Compressive Sensing-Based Radar Imaging and Subcarrier Allocation for Joint MIMO OFDM Radar and Communication System

Abstract

In this paper, a joint multiple-input multiple-output (MIMO OFDM) radar and communication (RadCom) system is proposed, in which orthogonal frequency division multiplexing (OFDM) waveforms carrying data to be transmitted to the information receiver are exploited to get high-resolution radar images at the RadCom platform. Specifically, to get two-dimensional (i.e., range and azimuth angle) radar images with high resolution, a compressive sensing-based imaging algorithm is proposed that is applicable to the signal received through multiple receive antennas. Because both the radar imaging performance (i.e., the mean square error of the radar image) and the communication performance (i.e., the achievable rate) are affected by the subcarrier allocation across multiple transmit antennas, by analyzing both radar imaging and communication performances, we also propose a subcarrier allocation strategy such that a high achievable rate is obtained without sacrificing the radar imaging performance.

Complex-Valued Phase Transmittance RBF Neural Networks for Massive MIMO-OFDM Receivers

“…Using simulations, we extend the concept introduced by [56] for QOSTBC with 8 × 1 and 16 × 1 antennas. In order to simulate these scenarios, we employ the proposed coding algorithm presented in Equation (8). As expected, the solid green curve with diamonds and the solid orange curve with circles are between the theoretical 4th and 5th order and the 8th and 10th order, respectively.…”

“…In the proposed scheme, M s M T and the code rate is R = M T /M s = 1 [11]. The recurrence is performed until we find (8).…”

“…Multicarrier modulation schemes, such as OFDM, have been widely employed in digital communications systems due to their low susceptibility to intersymbol interference (ISI) [6][7][8][9]. OFDM divides the channel bandwidth into K orthogonal subcarriers [10].…”

Compressive Sensing via Variational Bayesian Inference under Two Widely Used Priors: Modeling, Comparison and Discussion

“…Compressibility or sparsity means that the signal has few dominating elements under some proper basis. CS has been used in a variety of applications such as the single-pixel camera, missing pixels and inpainting removal of images, biomedical such as heart rate estimation, internet of things (IoT), geostatistical data analysis, seismic tomography, communications such as blind multi-narrowband signals sampling and recovery, the direction of arrival (DoA) estimation, spectrum sharing of radar and communication signals, wireless networks and many more [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. In the linear CS framework, the problem is posed as where contains the measurements, is the sparse signal of interest, is the noise representing either the measurement noise or the insignificant coefficients of and, generally, [ 1 , 2 ].…”

“…This is of more interest to CS applications such as blind multinarrowband signals, spectrum sharing RADAR, etc. [ 11 , 12 , 13 , 14 , 15 ].…”