Drone-based communications is a novel and attractive area of research in cellular networks. It provides several degrees of freedom in time (available on demand), space (mobile) and it can be used for multiple purposes (self-healing, offloading, coverage extension or disaster recovery). This is why the wide deployment of drone-based communications has the potential to be integrated in the 5G standard. In this paper, we utilize a grid of drones to provide cellular coverage to disaster-struck regions where the terrestrial infrastructure is totally damaged due to earthquake, flood, etc. We propose solutions for the most challenging issues facing drone networks which are limited battery energy and limited backhauling. Our proposed solution based mainly on using three types of drones; tethered backhaul drone (provides high capacity backhauling), untethered powering drone (provides on the fly battery charging) and untethered communication drone (provides cellular connectivity). Hence, an optimization problem is formulated to minimize the energy consumption of drones in addition to determining the placement of these drones and guaranteeing a minimum rate for the users. The simulation results show that we can provide unlimited cellular service to the disaster-affected region under certain conditions with a guaranteed minimum rate for each user.

As the demand for high-speed data transmission grows with the expected emergence of 6G networks and the proliferation of wireless devices, more than traditional wireless infrastructure may be required. Small cell networks (ScNs) integrated with reconfigurable intelligent surfaces (RISs) and multiple-input-multiple-output (MIMO) have emerged as promising solutions to address this issue. However, ScNs have resource allocation limitations, and traditional RISs can only reflect signals in a limited propagation space of $180^{0}$ with fixed reflection properties. This paper proposes a novel approach to overcome these challenges by introducing actively simultaneously transmitting and reflecting (ASTAR)-RISs. Unlike conventional RIS, ASTAR-RISs actively amplify and transmit signals, effectively mitigating the limited propagation challenge and improving signal strength, especially in dense ScNs. This approach enhances the quality of service in complex channel environments by amplifying, on top of reflection, from the macro base station (mBS), improving the overall signal strength, and providing $360^{0}$ flexible propagation space. Furthermore, ASTAR-RIS enables dynamic beam management, significantly improving signal coverage and interference management, which are crucial in dense deployments. In this work, we propose a network architecture where distributed ASTAR-RIS units are deployed to assist small cell mBSs by optimizing signal coverage and enhancing communication performance. ASTAR-RISs dynamically control signal reflection and amplification, complementing the functionality of traditional small-cell BSs in dense network environments. Using the MIMO technique, we design phase shifts for ASTAR elements and develop optimal hybrid beamforming for users at the mBS. We dynamically control the ON/OFF status of the ASTAR-RIS based on active or idle status. We propose an efficient model that ensures fairness of signal-to-noise ratio (SNR) for all users and minimizes overall power consumption while meeting user SNR and phase shift constraints. To this end, we integrate robust beamforming and power allocation strategies, ensuring the system maintains reliable performance even under imperfect channel state information (CSI). We formulate a max-min optimization problem that optimizes the SNR and power consumption, subject to the ON/OFF status, phase shift, and power budget of the ASTAR-RIS. Our proposed method uses an alternating optimization algorithm to optimize the phase shift matrix at the ASTAR-RIS and the hybrid beamforming at the mBS. The approach includes two transmission schemes, and the phase optimization problem is solved using a successive convex approximation method that offers a closed-form solution at each step. Additionally, we use the dual method to determine the optimal ON/OFF status of the ASTAR-RIS. Comprehensive simulations validate the robustness and scalability of our proposed solution, particularly under varying network densities and CSI uncertainties. provides significant performance improvements over 170% compared to traditional RIS schemes.

Environmental monitoring of delicate ecosystems or pristine sites is critical to their preservation. The communication infrastructure for such monitoring should have as little impact on the natural ecosystem as possible. Because of their wide range capabilities and independence from heavy infrastructure, low-power wide area network protocols have recently been used in remote monitoring. In this regard, we propose a mobile vehicle-mounted gateway architecture for IoT data collection in communication-network-free areas. The limits of reliable communication are investigated in terms of gateway speed, throughput, and energy consumption. We investigate the performance of various gateway arrival scenarios, focusing on the trade-off between freshness of data, data collection rate, and end-node power consumption. Then we validate our findings using both real-world experiments and simulations. In addition, we present a case study exploiting the proposed architecture to provide coverage for Wadi El-Gemal national park in Egypt. The results show that reliable communication is achieved over all spreading factors ($\mathrm{SFs}$) for gateway speeds up to 150 km/h with negligible performance degradation at $\mathrm{SFs}=11,12$ at speeds more than 100 km/h. The synchronized transmission model ensures the best performance in terms of throughput and power consumption at the expense of the freshness of data. Nonsynchronized transmission allows time-flexible data collection at the expense of increased power consumption. The same throughput as semisynchronized transmission is achieved using four gateways at only five times the energy consumption, while a single gateway requires seventeen times the amount of energy. Furthermore, increasing the number of gateways to ten increases the throughput to the level achieved by the synchronized scenario while consuming eight times the energy.

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.

Enhancing Internet of Things (IoT) communications through Reconfigurable Intelligent Surfaces (RIS) necessitates novel approaches that go beyond the conventional deployment of passive elements. This paper introduces an efficient method to enhance IoT system performance by combining active and passive functionality in RISs: an active and passive integrated approach. One identified enhancement area is efficiently managing signal throughput and exchanges in IoT systems, which is essential for supporting numerous devices. For this challenge, we propose a thorough process model that describes channel gains, develops a nonlinear energy harvesting, and identifies device-to-device communication schemes without violating information causality. The innovative component of our work is the utilization and strategic application of RIS panels that benefit from the advantages of active and passive components synergism to solve thermal noise issues and optimize signal reflection and transmission. An advanced optimization mechanism is developed based on mixed-integer nonlinear programming: an enabling approach between performance efficiency and maximum service utilization. Our simulation analysis show that developed RIS panels optimize IoT system performance and surpass existing performance indicators in conventional RIS-less systems.