Approximate computing in B5G and 6G wireless systems: A survey and future outlook

Damsgaard, Hans Jakob; Ometov, Aleksandr; Mowla, Md. Munjure; Flizikowski, Adam; Nurmi, Jari

doi:10.1016/j.comnet.2023.109872

Cited by 7 publications

(1 citation statement)

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“…These techniques allow a controlled trade-off in system performance, contributing to improved efficiency in communication systems. A comprehensive survey [26] on the potential of approximate computing techniques for existing and future B5G communication highlights SER as a crucial Key Performance Indicator (KPI) for channel-related problems while energy efficiency as a prime KPI for resource allocation. By employing approximate computing techniques within a fixed power budget for communication systems, it becomes possible to reduce overall system power consumption.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Wireless Signal Detection: A Revisit through the Lens of Approximate Computing

Kulkarni,

Ouameur,

Massicotte

2024

Electronics

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In the pursuit of energy efficiency in next-generation communication systems, approximate computing is emerging as a promising technique. In the proposed work, efforts are made to address the challenge of bridging the gap between the level of approximation and the Quality-of-Service (QoS) of the system. The application of approximate multiplication to wireless signal detection is explored systematically, illustrated by employing Truncated Multiplication (TM) on Quadrature Phase Shift Keying (QPSK) Minimum Mean Square Error (MMSE) detection. The irregularities induced by approximation in the multiplication operation employed in wireless signal detection are captured by the Approximate Multiplication Noise (AMN) model, which aids in the analysis of signal fidelity and resiliency of the system. The energy efficiency gains through approximation are highlighted in the approximation analysis. Signal fidelity analysis provides the capability to predict system output for varying levels of approximation, which aids in improving the stability of the system. The higher approximation levels are advantageous in low Signal-to-Noise Ratio (SNR) regimes, whereas lower approximation levels prove beneficial in high SNR regimes.

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Section: Related Workmentioning

confidence: 99%