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In this study, we analyze index modulation (IM) based on circularly-shifted chirps (CSCs) for dual-function radar & communication (DFRC) systems. We develop a maximum likelihood (ML) range estimator that considers multiple scatters. To improve the correlation properties of the transmitted waveform and estimation accuracy, we propose index separation (IS) which separates the CSCs apart in time. We theoretically show that the separation can be large under certain conditions without losing the spectral efficiency (SE). Our numerical results show that the IS combined ML and linear minimum mean square error (LMMSE)-based estimators can provide approximately 3 dB signal-to-noise ratio (SNR) gain in some cases while improving estimation accuracy substantially without causing any bit-error ratio (BER) degradation at the communication receiver.
In this study, we propose a wideband index modulation (IM) based on circularly-shifted chirps. To derive the proposed method, we first prove that a Golay complementary pair (GCP) can be constructed by linearly combining the Fourier series of chirps.
In this work we consider a multiple-input multiple-output (MIMO) dual-function radar-communication (DFRC) system that employs an orthogonal frequency division multiplexing (OFDM) and a differential phase shift keying (DPSK) modulation, and study the
Rate-Splitting Multiple Access (RSMA), relying on multi-antenna Rate-Splitting (RS) techniques, has emerged as a powerful strategy for multi-user multi-antenna systems. In this paper, RSMA is introduced as a unified multiple access for multi-antenna
A novel multiple-input multiple-output (MIMO) dual-function radar communication (DFRC) system is proposed. The system transmits wideband, orthogonal frequency division multiplexing (OFDM) waveforms using a small subset of the available antennas in ea
Channel estimation is of crucial importance in massive multiple-input multiple-output (m-MIMO) visible light communication (VLC) systems. In order to tackle this problem, a fast and flexible denoising convolutional neural network (FFDNet)-based chann