The zero padding (ZP) variants of orthogonal frequency-division multiplexing (OFDM) exhibit a lower bit error rate (BER) and higher energy efficiency compared to their cyclic prefix (CP) counterparts. However, the employment of ZP-OFDM demands strict time synchronization, which is challenging in the absence of pilots or CP. Moreover, time synchronization in OFDM systems is even more challenging when impulsive noise is present. It is well known that urban noise, which consists largely of impulsive noise generated by spark plugs used in internal combustion engines, switching and industrial activities, and discharge of high voltage distribution lines, has a strong influence on digital mobile communications. In this paper, we propose a new low-complexity approximate maximum likelihood (A-ML) timing offset (TO) estimator for ZP multiple-input multiple-output (MIMO)-OFDM in impulsive-noise environments. Performance comparison of the A-ML estimator with existing TO estimators demonstrates a superior performance in terms of lock-in probability with similar computational complexity. Also, compared to the optimal ML TO estimator, it offers a significantly lower computational complexity with negligible performance loss. The A-ML estimator can be employed for both frame and symbol synchronization.

Due to the limited number of radio frequency (RF) chains in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) receivers using analog beamforming/hybrid beamforming, there is a restriction in scheduling the number of users in each transmission time interval. Therefore, fast and low-complexity user scheduling methods based on the instantaneous channel state information (CSI) are needed. In this paper, we propose novel user scheduling methods based on deep learning (DL) to reduce the size of the search space by using the learning capability of a deep neural network (DNN). We formulate the user scheduling combinatorial optimization problem as a regression problem followed by a user separation procedure through decision boundaries that are learned by a trained DNN. The decision boundaries are used to separate the users into two subsets. Then, one of the subsets is selected to be searched to find the users that maximize the sum-rate capacity. The proposed method can achieve a very low outage probability with a few number of searches. In order to achieve ergodic capacity with lower computation complexity, the proposed method is employed in combination with the genetic algorithm (GA) algorithm to take advantage of intelligent initial population selection. Our simulation results show that the proposed user scheduling methods can offer remarkably low complexity.

Non-line-of-sight (NLOS) error is one of the dominant sources of error in localization applications. Existing algorithms rely on solving a set of highly nonlinear equations to compensate for this error, which is intractable in practice. In this paper, we propose an efficient NLOS identification algorithm based on supervised machine learning. This approach enables us to improve localization accuracy by taking advantage of the NLOS measurements if the location of the reflector is known. Hence, our approach can be employed in combination with 5G intelligent reflecting surface systems to provide location-based wireless services. We also analytically derive the Cramer-Rao lower bound for the localization problem at hand. Finally, we investigate the performance of our proposed NLOS identification algorithm under different simulation setups.

In this paper, a statistical-based sphere decoding with increasing radius search (S-SD-IRS) algorithm is proposed, where the radiuses of the decoding hyperspheres are determined based on the statistical properties of the communication channel and additive noise. We show that the probability density functions (PDFs) of the q lowest squared distances in the closest lattice point problem can be approximated by Gumbel distributions with different parameters. Based on the obtained PDFs and by considering the characteristics of the fading channels and additive noise, we choose the radiuses for sphere decoding more efficiently than the conventional methods that ignore the characteristics of system. The performance achieved by the proposed algorithm is very close to the optimal maximum likelihood decoding (MLD) over a wide range of signal-to-noise ratios (SNRs), while the computational complexity, compared to existing sphere decoding variants, is significantly reduced. It is shown that the average number of lattice points inside the decoding hyperspheres drastically reduces in the proposed S-SD-IRS algorithm.

