Low-Complexity and High-Resolution DOA Estimation for Hybrid Analog and Digital Massive MIMO Receive Array (1712.02085v1)

Abstract: A large-scale fully-digital receive antenna array can provide very high-resolution direction of arrival (DOA) estimation, but resulting in a significantly high RF-chain circuit cost. Thus, a hybrid analog and digital (HAD) structure is preferred. Two phase alignment (PA) methods, HAD PA (HADPA) and hybrid digital and analog PA (HDAPA), are proposed to estimate DOA based on the parametric method. Compared to analog phase alignment (APA), they can significantly reduce the complexity in the PA phases. Subsequently, a fast root multiple signal classification HDAPA (Root-MUSIC-HDAPA) method is proposed specially for this hybrid structure to implement an approximately analytical solution. Due to the HAD structure, there exists the effect of direction-finding ambiguity. A smart strategy of maximizing the average receive power is adopted to delete those spurious solutions and preserve the true optimal solution by linear searching over a set of limited finite candidate directions. This results in a significant reduction in computational complexity. Eventually, the Cramer-Rao lower bound (CRLB) of finding emitter direction using the HAD structure is derived. Simulation results show that our proposed methods, Root-MUSIC-HDAPA and HDAPA, can achieve the hybrid CRLB with their complexities being significantly lower than those of pure linear searching-based methods, such as APA.

• The paper develops several low-complexity, high-resolution DOA estimation techniques specifically for hybrid analog and digital massive MIMO receive arrays.
• Proposed methods like HADPA, HDAPA, and Root-MUSIC-HDAPA significantly reduce computational complexity compared to traditional techniques, achieving orders of magnitude improvement.
• Numerical analysis shows these methods can achieve performance near the hybrid Cramer-Rao Lower Bound, offering efficient solutions suitable for real-time applications like 5G and IoT.

Analysis of DOA Estimation Techniques for Hybrid Analog and Digital MIMO Arrays

The paper “Low-Complexity and High-Resolution DOA Estimation for Hybrid Analog and Digital Massive MIMO Receive Array” investigates methods for optimizing Direction of Arrival (DOA) estimates in hybrid analog and digital (HAD) massive Multiple Input Multiple Output (MIMO) systems. This paper is significant given the importance of precise DOA estimation in applications ranging from wireless communications to radar and sonar systems. The primary challenge addressed is the high cost associated with fully-digital receive antenna arrays and the complexity involved in high-resolution DOA estimation.

Key Contributions and Methods

The novelty of this work is the development and assessment of several DOA estimation techniques tailored to HAD structures, specifically designed to balance computational complexity and estimation accuracy. Three notable methods are proposed:

1. HADPA and HDAPA Methods: These are aimed at reducing complexity in phase alignment by an order of magnitude compared to conventional analog phase alignment (APA). The HADPA method lowers computational requirements from $O(KM)$ to $O(K+M)$, where $K$ denotes the number of subarrays and $M$ the number of antennas per subarray. HDAPA further reduces the complexity by leveraging the periodic nature of digital array structures, confining the search for optimal solutions to a finite set of candidate directions.
2. Root-MUSIC-HDAPA Algorithm: This method offers significant computational savings and improves search efficiency by providing an approximately analytical solution. Incorporating efficient linear searching, this scheme allows for ambiguity resolution in direction-finding, yielding a significant reduction in the overall complexity compared to methods purely based on linear searches like APA.
3. Derivation of the Hybrid Cramer-Rao Lower Bound (CRLB): The theoretical groundwork laid by deriving the hybrid CRLB provides a benchmark for DOA estimation performance under the HAD framework, ensuring the methods achieve estimation accuracy potentially close to the ideal CRLB as signal-to-noise ratio (SNR) increases.

Numerical and Analytical Insights

The numerical analysis and simulations presented in the paper show that the HADPA and HDAPA methods can achieve the hybrid CRLB with lower complexity than traditional methods. Specifically, Root-MUSIC-HDAPA demonstrates its effectiveness by approaching CRLB with substantially less computational effort due to its reduced search space and strategic combination of Root-MUSIC and limited exhaustive search.

An evaluation of the computational complexity against APA, HADPA, and HDAPA illustrates that the Root-MUSIC-HDAPA method is, by a considerable margin, the most efficient algorithm in terms of both complexity and speed, making it highly suitable for real-time applications.

Future Implications and Developments

The implications of this research are profound, offering potential improvements in various fields that rely on high-resolution DOA estimation, including internet of things (IoT), UAV navigation, and advanced 5G communication systems. The hybrid CRLB offers a robust framework for assessing the limitations and optimal configurations of hybrid systems, providing a foundational reference for subsequent improvements in MIMO technology.

Given the efficiency gains demonstrated, future research is encouraged to further investigate hybrid systemic approaches in MIMO applications and explore advanced configuration possibilities to reduce existing constraints. The integration of HAD architectures in next-generation wireless communication systems stands as a promising area for ongoing inquiry and development.