Millimeter Wave MIMO Channel Estimation Based on Adaptive Compressed Sensing

Multiple-input multiple-output (MIMO) systems are well suited for millimeter-wave (mmWave) wireless communications where large antenna arrays can be integrated in small form factors due to tiny wavelengths, thereby providing high array gains while supporting spatial multiplexing, beamforming, or antenna diversity. It has been shown that mmWave channels exhibit sparsity due to the limited number of dominant propagation paths, thus compressed sensing techniques can be leveraged to conduct channel estimation at mmWave frequencies. This paper presents a novel approach of constructing beamforming dictionary matrices for sparse channel estimation using the continuous basis pursuit (CBP) concept, and proposes two novel low-complexity algorithms to exploit channel sparsity for adaptively estimating multipath channel parameters in mmWave channels. We verify the performance of the proposed CBP-based beamforming dictionary and the two algorithms using a simulator built upon a three-dimensional mmWave statistical spatial channel model, NYUSIM, that is based on real-world propagation measurements. Simulation results show that the CBP-based dictionary offers substantially higher estimation accuracy and greater spectral efficiency than the grid-based counterpart introduced by previous researchers, and the algorithms proposed here render better performance but require less computational effort compared with existing algorithms.Comment: 7 pages, 5 figures, in 2017 IEEE International Conference on Communications Workshop (ICCW), Paris, May 201

mm-Wave channel estimation with accelerated gradient descent algorithms

Abstract The availability of millimeter wave (mm-Wave) band in conjunction with massive multiple-input-multiple-output (MIMO) technology is expected to boost the data rates of the fifth-generation (5G) cellular systems. However, in order to achieve high spectral efficiencies, an accurate channel estimate is required, which is a challenging task in massive MIMO. By exploiting the small number of paths that characterize the mm-Wave channel, the estimation problem can be solved by compressed-sensing (CS) techniques. In this paper, we propose a novel CS channel estimation method based on the accelerated gradient descent with adaptive restart (AGDAR) algorithm exploiting a ℓ 1-norm approximation of the sparsity constraint. Moreover, a modified re-weighted compressed-sensing (RCS) technique is considered that iterates AGDAR using a weighted version of the ℓ 1-norm term, where weights are adapted at each iteration. We also discuss the impact of cell sectorization and tracking on the channel estimation algorithm. We compare the proposed solutions with existing channel estimations with an extensive simulation campaign on downlink third-generation partnership project (3GPP) channel models

Linear Block Coding for Efficient Beam Discovery in Millimeter Wave Communication Networks

The surge in mobile broadband data demands is expected to surpass the available spectrum capacity below 6 GHz. This expectation has prompted the exploration of millimeter wave (mm-wave) frequency bands as a candidate technology for next generation wireless networks. However, numerous challenges to deploying mm-wave communication systems, including channel estimation, need to be met before practical deployments are possible. This work addresses the mm-wave channel estimation problem and treats it as a beam discovery problem in which locating beams with strong path reflectors is analogous to locating errors in linear block codes. We show that a significantly small number of measurements (compared to the original dimensions of the channel matrix) is sufficient to reliably estimate the channel. We also show that this can be achieved using a simple and energy-efficient transceiver architecture.Comment: To appear in the proceedings of IEEE INFOCOM '1

Pilot Beam Sequence Design for Channel Estimation in Millimeter-Wave MIMO Systems: A POMDP Framework

In this paper, adaptive pilot beam sequence design for channel estimation in large millimeter-wave (mmWave) MIMO systems is considered. By exploiting the sparsity of mmWave MIMO channels with the virtual channel representation and imposing a Markovian random walk assumption on the physical movement of the line-of-sight (LOS) and reflection clusters, it is shown that the sparse channel estimation problem in large mmWave MIMO systems reduces to a sequential detection problem that finds the locations and values of the non-zero-valued bins in a two-dimensional rectangular grid, and the optimal adaptive pilot design problem can be cast into the framework of a partially observable Markov decision process (POMDP). Under the POMDP framework, an optimal adaptive pilot beam sequence design method is obtained to maximize the accumulated transmission data rate for a given period of time. Numerical results are provided to validate our pilot signal design method and they show that the proposed method yields good performance.Comment: 6 pages, 6 figures, submitted to IEEE ICC 201