Bayesian Matching Pursuit Based Channel Estimation for Millimeter Wave Communication

Hybrid precoding is considered as a solution to reduce the high power consumption caused by devices operating at radio frequency (RF) in millimeter wave (mmWave) communication. For hybrid precoding, the channel state information (CSI) is critical but hard to obtain because of the analog precoding at RF and the large number of antennas. mmWave channel has been proved to be sparse by real-world experiments. Compressive sensing (CS) methods can be applied to the channel estimation to decrease complexity. However, there is a distinct performance gap between the estimation of the existing CS methods with or without given sparsity pattern (SP). In this letter, a new method based on Bayesian matching pursuit(BMP) idea is proposed to improve sparse channel estimation performance. We make appropriate assumptions according to the characteristics of mmWave channel. We select a set of candidate SPs with high posterior probabilities to estimate CSI. Numerical simulation shows that our proposed method has significantly improved channel estimation performance with acceptable complexity compared to existing methods including orthogonal matching pursuit, sparse Bayesian learning and Bayesian compressive sensing

Position and Orientation Estimation through Millimeter Wave MIMO in 5G Systems

Millimeter wave signals and large antenna arrays are considered enabling technologies for future 5G networks. While their benefits for achieving high-data rate communications are well-known, their potential advantages for accurate positioning are largely undiscovered. We derive the Cram\'{e}r-Rao bound (CRB) on position and rotation angle estimation uncertainty from millimeter wave signals from a single transmitter, in the presence of scatterers. We also present a novel two-stage algorithm for position and rotation angle estimation that attains the CRB for average to high signal-to-noise ratio. The algorithm is based on multiple measurement vectors matching pursuit for coarse estimation, followed by a refinement stage based on the space-alternating generalized expectation maximization algorithm. We find that accurate position and rotation angle estimation is possible using signals from a single transmitter, in either line-of- sight, non-line-of-sight, or obstructed-line-of-sight conditions.Comment: The manuscript has been revised, and increased from 27 to 31 pages. Also, Fig.2, Fig. 10 and Table I are adde

Sub-Nyquist Channel Estimation over IEEE 802.11ad Link

Nowadays, millimeter-wave communication centered at the 60 GHz radio frequency band is increasingly the preferred technology for near-field communication since it provides transmission bandwidth that is several GHz wide. The IEEE 802.11ad standard has been developed for commercial wireless local area networks in the 60 GHz transmission environment. Receivers designed to process IEEE 802.11ad waveforms employ very high rate analog-to-digital converters, and therefore, reducing the receiver sampling rate can be useful. In this work, we study the problem of low-rate channel estimation over the IEEE 802.11ad 60 GHz communication link by harnessing sparsity in the channel impulse response. In particular, we focus on single carrier modulation and exploit the special structure of the 802.11ad waveform embedded in the channel estimation field of its single carrier physical layer frame. We examine various sub-Nyquist sampling methods for this problem and recover the channel using compressed sensing techniques. Our numerical experiments show feasibility of our procedures up to one-seventh of the Nyquist rates with minimal performance deterioration.Comment: 5 pages, 5 figures, SampTA 2017 conferenc

FCFGS-CV-Based Channel Estimation for Wideband MmWave Massive MIMO Systems with Low-Resolution ADCs

In this paper, the fully corrective forward greedy selection-cross validation-based (FCFGS-CV-based) channel estimator is proposed for wideband millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with low-resolution analog-to-digital converters (ADCs). The sparse nature of the mmWave virtual channel in the angular and delay domains is exploited to convert the maximum a posteriori (MAP) channel estimation problem to an optimization problem with a concave objective function and sparsity constraint. The FCFGS algorithm, which is the generalized orthogonal matching pursuit (OMP) algorithm, is used to solve the sparsity-constrained optimization problem. Furthermore, the CV technique is adopted to determine the proper termination condition by detecting overfitting when the sparsity level is unknown.Comment: to appear in IEEE Wireless Communications Letter