Non-orthogonal multiple access (NOMA) is the technique proposed for multiple access in the fifth generation (5G) cellular network. In NOMA, different users are allocated different power levels and are served using the same time/frequency resource blocks (RBs). The main challenges in existing NOMA systems are the limited channel feedback and the difficulty of merging it with advanced adaptive coding and modulation schemes. Unlike formerly proposed solutions, in this paper, we propose an effective channel estimation (CE) algorithm based on the long-short term memory (LSTM) neural network. The LSTM has the advantage of adapting dynamically to the behavior of the fluctuating channel state. On average, the use of LSTM results in a 10% lower outage probability and a 37% increase in the user sum rate as well as a maximal reduction in the bit error rate (BER) of 50% in comparison to the conventional NOMA system. Furthermore, we propose a novel power coefficient allocation algorithm based on binomial distribution and Pascal’s triangle. This algorithm is used to divide power among N users according to each user’s channel condition. In addition, we introduce adaptive code rates and rotated constellations with cyclic Q-delay in the quadri-phase shift keying (QPSK) and quadrature amplitude modulation (QAM) modulators. This modified modulation scheme overcomes channel fading effects and helps to restore the transmitted sequences with fewer errors. In addition to the initial LSTM stage, the added adaptive coding and modulation stages result in a 73% improvement in the BER in comparison to the conventional NOMA system.

Nowadays, visible light communication (VLC) systems have become one of the candidate technologies for high data rate indoor communications. However, the main challenge to develop a high data rate VLC system is the narrow modulation bandwidth of light-emitting diodes (LEDs). Power domain non-orthogonal multiple access (PD-NOMA) is a promising scheme to enhance the spectral efficiency of downlink VLC systems. In this paper, we introduce cooperative PD-NOMA to the system to improve the signal reception for the far users. We evaluate the bit error rate (BER) and achievable rate performance of non-cooperative and cooperative PD-NOMA under perfect channel state information (CSI). Moreover, we drive analytic expressions for the BER and provide a Monte Carlo simulation results for verifying the validity of the derived analytical BER results. The results show that cooperative PD-NOMA outperforms non-cooperative PD-NOMA by 8.2 dB at BER 10⁻⁶ and by achievable rate 14.1 bit/s/Hz at 45 dB in a two-user scenario.

Wireless and mobile communication systems are in continuous evolution every day. The fifth generation (5G) is the next major phase of the mobile telecommunication and wireless system which aims to improve Quality of Service (QoS) and integrate human-to-machine and machine-to-human communications. In 5G mobile networks, mobility and small coverage areas are the biggest challenges for mobile users. Mobile users may not have the time required to acquire network resources to transfer from one cell to another, resulting in a large number of handoff failures. In this paper, we propose a handoff scheme for the 5G mobile network to minimize the probability of handoff failures using Markovian queuing model. We use the ECC33 channel propagation model to calculate the received signal strength (RSS) of the different users to decide the handoff users. We evaluate system performance based on blocking probability, overall system delay, and overall system throughput. The results of the proposed system model give a better QoS in the system and significantly affect the system performance.

Electrocardiogram (ECG) signal analysis is a critical task in diagnosing the presence of any cardiac disorder. There are limited studies on detecting P-waves in various atrial arrhythmias, such as atrial fibrillation (AFIB), atrial flutter, junctional rhythm, and other arrhythmias due to P-wave variability and absence in various cases. Thus, there is a growing need to develop an efficient automated algorithm that annotates a 2D printed version of P-waves in the well-known ECG signal databases for validation purposes. To our knowledge, no one has annotated P-waves in the MIT-BIH atrial fibrillation database. Therefore, it is a challenge to manually annotate P-waves in the MIT-BIH AF database and to develop an automated algorithm to detect the absence and presence of different shapes of P-waves. In this paper, we present the manual annotation of P-waves in the well-known MIT-BIH AF database with the aid of a cardiologist. In addition, we provide an automatic P-wave segmentation for the same database using a fully convolutional neural network model (U-Net). This algorithm works on 2D imagery of printed ECG signals, as this type of imagery is the most commonly used in developing countries. The proposed automatic P-wave detection method obtained an accuracy and sensitivity of 98.56% and 98.78%, respectively, over the first 5 min of the second lead of the MIT-BIH AF database (a total of 8280 beats). Moreover, the proposed method is validated using the well-known automatically and manually annotated QT database (a total of 11,201 and 3194 automatically and manually annotated beats, respectively). This results in accuracies of 98.98 and 98.9%, and sensitivities of 98.97 and 97.24% for the automatically and manually annotated QT databases, respectively. Thus, these results indicate that the proposed automatic method can be used for analyzing long-printed ECG signals on mobile battery-driven devices using only images of the ECG signals, without the need for a cardiologist.

An explosion of traffic volume is the main driver behind launching various 5G services. The 5G network will utilize the IP Multimedia Subsystems (IMS) as a core network, same as in 4G networks. Thus, ensuring a high level of survivability and efficient failure management in the IMS is crucial before launching 5G services. We introduce a new methodology based on machine learning to predict the call failures occurring inside the IMS network using the traces for the Session Initiation Protocol (SIP) communication. Predicting that the call will fail enables the operator to prevent the failure by redirecting the call to another radio access technique by initiating the Circuit Switching fallback (CS-fallback) through a 380 SIP error response sent to the handset. The advantage of the model is not limited to call failure prediction, but also to know the root causes behind the failure; more specifically, the multi-factorial root is caused by using machine learning, which cannot be obtained using the traditional method (manual tracking of the traces). We built eight different machine learning models using four different classifiers (decision tree, naive Bayes, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM)) and two different feature selection methods (Filter and Wrapper). Finally, we compare the different models and use the one with the highest prediction accuracy to obtain the root causes beyond the call failures. The results demonstrate that using SVM classifier with Wrapper feature selection method conducts the highest prediction accuracy, reaching 97.5%.

The huge traffic demand envisioned in 5G requires radical changes in mobile network architecture. A Centralized Radio Access Network (C-RAN) was introduced as a novel mobile network architecture, designed to effectively support the challenging requirements of future 5G networks. Coordinated Multi-point (CoMP) is one of the technologies aiming to increase user traffic by transforming inter-cell interference into useful signals to maximize cell-edge users throughput. Intra-CoMP means that cooperating RRHs are assigned to the same Base Band Unit (BBU). On the other hand, in inter-CoMP, the cooperating RRHs are assigned to different BBUs, which introduce overhead signalling over an X2 interface. This paper proposes a model for BBU placement in C-RAN deployment over a 5G optical aggregation network. The model aims to minimize the number of users undergoing inter-CoMP, therefore reducing X2 signalling overhead. First, we solve the BBU placement problem using Integer Linear Programming (ILP), which minimizes the number of BBUs and the number of used links. Second, given the output of the ILP model (i.e., BBU locations and routes), we propose a heuristic algorithm to reconfigure the BBUs (i.e., the assignment of the RRHs to their corresponding BBUs), which aims at minimizing the number of users undergoing inter-CoMP. The proposed heuristic algorithm considers minimizing end-to-end delay, the number of used wavelengths, and maximizing multiplexing gain. The results show that up to 97% of inter-CoMP users migrated to intra-CoMP users. This results in a decrease in the X2 traffic, which is mainly used for the coordination between the BBUs.