Results and progress
WP1: Signal Processing for Coexisting Communication and Radar Systems (C-ComRad)
T1.1 Interference Channel Estimation using Radar Signals as Pilots
We study the interfering channel estimation between coexisted MIMO radar and MIMO base station (BS), where the radar is operated in the "search and track" mode, and the BS receives the interference from the radar.
T1.2 Power-efficient Transmit Beamforming Enabled by Interference Exploitation
We propose a novel approach to enable the coexistence between Multi-Input-Multi-Output (MIMO) radar and downlink multiuser multi-input single-output communication system. By exploiting the constructive multiuser interference (MUI), the proposed approach tradeoff useful MUI power for reducing the transmit power, to obtain a power efficient transmission.
T1.3 Receiving Techniques for Interference Mitigation
In C-ComRad scenarios, the BS may receive both the target echoes and the UL signals, as the two types of signals may partially overlap with each other, we propose to exploit the non-overlapped part of the radar echoes to recover the overall radar signal, and then subtract its interfrence to the UE's signal by successive interference cancellation (SIC).
WP2: Signal Processing for Dual-functional Communication-Radar Systems (D-ComRad)
T2.1 Multi-functional Waveform Design for Target Detection, Channel Estimation and Communications
We propose multi-input multi-output (MIMO) beamforming designs towards joint radar sensing and multi-user communications. We employ the Cramer-Rao bound (CRB) as a performance metric of target/channel estimation, under both point and extended target scenarios.
T2.2 Joint Symbol-level Transmit Beamforming using Manifold optimisation and Interference Exploitation
We focus on a dual-functional multi-input-multioutput (MIMO) radar-communication (RadCom) system, where a single transmitter with multiple antennas communicates with downlink cellular users and detects radar targets simultaneously. Several design criteria are considered for minimizing the downlink multiuser interference.
T2.3 Joint Receiving based on Signal Recognition and Classification
In D-ComRad scenarios, the BS may also receive both the target echoes and the UL signals, in which case the method adopted in T1.3 can be applied unaltered. On top of that, we have also proposed a tailored D-ComRad frame structure for coordinating the radar and communication operations.
WP3: Demonstration and Verification for the Proposed CRSS Approaches
T3.1 System-Level Simulations for the Proposed CRSS Approaches
We have built system-level simulators based on both MATLAB and LabVIEW. The LabVIEW platform was built during the secondment at Athens Information Technology (AIT), Athens, Greece, where the Research Fellow was hosted by Prof. Constantinos Papadias, IEEE Fellow, the Dean of AIT, as a Visiting Scholar. Fig. 1 shows the UI of the simulator built by LabVIEW.
T3.2 Basic Hardware Demo using USRP and LabVIEW
We have also bulit a basic D-ComRad hardware demo using USRP-2935R, as shown in Fig. 2, provided by the UCL’s Aeroflex lab. We have tested basic communication transmission performance and the radar beampattern performance of the designed D-ComRad waveform in T2.2 of WP2, with a 6-antenna MIMO BS and 2 single-antenna users. The results are shown in Fig. 1, where the tradeoff between a QPSK constellation and a omnidirectional MIMO radar beampattern has been explicitly shown. One can adjust the weights for comms and radar given the specific preference. The experimental results prove again the effectiveness of our D-ComRad designs.
