In this work, we investigate the probability distribution function of the channel fading between a base station, an array of intelligent reflecting elements, known as large intelligent surfaces (LIS), and a single-antenna user. We assume that both fading channels, i.e., the channel between the base station and the LIS, and the channel between the LIS and the single user are Nakagami-m distributed. Additionally, we derive the exact bit error probability considering quadrature amplitude (M-QAM) and binary phase-shift keying (BPSK) modulations when the number of LIS elements, n, is equal to 2 and 3. We assume that the LIS can perform phase adjustment, but there is a residual phase error modeled by a Von Mises distribution. Based on the central limit theorem, and considering a large number of reflecting elements, we also present an accurate approximation and upper bounds for the bit error rate. Through several Monte Carlo simulations, we demonstrate that all derived expressions perfectly match the simulated results. INDEX TERMS Bit error rate, large intelligent surfaces, massive MIMO, Nakagami-m fading, von Mises circular distribution.

Channel estimation is crucial for massive massive multiple-input multiple-output (MIMO) systems to scale up multi-user (MU) MIMO, providing great improvement in spectral and energy efficiency. This paper presents a simple and practical channel estimator for multi-cell MU massive MIMO time division duplex (TDD) systems with pilot contamination in flat Rayleigh fading channels, i.e., the gains of the channels follow the Rayleigh distribution. We also assume uncorrelated antennas. The proposed estimator addresses performance under moderate to strong pilot contamination without previous knowledge of the cross-cell large-scale channel coefficients. This estimator performs asymptotically as well as the minimum mean square error (MMSE) estimator with respect to the number of antennas. An approximate analytical mean square error (MSE) expression is also derived for the proposed estimator.

Radio spectrum has become a scarce commodity due to the advent of several non-collaborative radio technologies that share the same spectrum. Recognizing a radio technology that accesses the spectrum is fundamental to define spectrum management policies to mitigate interference. State-of-the-art approaches for technology recognition using machine learning are based on supervised learning, which requires an extensive labeled data set to perform well. However, if the technologies and their environment are entirely unknown, the labeling task becomes time-consuming and challenging. In this work, we present a Semisupervised Learning (SSL) approach for technology recognition that exploits the capabilities of modern Software Defined Radios (SDRs) to build large unlabeled data sets of IQ samples but requires only a few of them to be labeled to start the learning process. The proposed approach is implemented using a Deep Autoencoder, and the comparison is carried out against a Supervised Learning (SL) approach using Deep Neural Network (DNN). Using the DARPA Colosseum test bed, we created an IQ sample data set of 16 unknown radio technologies and obtain a classification accuracy of > 97% using the entire labeled data set using both approaches. However, the proposed SSL approach achieves a classification accuracy of ≥ 70% while using only 10% of the labeled data. This performance is equivalent to 4.6x times better classification accuracy than the DNN using the same reduced labeled data set. More importantly, the proposed approach is more robust than the DNN under corrupted input, e.g., noisy signals, which gives us to 2x and 3x better accuracy at Signal-to-Noise Ratio (SNR) of -5 dB and 0 dB, respectively.

It is known that the Central Limit Theorem (CLT) is not always the most appropriate tool for deriving closed-form expressions. We evaluate a Single-Input Single-Output (SISO) system performance in which the Large Intelligent Surface (LIS) acts as a scatterer. The direct link between the transmitting and receiving devices is negligible. Quantization phase errors are considered since the high precision configuration of the reflection phases is not always feasible. We derive exact closed-form expressions for the spectral efficiencies, outage probabilities, and average symbol error rate (SER) of different modulations. We assume a more comprehensive scenario in which $b$ bits are dedicated to the LIS elements' phase adjustment. From Monte Carlo simulations, we prove the excellent accuracy of our approach and investigate the behavior of power scaling law and power required to reach a specific capacity, depending on the number of reflecting elements. We show that the LIS with approximately fifty elements and four dedicated bits for phase quantization outperforms the conventional system performance without LIS.

With a growing number of connected devices relying on the Industrial, Scientific, and Medical radio bands for communication, spectrum scarcity is one of the most important challenges currently and in the future. The existing collision avoidance techniques either apply a random back-off when spectrum collision is detected or assume that the knowledge about other nodes' spectrum occupation is known. While these solutions have shown to perform reasonably well in intra-Radio Access Technology environments, they can fail if they are deployed in dense multi-technology environments as they are unable to address the inter-Radio Access Technology interference. In this paper, we present Spectrum Prediction Collision Avoidance (SPCA): an algorithm that can predict the behavior of other surrounding networks, by using supervised deep learning; and adapt its behavior to increase the overall throughput of both its own Multiple Frequencies Time Division Multiple Access network as well as that of the other surrounding networks. We use Convolutional Neural Network (CNN) that predicts the spectrum usage of the other neighboring networks. Through extensive simulations, we show that the SPCA is able to reduce the number of collisions from 50% to 11%, which is 4.5 times lower than the regular Multiple Frequencies Time Division Multiple Access (MF-TDMA) approach. In comparison with an Exponentially Weighted Moving Average (EWMA) scheduler, SPCA reduces the number of collisions from 29% to 11%, which is a factor 2.5 lower. INDEX TERMS Collaborative wireless networks, deep learning, machine learning, wireless MAC.

The advances mobile communications has seen in recent years has rendered the radio spectrum a limited and, hence, an expensive resource. Therefore, technologies that support unlicensed access to spectrum are needed. Therefore, the adoption of novel modulation schemes becomes of utmost importance to obtain better spectral-localization and reduce the OOBE (\textit{Out of Band Emission}) inherent to OFDM (\textit{Orthogonal Frequency Division Multiplexing}) and, consequently, mitigating the interference between secondary (\textit{unlicensed}) and primary users. In this scenario, we assess the gain in the bit error probability using f-OFDM (\textit{filtered-OFDM}) in MIMO systems, both used in the 5G RANGE project.

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This paper evaluates the feasibility of applying massive multiple-input multiple-output (MIMO) to tackle the uplink mixed-service communication problem. Under the assumption of an available physical narrowband shared channel, devised to exclusively consume data traffic from machine type communications (MTC) devices, the capacity (i.e., number of connected devices) of MTC networks and, in turn, that of the whole system, can be increased by clustering such devices and letting each cluster share the same time-frequency physical resource blocks. Following this research line, we study the possibility of employing sub-optimal linear detectors to the problem and present a simple and practical channel estimator that works without the previous knowledge of the large-scale channel coefficients. Our simulation results suggest that the proposed channel estimator performs asymptotically, as well as the MMSE estimator, with respect to the number of antennas and the uplink transmission power. Furthermore, the results also indicate that, as the number of antennas is made progressively larger, the performance of the sub-optimal linear detection methods approaches the perfect interference-cancellation bound. The findings presented in this paper shed light on and motivate for new and exciting research lines toward a better understanding of the use of massive MIMO in MTC networks. INDEX TERMS Large-scale antenna systems, 5G networks, machine type communications, channel estimation, linear detection.