Sparse Nature Of mmWave Channels Research Articles

Fast Beam Training Architecture for Hybrid mmWave Transceivers

Millimeter-wave (mmWave) communications have attracted considerable interest due to the massive available spectrum. However, to establish communication links, a beam training procedure is indispensable. How to accelerate the beam training process is one of the key challenges towards realizing mmWave communications in practice. In this study, we first propose a novel low-cost digital beamforming (DBF) module assisted hybrid (DAhybrid) architecture, by exploiting both the capabilities of analog and digital modules. To make this topology practical, we deploy coarse radio frequency (RF) chains and low-resolution analog-to-digital converters in the low-cost DBF module to reduce cost and power consumption. Second, we design a fast beam training method (named DAH-BT) by utilizing the proposed DA-hybrid architecture and leveraging the sparse nature of mmWave channels, in which an internal calibration method is adapted to obtain the parameters of the RF impairments and the orthogonal matching pursuit algorithm is utilized to estimate beams. We also prove that the developed measurement matrices satisfy the restricted isometry property. Extensive simulation results show that the DA-hybrid architecture can not only provide close to 100% beam matching accuracy, but also dramatically reduce the system power consumption and cost. In addition, the proposed DAH-BT scheme consumes the shortest time for beam training over the state-of-art methods with comparable spectral efficiency.

Configuration Optimization and Channel Estimation in Hybrid Beamforming mmWave Systems With Channel Support Side Information

Hybrid beamforming (HBF) is a promising technique in millimeter-wave (mmWave) systems with a limited number of radio frequency (RF) chains. Acquiring channel state information is essential for HBF design. Various compressive-sensing-based channel estimation (CE) schemes that exploit the sparse nature of mmWave channels have been proposed. The design of an analog beamforming matrix plays a key role in providing high CE quality with low pilot overhead. In this paper, a novel and flexible HBF architecture called adaptive sum product network (ASPN) is proposed, which can realize more flexible analog beamforming with less hardwares. Based on the ASPN architecture, a novel variational-Bayesian-inference-based compressive CE algorithm is proposed, which can fully exploit the channel support side information (CSSI) at the base station under a general uncertain measurement matrix induced by the off-grid parameters for power leakage elimination. To optimally configure the degrees of freedom in the ASPN according to the CSSI, a dynamic configuration optimization algorithm is proposed by formulating the CE performance metric in terms of the configuration parameters of ASPN. The importance and effectiveness of the flexibility introduced in ASPN HBF architecture, the compressive CE algorithm as well as the configuration algorithm are illustrated by the superb performance of the proposed scheme compared to the representative state-of-the-art techniques through extensive simulation results.

Beam Selection and Power Allocation for Massive Connectivity in Millimeter Wave NOMA Systems

Massive connectivity is one of the killer use cases for fifth generation (5G) wireless systems over the millimeter wave (mmWave) band. Due to the sparse nature of mmWave channel, several users may choose the same beam as the strongest one, leading to severe intra-beam interference (intra-BI). Although intra-BI can be mitigated through digital precoding, the required number of RF chains for fulfilling massive connectivity becomes large. By applying non-orthogonal multiple access (NOMA) and properly allocating transmit power to each beam and user, the sum rate can be maximized using a smaller number of RF chains. However, the power allocated to each user depends on that allocated to other users within the same beam. Since the joint beam selection and power allocation problem contains a non-convex objective function with a large set of coupled and mixed integer variables, direct solutions may not exist particularly when both the number of users and antennas are large. In this work, we decompose the aforementioned problem into two subproblems, where the beam selection sub-problem is first solved under an equal power allocation. The outcome of beam selection is then used as the input to the power allocation sub-problem. For both subproblems, we develop numerous efficient algorithms to find the active beam set and the user power allocation, respectively. The notions of embedded methods from machine learning and that of intelligent searching from metaheuristics are adopted in our work as the key ingredient for algorithm constructions. Numerical results demonstrate that in the region of large user population, the proposed beam selection and power allocation algorithms can effectively improve the sum rate using a less number of RF chains in comparison with some existing solutions.

Message Passing-Based Joint CFO and Channel Estimation in mmWave Systems With One-Bit ADCs

Channel estimation at millimeter wave (mmWave) carrier frequencies is challenging when large antenna arrays are used. Prior work has leveraged the sparse nature of mmWave channels via compressed sensing-based algorithms for channel estimation. Most of these algorithms, though, assume perfect synchronization and are vulnerable to phase errors that arise due to carrier frequency offset and phase noise. Recently, sparsity-aware, non-coherent beamforming algorithms that are robust to phase errors were proposed for narrowband phased array systems with full resolution analog-to-digital converters. Such energy-based algorithms, however, are not robust to heavy quantization at the receiver. In this paper, we develop a joint carrier frequency offset and wideband channel estimation algorithm that is scalable across different hardware architectures. Our method exploits the sparse nature of mmWave channels in the angle-delay domain, in addition to the compressibility of the phase error vector. We formulate the joint estimation as a quantized sparse bilinear optimization problem and then use message passing for recovery. We also give an efficient implementation of a generalized bilinear message passing algorithm for the joint estimation in one-bit receivers. Simulation results show that our method is able to estimate the frequency offset and the channel compressibility, even in the presence of phase noise.

