Impact of Nonideal Phase Reference on Soft Decoding of Differentially Encoded Modulation

Abstract

The performance of coded modulation schemes based on the concatenation of a forward-error correction (FEC) code and differentially encoded quadrature amplitude modulations (QAMs) in the presence of Wiener phase noise is considered. Log-likelihood ratios, required to perform soft decoding, are first derived for the case in which the phase of the received carrier is assumed to be perfectly known, and then used to obtain the performance for nonideal reference of phase. Simulation results of the post-FEC bit-error rate are presented for a near-Shannon limit low-density parity check code with an overhead of 20%. The performance loss, compared to ideal coherent demodulation, is evaluated for two different linewidths of the phase noise in case of quadrature-phase shift keying and 16-QAM transmission.

Dimensioning BCH Codes for Coherent DQPSK Systems With Laser Phase Noise and Cycle Slips

“…The presence of singleand double-errors changes the error statistics and thereby FEC performance. This is considered for coherent optical systems using LDPC codes in [14]- [16]. In [17], we present semianalytical methods for dimensioning binary Bose-ChaudhuriHocquenghem (BCH) and Reed-Solomon (RS) codes for systems with PN.…”

New Lower Bound below the Information Rate of Phase Noise Channel Based on Kalman Carrier Recovery

“…Figures 4 and 5 report the results obtained with the second-order phase noise model of (9). Now the state space is multidimensional, and quantizing a multidimensional state space according to the trellis-based approach of [13,14] would lead to an exponential increase of the number of states of the trellis, making computation unfeasible. Therefore, in order to work out an approximation to channel capacity, we adopt the particle filter of [19].…”

“…Computation of the capacity of the multiplicative Wiener phase noise plus additive white Gaussian noise (AWGN) channel, which is a channel with memory and continuous state, is a challenging problem. A Monte Carlo approach based on phase space quantization and trellis representation of phase memory has been recently proposed in [13][14][15] for computing the constrained channel capacity (i.e. the capacity with a fixed source).…”

Lower bound based on Kalman carrier recovery below the information rate of Wiener phase noise channel

“…Several methods have been proposed in the literature to combat the detrimental effects of Wiener phase noise. Among these methods we cite the iterative demodulation and decoding techniques of [6]- [8], the insertion of pilot symbols [9]- [11], and soft differential demodulation [12].…”