35 research outputs found

    Impact and compensation of carrier synchronization errors in OFDM signals with very large QAM constellations

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    Publisher Copyright: © 2023 The Authors. IET Communications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.Low cost video sensors used for streaming video signals to help firefighters, require high bit rate due to uncompressed images. To increase spectral efficiency given a limited bandwidth, very high order constellations in high signal to noise ratio regimes can be used. However, noise is not the only factor effecting the high order constellations. These constellations are also sensitive to hardware impairments and system non-linearities. Therefore, in this paper, the effect of carrier frequency offset (CFO) on the performance of an orthogonal frequency division multiplexing (OFDM) system with high order quadrature amplitude modulation (QAM) is studied. A closed form expression is derived for the maximum normalized residual CFO that an OFDM system with M-QAM constellation can resist to have an error free symbol detection. Finally, the suitability of common previous CFO estimation techniques such as the cyclic prefix based technique and the Moose technique in these systems are investigate. The results show that the maximum residual CFO that an OFDM system with M-QAM constellation can resist is proportional to the inverse of (Formula presented.). The results also show that very large order QAM constellations such as 4096-QAM are very sensitive to even small residual CFO values and their performance degrades, significantly. However, the bit error rate analysis indicate that the Moose CFO estimation technique can be used in these systems to compensate the CFO effect, accurately.publishersversionpublishe

    On Investigations of Machine Learning and Deep Learning Techniques for MIMO Detection

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    This paper reviews in detail the various types of multiple input multiple output (MIMO) detector algorithms. The current MIMO detectors are not suitable for massive MIMO (mMIMO) scenarios where there are a large number of antennas. Their performance degrades with the increase in number of antennas in the MIMO system. For combatting the issues, machine learning (ML) and deep learning (DL) based detection algorithms are being researched and developed. An extensive survey of these detectors is provided in this paper, alongwith their advantages and challenges. The issues discussed have to be resolved before using them for final deployment

    Spectrally and Energy Efficient Wireless Communications: Signal and System Design, Mathematical Modelling and Optimisation

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    This thesis explores engineering studies and designs aiming to meeting the requirements of enhancing capacity and energy efficiency for next generation communication networks. Challenges of spectrum scarcity and energy constraints are addressed and new technologies are proposed, analytically investigated and examined. The thesis commences by reviewing studies on spectrally and energy-efficient techniques, with a special focus on non-orthogonal multicarrier modulation, particularly spectrally efficient frequency division multiplexing (SEFDM). Rigorous theoretical and mathematical modelling studies of SEFDM are presented. Moreover, to address the potential application of SEFDM under the 5th generation new radio (5G NR) heterogeneous numerologies, simulation-based studies of SEFDM coexisting with orthogonal frequency division multiplexing (OFDM) are conducted. New signal formats and corresponding transceiver structure are designed, using a Hilbert transform filter pair for shaping pulses. Detailed modelling and numerical investigations show that the proposed signal doubles spectral efficiency without performance degradation, with studies of two signal formats; uncoded narrow-band internet of things (NB-IoT) signals and unframed turbo coded multi-carrier signals. The thesis also considers using constellation shaping techniques and SEFDM for capacity enhancement in 5G system. Probabilistic shaping for SEFDM is proposed and modelled to show both transmission energy reduction and bandwidth saving with advantageous flexibility for data rate adaptation. Expanding on constellation shaping to improve performance further, a comparative study of multidimensional modulation techniques is carried out. A four-dimensional signal, with better noise immunity is investigated, for which metaheuristic optimisation algorithms are studied, developed, and conducted to optimise bit-to-symbol mapping. Finally, a specially designed machine learning technique for signal and system design in physical layer communications is proposed, utilising the application of autoencoder-based end-to-end learning. Multidimensional signal modulation with multidimensional constellation shaping is proposed and optimised by using machine learning techniques, demonstrating significant improvement in spectral and energy efficiencies
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