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Register a new userCapacity increases obtained extending the transmission bandwidth in optical communication systems
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Added by
Gabriel Saavedra
Publication date
2019
fields
Electronic Engineering
and research's language is
English
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The potential benefits of extending the optical fibre transmission bandwidth are studied. Even in the presence of Kerr nonlinearity and inter-channel stimulated Raman scattering, increasing the usable optical fibre bandwidth appears to be the most promising solution to increase system throughput.
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Meeting the ever-growing information rate demands has become of utmost importance for optical communication systems. However, it has proven to be a challenging task due to the presence of Kerr effects, which have largely been regarded as a major bottleneck for enhancing the achievable information rates in modern optical communications. In this work, the optimisation and performance of digital nonlinearity compensation are discussed for maximising the achievable information rates in spectrally-efficient optical fibre communication systems. It is found that, for any given target information rate, there exists a trade-off between modulation format and compensated bandwidth to reduce the computational complexity requirement of digital nonlinearity compensation.
The orbital angular momentum (OAM) wireless communication technology is widely studied in recent literatures. But the atmospheric turbulence is rarely considered in analyzing the capacity of OAM-based millimeter wave (OAM-mmWave) communication systems. The OAM-mmWave propagated in the atmosphere environments is usually interfered by the atmospheric turbulence, resulting in the crosstalk among OAM channels,capacity degradation, etc. By taking into account the atmospheric turbulence effect, this paper proposes a new purity model and a new capacity model for the OAM-mmWave communication systems. Simulation results indicate that the OAM-mmWave propagation in the atmosphere environments is evidently interfered by atmospheric turbulence, where the capacity of the OAMmmWave communication systems decreases with the increase of the transmission frequency.
We consider futuristic, intelligent reflecting surfaces (IRS)-aided communication between a base station (BS) and a user equipment (UE) for two distinct scenarios: a single-input, single-output (SISO) system whereby the BS has a single antenna, and a multi-input, single-output (MISO) system whereby the BS has multiple antennas. For the considered IRS-assisted downlink, we compute the effective capacity (EC), which is a quantitative measure of the statistical quality-of-service (QoS) offered by a communication system experiencing random fading. For our analysis, we consider the two widely-known assumptions on channel state information (CSI) -- i.e., perfect CSI and no CSI, at the BS. Thereafter, we first derive the distribution of the signal-to-noise ratio (SNR) for both SISO and MISO scenarios, and subsequently derive closed-form expressions for the EC under perfect CSI and no CSI cases, for both SISO and MISO scenarios. Furthermore, for the SISO and MISO systems with no CSI, it turns out that the EC could be maximized further by searching for an optimal transmission rate $r^*$, which is computed by exploiting the iterative gradient-descent method. We provide extensive simulation results which investigate the impact of the various system parameters, e.g., QoS exponent, power budget, number of transmit antennas at the BS, number of reflective elements at the IRS etc., on the EC of the system.
In this article, we first provide a brief overview of optical transmission systems and some of their performance specifications. We then present a simple, robust, and bandwidth-efficient OFDM synchronization method, and carry out measurements to validate the presented synchronization method with the aid of an experimental setup.
Reliably transmitting messages despite information loss due to a noisy channel is a core problem of information theory. One of the most important aspects of real world communication, e.g. via wifi, is that it may happen at varying levels of information transfer. The bandwidth-limited channel models this phenomenon. In this study we consider learning coding with the bandwidth-limited channel (BWLC). Recently, neural communication models such as variational autoencoders have been studied for the task of source compression. We build upon this work by studying neural communication systems with the BWLC. Specifically,we find three modelling choices that are relevant under expected information loss. First, instead of separating the sub-tasks of compression (source coding) and error correction (channel coding), we propose to model both jointly. Framing the problem as a variational learning problem, we conclude that joint systems outperform their separate counterparts when coding is performed by flexible learnable function approximators such as neural networks. To facilitate learning, we introduce a differentiable and computationally efficient version of the bandwidth-limited channel. Second, we propose a design to model missing information with a prior, and incorporate this into the channel model. Finally, sampling from the joint model is improved by introducing auxiliary latent variables in the decoder. Experimental results justify the validity of our design decisions through improved distortion and FID scores.
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