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Near Maximum-Likelihood Detector and Channel Estimator for Uplink Multiuser Massive MIMO Systems with One-Bit ADCs (1507.04452v3)

Published 16 Jul 2015 in cs.IT and math.IT

Abstract: In massive multiple-input multiple-output (MIMO) systems, it may not be power efficient to have a high-resolution analog-to-digital converter (ADC) for each antenna element. In this paper, a near maximum likelihood (nML) detector for uplink multiuser massive MIMO systems is proposed where each antenna is connected to a pair of one-bit ADCs, i.e., one for each real and imaginary component of the baseband signal. The exhaustive search over all the possible transmitted vectors required in the original maximum likelihood (ML) detection problem is relaxed to formulate an ML estimation problem. Then, the ML estimation problem is converted into a convex optimization problem which can be efficiently solved. Using the solution, the base station can perform simple symbol-by-symbol detection for the transmitted signals from multiple users. To further improve detection performance, we also develop a two-stage nML detector that exploits the structures of both the original ML and the proposed (one-stage) nML detectors. Numerical results show that the proposed nML detectors are efficient enough to simultaneously support multiple uplink users adopting higher-order constellations, e.g., 16 quadrature amplitude modulation. Since our detectors exploit the channel state information as part of the detection, an ML channel estimation technique with one-bit ADCs that shares the same structure with our proposed nML detector is also developed. The proposed detectors and channel estimator provide a complete low power solution for the uplink of a massive MIMO system.

Citations (412)

Summary

  • The paper presents a near maximum-likelihood detector that uses convex optimization to approximate optimal ML performance with reduced complexity.
  • It introduces a two-stage detection process that refines candidate vectors and achieves lower symbol error rates compared to traditional linear detectors.
  • The study also proposes a complementary channel estimator that accurately recovers channel direction and norm from one-bit ADC signals, enhancing overall system efficiency.

Near Maximum-Likelihood Detector and Channel Estimator for Uplink Multiuser Massive MIMO Systems with One-Bit ADCs

This paper explores the deployment of low-resolution analog-to-digital converters (ADCs) specifically, one-bit ADCs in massive multiple-input multiple-output (MIMO) systems. This approach to signal detection and channel estimation is particularly motivated by the potential to reduce power consumption at the base station, which traditionally requires high-power ADCs to handle high-resolution data.

Key Contributions

The primary contribution of this work is the development of a near maximum likelihood (nML) detector for uplink multiuser massive MIMO systems with one-bit ADCs. The nML detector circumvents the computational complexity of conventional maximum likelihood (ML) detectors, which becomes prohibitive as the number of users increases. Instead, the proposed nML detector employs convex optimization techniques to achieve performance that closely approximates the optimal ML detector by relaxing the constraint of maximum likelihood estimation to a simpler norm-constrained formulation. This relaxation allows the detection process to be framed as a solvable convex optimization problem, rather than an exhaustive search.

To enhance the detection performance further, a two-stage nML detector is introduced. This approach first applies the one-stage nML detector to narrow down potential transmitted vectors and then uses a refined ML strategy on the reduced set of candidates to approach the performance of the original ML detector more closely.

Additionally, the paper proposes an associated channel estimation technique that aligns structurally with the nML detector. This method can estimate the channel's direction and norm, thus fully complementing the detection mechanism with minimal additional complexity.

Numerical Results and Implications

The numerical simulations demonstrate that the nML detector efficiently supports multiple simultaneous uplink users, accommodating complex modulation schemes such as 16 QAM. This detector, when compared to linear alternatives like Zero-Forcing (ZF), achieves superior symbol error rates, particularly as the number of antennas increases - a key advantage inherent in massive MIMO configurations.

The findings highlight a couple of interesting insights:

  1. Error Floors: While all detectors suffer from error floors due to quantization, the proposed nML detector typically results in lower error floors than its ZF counterparts.
  2. Extensibility: The two-stage nML detector leverages large antenna arrays to achieve near-optimal performance, implying practical usefulness as MIMO systems scale.
  3. Channel Estimation: An effective channel estimation method is demonstrated, capable of utilizing one-bit quantized signals, which addresses a key challenge of implementing low-resolution ADC systems.

Broader Impact and Future Directions

The proposed framework offers significant implications for future development in low-power wireless communication systems, particularly in millimeter wave and beyond-5G implementations where data sampling rates further exacerbate ADC power issues. By providing a computationally feasible solution for one-bit quantization, this work pioneers advancements in making massive MIMO systems more viable and efficient.

Future developments may incline towards extending the proposed nML methodologies to systems with frequency-selective fading channels, improving robustness against such conditions. Additionally, integrating these detectors with advanced channel coding and error correction schemes, or applying them in mixed-resolution ADC environments, could further optimize performance and energy efficiency.

Overall, the paper establishes a foundational approach to energy-efficient signal processing in large-scale MIMO systems, underpinning its importance in the continuing evolution of wireless communication network architectures.