[Paper Review] Efficient MIMO Detection with Imperfect Channel Knowledge - A Deep Learning Approach

Qian Chen, Shunqing Zhang+2 more|arXiv (Cornell University)|Mar 19, 2019

Advanced MIMO Systems Optimization19 references4 citations

TL;DR

This paper proposes a deep neural network (DNN)-based MIMO detection method that achieves superior trade-offs between detection accuracy and computational complexity, especially under imperfect channel state information (CSI). By directly learning the end-to-end mapping from received signals to transmitted bits using a DNN, the approach outperforms conventional ZF, MMSE, and DetNet methods in both BER performance and robustness to CSI imperfections, while maintaining near-ZF throughput levels.

ABSTRACT

Multiple-input multiple-output (MIMO) system is the key technology for long term evolution (LTE) and 5G. The information detection problem at the receiver side is in general difficult due to the imbalance of decoding complexity and decoding accuracy within conventional methods. Hence, a deep learning based efficient MIMO detection approach is proposed in this paper. In our work, we use a neural network to directly get a mapping function of received signals, channel matrix and transmitted bit streams. Then, we compare the end-to-end approach using deep learning with the conventional methods in possession of perfect channel knowledge and imperfect channel knowledge. Simulation results show that our method presents a better trade-off in the performance for accuracy versus decoding complexity. At the same time, better robustness can be achieved in condition of imperfect channel knowledge compared with conventional algorithms.

Motivation & Objective

• To address the challenge of robust MIMO detection under imperfect channel state information (CSI), a common issue in practical 5G and LTE systems.
• To develop an efficient deep learning framework that reduces decoding complexity while maintaining high detection accuracy compared to conventional linear and non-linear MIMO detectors.
• To evaluate the generalization capability of deep learning models in handling imperfect CSI, where traditional methods suffer significant performance degradation.
• To compare DNN and CNN architectures for MIMO detection and identify the more effective and efficient approach.
• To demonstrate that the proposed DNN-based method achieves near-ZF throughput with significantly better BER performance than conventional schemes.

Proposed method

• A deep neural network (DNN) is trained to learn the non-linear mapping from received signals and channel matrix estimates to the detected bit streams, bypassing explicit channel inversion or iterative detection.
• The DNN is trained end-to-end using labeled data generated from known transmitted symbols and simulated fading channels, enabling it to generalize across imperfect CSI conditions.
• The network architecture is designed to be lightweight, with fully connected layers, and avoids the computational overhead of convolutional layers used in prior works like DetNet.
• The method relies solely on receiver-side knowledge, including the received signal vector and estimated channel matrix, without requiring training data from the transmitter.
• Performance is evaluated using bit error rate (BER) and throughput metrics under both perfect and imperfect CSI scenarios.
• The DNN is compared against ZF, MMSE, ML, and DetNet using QPSK and BPSK modulations in 2×2 and 4×4 MIMO systems.

Experimental results

Research questions

• RQ1 Can a deep learning-based MIMO detection method achieve better BER performance than conventional ZF and MMSE detectors under imperfect CSI?
• RQ2 Does a DNN-based approach offer superior robustness to channel estimation errors compared to traditional detection algorithms?
• RQ3 How does the DNN-based method compare in terms of decoding complexity and throughput to ML and ZF schemes?
• RQ4 Is the DNN-based method more effective than prior deep learning approaches like DetNet in handling imperfect CSI?
• RQ5 Can a simpler DNN architecture outperform more complex CNN-based models in MIMO detection while maintaining low computational complexity?

Key findings

• For a BER of 10⁻³, the DNN-based method outperforms DetNet by 4.5 dB in a 2×2 MIMO system with QPSK modulation under perfect CSI.
• In 4×4 MIMO with BPSK, the DNN-based method achieves a 3.5 dB gain over DetNet at a BER of 2×10⁻³, even when using imperfect CSI.
• Under imperfect CSI, the DNN-based method outperforms ZF and MMSE by more than 4.5 dB at BER = 10⁻² in 2×2 MIMO with QPSK.
• The DNN-based method achieves a throughput of 4.8×10⁴ Kbps in 4×4 MIMO with BPSK, matching the ZF method and significantly exceeding the ML method (8.8×10³ Kbps).
• The DNN-based method achieves better BER performance than DetNet even when DetNet is trained with perfect CSI, demonstrating superior robustness to CSI imperfections.
• The DNN-based method maintains stable performance across varying CSI quality, with minimal BER fluctuation compared to ML, which suffers significant performance loss under imperfect CSI.