The anticipated launch of the Sixth Generation (6G) of mobile technology by 2030 will mark a significant milestone in the evolution of wireless communication, ushering in a new era with advancements in technology and applications. 6G is expected to deliver ultra-high data rates and almost instantaneous communications, with three-dimensional coverage for everything, everywhere, and at any time. In the 6G Radio Access Networks (RANs) architecture, the Fronthaul connects geographically distributed Remote Units (RUs) to Distributed/Digital Units (DUs) pool. Among all possible solutions for implementing 6G fronthaul, optical technologies will remain crucial in supporting the 6G fronthaul, as they offer high-speed, low-latency, and reliable transmission capabilities to meet the 6G strict requirements. This survey provides an explanation of the 5G and future 6G optical fronthaul concept and presents a comprehensive overview of the current state of the art and future research directions in 6G optical fronthaul, highlighting the key technologies and research perspectives fundamental in designing fronthaul networks for 5G and future 6G. Additionally, it examines the benefits and drawbacks of each optical technology and its potential applications in 6G fronthaul networks. This paper aims to serve as a comprehensive resource for researchers and industry professionals about the current state and future prospects of 6G optical fronthaul technologies, facilitating the development of robust and efficient wireless networks of the future.

Currently, 5G and the forthcoming 6G mobile communication systems are the most promising cellular generations expected to beat the growing hunger for bandwidth and enable the fully connected world presented by the Internet of Everything (IoE). The cloud radio access network (CRAN) has been proposed as a promising architecture for meeting the needs and goals of 5G/6G (5G and beyond) networks. Nevertheless, the provisioning of cost-efficient connections between a large number of remote radio heads (RRHs) in the cell sites and the baseband unit (BBU) pool in the central location, known as the fronthaul, has emerged as a new challenge. Many wired and wireless solutions have been proposed to address this bottleneck. Specifically, optical technologies presented by passive optical networks (PONs) are introduced as the best suitable solution for 5G and beyond network fronthaul due to their properties of providing high capacity and low latency connections. We considered time and wavelength division multiplexed passive optical networks (TWDM-PONs) as a fronthaul for 5G and beyond. Taking that into consideration, in this paper, we propose an integer linear program (ILP) that results in the optimal optical fronthaul deployment while minimizing the total cost of 5G and beyond instances. However, for larger network instances, solving the ILP problem becomes unscalable and time-consuming. To address that, we developed two heuristic-based algorithms (the K-means clustering algorithm and the one based on the genetic algorithm—GA). We evaluated the suitability of our proposed ILP and heuristic algorithms in simulations by utilizing them to plan different network instances (dense and sparse).

Reducing power consumption is a pivotal challenge in 5G millimeter wave (mmWave) networks due to the density of the base stations (BSs) in these networks. In this paper, we focus on the joint user and power allocation problem in 5G mmWave networks, aiming to minimize power consumption while maintaining the user Quality of Service (QoS), considering the BSs switching on/off strategy. Initially, we formulate the problem as an Integer Linear Program (ILP), aiming to obtain the optimal solution. Due to the NP-hardness of the problem, we propose a Genetic Algorithm (GA)-based heuristic strategy. Extensive simulations are conducted to evaluate the performance of the GA. The obtained results demonstrate the efficiency of the proposed GA in providing close to optimal solutions, even in scenarios with a large number of users, when compared to the ILP. Additionally, the proposed GA outperforms the benchmark algorithms in terms of network power consumption, network throughput, energy efficiency, and the number of switched off BSs. Moreover, when comparing the running time of the different methods, the proposed GA shows a significantly reduced time compared to the optimal solution obtained by the ILP method. At the same time, it requires a running time close to the running times of the benchmark solutions. Further insights from power consumption patterns in residential and office areas affirm the consistent energy savings achieved by the proposed GA, emphasizing its applicability in real-world scenarios. In our case, the proposed GA yields energy savings up to 22.34% and 33.12% in residential and office areas, respectively.

