Impact Analysis of Antenna Array Geometry on Performance of Semi-blind Structured Channel Estimation for massive MIMO-OFDM systems
Do Hai Son, Tran Thi Thuy Quynh
TL;DR
The paper addresses channel estimation accuracy in massive MIMO-OFDM and investigates how antenna array geometry affects performance under a structured channel model in both training-based and semi-blind settings. It develops CRB-based analyses for both unstructured and structured channel models, deriving OP and SB Fisher information expressions and leveraging a 3D antenna geometry comparison between Uniform Linear Arrays ($N_t$, $N_r$, $K$) and Uniform Cylindrical Arrays. A key contribution is the SB CRB derivation for UCyA, demonstrating that structured modeling combined with SB estimation yields lower error bounds than traditional methods, particularly as the array becomes more geometrically rich. The results indicate substantial reductions in channel estimation error when employing a 3D UCyA geometry with SB estimation, offering practical benefits for reducing pilot overhead and enhancing reliability in massive MIMO-OFDM deployments.
Abstract
Channel estimation is always implemented in communication systems to overcome the effect of interference and noise. Especially, in wireless communications, this task is more challenging to improve system performance while saving resources. This paper focuses on investigating the impact of geometries of antenna arrays on the performance of structured channel estimation in massive MIMO-OFDM systems. We use Cram'er Rao Bound to analyze errors in two methods, i.e., training-based and semi-blind-based channel estimations. The simulation results show that the latter gets significantly better performance than the former. Besides, the system with Uniform Cylindrical Array outperforms the traditional Uniform Linear Array one in both estimation methods.