The Channel Shortening comparison between OFDM &amp; MC-CDMA System over an ADSL Channel using a Water-filling

fatima, bouasria

doi:10.11591/ijece.v9i5.pp3695-3700

Cited by 1 publication

(1 citation statement)

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“…In the literature, the investigation of advanced receiver techniques for the compensation of the ISI/ICI in an OFDM system is broad, but limited to specific scenarios. For ADSL systems, which are characterized by quasi-static channels, the authors of [7] proposed an approach to compensate the effect of the channel within the CP in a static channel condition. In [8,9], the authors proposed low-complexity equalizers for video broadcasting in timeand frequency-selective channels, although the considered CP length was larger than the delay spread.…”

Section: Introductionmentioning

confidence: 99%

Single-Frequency Network Terrestrial Broadcasting with 5GNR Numerology Using Recurrent Neural Network

Mosavat

Montorsi

2022

Electronics

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We explore the feasibility of Terrestrial Broadcasting in a Single-Frequency Network (SFN) with standard 5G New Radio (5GNR) numerology designed for uni-cast transmission. Instead of the classical OFDM symbol-by-symbol detector scheme or a more complex equalization technique, we designed a Recurrent-Neural-Network (RNN)-based detector that replaces the channel estimation and equalization blocks. The RNN is a bidirectional Long Short-Term Memory (bi-LSTM) that computes the log-likelihood ratios delivered to the LDPC decoder starting from the received symbols affected by strong intersymbol/intercarrier interference (ISI/ICI) on time-varying channels. To simplify the RNN receiver and reduce the system overhead, pilot and data signals in our proposed scheme are superimposed instead of interspersed. We describe the parameter optimization of the RNN and provide end-to-end simulation results, comparing them with those of a classical system, where the OFDM waveform is specifically designed for Terrestrial Broadcasting. We show that the system outperforms classical receivers, especially in challenging scenarios associated with large intersite distance and large mobility. We also provide evidence of the robustness of the designed RNN receiver, showing that an RNN receiver trained on a single signal-to-noise ratio and user velocity performs efficiently also in a large range of scenarios with different signal-to-noise ratios and velocities.

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Section: Introductionmentioning

confidence: 99%