Multi-Antenna Channel Interpolation via Tucker Decomposed Extreme Learning Machine

Abstract

Channel interpolation is an essential technique for providing high-accuracy estimation of the channel state information (CSI) for wireless systems design where the frequency-space structural correlations of multi-antenna channel are typically hidden in matrix or tensor forms. In this letter, a modified extreme learning machine (ELM) that can process tensorial data, or ELM model with tensorial inputs (TELM), is proposed to handle the channel interpolation task. The TELM inherits many good properties from ELMs. Based on the TELM, the Tucker decomposed extreme learning machine (TDELM) is proposed for further improving the performance. Furthermore, we establish a theoretical argument to measure the interpolation capability of the proposed learning machines. Experimental results verify that our proposed learning machines can achieve comparable mean squared error (MSE) performance against the traditional ELMs but with 15% shorter running time, and outperform the other methods for a 20% margin measured in MSE for channel interpolation. Index TermsChannel interpolation, extreme learning machine, tensor decomposition.

Antenna Optimization Based on Auto-Context Broad Learning System

“…APE given by the above ( 7) is used as the performance evaluation index. We perform a grid search for the original BLS from [1,10] × [1,10] × [1,70] to determine the optimal structure (F n , M g , E n ), and the searching step is set to 1.…”

“…e modeling time of BLS is 111.87 s, however, considering that AC needs iteration if, in order to further save the training time, when executing AC on BLS, the scope of grid search can be narrowed down to [1,10] × [1,10] × [1,10], and the training time of each AC is only 26.33 s. Clearly, the total training time of ACBLS is the sum of the training time spent by the original BLS and the time required for K iterations of AC, and the optimization times of related methods are listed in Table 9.…”

“…For this case, the iteration is chosen as 8, the training time here is 111.87s(BLS) +8 * 26.33s(8 AC iterations), which is only 322.51s, compared with the direct EM simulation for optimization, and the proposed method takes much less CPU time. One set of obtained optimal S 11 solution satisfying the antenna criterion is plotted in Figure 7, and the corresponding geometric value is X � [22,21,10,12,3]mm. Meanwhile, the blue line of "HFSS" is the simulation result, and the red line of "proposed" is the prediction result.…”

“…erefore, the use of surrogate models instead of EMSS for evaluating the fitness of electromagnetic components can save much optimization time, which is currently a hot topic in electromagnetic optimization design. Many popular modeling methods have been widely used like Gaussian process (GP) [1,2], backpropagation (BP) [3], artificial neural network (ANN) [4][5][6], support vector machine (SVM) [7,8], extreme learning machine (ELM) [9,10], kernel ELM (KELM) [11], and so on. Traditional deep neural networks are generally composed of multilayer learning to mine complex knowledge and abstract data characteristics from simple concepts, which have achieved breakthrough success [12], such as deep belief networks (DBN) [13] and convolutional neural networks (CNNs) [14].…”

OP Performance Prediction for Complex Mobile Multiuser Networks Based on Extreme Learning Machine

“…Reference [26] proposed an online channel estimation and equalization scheme using fully complex ELM for OFDM systems. ELM was proposed to handle the channel interpolation task in [27].…”