This paper proposes the multipleinput multipleoutput (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.
In the future, studies aim to increase channel capacity in the overall wireless communication systems in order to handle explosive data traffic [
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 nonconvex problems [
This paper considers downlink singleuser MIMO system where one base station which has
where
This paper deals with the ML detection as a conventional scheme. Several lowcomplexity 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,
where
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.
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
where
The basic structure of the fully connected DNN is shown in
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 multiclass 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,
Data 1  Data 2  Data 


Label 1  Label 2  Label 
Data bit (T 
Data bit (T 
Label 

00  00  0 
00  01  1 
00  10  2 
00  11  3 
01  00  4 
01  01  5 
11  11  15 
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.
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
where
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
where
The rule of the combination using the class probability is as follows,
The proposed scheme calculates
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 multipath Rayleigh fading. Finally, the simulations are performed on
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
For verification of performance results in
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.