In fifth generatin (5G) and the future beyond 5G (6G) radio access networks (RANs), the cost of fronthaul deployment is a main challenge for mobile network operators. Depending on different constraints, there are various solutions to deploy an efficient fronthaul. Fiber-optic-based fronthaul offers long-term support with regard to a rapid increase in capacity demands. When fiber connections, either point-to-point (P2P) or point-to-multipoint [i.e., passive optical networks (PONs)], are not available due to economic or geographical constraints, new optical fronthaul solutions such as free space optics (FSO) can be applied. Before deploying any optical fronthaul architecture, mobile operators must assess its impact on the total cost of ownership (TCO) of the network (i.e., capital and operational expenditures). To assist operators in choosing the most cost-effective fronthaul architecture, in this paper, we show how to evaluate the TCO of 5G and beyond RANs while taking various fronthaul architectures (P2P, PON, and hybrid PON-FSO) into consideration. Furthermore, this paper answers the question of how much energy is needed to run a network using each of the considered optical fronthaul architectures. To do so, we propose a holistic framework based on an integer linear program that minimizes the TCO of the network. Furthermore, we propose a heuristic algorithm to solve large-sized problems. We run simulations to compare different fronthaul architectures for two deployment areas: dense and sparse.

The fifth generation (5G) of mobile networks utilizing millimeter Wave (mmWave) bands can be considered the leading player in meeting the continuously increasing hunger of the end user demands in the near future. However, 5G networks are characterized by high power consumption, which poses a significant challenge to the efficient management of base stations (BSs) and user association. Implementing new power consumption and user association strategies is imperative to address this issue. For that in this work, we focus on the Dual Connectivity-User and Power Allocation (DC-UPA) problem utilizing BS switching on/off along with user dual connectivity. The problem is mathematically formulated as an Integer Linear Program (ILP), and its NP-hardness is proven by showing its equivalence to a variant of the set covering problem. Moreover, we developed two heuristic algorithms: Simulated Annealing (SA) and Distance-Aware (DA) greedy, to mitigate the impact of the problem complexity and resolve the ILP scalability issue. We conducted extensive simulations to validate the effectiveness of the proposed heuristics in a two-dimensional area containing multiple BSs and users with uniform and nonuniform geographical distributions. The performance of the SA and DA algorithms was compared against the ILP approach. We evaluated the performance of the proposed solutions considering different aspects such as the number of users, the BS radius, and the traffic load changes. The numerical results show that SA outperforms the DA in both uniform and nonuniform geographical distributions of users. The SA provides a sub-optimal solution with an optimality gap of about 3.2%, while the optimality gap of the DA is 8.62% in the case of the uniform distribution. Moreover, the optimality gap in the case of nonuniform distribution is equal to about 1% and 5.2% of SA and DA, respectively. Additionally, by utilizing our solutions, the reduction of the level of power consumption up to 16.1% and 20% in the case of uniform and nonuniform distributions can be achieved. The obtained results highlight the efficiency of the proposed algorithms in addressing the DC-UPA problem, providing practical solutions for managing power consumption and maintaining continuous user connectivity in 5G mmWave networks.

The Industry 4.0 initiative has been showing the way for industrial production to optimize operations based on collecting, processing, and sharing data. There are new requirements on the production floor: flexible but ultra-reliable, low latency wireless communications through interoperable systems can share data. Further challenges of data sharing and storage arise when diverse systems come into play at the Manufacturing Operations Management and Business Planning & Logistics levels. The emerging complex cyber-physical systems of systems need to be engineered with care. Regarding industrial requirements, the telecommunication industry has many similarities to production—including ultra-reliability, high complexity, and having humans “in-the-loop”. The current paper aims to provide an overview of converging telco-grade solutions that can be successfully applied in the wide sense of industrial production. These toolsets range from model-driven engineering through system interoperability frameworks, 5G- and 6G-supported manufacturing, and the telco-cloud to speech recognition in noisy environments.