Learning-Based One-Bit Maximum Likelihood Detection for Massive MIMO Systems with Dithering-Aided Adaptive Approach

Core Concepts

Proposing a learning-based detection framework for uplink massive MIMO systems with one-bit ADCs, utilizing dithering to overcome under-trained likelihood functions.

Abstract

The paper introduces a learning-based approach for one-bit maximum likelihood detection in massive MIMO systems. It addresses the challenges of under-trained likelihood functions by incorporating dithering signals and adaptive learning techniques. The content is structured into sections covering system models, naive detection methods, adaptive statistical learning without CSI, and extension to channel coding. Simulation results validate the proposed methods' performance in uncoded and coded scenarios. System Model: Massive MIMO systems with one-bit ADCs. Channel estimation challenges addressed through learning-based approaches. Naive Detection: Counting-based method for estimating likelihood probabilities. Under-trained likelihood functions identified as a critical issue at high SNR. Adaptive Statistical Learning: Dither-and-learning technique introduced to prevent under-trained likelihood functions. Adaptive dithering power update based on feedback from observations. Extension to Channel Coding: Introduction of frame structure for channel-coded communication frameworks. Calculation of soft metrics using trained likelihood probabilities for LLR computation in decoding process.

Stats

The proposed method aims to address the challenges of under-trained likelihood functions by incorporating dithering signals and adaptive learning techniques. The number of under-trained likelihood functions among 2Nr likelihood functions is evaluated for Nu = 4 users, 4-QAM, Nr = 32 antennas, and Ntr = 45 pilot signals with Rayleigh channels. The proposed adaptive dither-and-learning (ADL) method divides the training period into Ns ∈{1, 3, 5} sub-blocks for feedback-driven updates of dithering power.

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Key Insights Distilled From

Learning-Based One-Bit Maximum Likelihood Detection for Massive MIMO Systems

by Yunseong Cho... at arxiv.org 03-25-2024

https://arxiv.org/pdf/2304.07696.pdf

Deeper Inquiries

How can the proposed learning-based approach be adapted to different modulation schemes

The proposed learning-based approach can be adapted to different modulation schemes by adjusting the constellation mapping function and the likelihood computation based on the specific characteristics of each scheme. For example, for higher-order modulation schemes like 16-QAM or 64-QAM, the constellation mapping function would map multiple bits to a single symbol, requiring adjustments in the likelihood computation to account for these mappings. Additionally, the training duration may need to be optimized differently for different modulation schemes based on their complexity and signal-to-noise ratio requirements.

What are the implications of reducing the training duration on overall system performance

Reducing the training duration can have several implications on overall system performance. Firstly, a shorter training duration may lead to an increase in under-trained likelihood functions, which can result in degraded detection performance due to inaccurate probability estimations. This could potentially increase symbol error rates and decrease system reliability. Secondly, reducing training duration may limit the ability of the system to adapt and learn from varying channel conditions effectively, impacting robustness and adaptability. Therefore, finding a balance between training duration and accuracy is crucial for optimizing system performance.

How does the use of dithering signals impact energy efficiency in massive MIMO systems

The use of dithering signals impacts energy efficiency in massive MIMO systems by enabling more efficient data detection with one-bit ADCs while maintaining reliable communication quality. By adding dithering signals before quantization during pilot transmissions, it helps prevent under-trained likelihood functions without significantly increasing power consumption. The adaptive adjustment of dithering power through feedback-driven methods optimizes its impact on signal reception while minimizing unnecessary noise generation that could lead to increased energy consumption. Overall, incorporating dithering signals enhances energy efficiency by improving detection accuracy without compromising system performance.