Title :
Detecting OFDM Signals in Alpha-Stable Noise
Author :
Mahmood, Arif ; Chitre, Mandar ; Armand, Marc A.
Author_Institution :
Acoust. Res. Lab. (ARL), Nat. Univ. of Singapore, Singapore, Singapore
Abstract :
This paper analyzes various receiver schemes for orthogonal frequency division multiplexing (OFDM) transmission in impulsive noise. We consider Rayleigh block-fading and model the noise process by additive white symmetric α-stable noise (AWSαSN). We begin by discussing maximum-likelihood (ML) detection performance of baseband OFDM. Though optimal, the computational cost increases exponentially with the number of carriers. Alternatively, one may evaluate soft-estimates of the transmitted symbol block and employ carrier-wise detection to lower computational complexity. We analyze such schemes under the frameworks of M-estimation and compressed sensing theory. Moving on, we highlight important rules that ensure the passband AWSαSN process is converted to a baseband form suitable for the discussed schemes. Finally, it is shown that linear passband-to-baseband conversion actually reduces the signal-to-noise ratio (SNR) at the receiver and that all these rules may be avoided by applying an estimation scheme directly on the passband samples.
Keywords :
OFDM modulation; compressed sensing; computational complexity; maximum likelihood detection; maximum likelihood estimation; M-estimation; ML detection performance; OFDM signal detection; OFDM transmission; Rayleigh block-fading; SNR reduction; additive white symmetric α-stable noise; alpha-stable noise; baseband OFDM; carrier-wise detection; compressed sensing theory; computational complexity; computational cost; estimation scheme; impulsive noise; linear passband-to-baseband conversion; maximum-likelihood detection performance; noise process model; orthogonal frequency division multiplexing; passband AWSαSN process; signal-to-noise ratio reduction; soft-estimates; transmitted symbol block; Baseband; OFDM; Passband; Signal to noise ratio; Tin; Vectors; AWS $alpha$SN; M-estimation; ML; OFDM; compressed sensing; impulsive noise;
Journal_Title :
Communications, IEEE Transactions on
DOI :
10.1109/TCOMM.2014.2351809