|Computers, Materials & Continua
Improved MIMO Signal Detection Based on DNN in MIMO-OFDM System
1Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea
2Department of Information and Communication Engineering, Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea
3Department of Computer Engineering, Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea
*Corresponding Author: Hyoung-Kyu Song. Email: firstname.lastname@example.org
Received: 30 May 2021; Accepted: 12 July 2021
Abstract: This paper proposes the multiple-input multiple-output (MIMO) detection scheme by using the deep neural network (DNN) based ensemble machine learning for higher error performance in wireless communication systems. For the MIMO detection based on the ensemble machine learning, all learning models for the DNN are generated in offline and the detection is performed in online by using already learned models. In the offline learning, the received signals and channel coefficients are set to input data, and the labels which correspond to transmit symbols are set to output data. In the online learning, the perfectly learned models are used for signal detection where the models have fixed bias and weights. For performance improvement, the proposed scheme uses the majority vote and the maximum probability as the methods of the model combinations for obtaining diversity gains at the MIMO receiver. The simulation results show that the proposed scheme has improved symbol error rate (SER) performance without additional receive antennas.
Keywords: MIMO; DNN; ensemble machine learning; ML
In the future, studies aim to increase channel capacity in the overall wireless communication systems in order to handle explosive data traffic . Multiple-input multiple-output (MIMO) technology has been studied in the wireless communication systems since tremendous increase of channel capacity is implemented [2–7]. In the MIMO systems, accurate signal detection is one of the main issues and various researches for the MIMO detection have been proposed for efficient signal detection [8–11]. Among various MIMO detection techniques, a maximum likelihood (ML) detection has optimal error performance. The ML detection has high complexity in huge MIMO system which has several antennas at the transmitter and uses high order modulation. When the constellation size is
The machine learning algorithm is very popular area and several technologies which use the machine learning algorithm in the MIMO systems have been studied in various fields of physical layer such as signal detection, channel estimation, and solution for non-convex problems [12–15]. Among several machine learning algorithms, the deep neural network (DNN) has a structure for dense-layer based several neurons. The DNN based MIMO signal detection is attractive since it does not require new algorithms regardless of transmitter structure and it can operate without complex mechanism when the number of learning data is enough. Also, the learning is performed in the offline in advance and the complete learning model can be used in the online. Surely, the learning model is not fitted in time-variant channel since the weights and biases which are calculated in the offline learning are not no longer valid, and it is impractical for updating these parameters according to quickly varied wireless channels. Nonetheless, this paper proposes the DNN based signal detection in time-invariant channels to show the possibility of MIMO signal detector based on the DNN which has high error performance despite of high complexity for the learning. These new trials are attractive in terms of high error performance since most researches were low-complexity signal detection which has nearly the same error performance as ML detection. However, the DNN based signal detection which has only one learning model at the receiver cannot have better error performance than the ML detection in a linear system. Thus, this paper proposes the DNN based ensemble machine learning for the MIMO signal detection which is a combination of several learning models and it can improve error performance when the learning models are accurately combined since the diversity gain is obtained. The proposed scheme introduces the encoding method of appropriate training data sets for the DNN generation and the structures for the MIMO signal detector based on the ensemble machine learning.
2 MIMO Detection Technique
2.1 System Model
This paper considers downlink single-user MIMO system where one base station which has
2.2 Conventional MIMO Detection Scheme
This paper deals with the ML detection as a conventional scheme. Several low-complexity detectors which have optimal error performance have been studied. However, the past detectors are not considered as conventional schemes. The ML detection is performed by comparing the squared Euclidean distance between the received symbols and the combination of all reference symbols as follows,
3 Proposed MIMO DNN Based Ensemble Machine Learning
This paper uses the ensemble machine learning algorithm in the MIMO detector for obtaining diversity gain. The error performance for the proposed scheme is improved compared with the detector which uses only one DNN model.
3.1 Quick Overview of DNN
The DNN can be viewed as a mapping function between the input and output. Therefore, the DNN describes a function as follows,
where the mapping function
The basic structure of the fully connected DNN is shown in Fig. 2. The fully connected DNN is composed of an input layer, several hidden layers and an output layer.
