Zobrazeno 1 - 10
of 370
pro vyhledávání: '"Jun, Angela"'
The increasing concern for data privacy has driven the rapid development of federated learning (FL), a privacy-preserving collaborative paradigm. However, the statistical heterogeneity among clients in FL results in inconsistent performance of the se
Externí odkaz:
http://arxiv.org/abs/2410.20141
This paper addresses the problem of end-to-end (E2E) design of learning and communication in a task-oriented semantic communication system. In particular, we consider a multi-device cooperative edge inference system over a wireless multiple-input mul
Externí odkaz:
http://arxiv.org/abs/2408.17397
Autor:
Park, Jung In, Abbasian, Mahyar, Azimi, Iman, Bounds, Dawn, Jun, Angela, Han, Jaesu, McCarron, Robert, Borelli, Jessica, Li, Jia, Mahmoudi, Mona, Wiedenhoeft, Carmen, Rahmani, Amir
Objective: This study aims to develop and validate an evaluation framework to ensure the safety and reliability of mental health chatbots, which are increasingly popular due to their accessibility, human-like interactions, and context-aware support.
Externí odkaz:
http://arxiv.org/abs/2408.04650
Extremely large-scale arrays (XL-arrays) have emerged as a key enabler in achieving the unprecedented performance requirements of future wireless networks, leading to a significant increase in the range of the near-field region. This transition neces
Externí odkaz:
http://arxiv.org/abs/2407.13491
This paper studies the multi-intelligent reflecting surface (IRS)-assisted cooperative sensing, in which multiple active IRSs are deployed in a distributed manner to facilitate multi-view target sensing at the non-line-of-sight (NLoS) area of the bas
Externí odkaz:
http://arxiv.org/abs/2406.12426
By extracting task-relevant information while maximally compressing the input, the information bottleneck (IB) principle has provided a guideline for learning effective and robust representations of the target inference. However, extending the idea t
Externí odkaz:
http://arxiv.org/abs/2405.04144
Autor:
Xie, Songjie, He, Hengtao, Li, Hongru, Song, Shenghui, Zhang, Jun, Zhang, Ying-Jun Angela, Letaief, Khaled B.
Deep learning-based joint source-channel coding (DJSCC) is expected to be a key technique for {the} next-generation wireless networks. However, the existing DJSCC schemes still face the challenge of channel adaptability as they are typically trained
Externí odkaz:
http://arxiv.org/abs/2401.11155
Artificial intelligence (AI) technologies have emerged as pivotal enablers across a multitude of industries largely due to their significant resurgence over the past decade. The transformative power of AI is primarily derived from the utilization of
Externí odkaz:
http://arxiv.org/abs/2312.00333
In task-oriented communications, most existing work designed the physical-layer communication modules and learning based codecs with distinct objectives: learning is targeted at accurate execution of specific tasks, while communication aims at optimi
Externí odkaz:
http://arxiv.org/abs/2309.02888
In future B5G/6G broadband communication systems, non-linear signal distortion caused by the impairment of transmit power amplifier (PA) can severely degrade the communication performance, especially when uplink users share the wireless medium using
Externí odkaz:
http://arxiv.org/abs/2308.12662