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Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) are innovative technologies that aim to enhance the quality of communication in next-generation networks. These technologies are individually tailored to specific applications and have demonstrated impressive results. UAVs are known for their flexibility and ease of deployment, while RIS can adapt to changing environments by altering the traditional propagation and reflection schemes. Recent research has combined these two technologies for various applications, improving communication and data transmission in next-generation networks. This paper surveys recent advancements in using tethered and untethered UAVs and RIS in 5G/6G networks. The main objective of this survey is to inspire further solutions that support RIS-assisted UAVs and to provide insights not covered in published UAV–RIS surveys. The survey summarizes how UAVs and RIS can be used to address communication issues. Two case studies are presented to further illustrate this integration's potential benefits. The first case study demonstrates a UAV mounting an RIS to mitigate the impact of base station fronthaul failure. The results suggest that co-locating tethered UAVs with a selected number of base stations can proactively restore any failed fronthaul links. The second case study explores the 3D trajectory of the UAV and the phase shift design of the RIS based on the number of RIS elements and height placement of the UAV. The study considers an untethered UAV with a terrestrial RIS and shows that the propulsion energy of the UAV can be reduced by changing and adjusting the height of the UAV. [ABSTRACT FROM AUTHOR]

This paper proposes an energy management framework for cellular heterogeneous networks (HetNets) supported by dynamic solar powered drones. A HetNet composed of a macrocell base station (BS), micro cell BSs, and drone small cell BSs are deployed to serve the networks’ subscribers. The drones can land at pre-planned locations defined by the mobile operator and at the macrocell BS site where they can charge their batteries. The objective of the framework is to jointly determine the optimal trips of the drones and the MBSs that can be safely turned off in order to minimize the total energy consumption of the network. This is done while considering the cells’ capacities and the minimum receiving power guaranteeing successful communications. To do so, an integer linear programming problem is formulated and optimally solved for three cases based on the knowledge level about future renewable energy statistics of the drones. A low complex relaxed solution is also developed. Its performances are shown to be close to those of the optimal solutions. However, the gap increases as the network becomes more congested. Numerical results investigate the performance of the proposed drone-based approach and show notable improvements in terms of energy saving and network capacity. [ABSTRACT FROM AUTHOR]

Fourth/Fifth Generation heterogeneous wireless networks (4G/5G HetNets) use or will use small cells (SCs) to extend network coverage and increase spectrum efficiency. However, the standard and technical specifications do not specify how to plan the locations of the SCs within the network. Several papers introduced strategies for planning the locations of SCs in the 4G HetNet architecture. However, SCs placement strategies to support the self-healing functionality of the 4G/5G self organizing networks framework has not been studied in the literature. The placement of SCs in 4G HetNets such that an SC failure will not interrupt service, hence making the network fault tolerant, is an important design and planning problem that is addressed in this paper. We present an integer linear program formulation for planning operators of managed SC locations with fault tolerance. We allow one SC to fail and by using self-healing, a fault-tolerance service is provided at designated fail-over levels (defined in terms of users throughput). We consider the problem of SC location planning by using offloading in both out-band and in-band modes, and an interference model is presented to consider the in-band mode and to address the effect of interference on SCs placement planning. A novel approach to provide a linear interference model by using an expanded state space to get rid of nonlinearity is introduced. We present numerical results that show how our model can be used to plan the positions of SCs. We also incorporate the existence of obstacles in the planning, such as large structures or natural formations, that might happen in real life. To the best of our knowledge, this is the first work that addresses the planning of SC locations in 4G/5G HetNets in a fault-tolerant manner. [ABSTRACT FROM AUTHOR]

5G networks are expected to bring the gigabits per second throughput level per user to reality by 2020. This is done using a combination of new and well known technologies such as C-RAN, self-organizing networks, ultra dense networks, massive MIMO, and millimeter waves. In new RAN architectures, C-RAN has been viewed as a promising 5G architecture that centralizes baseband processing units and virtualizes them into a resource pool. The baseband units are connected to the remote radio heads via high speed fronthaul links. Failure of any 5G cell site fronthaul means the loss of hundreds of gigabits, or even terabits. In this article, we present a novel cell outage compensation approach using new SHRs added to each cell site in the 5G network. These SHRs operate only in case of fronthaul/ backhaul failure of any cell site in the network. A new software defined controller is introduced to handle the self-healing procedures. The article also introduces a high-level simulation study that is carried out to assess the proposed approach. The simulation results confirm the advantages of the proposed approach in terms of the degree of recovery from failures. [ABSTRACT FROM AUTHOR]

In this paper, we study the channel allocation problem in cognitive radio wireless mesh networks (CR-WMNs). We aim at finding an allocation strategy that guarantees quality of service (QoS) (link reliability), maximizes network coverage, and alleviates the need for a common control channel to coordinate the communication process. The allocation of a particular channel to a mesh client (MC) is considered feasible if the MC can establish connectivity with the backbone network in both the upstream and the downstream directions, and has the signal-to-interference-plus-noise ratio (SINR) of the uplink and the downlink with its parent mesh router (MR) within a predetermined threshold. A receiver-based channel allocation (RBA) model that achieves the aforementioned objectives is proposed (channel assignment under this model can be proven to be NP-hard). We then formulate a mixed integer linear program, of the channel allocation problem under the proposed model, and compare its performance to that of two other baseline models, namely, transmitter-based and all-tunable channel allocation strategies. The results prove the superiority of the proposed model. We also developed a heuristic algorithm, which is shown to be an accurate algorithm. [ABSTRACT FROM PUBLISHER]

Welcome to the November issue of the Artificial Intelligence and Data Science for Communications Series of IEEE Communications Magazine. Artificial intelligence (AI) and machine learning (ML) are generally considered of utmost importance, enabling information extraction, knowledge acquisition, and intelligent decision making, efficiently exploiting the vast amount of data available, supporting systems in accomplishing end-to-end goals and objectives in an efficient and effective manner. However, many challenges need to be solved in order to exploit the full potential of AI and ML application in communications. [ABSTRACT FROM AUTHOR]