Massive MIMO with 1-Bit DACs: Data Detection for Quantized Linear Precoding with Dithering
원문 초록
To leverage high-frequency bands in 6G wireless systems and beyond, employing massive multiple-input multipleoutput (MIMO) arrays at the transmitter and/or receiver side is crucial. To mitigate the power consumption and hardware complexity across massive frequency bands and antenna arrays, a sacrifice in the resolution of the data converters will be inevitable. In this paper, we consider a point-to-point massive MIMO system with 1-bit digital-to-analog converters at the transmitter, where the linearly precoded signal is supplemented with dithering before the 1-bit quantization. For this system, we propose a new maximumlikelihood (ML) data detection method at the receiver by deriving the mean and covariance matrix of the received signal, where symbol-dependent linear minimum mean squared error estimation is utilized to efficiently linearize the transmitted signal. Numerical results show that the proposed ML method can provide gains of more than two orders of magnitude in terms of symbol error rate over conventional data detection based on soft estimation.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
MGVQ: Could VQ-VAE Beat VAE? A Generalizable Tokenizer with Multi-group Quantization
Compress Any Segment Anything Model (SAM)
OpenDPDv2: A Unified Learning and Optimization Framework for Neural Network Digital Predistortion
EdgeCodec: Onboard Lightweight High Fidelity Neural Compressor with Residual Vector Quantization
any4: Learned 4-bit Numeric Representation for LLMs
CycleVAR: Repurposing Autoregressive Model for Unsupervised One-Step Image Translation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.