Millimeter wave (mm-Wave) communications are characterized by wideband channels with few directional paths, mostly in line-of-sight. Antenna arrays are mandatory to cope with severe path-loss, and the resulting channel response is sparse in the space-time (ST) domain. This paper addresses the sparsity by proposing a channel estimation method that exploits the algebraic structure of channel and interference, without requiring complex antenna-array calibration procedures. The method relies on the recognition that the ST channel is lowrank and exhibits slowly and fast-varying features (angles/delays of arrival and fading amplitudes, respectively) and, accordingly, that the interference has a slowly-varying spatial covariance with fast-varying amplitudes. The accuracy of the estimation of quasistationary components is increased by introducing averaging mechanisms over multiple sequences. Numerical results show that: i) rank-1 is an effective channel-interference representation in mm-Wave setting with severe interference; ii) fundamental limits (derived in closed form) prove the remarkable performance gains in terms of signal-to interference ratio; iii) circular array arrangement with directive elements is preferable compared to square or triangular configurations.

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A widely-distributed radar system is a promising architecture to enhance imaging performance. However, most existing algorithms rely on isotropic scattering assumption, which is only satisfied in colocated radar systems. Moreover, due to noise and imaging model imperfections, artifacts such as layovers are common in radar images. In this paper, a novel l1-regularized, consensus alternating direction method of multipliers (CADMM) based algorithm is proposed to mitigate artifacts by exploiting the spatial diversity in a widely-distributed radar system. By imposing the consensus constraints on the local images formed by the distributed antenna clusters and solving the resulting distributed optimization problem, the scenario's spatially-invariant common features are retained and the spatially-variant artifacts are mitigated in a data-driven fashion. The iterative procedure will finally will finally converge to a high-quality global image in the consensus of all widely-distributed measurements. The proposed algorithm outperforms the existing joint sparsity-based composite imaging (JSC) algorithm in terms of artifacts mitigation. It can also reduce the computation and storage burden of largescale imaging problems through its distributed and parallelizable optimization scheme.