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As Wi-Fi "strikes again" with 802.11be, this forum will host a discussion on its evolution, the ongoing 802.11be standardization, the opportunities created by the progressive adoption of the 6 GHz spectrum, and the increased interest in supporting not only higher capacity but also reliable and low latency applications using Wi-Fi. Experts from industry and academia will share their experience in driving standard and product development, spectrum and technology regulations, and research visions.
KEYNOTE 1: DISTRIBUTED MACHINE LEARNING AT THE WIRELESS EDGE SPEAKER: PROF. DENIZ GÜNDÜZ IMPERIAL COLLEGE LONDON, UK Abstract: IoT devices collect significant amount of data at the wireless edge, opening up new potentials for machine learning applications. Current approach to edge intelligence is to offload all the collected data to a cloud server for central processing. This approach is not sustainable considering the expected growth in the number of IoT devices and the traffic they generate. Moreover, it creates significant privacy risks for the users, and introduces delays that cannot be tolerated by most applications. The alternative is to bring the intelligence to the edge, by distributing both the training and the inference tasks across edge devices and servers. In this talk, I will present recent results on efficient distributed inference and training over wireless channels taking into account channel impairments as well as power and bandwidth limitations of wireless devices. This will involve bringing together novel communication and coding techniques with distributed learning algorithms. SPEAKER: JULIEN FORGEAT, ARTIFICIAL INTELLIGENCE, ERICSSON RESEARCH Bio: Julien Forgeat is an artificial intelligence principal researcher at Ericsson Research. He joined Ericsson in 2010 after spending several years working on network analysis and optimization. He holds an M.Eng. in computer science from the National Institute of Applied Sciences in Lyon, France. At Ericsson, Julien has worked on mobile learning, Internet of Things and big data analytics before specializing in machine learning and AI infrastructure. His current research focuses on the software components required to run AI and machine learning workloads on distributed infrastructures as well as the algorithmic approaches that are best suited for complex distributed and decentralized use-cases.
With the advent of the Industrial Internet of Things (IIoT), the industrial sector has witnessed substantial changes over the last couple of years. These changes when integrated with the conventional systems not only help to upscale their productions but also help them to achieve their business goals. The ongoing expansion across different domains such as manufacturing, energy systems, transportation, automated vehicles, etc is a perfect example of actively applying this innovative technology to the industrial sector. The major precursors behind this advancement of IIoT can be attributed to more product variability, enhanced quality of products, growing global competition, decreased product cost, and shorter manufacturing time. To cater to the above-mentioned challenges, IIoT leverages the concept of Industrial Cyber-Physical Systems (I-CPS); which enable interactions amongst the machines, data, and humans.Sensing-as-a-Service (S2aaS) is considered as key component of the I-CPS and is assumed to resides somewhere between the cyber and physical worlds. It also helps in integrating the IIoT data with the marketplace analysing and trading on the gathered data. Nonetheless, the sensors are not necessarily the physical sensors interacting with the environment, but they can be virtual too. In S2aaS, the virtual sensors can be any entity that produces data such as social media accounts, quantified self apps, weather APIs, etc. The data generated from these sensors needs real-time analysis to derive value for its marketplace; and Edge/Fog/Cloud Computing has a significant role to play in the context. This computing paradigm can offer promising results for CPS applications that are characterised by geo-distribution, latency-sensitivity and high-resilience.
Unmanned aerial vehicles (UAVs) have found fast growing applications during the past few years. As such, it is imperative to develop innovative communication technologies for supporting reliable UAV command and control (C&C), as well as mission-related payload communication. However, traditional UAV systems mainly rely on the simple direct communication between the UAV and the ground pilot over unlicensed spectrum (e.g., ISM 2.4GHz), which is typically of low data rate, unreliable, insecure, vulnerable to interference, difficult to legitimately monitor and manage, and can only operate within the visual line of sight (LoS) range. To overcome the above limitations, there has been significant interest in integrating UAVs into cellular communication systems. On the one hand, UAVs with their own missions could be connected into cellular networks as new aerial users. Thanks to the advanced cellular technologies and almost ubiquitous accessibility of cellular networks, cellular-connected UAVs are expected to achieve orders-of-magnitude performance improvement over the existing point-to-point UAV communications. It also offers an effective option to strengthen the legitimate UAV monitoring and management, and achieve more robust UAV navigation by utilizing cellular signals as a complement to GPS (Global Position System). On the other hand, dedicated UAVs could be deployed as aerial base stations (BSs), access points (APs), or relays, to assist terrestrial wireless communications from the sky, leading to another paradigm known as UAV-assisted communications. UAV-assisted communications have several promising advantages, such as the ability to facilitate on-demand deployment, high flexibility in network reconfiguration, high chance of having LoS communication links, and enable numerous applications such as BS traffic offloading, information dissemination and collection for Internet of Things (IoTs). UAV communications are significantly different from conventional communication systems, due to the high altitude and high mobility of UAVs, the unique channel of UAV-ground links, the asymmetric quality of service (QoS) requirements for downlink C&C and uplink mission-related data transmission, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the additional design degrees of freedom enabled by joint UAV mobility control and communication resource allocation.
This academic keynote is on Future of MIMO Communication. Bio: Robert W. Heath Jr. received the Ph.D. in EE from Stanford University. He is a Distinguished Professor at North Carolina State University. He is also the President and CEO of MIMO Wireless Inc. Prof. Heath is a recipient of several awards including recently the 2016 IEEE Communications Society Fred W. Ellersick Prize, the 2016 IEEE Communications Society and Information Theory Society Joint Paper Award, the 2017 IEEE Marconi Prize Paper Award, the 2017 EURASIP Technical Achievement Award, the 2019 IEEE Communications Society Stephen O. Rice Prize, the 2019 IEEE Kiyo Tomiyasu Award, and the 2020 IEEE SPS Donald G. Fink Overview Paper Award. He co-authored “Millimeter Wave Wireless Communications” (Prentice Hall in 2014) and "Foundations of MIMO Communications" (Cambridge 2019). He was EIC of IEEE Signal Processing Magazine from 2018-2020. He is a current member-at-large of the IEEE Communications Society Board-of-Governors (2020-2022) and a past member-at-large of the IEEE Signal Processing Society Board-of-Governors (2016-2018). He is a licensed Amateur Radio Operator, a registered Professional Engineer in Texas, a Private Pilot, a Fellow of the National Academy of Inventors, and a Fellow of the IEEE.