The last few years have witnessed a tremendous growth of the demand for wireless services and a significant increase of the number of mobile subscribers. A recent data traffic forecast from Cisco reported that the global mobile data traffic reached 1.2 zettabytes per year in 2016, and the global IP traffic will increase nearly threefold over the next 5 years. Based on these predictions, a 127-fold increase of the IP traffic is expected from 2005 to 2021. It is also anticipated that the mobile data traffic will reach 3.3 zettabytes per year by 2021, and that the number of mobile-connected devices will reach 3.5 per capita.
With such demands for higher data rates and for better quality of service (QoS), fifth generation (5G) standardization initiatives, whose initial phase was specified in June 2018 under the umbrella of Long Term Evolution (LTE) Release 15, have been under vibrant investigation. In particular, the International Telecommunication Union (ITU) has identified three usage scenarios (service categories) for 5G wireless networks: (i) enhanced mobile broadband (eMBB), (ii) ultra-reliable and low latency communications (uRLLC), and (iii) massive machine type communications (mMTC). The vast variety of applications for beyond 5G wireless networks has motivated the necessity of novel and more flexible physical layer (PHY) technologies, which are capable of providing higher spectral and energy efficiencies, as well as reduced transceiver implementations.
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This month, we are happy to introduce Prof. Alexander Bertrand who is currently Assistant Professor at the Electrical Engineering Depatrtment (ESAT) of KU Leuven, Belgium. His research involes signal processing design with a focus on biomedical applications, such as electroencephalography (EEG), neuro probes, body area networks, hearing aids and cochlear implants.He also conducts research in generic signal processing techniques, with a focus on multi-channel and distributed signal processing, spatial filtering, beamforming, adaptive filtering, and signal enhancement.
What are your research interests in the signal processing field?
My signal processing interests are mainly in multi-channel and array processing, distributed signal processing, adaptive filtering, blind source separation. In terms of applications, I am mainly interested in biomedical and audio signal processing, with a particular focus on those modalities that require multi-channel and array processing techniques such as, e.g., microphone arrays, EEG, neuroprobe arrays, etc.
Could you briefly introduce your research?
A major part of my research is on distributed signal processing algorithm design, e.g., for parameter or signal estimation, subspace estimation, and topology inference. We typically also aim to translate the rather generic results in this field to specific applications, such as speech enhancement in acoustic sensor networks, and brain computer interfaces in ‘EEG sensor networks’. The latter can be viewed as miniature wireless body area networks that measure brain activity at different locations on the scalp.
Apart from our work on distributed algorithms, we also work on signal processing problems in auditory neuroscience, in which we envisage so-called neuro-steered hearing prostheses that simultaneously analyze audio and neural signals. For example, one could think of a hearing aid or cochlear implant that uses EEG sensors to detect which speech source a subject is attending to, and then steer an acoustic beamformer towards that particular speaker in a cocktail party environment. Of course, it will take many years before such applications will truly see the day. A lot of obstacles still have to be overcome, not only on the signal processing side, but also on the hardware side, as truly wearable (discreet) EEG systems do not exist yet today. But significant progress has been made in the past few years.
We also collaborate with UC Berkeley (Prof. Jan Rabaey et al.) on signal processing challenges in next-generation neural implants, such as ‘neural dust’. The latter consists of a grid of free-floating neural dust motes, which are implanted at 3mm depth in the cortex to measure extracellular action potentials or 'spikes'. The spike signals observed by the neural dust motes are read out using an array of ultrasonic ‘interrogators’ relying on passive backscattering, which generates an interesting MIMO source separation problem.
Could you introduce an important state-of-the-art research issue (or technology) in this field (Other than your research)?
In the field of biomedical signal processing, a major concern is currently how to deal with the relatively poor signal quality from wearable devices. This low quality is both due to the wearable sensor technology, which has not matured yet for most modalities, as well as due to the many artifacts that appear in the signals, where motion artifacts due to body movement are the most notorious. There is indeed a huge difference between measuring body signals in a well-controlled environment such as a lab or a hospital, and measuring the same signals 24/7 during the normal every-day routine of a person.
If we return to the field of distributed signal processing, an important research issue is currently how to organize and analyze interactions or collaborations in a network where the nodes or agents do not necessarily have the same interest or task. For example, consider a large room where many people are simultaneously using audio devices such as smartphones, hearing aids, wireless headsets, etc. All these devices can be combined in a large sensor network consisting of many microphones observing the same environment, but each user is interested in a different sound source, i.e., a desired speaker for user A may be an interfering speaker for user B and vice versa. This leads to interesting research challenges, involving coalitional game theory, multi-source detection and labeling, source separation, etc.
In your opinion, what was the most impressive result published in IEEE SPS journals and conferences within the last 12 months?
This is a tough question, as I have seen so many interesting things. I am not sure if this counts as an answer, but I really liked the paper “Signal Processing and Optimization Tools for Conference Review and Session Assignment” (N. D. Sidiropoulos and E. E. Tsakonas) in one of the recent issues of the Signal Processing Magazine. It’s not the most impressive result I’ve seen, but it was definitely one of the most interesting and the most fun to read. It’s intriguing to see how signal processing tools can actually help us, signal processing researchers, making our own life easier.
In which way have you been connected first with IEEE (university, conference, etc…)?
This was during my masters, when the IEEE student branch organized a wine-tasting course. There was a reduced entrance fee for those participants who also subscribed as an IEEE member, which is how they 'tricked' me. I became a fan of the IEEE Spectrum magazine, so I kept renewing my membership. During my Ph.D., I also became a member of the SPS.
In which way did you know the IEEE SPS e-NewsLetter?
That I honestly can’t remember. I guess it just automatically started to appear in my mailbox because of my SPS membership. Anyway, it is a very useful tool to stay up to date with all the novelties in the community.
Alexander Bertrand was born in Roeselare, Belgium, in 1984. He received the M.Sc. degree in Electrical Engineering (2007) and the Ph.D. degree in Engineering Sciences (2011), both from KU Leuven, Belgium. He is currently assistant professor at the Electrical Engineering Department (ESAT) of KU Leuven. He was a visiting researcher at University of California, Los Angeles (in 2010) and at University of California, Berkeley (in 2013).
His research involves signal processing algorithm design with a focus on biomedical applications, such as electroencephalography (EEG), neural implants, body area networks, hearing aids and cochlear implants. His EEG-related research focuses on signal processing challenges in mobile EEG systems, distributed EEG sensor networks, and auditory neuroscience (auditory steady-state responses, auditory attention detection, etc.). He also conducts research in generic signal processing techniques, with a focus on multi-channel and distributed signal processing, spatial filtering, beamforming, adaptive filtering, and signal enhancement.
Dr. Bertrand received the 2012 FWO/IBM-Belgium Award, the 2013 KU Leuven Research Council Award (Science & Technology), an Honorary Mention in the ERCIM Cor Baayen Award (2013), and was awarded several research fellowships/grants. He has served as a Technical Program Committee (TPC) Member for several conferences and Workshops, as a lead guest editor for Signal Processing (Elsevier), and he is currently co-editor of the newsletter of the European Association for Signal Processing (EURASIP).
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