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

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

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

By: 
Xingjian Li; Jun Fang; Huiping Duan; Zhi Chen; Hongbin Li

In this paper, we study the problem of beam alignment for millimeter wave (mmWave) communications, where 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 a sparse signal from compressive phaseless measurements. A sparse bipartite graph code 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 a probability exceeding a pre-specified value from O(K2 )  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 reliable beam alignment even in the low signal-to-noise ratio regime and presents a clear performance advantage over existing methods.

Table of Contents:

TSP Featured Articles

SPS on Twitter

  • We are happy to welcome Prof. Jiebo Luo as the new Editor-in-Chief of IEEE Transactions on Multimedia beginning in… https://t.co/9ZgBrgkFXv
  • wants your talents! Our tenure-track position in engineering applications of information and data science a… https://t.co/QrqTAFGlyM
  • If you’re missing out on , don’t worry - we’ll be tweeting all week long. Follow along with us to see the action!

SPS Videos


Signal Processing in Home Assistants

 


Multimedia Forensics


Careers in Signal Processing             

 


Under the Radar