Bayes-Optimal Joint Channel-and-Data Estimation for Massive MIMO with Low-Precision ADCs (1507.07766v2)

Abstract: This paper considers a multiple-input multiple-output (MIMO) receiver with very low-precision analog-to-digital convertors (ADCs) with the goal of developing massive MIMO antenna systems that require minimal cost and power. Previous studies demonstrated that the training duration should be {\em relatively long} to obtain acceptable channel state information. To address this requirement, we adopt a joint channel-and-data (JCD) estimation method based on Bayes-optimal inference. This method yields minimal mean square errors with respect to the channels and payload data. We develop a Bayes-optimal JCD estimator using a recent technique based on approximate message passing. We then present an analytical framework to study the theoretical performance of the estimator in the large-system limit. Simulation results confirm our analytical results, which allow the efficient evaluation of the performance of quantized massive MIMO systems and provide insights into effective system design.

• The paper proposes a Bayes-optimal joint channel-and-data (JCD) estimation method for massive MIMO systems using low-precision ADCs, leveraging approximate message passing techniques.
• An analytical framework based on the replica method demonstrates that the Bayes-optimal estimator decouples the system into scalar AWGN channels for performance analysis.
• Simulations show the Bayes-optimal JCD estimator significantly outperforms pilot-based schemes in quantized MIMO, although it requires longer training sequences.

Overview of Bayes-Optimal Joint Channel-and-Data Estimation for Massive MIMO with Low-Precision ADCs

The paper "Bayes-Optimal Joint Channel-and-Data Estimation for Massive MIMO with Low-Precision ADCs" by Wen et al. explores the challenges and solutions for massive multiple-input multiple-output (MIMO) systems operating with very low-precision analog-to-digital converters (ADCs). The focus of the research is on improving channel estimation and data detection in such systems.

In high-dimensional massive MIMO systems, hardware complexity and power consumption grow substantially, especially with the precision of ADCs. As a reaction to these challenges, there's an interest in using low-cost, low-precision ADCs. However, coarse quantization poses significant problems for communication theories and existing signal processing techniques that are designed for high-resolution scenarios.

Joint Channel-and-Data Estimation (JCD)

To tackle the limitations imposed by low-precision ADCs, the paper proposes a joint channel-and-data (JCD) estimation method. The method leverages Bayes-optimal inference to minimize the mean square error (MSE) of estimations using approximate message passing (AMP) techniques. By implementing a version of the generalized approximate message passing (BiG-AMP) algorithm, the researchers approximate the marginal distributions of both data and channel components, thus making the theoretical optimal estimator practically achievable.

Analytical and Simulation Framework

The authors present an analytical framework based on the replica method from statistical physics to assess the performance of their Bayes-optimal estimator in the large-system limit. The analyses show that the input and output relations within a quantized MIMO system employing the Bayes-optimal JCD estimator can be decoupled into scalar additive white Gaussian noise (AWGN) channels. This decoupling simplifies the evaluation of performance metrics such as the symbol error rate (SER) and MSE. The results are validated via simulations, which confirm the practical applicability and effectiveness of the proposed techniques in designing quantized MIMO systems.

Results and Implications

• Quantization Effects: The paper highlights that reducing ADC precision to as low as 1-3 bits significantly affects performance, emphasizing the need for more sophisticated channel estimation techniques like JCD.
• Numerical Results: Simulations demonstrate that the Bayes-optimal JCD estimator outperforms pilot-based schemes dramatically in quantized MIMO systems, even when very low quantization (e.g., 1-bit) is used.
• Training Length: The work indicates that longer training sequences (approximately 50 times the number of users in 1-bit quantization scenarios) are necessary to achieve desirable performance.
• Decoupling Principle: The decoupling into scalar AWGN channels discussed in the paper can streamline the design of future communication systems relying on quantized MIMO, facilitating straightforward performance analysis and system configuration.

Future Directions

The presented framework opens up several avenues for further research. With the evidence that JCD estimation can efficiently operate under extreme quantization conditions, future work might explore the implementation of mixed-precision systems where low-precision and high-precision ADCs are used collaboratively to balance performance and cost. Furthermore, optimizing the training strategies, quantization step sizing, and integration with adaptive algorithms could yield further practical gains, even extending the applicability to other wireless communication settings like millimeter-wave bands.

In summary, this paper addresses significant design challenges in massive MIMO systems with low-precision ADCs using advanced statistical inference methodologies, offering insightful theoretical analysis and practical performance verification.