Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays

Massive MIMO (multiple-input multiple-output) is no longer a "wild" or "promising" concept for future cellular networks - in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies - once viewed prohibitively complicated and costly - is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6 GHz and mmWave frequencies. In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication applications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin

Fusion of deep representations in multistatic radar networks to counteract the presence of synthetic jamming

Micro-Doppler signatures are extremely valuable in the classification of a wide range of targets. This paper investigates the effects of jamming on the micro-Doppler classification performance and explores a potential deep topology enabling low-bandwidth data fusion between nodes in a multistatic radar network. The topology is based on an array of three independent deep neural networks (DNNs) functioning cooperatively to achieve joint classification. In addition to this, a further DNN is trained to detect the presence of jamming, and from this, it attempts to remedy the degradation effects in the data fusion process. This is applied to the real experimental data gathered with the multistatic radar system, NetRAD, of a human operating with seven combinations of holding a rifle-like object and a heavy backpack that is slung on their shoulders. The resilience of the proposed network is tested by applying synthetic jamming signals into specific radar nodes and observing the networks' ability to respond to these undesired effects. The results of this are compared with a traditional voting system topology, serving as a convenient baseline for this paper

A preliminary approach to intelligent x-ray imaging for baggage inspection at airports

Identifying explosives in baggage at airports relies on being able to characterize the materials that make up an X-ray image. If a suspicion is generated during the imaging process (step 1), the image data could be enhanced by adapting the scanning parameters (step 2). This paper addresses the first part of this problem and uses textural signatures to recognize and characterize materials and hence enabling system control. Directional Gabor-type filtering was applied to a series of different X-ray images. Images were processed in such a way as to simulate a line scanning geometry. Based on our experiments with images of industrial standards and our own samples it was found that different materials could be characterized in terms of the frequency range and orientation of the filters. It was also found that the signal strength generated by the filters could be used as an indicator of visibility and optimum imaging conditions predicted