![]() Via the proper design, the reflected signals can be combined coherently at the intended UE, thereby enhancing the desired signal, and/or destructively at non-intended UEs, thereby eliminating co-channel interference. Specifically, an IRS is made up of many reflectors with reflecting passive elements, each of which can independently change the corresponding amplitude and phase angles of the incident signals. Among them, the intelligent reflecting surface (IRS) is regarded as a promising and efficient solution, which uses software-controlled signal reflection to reconfigure the wireless propagation environment. With the increasing demands for higher data rates, higher spectrum and energy efficiency, and ubiquitous connectivity for the beyond-fifth-generation and sixth-generation wireless communication networks, many improved wireless technologies have been proposed (e.g., unmanned aerial vehicle communication, satellite communication, Terahertz communication, etc.). In addition, our results show that the total transmit power at the BS decreases with the increasing number of reflecting units at the IRSs. Simulation results confirm that the proposed algorithms can achieve better power-saving performance and convergence with a noteworthy reduction in the computation time compared to the alternating optimization-based approaches. Specifically, a deep deterministic policy gradient (DDPG)-based algorithm is designed to address the GRNN algorithm’s restrictions and efficiently handle the dynamic UE scenario. Meanwhile, for the dynamic UE scenario, we propose a deep reinforcement learning (DRL)-based optimization algorithm. For the static UE scenario, we propose a low-complexity optimization algorithm based on the new generalized neural network (GRNN). Consequently, an alternative approach, i.e., machine learning (ML), is adopted to find the optimal solution with lower complexity. Although the conventional alternating approach is widely used to find converged solutions, its applicability is restricted by high complexity, which is more severe in a dynamic environment. Hence, we aim to jointly optimize the configuration of the BS active beamforming and reflection beamforming of the IRSs that meet the UE’s QoS while allowing the lowest transmit power consumption at the BS. In this work, we consider a single cellular network where multiple IRSs are deployed to assist the downlink transmissions from the base station (BS) to multiple user equipment (UE). These include incident action planning, unity of command, personal responsibility, span of control, check-in/check-out, and resource tracking.Due to the benefits of the spectrum and energy efficiency, intelligent reflecting surfaces (IRSs) are regarded as a promising technology for future networks. Accountability: Incident personnel should not waiver from principles of accountability.This clarifies the reporting relationships and reduces confusion caused by multiple, conflicting directives. Unity of command means that each individual reports to only one person. Chain of command and unity of command: Chain of command refers to the hierarchical ranks of the incident management organization.Instead, Unified Command manages the incident by jointly approved objectives. There is no one “commander” in Unified Command. Unified Command: Unified Command may be established when no one jurisdiction, organization, or agency has the primary authority or the resources to manage an incident completely independently.Command transfer must include a situation briefing that captures essential information for continuing safe and effective operations. Establishment and transfer of command: The incident commander should be established at the start of an incident.One supervisor to five subordinates defines the optimal span of control however, this ratio may vary depending on the circumstances of the crisis. Manageable span of control: Efficient operations maintain an appropriate span of control.Modular organization: ICS’ organizational structures develop in a modular fashion based on an incident’s size and complexity.Common terminology: The National Incident Management System establishes common terminology to support cooperation and ensure understanding between various agencies. ![]()
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