Синтез N-арилалкил-N'-ацилпроизводных мочевины и их фармакологическая активность

Source codec parameters determined by modern source encoders are usually very sensitive to noise on the transmission link. Natural residual redundancy of these parameters can be utilized by Soft Decision Source Decoding (SDSD) [1] to increase the error resistance. Serially concatenating Forward Error Correction (FEC) and SDSD leads to a Iterative Source-Channel Decoding (ISCD) scheme [2, 3] which offers further improvements in robustness. ISCD exploits natural residual source redundancy by SDSD and artificial channel coding redundancy due to FEC in an iterative Turbo-like process [4, 5]. The convergence behavior of ISCD schemes can be analyzed by EXtrinsic Information Transfer (EXIT) charts [6, 7]. However, if an advanced ISCD system design [8-11] is applied, offering incremental quality improvements for many iterations, the EXIT curves for SDSD do not specify a tight bound for the decoding trajectory anymore. In the initial iterations, the decoding trajectory "overshoo ts" the EXIT curves of SDSD, e.g. [12, 13]. In this paper, we give reasons for this overshooting effect. In addition, we propose a novel solution for determining the EXIT curve of SDSD which allows a more precise convergence analysis

Experimental demonstration of dispersion tolerant end-to-end deep learning-based IM-DD transmission system

We experimentally demonstrate an IM-DD system relying on deep neural networks from transmitter to receiver delivering 42 Gb/s over 20 and 40 km at 1550 nm below 3.8×10−3 . The ANN is trained to tolerate deviations in dispersion by as much as ±170ps/n

Optical Fiber Communication Systems Based on End-to-End Deep Learning: (Invited Paper)

We investigate end-to-end optimized optical transmission systems based on feedforward or bidirectional recurrent neural networks (BRNN) and deep learning. In particular, we report the first experimental demonstration of a BRNN auto-encoder, highlighting the performance improvement achieved with recurrent processing for communication over dispersive nonlinear channels

End-to-end optimized transmission over dispersive intensity-modulated channels using bidirectional recurrent neural networks

We propose an autoencoding sequence-based transceiver for communication over dispersive channels with intensity modulation and direct detection (IM/DD), designed as a bidirectional deep recurrent neural network (BRNN). The receiver uses a sliding window technique to allow for efficient data stream estimation. We find that this sliding window BRNN (SBRNN), based on end-to-end deep learning of the communication system, achieves a significant bit-error-rate reduction at all examined distances in comparison to previous block-based autoencoders implemented as feed-forward neural networks (FFNNs), leading to an increase of the transmission distance. We also compare the end-to-end SBRNN with a state-of-the-art IM/DD solution based on two level pulse amplitude modulation with an FFNN receiver, simultaneously processing multiple received symbols and approximating nonlinear Volterra equalization. Our results show that the SBRNN outperforms such systems at both 42 and 84 Gb/s, while training fewer parameters. Our novel SBRNN design aims at tailoring the end-to-end deep learning-based systems for communication over nonlinear channels with memory, such as the optical IM/DD fiber channel

Преобразователь переменного тока с время-импульсной модуляцией

Iterative source-channel decoding (ISCD) exploits the residual redundancy of source codec parameters by using the Turbo principle. In this paper we extend the excellent capabilities of ISCD to a hybrid automatic repeat request (HARQ) scheme by novel incremental redundant index assignments. The incremental redundancy for HARQ is supplied by the source encoder and not the channel encoder. Simulation results show an excellent performance over a wide range of channel conditions, with inherently adapted bandwidth and complexity

End-to-End Learning in Optical Fiber Communications: Experimental Demonstration and Future Trends

Fiber-optic auto-encoders are demonstrated on an intensity modulation/direct detection testbed, outperforming state-of-the-art signal processing. Algorithms for end-to-end optimization using experimentally collected data are discussed. The end-to-end learning framework is extended for performing optimization of the symbol distribution in probabilistically-shaped coherent systems

Low-complexity BCH codes with optimized interleavers for DQPSK systems with laser phase noise

The presence of high phase noise in addition to additive white Gaussian noise in coherent optical systems affects the performance of forward error correction (FEC) schemes. In this paper, we propose a simple scheme for such systems, using block interleavers and binary Bose–Chaudhuri–Hocquenghem (BCH) codes. The block interleavers are specifically optimized for differential quadrature phase shift keying modulation. We propose a method for selecting BCH codes that, together with the interleavers, achieve a target post-FEC bit error rate (BER). This combination of interleavers and BCH codes has very low implementation complexity. In addition, our approach is straightforward, requiring only short pre-FEC simulations to parameterize a model, based on which we select codes analytically. We aim to correct a pre-FEC BER of around (Formula presented.). We evaluate the accuracy of our approach using numerical simulations. For a target post-FEC BER of (Formula presented.), codes selected using our method result in BERs around 3(Formula presented.) target and achieve the target with around 0.2 dB extra signal-to-noise ratio

Сутність та класифікація ризиків інвестиційної діяльності

Наводиться визначення поняттю "ризики інвестиційної діяльності" за рахунок поєднання його сутнісних характеристик, виконано узагальнення класифікації цих ризиків, запропоновано введення нової класифікаційної групи – "корпоративні ризики", які пов'язані з можливістю втрати контролю над підприємством інвестором-акціонером

Investigations on multi-user MIMO feature in IEEE 802.11ac networks

This paper studies the multi-user MIMO feature of IEEE 802.11ac networks that serve, along with IEEE 802.11ac nodes, also legacy IEEE 802.11n nodes. For this purpose, we develop a simulator that models the IEEE 802.11ac and IEEE 802.11n networks. Then, using a setup, we first study the tradeoff between the amount of overhead used in channel sounding and the corresponding rate of information, concluding that in this setting, channel sounding with all clients lead to better throughput. Secondly, we observe the negative impact of IEEE 802.11n nodes on the IEEE 802.11ac traffic due to the deafness problem, and analyze the performance of the usage of RTS/CTS handshake and cts2self mechanisms to mitigate this effect. We show that the regular RTS/CTS handshake mitigates the deafness problem to a certain degree. However, the cts2self mechanism achieves a better performance since no airtime is wasted to collisions with the RTS frames

Predicting the performance of nonbinary forward error correction in optical transmission experiments

It is shown that the correct metric to predict the performance of coded modulation based on nonbinary FEC is the mutual information. The accuracy of the prediction is verified in an optical experiment