IEEE Journal of Selected Topics in Signal Processing

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

The papers in this special section focus on self-supervised learning for speech and audio processing. A current trend in the machine learning community is the adoption of self-supervised approaches to pretrain deep networks. Self-supervised learning utilizes proxy-supervised learning tasks (or pretext tasks) - for example, distinguishing parts of the input signal from distractors or reconstructing masked input segments conditioned on unmasked segments—to obtain training data from unlabeled corpora. 

04 May

JSTSP Webinar: Overview of Special Issue on Joint Communication and Radar Sensing (JCR) for Emerging Applications

Date: May 4, 2022
Time: 8:00 AM EDT (New York Time)
Presenters: Dr. Christos Masouros, Dr. Fan Liu, Dr. J. Andrew Zhang

Network slicing to support multi-tenancy plays a key role in improving the performance of 5G and beyond networks. In this paper, we study dynamically slicing network resources in the backhaul and Radio Access Network (RAN) prior to user demand observations across multiple tenants, where each tenant owns and operates several slices to provide different services to users.
Dual-Functional Radar-Communication (DFRC) is a promising paradigm to achieve Integrated Sensing and Communication (ISAC) in beyond 5G. In parallel, Rate-Splitting Multiple Access (RSMA), relying on multi-antenna Rate-Splitting (RS) by splitting messages into common and private streams at the transmitter and Successive Interference Cancellation (SIC) at the receivers, has emerged as a new strategy for multi-user multi-antenna communications systems. I
A receiver architecture is proposed to cognitively extract navigation observables from fifth generation (5G) new radio (NR) signals of opportunity. Unlike conventional opportunistic receivers which require knowledge of the signal structure, particularly the reference signals (RSs), the proposed cognitive opportunistic navigation (CON) receiver requires knowledge of only the frame duration and carrier frequency of the signal. In 5G NR, some of these RSs are only transmitted on demand, which limits the existing opportunistic...

Pages

SPS Social Media

IEEE SPS Educational Resources

IEEE SPS Resource Center

IEEE SPS YouTube Channel