Fast Beam Alignment for Millimeter Wave Communications: A Sparse Encoding and Phaseless Decoding Approach

Abstract

In this paper, we studied the problem of beam alignment for millimeter wave (mmWave) communications, in which we assume a hybrid analog and digital beamforming structure is employed at the transmitter (i.e. base station), and an omni-directional antenna or an antenna array is used at the receiver (i.e. user). By exploiting the sparse scattering nature of mmWave channels, the beam alignment problem is formulated as a sparse encoding and phaseless decoding problem. More specifically, the problem of interest involves finding a sparse sensing matrix and an efficient recovery algorithm to recover the support and magnitude of the sparse signal from compressive phaseless measurements. A sparse bipartite graph coding (SBG-Coding) algorithm is developed for sparse encoding and phaseless decoding. Our theoretical analysis shows that, in the noiseless case, our proposed algorithm can perfectly recover the support and magnitude of the sparse signal with probability exceeding a pre-specified value from $\mathcal{O}(K^2)$ measurements, where $K$ is the number of nonzero entries of the sparse signal. The proposed algorithm has a simple decoding procedure which is computationally efficient and noise-robust. Simulation results show that our proposed method renders a reliable beam alignment in the low and moderate signal-to-noise ratio (SNR) regimes and presents a clear performance advantage over existing methods.

References in corpus (16)

• Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems
• Millimeter Wave Cellular Wireless Networks: Potentials and Challenges
• Millimeter Wave Beamforming for Wireless Backhaul and Access in Small Cell Networks
• Limited Feedback Hybrid Precoding for Multi-User Millimeter Wave Systems
• Off-grid Direction of Arrival Estimation Using Sparse Bayesian Inference
• Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO
• GESPAR: Efficient Phase Retrieval of Sparse Signals
• Compressed Sensing Based Multi-User Millimeter Wave Systems: How Many Measurements Are Needed?
• Compressive Phase Retrieval via Generalized Approximate Message Passing
• Newtonized Orthogonal Matching Pursuit: Frequency Estimation over the Continuum
• Compressive channel estimation and tracking for large arrays in mm wave picocells
• Efficient Compressive Phase Retrieval with Constrained Sensing Vectors
• A compressive channel estimation technique robust to synchronization impairments
• Joint CFO and Channel Estimation in Millimeter Wave Systems with One-Bit ADCs
• Efficient Beam Alignment for mmWave Single-Carrier Systems with Hybrid MIMO Transceivers
• Message passing-based joint CFO and channel estimation in millimeter wave systems with one-bit ADCs

Cited by in corpus (6)

• Compressed Channel Estimation and Joint Beamforming for Intelligent Reflecting Surface-Assisted Millimeter Wave Systems
• Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms
• Machine Learning Assisted Phase-less Millimeter-Wave Beam Alignment in Multipath Channels
• Efficient Sparse Coding using Hierarchical Riemannian Pursuit
• Fast Beam Training and Alignment for IRS-Assisted Millimeter Wave/Terahertz Systems
• Beam Alignment in mmWave User-Centric Cell-Free Massive MIMO Systems