In this paper, we compare two artificial neural networks (ANNs) approaches designed to perform channel equalization for millimeter-wave (mmWave) signals operating in the 28 GHz frequency band. We used an in-house deterministic Three-Dimensional Ray-Launching (3D-RL) code to simulate the spatial structure of mmWave channels considering the material properties of the obstacles within the scenario at the frequency under analysis. We performed offline training of a multilayer perceptron (MLP) neural network with the simulated mmWave channels to equalize the received signal. We also performed online training of an extreme learning machine (ELM) neural network to directly get the equalized symbols at the receiver, given as input the received mmWave signal. The ANN solutions were tested in terms of the achievable spectral efficiency, bit error rate, and time to process. We compared the ANN techniques to the minimum mean square error and the zero-forcing equalizers, considering an orthogonal frequency-division multiplexing communication based on the 5G New Radio standard. We present numerical results on the performance of the proposed ANNs and show that the ELM strategy outperforms the MLP method, requiring significantly less processing time than the reviewed equalization methods.

In this work, a comparative analysis is performed to study the bit error rate and error vector magnitude achieved with the least‐squares (LS), the minimum mean squared error (MSE), and the Kalman filter (KF) channel estimators when these are applied to the maximum‐ratio combining (MRC) and the regularised zero‐forcing (RZF) receivers. The MSE achieved with the different channel estimators was also compared by varying the noise and interference power at the receiver. The proposed methodology relies on the characterisation of a massive multiple‐input multiple‐output (MIMO) channel with a quasi‐deterministic radio channel generator and a cyclic prefix orthogonal frequency division multiplexing link‐level radio simulation. The fifth‐generation (5G) new radio (NR) frame structure was used to perform channel estimation and equalisation for operation frequencies below 6 GHz. Numerical results show that the MRC receiver achieves its maximum performance with the KF estimator, especially at low signal‐to‐noise ratio scenarios, while the RZF receiver achieves its maximum performance with the LS estimation even in high interference scenarios.

Reliable wireless communications are crucial for ensuring workers’ safety in underground tunnels and mines. Visible light communications (VLC) have been proposed as auxiliary systems for short-range wireless communications in underground environments due to their seamless availability, immunity to electromagnetic interference, and illumination capabilities. Although multiple VLC channel models have been proposed for underground mines (UM) so far, none of these models have considered the wavelength dependence of the underground mining VLC channel (UM-VLC). In this paper, we propose a single-input, single-output (SISO), wavelength-dependent UM-VLC channel model considering the wavelength dependence of the light source, reflections, light scattering, and the attenuation due to dust and the photodetector. Since wavelength dependence allows us to model VLC systems more accurately with color-based modulation, such as color-shift keying (CSK), we also propose a wavelength-dependent CSK-based UM-VLC channel model. We define a simulation scenario in an underground mine roadway and calculate the received power, channel impulse response (CIR), signal-to-noise ratio (SNR), signal-to-interference ratio (SIR), root mean square (RMS) delay, and bit error rate (BER). For comparison, we also calculate these parameters for a monochromatic state-of-the-art UM-VLC channel and use it as a reference channel. We find that the inclusion of wavelength-dependency in CSK-based UM-VLC systems plays a significant role in their performance, introducing color distortion that the color calibration algorithm defined in the IEEE 802.15.7 VLC standard finds harder to revert than the linear color distortion induced by monochromatic CSK channels.

An extreme learning machine (ELM)-based receiver for multi-user massive MIMO systems is introduced. The proposed ELM combining method, defined in the complex plane, is designed to directly perform MIMO combining processing to the received uplink signals, based on the adoption of the pilot symbols as training data. Numerical results show that by appropriately setting the number of hidden neurons, the ELM achieves higher spectral efficiency and smaller BER, with fewer floating-point operations than the conventional linear MIMO receivers, namely the minimum mean squared error and maximum ratio receivers.

In this paper, we study the problem of optimizing the performance of multi-user millimeter-wave (mmWave) communications in three steps. The first one is given by the use of a new pilot mapping to reduce the inter-user interference effect and to perform more accurate channel estimation. In the second step, we designed a hybrid receiver that, based on the accuracy of the channel state information, chooses between the minimum mean square error (MMSE) and the multi-user regularized zero-forcing beamforming (RZFBF) receivers, to combine/precode the massive multiple-input multiple-output (MIMO) signal. In the third step, we propose to improve the beam direction with a slight change in the azimuth angle during the uplink communications to increase the multi-user efficiency and reduce inter-user interference. Numerical results show the performance increase using the proposed solutions in terms of the spectral efficiency by comparing the MMSE, RZFBF, and hybrid receivers.