IEEE Journal of Selected Topics in Signal Processing

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  • CURRENT ISSUE
    Distributed Signal Processing for Edge
    Learning in B5G IoT Networks
  • October 2022
    Self-Supervised Learning for speech
    and Audio Processing
  • August 2022
    Advanced Signal Processing
    for Local and Private 5G Networks
  • June 2022
    Deep Learning for High Dimensional Sensing

JSTSP Volume 17 Issue 1

Online Edge Learning Offloading and Resource Management for UAV-Assisted MEC Secure Communications

JSTSP Volume 17 Issue 1
JSTSP Articles

The mobile and flexible unmanned aerial vehicle (UAV) with mobile edge computing (MEC) can effectively relieve the computing pressure of the massive data traffic in 5G Internet of Things. In this paper, we propose a novel online edge learning offloading (OELO) scheme for UAV-assisted MEC secure communications, which can improve the secure computation performance. Moreover, the problem of information security is further considered since the offloading information of terminal users (TUs) may be eavesdropped due to the light-of-sight characteristic of UAV transmission.

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Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control

JSTSP Volume 17 Issue 1
JSTSP Articles

The explosive growth of dynamic and heterogeneous data traffic brings great challenges for 5G and beyond mobile networks. To enhance the network capacity and reliability, we propose a learning-based dynamic time-frequency division duplexing (D-TFDD) scheme that adaptively allocates the uplink and downlink time-frequency resources of base stations (BSs) to meet the asymmetric and heterogeneous traffic demands while alleviating the inter-cell interference. 

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Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing

JSTSP Volume 17 Issue 1
JSTSP Articles

To process and transfer large amounts of data in emerging wireless services, it has become increasingly appealing to exploit distributed data communication and learning. Specifically, edge learning (EL) enables local model training on geographically disperse edge nodes and minimizes the need for frequent data exchange.

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Guest Editorial Distributed Signal Processing for Edge Learning in B5G IoT Networks

JSTSP Volume 17 Issue 1
JSTSP Articles

The papers in this special section focus on distributed signal processing for edge learning (EL). EL is a new and promising technology for implementing artificial intelligence (AI) algorithms at edge devices over wireless networks.

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JSTSP Volume 16 Issue 6

Towards Better Domain Adaptation for Self-Supervised Models: A Case Study of Child ASR

JSTSP Volume 16 Issue 6
JSTSP Articles

Recently, self-supervised learning (SSL) from unlabelled speech data has gained increased attention in the automatic speech recognition (ASR) community. Typical SSL methods include autoregressive predictive coding (APC), Wav2vec2.0, and hidden unit BERT (HuBERT). However, SSL models are biased to the pretraining data. When SSL models are finetuned with data from another domain, domain shifting occurs and might cause limited knowledge transfer for downstream tasks.

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