Performance of multilevel‐turbo codes with blind/non‐blind equalization over WSSUS multipath channels

Abstract

SUMMARYIn this paper, in order to improve error performance, we introduce a new type of turbo codes, called 'multilevel-turbo codes (ML-TC)' and we evaluate their performance over wide-sense stationary uncorrelated scattering (WSSUS) multipath channels. The basic idea of ML-TC scheme is to partition a signal set into several levels and to encode each level separately by a proper component of the turbo encoder. In the considered structure, the parallel input data sequences are encoded by our multilevel scheme and mapped to any modulation type such as MPSK, MQAM, etc. Since WSSUS channels are very severe fading environments, it is needed to pass the received noisy signals through non-blind or blind equalizers before turbo decoders. In ML-TC schemes, noisy WSSUS corrupted signal sequence is first processed in equalizer block, then fed into the first level of turbo decoder and the first sequence is estimated from this first Turbo decoder. Subsequently, the other following input sequences of the frame are computed by using the estimated input bit streams of previous levels. Here, as a ML-TC example, 4PSK 2 level-turbo codes (2L-TC) is chosen and its error performance is evaluated in WSSUS channel modelled by COST 207 (Cooperation in the field of Science & Technology, Project #207). It is shown that 2L-TC signals with equalizer blocks exhibit considerable performance gains even at lower SNR values compared to 8PSK-turbo trellis coded modulation (TTCM). The simulation results of the proposed scheme have up to 5.5 dB coding gain compared to 8PSK-TTCM for all cases. It is interesting that after a constant SNR value, 2L-TC with blind equalizer has better error performance than non-blind filtered schemes. We conclude that our proposed scheme has promising results compared to classical schemes for all SNR values in WSSUS channels.

Performance of joint multilevel/AES‐LDPCC‐CPFSK schemes over wireless sensor networks

“…In order to improve bit error performance of our combined scheme, we can use some of the bit streams in the last input level of AES to estimate channel parameters. The considered bit streams will be known by receiver block as in pilot symbol communication [25].…”

Performance of multi level‐turbo coding with neural network‐based channel estimation over WSSUS MIMO channels

“…Each turbo encoder has only one input, and the encoding is processed simultaneously. The outputs of these encoders can be punctured and then are mapped to any modulated scheme using group set partitioning technique [20] as in Figure 6. Here, if the d k is the input of multi level encoder at time k, the encoder output x k is equal to where f (·) corresponds to the encoder structure.…”

“…For improving the bit error probability, many scientists considered multi level coding [18][19][20][21] as an important band and power-efficient technique, as it provides significant amount of encoding gain and low coding complexity. Nowadays, there are also many attempts to improve the performance of turbo-based systems.…”

Blind equalization of multilevel turbo coded‐continuous phase frequency shift keying over MIMO channels

“…In [9], the impact of interleaver size on the performance of multilevel turbo-based systems is discussed. Power and bandwidth efficiencies of multilevel turbo codes are also discussed in [10][11][12].…”