Bit Error Ratio Estimation Research Articles

Adaptive Modulation and Coding for Underwater Acoustic Communications Based on Data-Driven Learning Algorithm

With the development of the underwater acoustic (UWA) adaptive communication system, energy-efficient transmission has become a critical topic in underwater acoustic (UWA) communications. Due to the unique characteristics of the underwater environment, the transmitter node will almost always have outdated channel state information (CSI), which results in low energy efficiency. In this paper, we take full advantage of bidirectional links and propose an adaptive modulation and coding (AMC) scheme that aims to maximize the long-term energy efficiency of a single link by jointly scheduling the coding rate, modulation order, and transmission power. Considering the complexity characteristics of UWA channels, we proposed a bit error ratio (BER) estimation method based on deep neural networks (DNN). The proposed network could realize channel estimation, feature extraction, and BER estimation by using a fixed pilot of the feedback link. Then, we design a channel classification method based on the estimated BERs of the modulation and coding scheme (MCS) and further model the UWA channels as a finite-state Markov chain (FSMC) with an unknown transition probability. Thus, we formulate the AMC problem as a Markov Decision Process (MDP) and solve it through a reinforcement learning framework. Considering the large state-action pairs, a double deep Q-network (DDQN) based scheme is proposed. Simulation results demonstrate that the proposed AMC scheme outperforms the fixed MCS with a perfect channel information state, and achieves near-optimal energy efficiency.

Optical performance monitoring using digital coherent receivers and convolutional neural networks.

We experimentally demonstrate accurate modulation format identification, optical signal to noise ratio (OSNR) estimation, and bit error ratio (BER) estimation of optical signals for wavelength division multiplexed optical communication systems using convolutional neural networks (CNN). We assess the benefits and challenges of extracting information at two distinct points within the demodulation process: immediately after timing recovery and immediately prior to symbol unmapping. For the former, we use 3D Stokes-space based signal representations. For the latter, we use conventional I-Q constellation images created using demodulated symbols. We demonstrate these methods on simulated and experimental dual-polarized waveforms for 32-GBaud QPSK, 8QAM, 16QAM, and 32QAM. Our results show that CNN extracts distinct and learnable features at both the early stage of demodulation where the information can be used to optimize subsequent stages and near the end of demodulation where the constellation images are readily available. Modulation format identification is demonstrated with >99.8% accuracy, OSNR estimation with <0.5 dB average discrepancy and BER estimation with percentage error of <25%.

Calibration of Estimated BER from Error Vector Magnitude with Carrier Phase Recovery

This paper presents the estimation of bit error ratio (BER) from error vector magnitude (EVM) for the quadrature phase shift keying (QPSK) modulation format with the Viterbi & Viterbi algorithm and differential decoding to compensate for laser phase noise. It is demonstrated analytically and through simulation that a calibration is necessary when using the established relationship between EVM and BER to take into account the implementation penalty associated with the carrier phase recovery algorithm. Simulation results using the proposed calibration technique are presented for different combined linewidth symbol duration product in a QPSK coherent system. The impact on the number of symbols used to estimate the BER from EVM analysis is also investigated and compared to error counting. An estimation with lower uncertainty is demonstrated to be achievable with EVM analysis compared to error counting for different number of symbols. Finally, the estimation of BER for a 16-ary quadrature amplitude modulation (16- QAM) optical coherent system is investigated using the blind phase search algorithm for carrier phase recovery.

Estimation of BER from Error Vector Magnitude for Optical Coherent Systems

This paper presents the estimation of bit error ratio (BER) from error vector magnitude (EVM) for M-ary quadrature amplitude modulation (QAM) formats in optical coherent systems employing carrier phase recovery with differential decoding to compensate for laser phase noise. Simulation results show that the relationship to estimate BER from EVM analysis for data-aided reception can also be applied to nondata-aided reception with a correction factor for different combined linewidth symbol duration product at the target BER of 10 − 3 . It is demonstrated that the calibrated BER, which would otherwise be underestimated without the correction factor, can reliably monitor the performance of optical coherent systems near the target BER for quadrature phase shift keying (QPSK), 16-QAM, and 64-QAM.