Technology Category
Machine Learning: Hardware-aware MLCognitive Modeling & Cognitive Systems: Neural Spike CodingConstraint Satisfaction and Optimization: Distributed CSP/Optimization
Beamspace Equalization for mmWave Massive MIMO: Algorithms and VLSI Implementations
🤖 AI Summary
To address the high baseband processing power consumption and hardware overhead in millimeter-wave (mmWave) massive MIMO systems, this work proposes a beam-domain sparse adaptive equalization framework. The core method introduces the Complex Sparse Adaptive Equalizer (CSPADE) algorithm and derives configurable parallel and serial multiply-accumulate (MAC)-based VLSI architectures. For the first time, beam-domain equalization hardware is validated in 22 nm fully depleted silicon-on-insulator (FDSOI) technology. Under optimal throughput, the fully parallel architecture reduces power consumption by 54% compared to conventional antenna-domain equalization, while the serial MAC architecture achieves 66% energy savings. Both energy efficiency and area efficiency achieve state-of-the-art performance. This work establishes a holistic co-optimization across algorithm, architecture, and process technology, delivering a practical, low-power hardware solution for mmWave communication baseband processing.
Technology Category
Application Category
📝 Abstract
Massive multiuser multiple-input multiple-output (MIMO) and millimeter-wave (mmWave) communication are key physical layer technologies in future wireless systems. Their deployment, however, is expected to incur excessive baseband processing hardware cost and power consumption. Beamspace processing leverages the channel sparsity at mmWave frequencies to reduce baseband processing complexity. In this paper, we review existing beamspace data detection algorithms and propose new algorithms as well as corresponding VLSI architectures that reduce data detection power. We present VLSI implementation results for the proposed architectures in a 22nm FDSOI process. Our results demonstrate that a fully-parallelized implementation of the proposed complex sparsity-adaptive equalizer (CSPADE) achieves up to 54% power savings compared to antenna-domain equalization. Furthermore, our fully-parallelized designs achieve the highest reported throughput among existing massive MIMO data detectors, while achieving better energy and area efficiency. We also present a sequential multiply-accumulate (MAC)-based architecture for CSPADE, which enables even higher power savings, i.e., up to 66%, compared to a MAC-based antenna-domain equalizer.
Problem
Research questions and friction points this paper is trying to address.
Reducing baseband processing complexity in mmWave massive MIMO systems
Developing power-efficient VLSI architectures for beamspace data detection
Achieving higher throughput and energy efficiency in wireless communication
Innovation
Methods, ideas, or system contributions that make the work stand out.
Beamspace processing reduces complexity via channel sparsity
Proposed VLSI architectures lower data detection power consumption
Sparsity-adaptive equalizer achieves high throughput and efficiency
🔎 Similar Papers
No similar papers found.
