Energy Efficient Extreme MIMO: Design Goals and Directions
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Abstract (arXiv)
Ever since the invention of Bell Laboratories Layer Space-Time (BLAST) in mid 1990s, the focus of MIMO research and development has been largely on pushing the limit of spectral efficiency. While massive MIMO technologies laid the foundation of high spectrum efficiency in 5G and beyond, the challenge remains in improving energy efficiency given the increasing complexity of the associated radio systems. With the substantial negative implications of climate change looming ever closer, minimizing energy use is a key dimension of achieving sustainability and is of paramount importance for any future technology. Thus, every aspect of future extreme MIMO system design, implementation, and operation will be scrutinized to maximize energy efficiency. An analysis of the massive MIMO 5G radio energy consumption at different loads leads to qualitative energy efficiency design goals for emerging extreme MIMO systems. Following this, we focus on novel operational and component technology innovations to minimize energy consumption.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 Jan 2023.
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Citing papers per year: 2024: 2; 2025: 2.
4 papers.
- 2024OpenDPD: An Open-Source End-to-End Learning & Benchmarking Framework for Wideband Power Amplifier Modeling and Digital Pre-Distortioncited by 7
- 2024MP-DPD: Low-Complexity Mixed-Precision Neural Networks for Energy-Efficient Digital Predistortion of Wideband Power Amplifierscited by 3
- 2025DeltaDPD: Exploiting Dynamic Temporal Sparsity in Recurrent Neural Networks for Energy-Efficient Wideband Digital Predistortioncited by 1
- 2025OpenDPDv2: A Unified Learning and Optimization Framework for Neural Network Digital Predistortioncited by 0
- 2025OpenDPDv2: A Unified Learning and Optimization Framework for Neural Network Digital Predistortion9 Jul 2025
- 2025DeltaDPD: Exploiting Dynamic Temporal Sparsity in Recurrent Neural Networks for Energy-Efficient Wideband Digital Predistortion29 Apr 2025
- 2024MP-DPD: Low-Complexity Mixed-Precision Neural Networks for Energy-Efficient Digital Predistortion of Wideband Power Amplifiers18 Apr 2024
- 2024OpenDPD: An Open-Source End-to-End Learning & Benchmarking Framework for Wideband Power Amplifier Modeling and Digital Pre-Distortion16 Jan 2024