Network energy saving has received great attention from operators and vendors to reduce energy consumption and CO2 emissions to the environment as well as significantly reduce costs for mobile network operators. However, the design of energy-saving networks also needs to ensure that mobile users' (MUs) QoS requirements such as throughput requirements (TR). This work considers a mobile cellular network including many ground base stations (GBSs), and some GBSs are intentionally turned off due to network energy saving (NES) or crash, so the MUs located in these outage GBSs are not served in time. Based on this observation, we propose the problem of maximizing the total achievable throughput in the network by optimizing the GBSs' antenna tilt and adaptive transmission power with a given number of served MUs satisfied. Notice that, the MU is considered successfully served if its Reference Signal Received Power (RSRP) and throughput requirement are satisfied. The formulated optimization problem becomes difficult to solve with multiple binary variables and nonconvex constraints along with random throughput requirements and random placement of MUs. We propose a Deep Q-learning-based algorithm to help the network learn the uncertainty and dynamics of the transmission environment. Extensive simulation results show that our proposed algorithm achieves much better performance than the benchmark schemes.

Unmanned aerial vehicles (UAVs) offer a promising solution for enhancing network coverage, reliability, and data speed in future wireless network generations. However, deploying UAVs as aerial base stations requires careful consideration of crucial design factors, including three-dimensional (3D) placement and performance optimization tailored to specific applications. In this paper, the 3D placement of multiple UAVs, acting as aerial base stations, is investigated in a dynamic user scenario. First, a closed-form expression for the coverage probability is derived. Then, to maximize the network coverage and sum rate while ensuring reliable and energy efficient system, a joint multi-objective optimization problem is formulated considering the real-time user movements. To solve the problem, an improved Chaos-based Bonobo Optimizer (CBO) scheme is proposed which combines chaotic maps with the Bonobo Optimizer (BO) algorithm. The obtained results demonstrate the superior performance of the proposed approach compared with different benchmark algorithms. The results reveal that the proposed CBO algorithm offers a minimum of $\mathbf{1 5 \%}$ and $\mathbf{9 0} \mathbf{~ M b i t / s ~ i m p r o v e m e n t s ~}$ in coverage and sum rate, respectively.

In this paper, we study the problem of how to provide reliable communications for energy harvesting (EH) wireless sensor network (WSN). Using the example of an autonomous quarry, where self-driving trucks autonomously collect and transport goods, there is a need for multiple wireless sensors collecting data about where and when goods can be collected, while guaranteeing reliable operation of the quarry. The vehicles transfer energy to the wireless sensors within range, forming a cluster. The sensors use this energy to transmit data to the vehicles. Finally, the vehicles relay information to an access point (AP). The AP processes the collected information and synchronize the operation of all vehicles. We propose an interference channel selection policy for the sensors-to-vehicles links and vehicles-to-AP links to improve the reliability of the communications, while enhancing the energy utilization. Accordingly, closed-form expression on how to achieve reliable communication within the considered system is derived and numerical results show that the proposed channel selection strategy not only improves the probability of achieving sufficiently reliable communication but also enhances the energy utilization.