Efficient Beam Alignment for Millimeter Wave Single-Carrier Systems With Hybrid MIMO Transceivers

Communication at millimeter wave (mm-wave) bands is expected to become a key ingredient of the next generation (5G) wireless networks. Effective mm-wave communications require fast and reliable methods for beamforming at both the user equipment (UE) and the base station sides, in order to achieve a sufficiently large signal-to-noise ratio after beamforming. We refer to the problem of finding a pair of strongly coupled narrow beams at the transmitter and receiver as the beam alignment problem. In this paper, we propose an efficient BA scheme for single-carrier mm-wave communications. In the proposed scheme, the BS periodically probes the channel in the downlink via a pre-specified pseudo-random beamforming codebook and pseudo-random spreading codes, letting each UE estimate the angle-of-arrival/angle-of-departure (AoA-AoD) pair of the multipath channel for which the energy transfer is maximum. We leverage the sparse nature of mm-wave channels in the AoA-AoD domain to formulate the BA problem as the estimation of a sparse non-negative vector. Based on the recently developed non-negative least squares technique, we efficiently find the strongest AoA-AoD pair connecting each UE to the BS. We evaluate the performance of the proposed scheme under a realistic channel model, where the propagation channel consists of a few multipath components each having different delays, AoAs-AoDs, and Doppler shifts. The channel model parameters are consistent with the experimental channel measurements. The simulation results indicate that the proposed method is highly robust to fast channel variations caused by the large Doppler spread between the multipath components. Furthermore, we also show that after achieving BA, the beamformed channel is essentially frequency-flat, such that single-carrier communication needs no equalization in the time domain.

Time-Varying Channel Estimation for Millimeter Wave Multiuser MIMO Systems

Channel estimation for downlink millimeter wave (mmWave) multiuser multiple-input-multiple-output (MU-MIMO) communications over a time-varying channel is demandingly challenging. This is due to a massive number of channel coefficients to be estimated and the severe propagation loss of mmWave transmissions. To tackle this problem, this paper proposes an effective channel estimation scheme for time-varying mmWave channels. Discovering the property that the variations of angles of arrival/departure (AoAs/AoDs) are much slower than that of path gains, we propose a novel transmission frame structure, where the channel estimation is decoupled into two separated stages including AoAs/AoDs estimation followed by path gains estimation. In the first stage, the sparse nature of mmWave channels is leveraged to formulate the AoAs/AoDs estimation into a block-sparse signal recovery problem, and we develop an adaptive angle estimation algorithm to estimate the AoAs/AoDs. In the second stage, the pilot beamforming based on the estimated AoAs/AoDs is designed aiming for maximizing the desired pilot power so that the path gains can be accurately estimated. Simulation results demonstrate that the proposed scheme can effectively estimate the time-varying mmWave channels with a small number of pilots.

Channel Estimation via Orthogonal Matching Pursuit for Hybrid MIMO Systems in Millimeter Wave Communications

We propose an efficient open-loop channel estimator for a millimeter-wave (mm-wave) hybrid multiple-input multiple-output (MIMO) system consisting of radio-frequency (RF) beamformers with large antenna arrays followed by a baseband MIMO processor. A sparse signal recovery problem exploiting the sparse nature of mm-wave channels is formulated for channel estimation based on the parametric channel model with quantized angles of departures/arrivals (AoDs/AoAs), called the angle grids. The problem is solved by the orthogonal matching pursuit (OMP) algorithm employing a redundant dictionary consisting of array response vectors with finely quantized angle grids. We suggest the use of non-uniformly quantized angle grids and show that such grids reduce the coherence of the redundant dictionary. The lower and upper bounds of the sum-of-squared errors of the proposed OMP-based estimator are derived analytically: the lower bound is derived by considering the oracle estimator that assumes the knowledge of AoDs/AoAs, and the upper bound is derived based on the results of the OMP performance guarantees. The design of training vectors (or sensing matrix) is particularly important in hybrid MIMO systems, because the RF beamformer prevents the use of independent and identically distributed random training vectors, which are popular in compressed sensing. We design training vectors so that the total coherence of the equivalent sensing matrix is minimized for a given RF beamforming matrix, which is assumed to be unitary. It is observed that the estimation accuracy can be improved significantly by randomly permuting the columns of the RF beamforming matrix. The simulation results demonstrate the advantage of the proposed OMP with a redundant dictionary over the existing methods such as the least squares method and the OMP based on the virtual channel model.