CHINACOM 2015, the Tenth EAI International Conference on Communications and Networking in China, took successfully place in August, in Shanghai. Among the researches presented at the conference, the paper by Wei Xiao, Peng Wei and Jiang Tao won one of the six Best Paper Awards.
‘Correlation Based Direction of Arrival Estimation for Large Scale Multi-user MIMO Systems’ is the title of the study, which proposes a correlation-based low complexity DOA estimation method aiming at obtaining a better performance than the existing methods with a much lower computational complexity.
State-of-the-art Due to its ability to improve the system energy efficiency (EE) and spectrum efficiency (SE), MIMO technology is one of the key-element of the future wireless communication. Although the direction of arrival (DOA) estimation can help to improve the performance of MIMO systems, when large-scale MIMO are considered, the DOA estimation becomes very difficult due to its large dimensions. Among the existing methods for DOA estimation, the estimation of signal parameters via rotational invariance techniques (ESPRIT) has been estimated the more efficient so far; however, its performance decreases the higher the number of computable parameters grows.
A new correlation-based system The team led by Wei Xiao presented a DOA estimation method, which is based on the assumption that correlation exists between the neighboring antennas, caused by the limited space. Through a comparative study with the ESPRIT method, the results show that:
- The performances of both the ESPRIT method and the proposed DOA estimation method improve when more samples are used for the DOA estimation
- The proposed DOA estimation method can achieve a much better performance than the ESPRIT method, even with a smaller number of samples
Based on the observation exposed in the paper, the proposed DOA estimation method calculates the phase offset between the neighboring antennas, and then evaluate the DOA of each user, proving to be an unbiased estimator.
The full paper will be available online, on the European Union Digital Library (EUDL)