We consider the use of deep neural network (DNN) to develop a decision-directed (DD)-channel estimation (CE) algorithm for multiple-input multiple-output (MIMO)-space-time block coded systems in highly dynamic vehicular environments. We propose the use of DNN for k -step channel prediction for space-time block code (STBC), and show that deep learning (DL)-based DD-CE can remove the need for Doppler rate estimation in fast time-varying quasi stationary channels, where the Doppler rate varies from one packet to another. Doppler rate estimation in this kind of vehicular channels is remarkably challenging and requires a large number of pilots and preambles, leading to lower power and spectral efficiency. We train two DNNs which learn the real and imaginary parts of the MIMO fading channels over a wide range of Doppler rates. We demonstrate that by these DNNs, DD-CE can be realized with only priori knowledge about Doppler rate range and not the exact value. For the proposed DD-CE algorithm, we also analytically derive the maximum likelihood (ML) decoding algorithm for STBC transmission. The proposed DL-based DD-CE is a promising solution for reliable communication over vehicular MIMO fading channels without accurate mathematical models. This is because DNNs can intelligently learn the statistics of the fading channels. Our simulation results show that the proposed DL-based DD-CE algorithm exhibits lower error propagation compared to existing DD-CE algorithms which require perfect knowledge of the Doppler rate.

In this paper, a deep learning (DL)-based sphere decoding algorithm is proposed, where the radius of the decoding hypersphere is learned by a deep neural network (DNN). The performance achieved by the proposed algorithm is very close to the optimal maximum likelihood decoding (MLD) over a wide range of signal-to-noise ratios (SNRs), while the computational complexity, compared to existing sphere decoding variants, is significantly reduced. This improvement is attributed to the DNN's ability of intelligently learning the radius of the hypersphere used in decoding. The expected complexity of the proposed DL-based algorithm is analytically derived and compared with existing ones. It is shown that the number of lattice points inside the decoding hypersphere drastically reduces in the DL-based algorithm in both the average and worst-case senses. The effectiveness of the proposed algorithm is shown through the simulation for high-dimensional multiple-input multiple-output (MIMO) systems, using high-order modulations.

One of the main challenges in high-speed mobile communications is the presence of large Doppler spreads. Thus, accurate estimation of maximum Doppler spread (MDS) plays an important role in improving the performance of the communication link. In this paper, we derive the data-aided (DA) and non-data-aided (NDA) Cramér-Rao lower bounds (CRLBs) and maximum likelihood estimators (MLEs) for the MDS in multiple-input multiple-output (MIMO) frequency-selective fading channel. Moreover, a low-complexity NDA-moment-based estimator (MBE) is proposed. The proposed NDA-MBE relies on the second- and fourth-order moments of the received signal, which are employed to estimate the normalized squared autocorrelation function of the fading channel. Then, the problem of MDS estimation is formulated as a non-linear regression problem, and the least-squares curve-fitting optimization technique is applied to determine the estimate of the MDS. This is the first time in the literature, when DA- and NDA-MDS estimation is investigated for MIMO frequency-selective fading channel. Simulation results show that there is no significant performance gap between the derived NDA-MLE and NDA-CRLB, even when the observation window is relatively small. Furthermore, the significant reduced-complexity in the NDA-MBE leads to low root-mean-square error over a wide range of MDSs, when the observation window is selected large enough.

Two new non-data-aided (NDA) signal-to-noise ratio (SNR) estimators for multiple antenna systems are presented. The proposed estimators rely on higher-order moments of the received signal and employ the time-diversity of the fading channel. While one requires a priori knowledge about the number of transmit antennas, the other estimates the SNR without such knowledge. The performance of the proposed estimators is evaluated for different numbers of transmit antennas and over a wide SNR range. Experimental results show that the proposed estimators exhibit a good performance, over a wide range of SNRs, in terms of normalized-root-mean-square-error (NRMSE) with small bias.

A cooperative spectrum‐sensing problem for a cognitive radio (CR) system is investigated, where CR users collaborate to sense and track the primary users’ (PUs) activities in frequency‐selective fading channels. To sense PUs activities, channel gain estimation is performed by CRs through space–time extended Kalman filtering (STEKF). The STEKF method captures the channel gain from any point in space to each CR at each frame for a specific range of frequencies. The proposed channel gain tracking enables CRs to detect the transmit power, location and the number of active subcarriers of each PU via a time spatial weighted non‐negative Lasso (TSWNL) algorithm. The TSWNL exploits the sparsity of the PUs activities in a geographical area to track PUs activities in frequency‐selective fading channels. Numerical results indicate that the proposed spectrum sensing based on STEKF significantly improves the performance of CRs in tracking of PUs activities.