3.2 MIMO Detection Based on DNN
The estimation of the transmit symbols from the received symbols is performed by using the DNN through supervised learning. The signal detection using the DNN in the MIMO system can be interpreted as a multi-class classification for detecting damaged symbols at the receiver. The accurate weights and biases in the DNN are generated through the offline learning which solves the problem between input and output values. For the MIMO detection using the DNN,
Tab. 2 represents an example of encoding of the transmit symbol in the
After the learning period, the DNN finally acts as one MIMO detector. The receiver inputs the received symbol and the channel states into the DNN based signal detection model to estimate the transmit symbols.
3.3 MIMO Detection Based on Ensemble Machine Learning
The proposed scheme uses multiple DNN models for obtaining additional performance gains. When the ensemble machine learning is used, the error performance for the MIMO detection is improved since the receiver obtains diversity gain by combining the results which are predicted by several different models. The ensemble method is an approach to make more informed decision which is made by combining multiple results from different models in an appropriate way. To implement the ensemble method, the two problems which generate multiple predictors and ensemble combination have to be considered. In the ensemble, there are many ways to construct different predictors. The property of individual classifiers which participate in ensemble combinations should be different to increase diversity gain. The proposed scheme uses random sampling and several DNN models with different structures. The random sampling repeats the random selection of training data to create several different training data sets. These training data sets create several classifiers. Therefore, different DNN structures lead
For the output value, the majority vote selects the label with the most vote as follows,
For the maximum probability, the probability of the label contains information for the models, and it can be used itself as information. The output value of the
The rule of the combination using the class probability is as follows,
The proposed scheme calculates
Fig. 4 shows a diagram which illustrates a combination of the DNN based MIMO signal detection. The
4 Simulation Results
For evaluating the performance of the proposed scheme, symbol error rate (SER) and obtained diversity gain is measured. For performance evaluations, the training data which is a form of complex number is generated by MATLAB software and all models are learned by Keras library. The used channel model in the simulations is 7 multi-path Rayleigh fading. Finally, the simulations are performed on
Fig. 5 shows the SER performances for the conventional ML and the DNN based signal detection in
For performance comparisons, the SER performance for the conventional ML detection is shown. Again, this paper does not consider required complexity for showing improvement of the error performance clearly unlike existing algorithms. The DNN based signal detection is a special version of the proposed scheme with
Fig. 6 shows the SER performances for the proposed scheme with respect to the number of combined models in
Fig. 7 shows the SER performances for the proposed scheme
For verification of performance results in Fig. 7, the obtained diversity gains are measured in Fig. 8 with same system parameters which are used in Fig. 7. In Fig. 8, the diversity gain is defined as the gain for the ensemble learning where it is calculated by dividing the value of each ensemble detector which shows different results. Therefore, the results in Fig. 8 show how much the gain is obtained by using the ensemble machine learning. The diversity gain is increased as the signal to noise ratio (SNR) increases, and the increased diversity gain leads to accurate signal detection in high SNR.
This paper proposes the ensemble machine learning based MIMO detection for high error performance. The proposed scheme suggests a possibility of machine learning in terms of performance gains in new aspects and solves the difficult part of conventional mathematical modeling and analysis in wireless communication. For the proposed scheme, the DNN based MIMO detector is introduced and the ensemble model is proposed for further improvement of the error performance. For efficient ensemble machine learning, the majority vote and maximum probability are used. The simulation results show that the proposed scheme has better SER performances than the conventional ML detection by obtaining diversity gain at the MIMO receiver. Also, the proposed scheme with the majority vote has better SER performance than the proposed scheme with the maximum probability. One of main advantages for the proposed scheme is performance improvement without additional receive antennas. Also, the proposed scheme can detect signals by using only data set. Thus, the proposed scheme can be effectively used as a highly reliable MIMO detector in wireless communication systems regardless of the structures of transmitter.
Funding Statement: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government(MSIT) (No. NRF-2021R1A2C2005777) and was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2020R1A6A1A03038540)
Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.
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