Orthogonal frequency division multiplexing (OFDM) is a superior technology for the high-speed data rate of wire-line and wireless communication systems. The OFDM has many advantages over other techniques such as its high capacity and immunity against multipath fading channels. However, one of the main drawbacks of the OFDM system is the high-peak-to-average power ratio (PAPR) that leads the system to produce in-band distortion and out-of-band radiation because of the non-linearity of the high-power amplifiers. Therefore, numerous techniques have been proposed to overcome the PAPR problem such as selective mapping, partial transmit sequence (PTS), clipping, and nonlinear companding. In this paper, the PTS technique was analytically reviewed as one of the important methods to reduce the high PAPR problem. The PAPR performance and the computational complexity level are discussed in terms of modifying the PTS technique in the frequency domain, time domain, and modulation stage (Inverse Fast Fourier Transform block). Moreover, the numerical statistic comparison of the current modified-PTS methods is introduced, and the criteria for selecting the suitable modified-PTS method in the OFDM system is also given. The simulation and the numerical calculations results show that the rows exchange-interleaving PTS (IL-Ex-PTS) scheme is the best method for reducing the PAPR value with low complexly in the frequency domain, and the cooperative PTS (CO-PTS) method is the best among the modulation stage methods, while the cyclic shift sequence PTS (CSS-PTS) method achieves the superior performance in PAPR reduction and computational complexity for the time domain methods.

The sixth generation (6G) wireless communication network presents itself as a promising technique that can be utilized to provide a fully data-driven network evaluating and optimizing the end-to-end behavior and big volumes of a real-time network within a data rate of Tb/s. In addition, 6G adopts an average of 1000+ massive number of connections per person in one decade (2030 virtually instantaneously). The data-driven network is a novel service paradigm that offers a new application for the future of 6G wireless communication and network architecture. It enables ultra-reliable and low latency communication (URLLC) enhancing information transmission up to around 1 Tb/s data rate while achieving a 0.1 millisecond transmission latency. The main limitation of this technique is the computational power available for distributing with big data and greatly designed artificial neural networks. The work carried out in this paper aims to highlight improvements to the multi-level architecture by enabling artificial intelligence (AI) in URLLC providing a new technique in designing wireless networks. This is done through the application of learning, predicting, and decision-making to manage the stream of individuals trained by big data. The secondary aim of this research paper is to improve a multi-level architecture. This enables user level for device intelligence, cell level for edge intelligence, and cloud intelligence for URLLC. The improvement mainly depends on using the training process in unsupervised learning by developing data-driven resource management. In addition, improving a multi-level architecture for URLLC through deep learning (DL) would facilitate the creation of a data-driven AI system, 6G networks for intelligent devices, and technologies based on an effective learning capability. These investigational problems are essential in addressing the requirements in the creation of future smart networks. Moreover, this work provides further ideas on several research gaps between DL and 6G that are up-to-date unknown.

Software-Defined Networking (SDN) is a groundbreaking technology that has transformed network management significantly. By integrating data and control, SDN offers unparalleled flexibility and responsiveness, thereby overcoming the limitations of conventional network architectures. However, a centralized controller, which is a hallmark of SDN, is a double-edged security sword that offers easy control. This also becomes a dangerous point of failure for the entire network. To the best of our knowledge, this is the first comprehensive study to explore traditional-based, artificial intelligence (AI)-based, and moving target defense (MTD) approaches to securing SDN. The study begins with a survey of traditional security solutions for SDN, encompassing authentication, authorization, encryption, security protocols, firewalls, and flow verification, by addressing security threats and vulnerabilities in both data and control planes. The study then investigates the application of AI-based security solutions in an SDN environment, focusing on how Machine Learning (ML) and Deep Learning (DL) techniques are leveraged to address advanced security threats. Additionally, the survey examines MTD mechanisms within data and control plane security. Several in-depth techniques, including the randomization of Internet Protocol (IP) and Media Access Control (MAC) addresses, port numbers, and flow tables, and delving into the relationship between security threats, MTD strategies, and the specific controllers employed in experimental implementations. We utilized the widely recognized STRIDE cybersecurity framework to systematically identify and evaluate the potential threats to SDN security. Our analysis resulted in a comprehensive list of security challenges, and we propose future research directions aimed at addressing emerging threats in both the data and control planes.

