Co-existence Between a Radar System and a Massive MIMO Wireless Cellular System

In this paper we consider the uplink of a massive MIMO communication system using 5G New Radio-compliant multiple access, which is to co-exist with a radar system using the same frequency band. We propose a system model taking into account the reverberation (clutter) produced by the radar system at the massive MIMO receiver. Then, we propose several linear receivers for uplink data-detection, ranging by the simple channel-matched beamformer to the zero-forcing and linear minimum mean square error receivers for clutter disturbance rejection. Our results show that the clutter may have a strong effect on the performance of the cellular communication system, but the use of large-scale antenna arrays at the base station is key to provide increased robustness against it, at least as far as data-detection is concerned.Comment: To be presented at 2018 IEEE SPAWC, Kalamata, Greece, June 201

Interfering Channel Estimation for Radar and Communication Coexistence

We investigate the interfering channel estimation in radar and communication coexistence, where a multi-input-multi-output (MIMO) radar is operated in a “search and track” mode, and a MIMO base station (BS) is attempting to acquire the interfering channel state information (ICSI) between them, which is required for the precoding designs. In contrast to conventional training based techniques, we exploit radar probing waveforms as pilot signals, which requires no coordination between the systems. As the radar randomly transmits searching and tracking waveforms, it is challenging for the BS to directly obtain the ICSI. We therefore propose a Rao test approach to firstly identify the working mode of the radar, and then estimate the channel. We further provide theoretical performance analysis for the Rao detector. Finally, we assess the effectiveness of the proposed approach by numerical simulations, which show that the BS is able to estimate the ICSI with limited information from the radar

Joint Design of surveillance radar and MIMO communication in cluttered environments

In this study, we consider a spectrum sharing architecture, wherein a multiple-input multiple-output communication system cooperatively coexists with a surveillance radar. The degrees of freedom for system design are the transmit powers of both systems, the receive linear filters used for pulse compression and interference mitigation at the radar receiver, and the space-time communication codebook. The design criterion is the maximization of the mutual information between the input and output symbols of the communication system, subject to constraints aimed at safeguarding the radar performance. Unlike previous studies, we do not require any time-synchronization between the two systems, and we guarantee the radar performance on all of the range-azimuth cells of the patrolled region under signal-dependent (endogenous) and signal-independent (exogenous) interference. This leads to a non-convex problem, and an approximate solution is thus introduced using a block coordinate ascent method. A thorough analysis is provided to show the merits of the proposed approach and emphasize the inherent tradeoff among the achievable mutual information, the density of scatterers in the environment, and the number of protected radar cells.Comment: Submitted to IEEE Transaction on Signal Processing on June 24, 201

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

Machine Learning Based Receiver Design for Radar Communications

With increasing congestion of Radio Frequency (RF) spectrum, enabling radar and communication systems to coexist is becoming an important area of research for efficient spectrum utilization. Designing radar systems that can function amidst communication interference is a major step in this direction. The non-optimality of matched filtering-based receivers under communication interference provides the need to look for alternative approaches in radar receiver design. In this thesis we propose a machine learning based radar receiver design to tackle the problem of communication interference. Three different neural network architectures were designed and evaluated. The matched filtering based Constant False Alarm Rate (CFAR) detector was considered as a baseline for the evaluations. The performance of these detectors was evaluated on signal datasets generated from two sets of parameters each with different configurations of Signal to Noise Ratio (SNR) and interference power. The results obtained from the simulations depict that most of the evaluated neural network architectures significantly outperform the baseline CFAR detector in most configurations of SNR and interference power. This shows that the designed neural network architectures are able to learn some form of filtering better than the matched filter