Improving Data Detection in Massive MIMO Systems via Exhaustive Search and SUD

In this article, we explore a new approach to detecting data in multibeam MIMO-OFDM systems, which are used in next-generation wireless networks. These systems use multiple antennas and advanced modulation techniques to improve data transmission rates and reliability. However, the increased complexity of these systems also makes them more prone to errors.
To address this challenge, we propose a low-complexity variant of joint data detection (JD), which is a well-known technique for improving data detection in multibeam MIMO-OFDM systems. JD involves analyzing the signals received by multiple antennas to identify the transmitted data symbols. By exploiting the spatial diversity offered by multibeam MIMO technology, JD can significantly improve detection accuracy compared to traditional single-antenna systems.
Our proposed variant of JD reduces the computational complexity by only analyzing a subset of the received signals that are closest to each user’s soft-estimated symbol. This approach allows us to focus on the most important signals and reduce the overall processing load, resulting in faster detection times and lower power consumption.
We evaluate the performance of our proposed variant using numerical simulations, comparing it with other state-of-the-art receivers such as maximum ratio combiner (MRC), zero-forcing (ZF), and minimum mean squared error (MMSE) receivers. Our results show that ZF and MMSE provide significant gains in terms of symbol error rate compared to MRC, and our proposed JD variant offers a substantial boost in detection accuracy compared to a naive approach that adapts the maximum-likelihood data detection to the 1-bit quantization.
In summary, this article presents a low-complexity variant of joint data detection for multibeam MIMO-OFDM systems that can significantly improve data detection accuracy while reducing computational complexity. By exploiting the spatial diversity offered by multibeam MIMO technology and focusing on the most important signals, our proposed approach offers a promising solution for next-generation wireless networks.