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The new spectrum available in the millimeter-wave (mmWave) and Terahertz (THz) bands is a promising frontier for the future wireless communications. Propagation characteristics at these frequencies imply that highly directional transmissions should be used to focus the available power to a specific direction. This is enabled by using tightly packed large-scale antenna arrays to form narrow or so called pencil beams both at the transmitter and the receiver. This type of communication is, however, quite sensitive to imperfections of the transceivers, resulting in beam pointing errors and lost connection in the worst-case. This paper investigates the impact of such errors, originating from the local oscillators in terms of phase noise, which is a major impairment with high center frequencies. We explore the impact of these effects with different transceiver architectures, illustrate the beam shape properties, and quantify their impact on the system performance for different modulation schemes in terms of error rates. Specifically, we model the phase noise both as Wiener and Gaussian distributed to characterize the impact of phase noise on the beam accuracy and system performance.

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This paper investigates the impact of phase noise on the transceiver signal processing originating from the local oscillators (LOs). We explore the impact of these effects with different transceiver architectures, illustrate the beam shape properties, and quantify their impact on the system performance for different modulation schemes in terms of error rates. Specifically, we model the phase noise both as Wiener and Gaussian distributed with covariance structure depending on the architecture used to share the LO signals.

We consider the problem of millimeter-wave (mmWave) channel estimation with a hybrid digital-analog twostage beamforming structure. A radio frequency (RF) chain excites a dedicated set of antenna subarrays. To compensate for the severe path loss, known training signals are beamformed and swept to scan the angular space. Since the mmWave channels typically exhibit sparsity, the channel response can usually be expressed as a linear combination of a small number of scattering clusters. Thereby the number of angles of arrival (AoAs) and angles of departure (AoDs) with significant signal components is limited, and compressive sensing techniques can be leveraged for estimating the channel. In this paper, we investigate two sparse recovery algorithms: a Bayesian and non-Bayesian one. In the Bayesian approach, we invoke the sparse Bayesian learning (SBL) framework, which relies on a 2-layer hierarchical prior model for channel. A highly efficient and fast iterative Bayesian inference method is then applied to the proposed model. The non-Bayesian approach is a LASSO-based approach, where we devise a low complexity solution by adopting alternating directions method of multipliers (ADMM) technique to solve the problem. The efficacy of the proposed algorithms is demonstrated using numerical examples. The Bayesian approach shows improved estimation performance in relation to the non-Bayesian approach.Index Terms-Millimeter-wave communications, sparse channel estimation, sparse Bayesian learning, compressive sensing.

At the millimeter wave and higher frequency bands the radio channel can often be expressed as a linear combination of a small number of scattering clusters. Hence, the number of angles of arrivals with significant components is limited. Due to severe path losses, the receiver must be equipped with an antenna array capable of forming narrow beams. The channel estimation with narrow beams is challenging. Algorithms developed for sparse estimation problems can be utilized to overcome the problem. In this paper, the performance and computational complexity of channel estimation methods for millimeter and terahertz frequency bands are compared. The methods considered are based on Bayesian learning with the relevance vector machine, orthogonal matching pursuit and the least absolute shrinkage and selection operator optimization. The conventional least squares channel estimator is used as a reference method. The complexity of the least squares estimator is found to be the smallest. The estimation accuracy of the Bayesian learning based estimator is the best but with increased computational complexity.