Superimposed training for channel shortening equalization in OFDM

Authors:
Xiaoli Ma;Robert J. Baxley;John Kleider;G. Tong Zhou
Affiliations:
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA;School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA;General Dynamics, C4 Systems, Scottsdale, AZ;School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA
Venue:
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
Year:
2006

Citing 6
Cited 0

Blind, adaptive channel shortening by sum-squared auto-correlation minimization (SAM)

IEEE Transactions on Signal Processing
Second-order statistical approaches to channel shortening in multicarrier systems

IEEE Transactions on Signal Processing
Superimposed training for OFDM: a peak-to-average power ratio analysis

IEEE Transactions on Signal Processing - Part I
On superimposed training for channel estimation: performance analysis, training power allocation, and frame synchronization

IEEE Transactions on Signal Processing
Parallel-interference-cancellation-assisted decision-directed channel estimation for OFDM systems using multiple transmit antennas

IEEE Transactions on Wireless Communications
Low-complexity blind synchronization and demodulation for (ultra-)wideband multi-user ad hoc access

IEEE Transactions on Wireless Communications

Quantified Score

Hi-index	0.00

Visualization

Abstract

In orthogonal frequency division multiplexing (OFDM) systems, the cyclic prefix (CP) is required to be greater than the length of the channel impulse response to avoid inter-block interference. However, a long CP decreases power and bandwidth efficiencies. Recently channel shortening equalizers (CSEs) have been introduced to enable the use of a shorter CP, but they either require perfect channel knowledge, or demand high complexity and long decoding delay when the channel is unknown. In this paper, we propose a low complexity CSE for OFDM with unknown channels by using superimposed training. Our unique design achieves bandwidth efficient channel estimation, and low complexity channel shortening equalization as well as symbol decoding. Simulation results demonstrate the effectiveness of the proposed scheme.