Program

The new trend in wireless communications is enabling connectivity solutions from the sky. In this context, networked flying platforms (NFPs) including drones, balloons, and high-altitude/medium-altitude/low-altitude platforms (HAPs/MAPs/LAPs) are proposed to act as airborne base-stations (BSs) to offer 5G, beyond 5G (B5G), and 6G services. However, one of the main challenges facing the deployment of these airborne BSs is the limited available energy at the NFPs, which limits their flight and hovering time. In addition, these NFPs require an extra wireless feeder link to connect users to the core network. In this talk, we introduce the potential and advantages of tethered NFPs (t-NFPs) which are connected to a ground station through a tether. The tether provides the t-NFPs with both energy and data making them stay for a longer time in the air in order to essentially offer uninterrupted connectivity. We then discuss how the networks involving t-NFPs can be optimized to increase the efficiency of urban deployments and provide much-needed and better access in remote rural areas. We finally conclude our talk by presenting some research directions for these networks which should eventually offer a very appealing solution as a bridge between fixed base stations and free-flying platforms.

Recently, deep learning (DL) in wireless communications has been receiving increased research attention for designing intelligent communication systems. Specifically, DL has been applied in two important ways at the physical layer: 1) as a replacement to existing communication blocks like channel coding and signal detection, and 2) for designing end-to- end communication systems without traditional communication blocks. Both the approaches have shown promising results. This talk will focus on deep neural networks (DNN) for MIMO signal detection. Starting with a brief introduction on DNNs, the talk will introduce a novel modularized DNN architecture suited for MIMO signal detection. The architecture uses small sub-DNNs to individually detect the information symbols originating from multiple transmit antennas. This is in contrast to using a single large DNN to detect all he information symbols jointly. A key advantage is that using small sub-DNNs reduces the required size of the NN and hence requires learning lesser number of parameters. Also, due to its inherent ability to effectively learn the underlying noise models in practical receivers, the DNN base detector achieves robust and better performance compared to maximum likelihood detection performance when deviations from the standard model are witnessed.

Title: Dynamic Spectrum Sharing in Beyond 5G Networks

The demand for additional spectrum is skyrocketing in recent years due to widespread proliferation of wireless devices and application of all types. All useful frequency bands allocated for wireless communications are now crowded and unable to cope up with this increase in the demand, thereby creating an apparent spectrum scarcity for new wireless applications. On the other hand, field measurements show that up to 90% of the spectrum remain idle in many locations at any given time. This has clearly highlighted inadequacy of legacy spectrum regulation techniques based on long-term static spectrum allocation and exclusive use. There have been research efforts going on around the world to move from static spectrum allocation to dynamic spectrum sharing to efficiently use the available spectrum for 5G and beyond networks. In this lecture, an overview and motivation for spectrum sharing in beyond 5G wireless networks is first provided. Various spectrum sharing models are then introduced. The progress in spectrum sharing research starting from Cognitive Radios (CR) to Licensed Shared Access (LSA) and Citizens Broadband Radio Service (CBRS) is discussed. Future spectrum sharing research challenges are then highlighted.

Title: Multiple Access in Next Generation Communication System

The exponential growth of mobile data and multimedia traffic in recent years has accelerated the demands for new spectrum and even higher spectrum efficiency. Multiple access techniques in next generation communication systems is an emerging research area to provide much higher spectrum efficiency compared to existing orthogonal techniques. In particular, Non-orthogonal multiple access (NOMA) has recently emerged as a promising technique for multiple access in future communication systems. In contrast to orthogonal multiple access techniques such as TDMA, FDMA, CDMA and OFDMA used in 2G/3G/4G systems, NOMA serves multiple users concurrently in the same resource block, such as time slots, frequency channels, spreading codes, etc. In this lecture, basic concepts of NOMA are first introduced. Various forms of NOMA such as single carrier NOMA, multi-carrier NOMA, cooperative NOMA, MIMO NOMA, and mmWave NOMA are then described. Future research challenges of NOMA in the next generation communication systems are highlighted.

Title: Communication & Sensing

Abstract:

Radar and wireless communication systems are similar and dissimilar in many ways. Making these two systems share resources (be it hardware or bandwidth) is an on going research problem. Recently, with the application of AI to communications, a range of interesting possibilities have come up. In this talk, we plan to present some of our recent work towards application of AI to extract information similar to that of radars using 4G and 5G communication systems.

Title: A relook at Coding for wireless communications

Abstract:

Coding was originally developed for wired channels. This resulted in some of the fundamental constraints on error correction. This notion was later adopted for wireless communications. In this talk we present some of our recent results on coding for fading channels. We show that for these channels the error correction results are no longer valid in a probabilistic sense. New decoders are then presented in support of our claims.