The spectrum scarcity at sub-6 GHz spectrum has made millimeter-wave (mmWave) frequency band a key component of the next-generation wireless networks. While mmWave spectrum offers extremely large transmission bandwidths to accommodate ever-increasing data rates, unique characteristics of this new spectrum need special consideration to achieve the promised network throughput. In this work, we consider the off-grid problem for mmWave communications, which has a significant impact on basic network functionalities involving beam steering and tracking. The off-grid effect naturally appears in compressed sensing (CS) techniques adopting a discretization approach for representing the angular domain. This approach yields a finite set of discrete angle points, which are an approximation to the continuous angular space, and hence degrade the accuracy of related parameter estimation. In order to cope with the off-grid effect, we present a novel parameter-perturbation framework to efficiently estimate the channel and the covariance for mmWave networks. The proposed algorithms employ a smart perturbation mechanism in conjunction with a low-complexity greedy framework of simultaneous orthogonal matching pursuit (SOMP), and jointly solve for the off-grid parameters and weights. Numerical results show a significant performance improvement through our novel framework as a result of handling the off-grid effects, which is totally ignored in the conventional sparse mmWave channel or covariance estimation algorithms.
The millimeter-wave (mmWave) communications is a promising technology for next-generation wireless networks with its available broad spectrum. Along with massive number of antennas employed at both end of the transceiver, the number of unknown channel coefficients become extremely large. Thanks to sparse nature of mmWave links, this paper proposes a parameter perturbation based sparse recovery technique for mmWave channel estimation. Recently, classical compressive sensing (CS) based sparse recovery techniques have been applied in this area. However, CS based reconstructions are highly effected by basis mismatch problems such as off-the-grid targets, or, equivalently, scattering points. The proposed iterative algorithm called parameter perturbed orthogonal matching pursuit (PPOMP) jointly solves for both the sparse signal, which is the unknown mmWave channel itself, and the basis mismatch due to off-the-grid problem. We verify through extensive numerical results that the proposed PPOMP algorithm achieves significantly better channel estimation performance compared to the state of the art sparse reconstruction techniques. In millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems, channel estimation in the presence of sparse multipath fading boils down to two-dimensional (2D) direction-of-arrival (DOA) estimation followed by path gain estimation. To achieve super-resolution angle estimation at affordable complexity, this paper develops an efficient channel estimation approach by applying a truncated atomic norm minimization (T-ANM) technique, which is implemented via partial antenna activation during training-based channel estimation. This technique makes use of a key observation that the sparse scattering characteristics of mmWave MIMO channel gives rise to a low-rank two-level Toeplitz structure in the angular domain. Because of the low-rank property, only a subset of the transceiver antennas needs to be activated to save training resources. Meanwhile, the Toeplitz structure enables ANM-based gridless 2D DOA estimation via reduced-size semidefinite programming. Simulation results show that the proposed reduced-size method can achieve comparable spectral efficiency as the full-size benchmark method at much lower computational complexity and shorter sensing time. In this letter, an angle domain off-grid channel estimation algorithm for the uplink millimeter wave (mmWave) massive multiple-input and multiple-output systems is proposed. By exploiting spatial sparse structure in mmWave channels, the proposed method is capable of identifying the angles and gains of the scatterer paths. Comparing the conventional channel estimation methods for mmWave systems, the proposed method achieves better performance in terms of mean square error. Numerical simulation results are provided to verify the superiority of the proposed algorithm.