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1. Diff-GO ⁿ : Enhancing Diffusion Models for Goal-Oriented Communications

   https://doi.org/10.1109/ICC52391.2025.11161394  

   Wanninayaka, Suchinthaka; Wijesinghe, Achintha; Wang, Weiwei; Chao, Yu-Chieh; Zhang, Songyang; Ding, Zhi (June 2025, IEEE)

   The rapid expansion of edge devices and Internet-of-Things (IoT) continues to heighten the demand for data transport under limited spectrum resources. The goal-oriented communications (GO-COM), unlike traditional communication systems designed for bit-level accuracy, prioritizes more critical information for specific application goals at the receiver. To improve the efficiency of generative learning models for GOCOM, this work introduces a novel noise-restricted diffusionbased GO-COM (Diff-GOn) framework for reducing bandwidth overhead while preserving the media quality at the receiver. Specifically, we propose an innovative Noise-Restricted Forward Diffusion (NR-FD) framework to accelerate model training and reduce the computation burden for diffusion-based GO-COMs by leveraging a pre-sampled pseudo-random noise bank (NB). Moreover, we design an early stopping criterion for improving computational efficiency and convergence speed, allowing highquality generation in fewer training steps. Our experimental results demonstrate superior perceptual quality of data transmission at a reduced bandwidth usage and lower computation, making Diff-GO n well-suited for real-time communications and downstream applications. 

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2. Task-Driven Semantic Quantization and Imitation Learning for Goal-Oriented Communications

   https://doi.org/10.1109/ICC52391.2025.11161655  

   Chao, Yu-Chieh; Chen, Yubei; Wang, Weiwei; Wijesinghe, Achintha; Wanninayaka, Suchinthaka; Zhang, Songyang; Ding, Zhi (June 2025, IEEE)

   Semantic communication marks a new paradigm shift from bit-wise data transmission to semantic information delivery for the purpose of bandwidth reduction. To more effectively carry out specialized downstream tasks at the receiver end, it is crucial to define the most critical semantic message in the data based on the task or goal-oriented features. In this work, we propose a novel goal-oriented communication (GO-COM) framework, namely Goal-Oriented Semantic Variational Autoencoder (GOS-VAE), by focusing on the extraction of the semantics vital to the downstream tasks. Specifically, we adopt a Vector Quantized Variational Autoencoder (VQ-VAE) to compress media data at the transmitter side. Instead of targeting the pixel-wise image data reconstruction, we measure the quality-of-service at the receiver end based on a pre-defined task-incentivized model. Moreover, to capture the relevant semantic features in the data reconstruction, imitation learning is adopted to measure the data regeneration quality in terms of goal-oriented semantics. Our experimental results demonstrate the power of imitation learning in characterizing goal-oriented semantics and bandwidth efficiency of our proposed GOS-VAE. 

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3. Whisper: a unilateral defense against VoIP traffic re-identification attacks

   https://doi.org/10.1145/3359789.3359807  

   Vaidya, Tavish; Walsh, Tim; Sherr, Micah (December 2019, Proceedings of the Annual Computer Security Applications Conference (ACSAC))

   Encrypted voice-over-IP (VoIP) communication often uses variable bit rate (VBR) codecs to achieve good audio quality while minimizing bandwidth costs. Prior work has shown that encrypted VBR-based VoIP streams are vulnerable to re-identification attacks in which an attacker can infer attributes (e.g., the language being spoken, the identities of the speakers, and key phrases) about the underlying audio by analyzing the distribution of packet sizes. Existing defenses require the participation of both the sender and receiver to secure their VoIP communications. This paper presents Whisper, the first unilateral defense against re-identification attacks on encrypted VoIP streams. Whisper works by modifying the audio signal before it is encoded by the VBR codec, adding inaudible audio that either falls outside the fixed range of human hearing or is within the human audible range but is nearly imperceptible due to its low amplitude. By carefully inserting such noise, Whisper modifies the audio stream's distribution of packet sizes, significantly decreasing the accuracy of re-identification attacks. Its use is imperceptible by the (human) receiver. Whisper can be instrumented as an audio driver and requires no changes to existing (potentially closed-source) VoIP software. Since it is a unilateral defense, it can be applied at will by a user to enhance the privacy of its voice communications. We demonstrate that Whisper significantly reduces the accuracy of re-identification attacks and incurs only a small degradation in audio quality. 

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4. A 49-63 GHz Phase-locked FMCW Radar Transceiver for High Resolution Applications

   Liu, Xuyang; Maktoomi, Md Hedayatullah; Alesheikh, Mahdi; Heydari, Payam; Aghasi, Hamidreza (October 2023, IEEE European Solid State Circuits Conference)

   TPC of IEEE ESSCIRC Conference (Ed.)

   This paper presents an mmWave FMCW radar that can achieve sub-centimeter-scale range resolution at 14- GHz chirp-bandwidth while maintaining the radar range beyond 50 meters. To meet the requirements on power efficiency, chirp linearity, and signal-to-noise ratio (SNR), a phase-locked steppedchirp FMCW radar architecture is introduced. Specifically, a fully integrated radar transceiver comprising an interleaved frequency-segmented phase-locked transmitter and a segmented receiver architecture with high sensitivity is presented. The proposed design addresses the limitations of conventional typeII phase-locked loops (PLLs) in extending the radar bandwidth across multiple sub-bands with identical chirp profiles. Fabricated in a 22nm FD-SOI technology, the prototype chip comprises two sub-bands with 14 GHz of free-running bandwidth and 10 GHz of phase-locked bandwidth. The system achieves -101.7 dBc/Hz phase noise at 1 MHz offset, 8 dBm of effective isotropic radiated power (EIRP), 10 dB noise figure (NF), and 362.6 mW collective power consumption of transmitter and receiver arrays. 

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5. Scheduling Policy and Power Allocation for Federated Learning in NOMA based MEC

   https://doi.org/10.1109/GLOBECOM42002.2020.9322270  

   Ma, Xiang; Sun, Haijian; Hu, Rose Qingyang (December 2020, IEEE Globecom 2020)

   null (Ed.)

   Federated learning (FL) is a highly pursued machine learning technique that can train a model centrally while keeping data distributed. Distributed computation makes FL attractive for bandwidth limited applications especially in wireless communications. There can be a large number of distributed edge devices connected to a central parameter server (PS) and iteratively download/upload data from/to the PS. Due to limited bandwidth, only a subset of connected devices can be scheduled in each round. There are usually millions of parameters in the state-of-art machine learning models such as deep learning, resulting in a high computation complexity as well as a high communication burden on collecting/distributing data for training. To improve communication efficiency and make the training model converge faster, we propose a new scheduling policy and power allocation scheme using non-orthogonal multiple access (NOMA) settings to maximize the weighted sum data rate under practical constraints during the entire learning process. NOMA allows multiple users to transmit on the same channel simultaneously. The user scheduling problem is transformed into a maximum-weight independent set problem that can be solved using graph theory. Simulation results show that the proposed scheduling and power allocation scheme can help achieve a higher FL testing accuracy in NOMA based wireless networks than other existing schemes within the same learning time. 

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