There is a growing demand for 5G applications in all fields of knowledge. Current applications, such as the Internet-of-Things, smart homes and clean energy, require sophisticated forms of 5G waveforms. Researchers and developers are investigating the requirements of 5G networks for better waveform types, which will result in high spectrum efficiency and lower latency with less complexity in systems. This work proposes an assessment of various 5G waveform candidates (F-OFDM, UFMC, FBMC and GFDM) under the Key Performance Indicators (KPIs). This work assesses the main KPI factors (Computational complexity, Peak Average Power Ratio (PAPR), Spectral efficiency, Filter length and Latency). Moreover, this study compares and evaluates all KPI factors in various 5G waveforms. Finally, this study highlights the strengths and weaknesses of each waveform candidate based on KPI factors for better outcomes in the industry. In conclusion, the current review suggests the use of optimized waveforms (FBMC and UFMC) for better flexibility to overcome the drawbacks encountered by previous works. Regarding coexistence, (FBMC and UFMC) showed better coexistence with (CP-OFDM) in 4G networks with a new radio spectrum. The rapprochement between the mentioned waveforms has been called green coexistence and is due to the mix between one waveform in 4G networks and two waveforms in 5G networks based on the subcarrier and subband shaping (FBMC and UFMC).

Software-Defined Networking (SDN) is a promising solution for large-scale network management that offers extensive opportunities for optimization. However, the centralized control inherent in SDN also exposes networks to security threats, notably Distributed Denial of Service (DDoS) attacks. To address these challenges, machine learning (ML) techniques have emerged as potent tools for anomaly detection and mitigation. This paper proposes a novel approach for traffic classification within SDN environments that distinguishes between benign and malicious traffic using supervised ML techniques. This study introduces a unique dataset tailored for DDoS attack detection, overcoming the limitations of existing datasets, such as unrealistic topologies and lack of public availability. Benchmarking against the CICDDoS2019 dataset validated the efficacy and relevance of the custom dataset. This research has significant implications for real-world applications, offering improved capabilities for detecting and mitigating DDoS attacks in SDN infrastructure. Experimental results demonstrated the effectiveness of the proposed random forest model, achieving a remarkable accuracy of 98.97% and a minimal False Alarm Rate (FAR) of 0.023. These findings underscore the potential of ML-based approaches in enhancing network security and resilience against DDoS attacks in SDN environments, paving the way for future advancements in network-defense strategies.

An edge intelligence-aided Internet-of-Things (IoT) network has been proposed to accelerate the response of IoT services by deploying edge intelligence near IoT devices. The transmission of data from IoT devices to the edge nodes leads to large network traffic in the wireless connections. Federated Learning (FL) is proposed to solve the high computational complexity by training the model locally on IoT devices and sharing the model parameters in the edge nodes. This paper focuses on developing an efficient integration of joint edge intelligence nodes depending on investigating an energy-efficient bandwidth allocation, computing Central Processing Unit (CPU) frequency, optimization transmission power, and the desired level of learning accuracy to minimize the energy consumption and satisfy the FL time requirement for all IoT devices. The proposal efficiently optimized the computation frequency allocation and reduced energy consumption in IoT devices by solving the bandwidth optimization problem in closed form. The remaining computational frequency allocation, transmission power allocation, and loss could be resolved with an Alternative Direction Algorithm (ADA) to reduce energy consumption and complexity at every iteration of FL time from IoT devices to edge intelligence nodes. The simulation results indicated that the proposed ADA can adapt the central processing unit frequency and power transmission control to reduce energy consumption at the cost of a small growth of FL time.