Multiple Measurement Vector-based Sparse Bayesian Learning Channel Estimation for Efficient D2D Discovery and Pairing

Authors

  • Iqra Javid Department of Electrical, Electronics & Communication Engineering, Sharda University, Greater Noida, UP 201310, India
  • Sibaram Khara Department of Electrical, Electronics & Communication Engineering, Sharda University, Greater Noida, UP 201310, India

DOI:

https://doi.org/10.13052/jmm1550-4646.213424

Keywords:

Device to Device (D2D), multiple measurement vector, sparse Bayesian learning, scheduling, rate predictions

Abstract

Device-to-Device (D2D) communication in next-generation networks enables the creation of localized networks by directly connecting nearby devices, reducing base station traffic and enhancing spectral efficiency through frequency reuse. This work developed multiple measurement vector-based compressed sensing problem for the D2D system, where the composite multiple measurement vector (MMV) D2D channel is low-rank and exhibits common sparsity across multiple measurements. To exploit this common sparsity channel structure, we propose the MMV-based sparse Bayesian learning (MSBL) algorithm that achieves precise channel estimation by leveraging common sparsity. These estimates are then utilized to calculate the achievable rates for scheduling decisions for both cellular users and D2D links. Simulation results demonstrate the efficacy of the proposed MSBL method in improving rate predictions and scheduling accuracy in dense D2D networks.

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Author Biographies

Iqra Javid, Department of Electrical, Electronics & Communication Engineering, Sharda University, Greater Noida, UP 201310, India

Iqra Javid is currently pursuing her Ph.D. in wireless Communication from Sharda University, India. She has obtained her M.Tech in Digital Communication from Sharda University, India and her B.E in the department of Electronics and Communication Engineering from SSM College of Engineering and Technology Pattan, Jammu and Kashmir, India . Her research interest includes wireless networks, device to device communications, Machine learning.

Sibaram Khara, Department of Electrical, Electronics & Communication Engineering, Sharda University, Greater Noida, UP 201310, India

Sibaram khara received his Ph. D. in engineering from Jadavpur University, Kolkata, in next-generation wireless heterogeneous network-essentially in the area of interworking network and protocol convergence techniques for cellular and WiFi integrated networks. He did PG in Digital Systems from National Institute of Technology, Allahabad. He received Best Paper awards for his analytical model of Cellular/WiFi system (IEEE ADCOM 2008 MIT Chennai and IEEE EWT 2004 (1st), I2IT Pune (3rd)). He was honored as best Research Faculty in the School of Electronics Engineering, VIT University, Vellore, India for year 2010. His research articles are presented at seminars and conferences in many countries, namely, WEAS02 Athens, IEEE VTC06 Melbourne, IEEE PWC07 Prague, IEEE/ACM SAC10 Switzerland, etc. His major research interests cover the areas of cluster based wireless sensor networks, spectrum mobility in cognitive radio system, call admission control in heterogeneous network and carrier aggregation in LTE-A technology.

References

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Published

2025-08-13

How to Cite

Javid, I. ., & Khara, S. . (2025). Multiple Measurement Vector-based Sparse Bayesian Learning Channel Estimation for Efficient D2D Discovery and Pairing. Journal of Mobile Multimedia, 21(3-4), 767–782. https://doi.org/10.13052/jmm1550-4646.213424

Issue

2025: Vol 21 Iss 3-4

Section

WPMC 2024