Energy Efficient Extreme MIMO: Design Goals and Directions

Wesemann, Stefan; Du, Jinfeng; Viswanathan, Harish

doi:10.1109/mcom.004.2200958

Cited by 13 publications

(1 citation statement)

References 10 publications

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“…Optimized trade-off between energy efficiency and spectral efficiency can be achieved through antenna muting, while maintaining cell coverage with the help of a flexible analogue beamforming network that connects the remaining set of active transceiver chains with all antenna elements. Optimized power saving modes will allow to deactivate not only analogue frontend components but also digital processing units as relevant especially at low and mid load [17]. MIMO research will also lead to improvements in the sub6GHz bands.…”

Section: A Upper Midband Spectrum and Extreme Mimomentioning

confidence: 99%

How 6G will enable the Industrial Metaverse

Ziegler,

Viswanathan,

Batra

2024

Preprint

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Section: A Upper Midband Spectrum and Extreme Mimomentioning

confidence: 99%

How 6G will enable the Industrial Metaverse

Ziegler,

Viswanathan,

Batra

2024

Preprint

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Enhanced channel prediction in large‐scale5G MIMO‐OFDMsystems using pyramidal dilation attention convolutional neural network

Rathish,

Manojkumar,

Thanga Mariappan

et al. 2024

Internet Technology Letters

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In order to enhance communication while minimizing complexity in 5G and beyond, MIMO‐OFDM systems need accurate channel prediction. In order to enhance channel prediction, decrease Error Vector Magnitude, Peak Power, and Adjacent Channel Leakage Ratio, this study employs the Pyramidal Dilation Attention Convolutional Neural Network (PDACNN). Simplified clipping with filtering (SCF) reduces PAPR data, and this technique employs a PDACNN trained with the reduced data. By combining attention techniques with pyramidal dilated convolutions, the suggested PDACNN architecture is able to extract OFDM channel parameters across several scales. Attention approaches enhance channel prediction by allowing the model to dynamically concentrate on essential information. The primary objective is to make use of the network's ability to comprehend intricate spatial–temporal connections in OFDM channel data. The goal of these techniques is to make channel forecasts more accurate and resilient while decreasing concerns about EVM, Peak Power, and ACLR. To confirm the effectiveness of the suggested CP‐LSMIMO‐OFDM‐PDACNN, we measure its spectral efficiency, peak‐to‐average power ratio, bit error rate (BER), signal‐to‐noise ratio (SNR), and throughput. Throughput gains of 23.76%, 30.45%, and 18.97% are achieved via CP‐LSMIMO‐OFDM‐PDACNN, while bit error rates of 20.67%, 12.78%, and 19.56% are reduced. PAPRs of 21.66%, 23.09%, and 25.11% are also decreased.

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