We look at the performance of turbo coded OFDM systems in the presence of narrowband interference (NBI) and co-channel interference (CCI). In systems employing standards such as the IEEE 802.16d/e, CCI behaves like NBI with the number of affected subcarriers ranging from 10% to 30%. Hence we treat symbol detection in such systems as detection in contaminated Gaussian (CG) noise and propose a robust log-likelihood ratio (LLR) computation for it. The proposed LLR computation method exploits the fact that NBI/CCI has a CG probability density function (pdf) but does not assume knowledge of the NBI power, NBI pdf and the fraction of subcarriers affected by NBI. Simulation results indicate that the proposed method performs very close to the optimal method which would have complete knowledge of the CG pdf parameters. ©2008 IEEE.

Sheetal Kalyani

Department of Electrical Engineering

K. Giridhar

Broadband networks

Cochannel interference

Frequency allocation

Frequency division multiplexing

LUNAR SURFACE ANALYSIS

Maximum likelihood estimation

Orthogonal frequency division multiplexing

PARTICLE BEAM INJECTION

Probability distributions

Telecommunication systems

Wireless telecommunication systems

CODED OFDM

International conferences

SUB-CARRIERS

Probability density function

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Interference Mitigation in Turbo-Coded OFDM Systems Using Robust LLRs

IEEE International Conference on Communications

We consider the decoding of LDPC codes in presence of non-Gaussian noise, especially a set of E-mixture models. For each of these models, the optimal LLRs are presented. We study the performance degradation due to the use of incorrectLLR in presence of a given noise model. Without modifying the existing LDPC decoder, we propose robust initial LLR which require minimum knowledge about the underlying noise modeland are computationally less complex. Since BER simulations are computationally heavy, we use density evolution to compare the thresholds of different LLRs.

Fulltext

IEEE International Symposium on Information Theory - Proceedings

Robust initial llrs for iterative decoders in presence of non-gaussian noise

A method for the mitigation of the effect of narrowband interference (NBI) on the turbo decoder in OFDM systems is proposed. The presence of NBI leads to a contaminated Gaussian (CG) noise probability density function (pdf) which induces an outlier effect in the data detection problem. The outlier effect leads to significant degradation in the performance of turbo coded OFDM systems which use Gaussian noise pdf based log likelihood ratios (LLRs), with the degradation increasing as a function of the power of NBI and the number of subcarriers affected by NBI. We propose to use outlier detection theory to detect subcarriers affected by NBI, and then downweigh the corresponding LLRs before passing them to the turbo decoder. Extreme value theory (EVT) is used to define the weight function in this weighted-LLR (W-LLR) method. The method is easy to implement, is of modest computational complexity, and shows a significant improvement in the simulated error rate performance when compared with the simple unweighted turbo decoder in the presence of NBI. Furthermore, frequency selectivity and diversity mapping in OFDM systems such as IEEE 802.16 d/e WMAN standard causes the co-channel interference (CCI) to look like NBI within the FEC block. Therefore, the W-LLR method can also be applied for CCI mitigation in these systems. Since reuse-one cellular systems have a CCI limited performance, the proposed method provides a significant improvement over the normal turbo decoder. © 2007 IEEE.

Narrowband interference mitigation in turbo-coded OFDM systems

Channel estimation in the presence of multitone narrowband interference (MNBI) in OFDM systems is addressed in this paper. While pilot based OFDM channel estimation in the presence of only thermal noise at the receiver is a Gaussian regression problem, the presence of MNBI leads to an outlier contaminated Gaussian regression problem. Since Gaussian probability density function (pdf) based maximum likelihood (ML) estimators are highly sensitive to outliers, we define a M estimator based on the theory of robust regression for channel estimation in the presence of MNBI. The proposed iterative M estimator minimizes the Huber's cost function for p iterations and then minimizes a cost function defined by a redescending M estimator based on extreme value theory in the last few iterations. Simulation results indicate that the proposed estimator outperforms both the Gaussian pdf based ML estimator and a M estimator based only on Huber's cost function. © 2006 IEEE.

GLOBECOM - IEEE Global Telecommunications Conference

Extreme value theory based OFDM channel estimation in the presence of narrowband interference

2007 IEEE 8th Workshop on Signal Processing Advances in Wireless Communications

OFDM channel estimation in the presence of NBI and the effect of misspecified NBI model

IEEE Transactions on Signal Processing

Impacts of Narrowband Interference on OFDM-UWB Receivers: Analysis and Mitigation

IEEE Globecom 2006

GEN01-3: Robust Decoding for Channels with Impulse Noise

Interference mitigation in turbo-coded OFDM systems using robust LLRs

Journal	IEEE International Conference on Communications
ISSN	05361486
Open Access	No