physical Uplink Control Channel (PUCCH), which is mainly utilized to convey Uplink Control Information (UCI), is a fundamental building component to enable NR system. Compared to Long Term Evolution (LTE), more flexible PUCCH structure is specified in NR, aiming to support diverse applications and use cases. This paper describes the design principles of various NR PUCCH formats and the underlying physical structures. Further, extensive simulation results are presented to explain the considerations behind the NR PUCCH design.

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