We propose projecting radar waveform onto the null space of an interference channel matrix between the radar and a communication system as a solution for coexistence of radar and communication systems in the same band. This approach assumes that the cognitive radar has full knowledge of the interference channel and tries to modify its signal vectors in such a way that they fall in the null space of the channel matrix. We investigate the effects of null space projections on radar performance and target parameter identification both analytically and quantita tively by using maximum likelihood and Cramer-Rao bound performance bounds to estimate target direction in the two cases of no null space projection and null space projection. Through simulation we demonstrate that by optimal choice of the number of antennas, the performance and target identification capabilities of radar in our method are competitive with that of traditional radar waveforms, while simultaneously guaranteeing coexistence between radar and communication systems.

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Knowledge of the channel state information (CSI) at the transmitter side is one of the primary sources of information that can be used for efficient allocation of wireless resources. Obtaining downlink (DL) CSI in Frequency Division Duplexing (FDD) systems from uplink (UL) CSI is not as straightforward as in TDD systems. Therefore, users usually feed the DL-CSI back to the transmitter. To remove the need for feedback (and thus having less signaling overhead), we propose to use two recent deep neural network structures, i.e., convolutional neural networks and generative adversarial networks (GANs) to infer the DL-CSI by observing the UL-CSI. The core idea of our data-driven scheme is exploiting the fact that both DL and UL channels share the same propagation environment. As such, we extracted the environment information from UL channel response to a latent domain and then transferred the derived environment information from the latent domain to predict the DL channel. To overcome incorrect latent domain and the problem of oversimplistic assumptions, in this work, we did not use any specific parametric model and instead used data-driven approaches to discover the underlying structure of data without any prior model assumptions. To overcome the challenge of capturing the UL-DL joint distribution, we used a mean square error-based variant of the GAN structure with improved convergence properties called boundary equilibrium GAN (BEGAN). For training and testing we used simulated data of Extended Vehicular-A (EVA) and Extended Typical Urban (ETU) models. Simulation results verified that our methods can accurately infer and predict the downlink CSI from the uplink CSI for different multipath environments in FDD communications.

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