Reliable and Low-Complexity MIMO Detector Selection using Neural Network
This work addresses efficiency in wireless communication systems, offering a domain-specific incremental improvement by optimizing detector selection to reduce computational load.
The paper tackles the problem of minimizing computational complexity in MIMO detection for 5G NR/LTE systems by dynamically selecting detectors per resource element using a neural network, achieving a ~10X reduction in complexity while maintaining error rates close to those of a high-complexity baseline.
In this paper, we propose to dynamically select a MIMO detector using neural network for each resource element (RE) in the transport block of 5G NR/LTE communication system. The objective is to minimize the computational complexity of MIMO detection while keeping the transport block error rate (BLER) close to the BLER when dimension-reduced maximum-likelihood (DR-ML) detection is used. A detector selection problem is formulated to achieve this objective. However, since the problem is high dimensional and NP-hard, we first decompose the problem into smaller problems and train a multi-layer perceptron (MLP) network to obtain the solution. The MLP network is trained to select a low-complexity, yet reliable, detector using instantaneous channel condition in the RE. We first propose a method to generate a labeled dataset to select a low-complexity detector. Then, the MLP is trained twice using quasi-Newton method to select a reliable detector for each RE. The performance of online detector selection is evaluated in 5G NR link level simulator in terms of BLER and the complexity is quantified in terms of the number of Euclidean distance (ED) computations and the number of real additions and multiplication. Results show that the computational complexity in the MIMO detector can be reduced by ~10X using the proposed method.