The document presents a low-complexity algorithm for neighbor discovery in wireless networks, addressing the challenge of identifying neighboring nodes using maximum a posteriori (MAP) estimation. The proposed solution involves a two-stage approach utilizing Lasso estimator for sparse solutions and a threshold-based hard-decision mechanism to identify neighbor nodes. Additionally, it outlines the hardware and software requirements for implementing the project.

1. GLOBALSOFT TECHNOLOGIES
IEEE PROJECTS & SOFTWARE DEVELOPMENTS
IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE
BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS
CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401
Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.com
A Low-Complexity Algorithm for Neighbor Discovery
in Wireless Networks
ABSTRACT:
We study a neighbor discovery problem in wireless networks for
which each node wishes to identify the so-called neighboring nodes
within a single-hop communication. This problem can be optimally
addressed using maximum a posteriori (MAP) estimation, but its
implementation is notoriously difficult in practice. In this letter, we
present a low-complexity algorithm consisting of two stages: 1) we
solve such problem using LASSO estimator that is a convex
relaxation of MAP estimator to encourage a sparse solution; and 2)
we find a desired binary vector (e.g., indicator of neighbor nodes) by
taking a “hard-decision” with threshold, carefully chosen by
exploiting fading statistics. Finally, we provide some numerical
results to confirm that the proposed algorithm performs quite well.

2. SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
 System : Pentium IV 2.4 GHz.
 Hard Disk : 40 GB.
 Floppy Drive : 1.44 Mb.
 Monitor : 15 VGA Colour.
 Mouse : Logitech.
 Ram : 512 Mb.
SOFTWARE REQUIREMENTS:
 Operating system : Windows XP/7.
 Coding Language : JAVA/J2EE
 IDE : Netbeans 7.4
 Database : MYSQL