AI & ML
Spectral Efficiency Analysis of Massive MIMO-OFDM Systems with Double Quantization
arXiv math.IT0 views
arXiv:2610.10032v1 Announce Type: new Abstract: Massive multiple-input multiple-output (MIMO) achieves a high spectral efficiency (SE) by employing a large number of receive antennas in the uplink. Employing many receive antennas requires a large number of analog-to-digital converters (ADCs) and a high fronthaul data rate. Low-resolution ADCs can reduce power consumption and cost, while fronthaul quantization can reduce the required fronthaul data rate, at the expense of quantization distortion at both stages. In wideband orthogonal frequency-division multiplexing systems, ADC quantization is performed in the time domain, while fronthaul quantization may be applied after the discrete Fourier transform to transmit only active subcarriers. In this study, we investigate the achievable uplink SE over fronthaul links under double quantization and imperfect channel estimation. We derive a linear minimum mean-squared error channel estimator from the double-quantized pilot observations and obtain an achievable SE using the use-and-then-forget bound. Numerical results demonstrate that the ADC and fronthaul quantization resolutions have comparable impacts on the achievable SE. When the two resolutions differ, the lower resolution becomes the dominant factor limiting the SE. Furthermore, our numerical results show that six-bit ADC and fronthaul quantization achieve more than 90% of the SE attained in the ideal case.
Source:https://arxiv.org/abs/2610.10032PreviousThe Price of Privacy: Randomness Complexity of Graph-Based Multi-Secret SharingNextVariational Bayesian Inference Based Progressive Multi-Target MIMO Sensing with Nuisance Parameters
Related posts

Use it, or lose it 🧠
0dev.to AI

AI Daily Digest November 3, 2026: OpenAI Wires Agents into ServiceNow, Australia Books a 40,000-GPU AI Factory
0dev.to AI
How Publishers Can Test Engagement Signals to Improve On-Site Discovery
0dev.to AI
What Meta's Muse actually reads: first data from 3 tiny businesses
0dev.to WebDev
Your AI apps should answer to you, not to their lab
0dev.to Security

Warp's Breach Reached Agent Decision Logs. What One Agent Trace Actually Contains
0dev.to Security