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The Internet of Things (IoT) application scenarios is becoming extensive due to the quick evolution of smart devices with fifth-generation (5G) network slicing technologies. Hence, IoTs are becoming significantly important in 5G/6G networks. However, communication with IoT devices is more sensitive in disasters because the network depends on the ma...
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With the advancements in fifth-generation (5G) network slicing technology and the proliferation of intelligent devices, the Internet of Things (IoT) has gained significant importance in the context of fifth-generation (B5G) networks. However, when the primary power source may become unreliable and IoT devices are vulnerable, communication becomes m...
Task assignment is a challenging problem in multiple unmanned aerial vehicle (UAV) missions. In this paper, we focus on the task assignment problem for a UAV swarm saturation attack, in which a deep reinforcement learning (DRL) framework is developed. Specifically, we first construct a mathematical model to formulate the task assignment problem for...
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Network energy efficiency is a main pillar in the design and operation of wireless communication systems. In this paper, we investigate a dense radio access network (dense-RAN) capable of radiated power management at the base station (BS). Aiming to improve the long-term network energy efficiency, an optimization problem is formulated by collaborat...
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Citations
... Based on the control parameter, the system QoS and power state are enhanced based on the simulation experiments. Gupta et al., [16] proposed UAV assisted mMTC network for 5G using Dueling DQN (DDQN). The EE of the system is addressed through the resource allocation approach, which improves the EE and reduces the transmission power. ...
... The evaluation of the proposed model is compared with four approaches such as DQN [17], DRL [16], Hierarchical-DQN [15] and horizontal trajectory optimization (HTO) [18]. Figure 5 illustrates the comparison of proposed with baseline approaches in terms of EE. ...
With the advancements in fifth-generation (5G) network slicing technology and the proliferation of intelligent devices, the Internet of Things (IoT) has gained significant importance in the context of fifth-generation (B5G) networks. However, when the primary power source may become unreliable and IoT devices are vulnerable, communication becomes more critical and challenging during disasters. To increase the quality of the service, we consider unmanned aerial vehicles (UAVs) in this study as flying base stations (BS) for the emergency communication system using 5G mMTC network slicing. The suggested approach also enhances the scheduling of time slots, power, and UAV trajectory management. Initially, we optimize the UAV trajectory with various numbers of base stations. The second step is to create a non-convex fractional power allocation issue using the Dinkelbach approach. Additionally, we make a time slot distribution mechanism that increases the energy efficiency rating by evenly allocating time slots to all users. The system model is then transformed using Markov Decision Process (MDP) theory into a stochastic optimization-based problem. To solve the resource allocation problem, we provide a Dueling-Deep-Q-Networks (DDQN) based strategy that uses the Reinforcement Learning (RL) technique to maximize energy efficiency. The numerical outcomes show that the UAV-based network and the base station’s efficiency have greatly improved by addressing the proposed sum-rate maximization problem by addressing trajectory optimization, power allocation and resource allocation problems. Proposed approach by efficient optimization consumes less energy of 1500 joules to 2200 joules with less resource utilization.
... Then, as per its local observations utilizing learning, the authors developed a MARL structure where all agents find their optimal method. In [16], the drone as a flying BS was considered for an emergency transmission system, including 5G mMTC network slicing, to enhance the service quality. The drone-related mMTC makes a BS selection method to maximize the system's energy efficiency. ...
Unmanned aerial vehicle (UAV) networks offer a wide range of applications in an overload situation, broadcasting and advertising, public safety, disaster management, etc. Providing robust communication services to mobile users (MUs) is a challenging task because of the dynamic characteristics of MUs. Resource allocation, including subchannels, transmit power, and serving users, is a critical transmission problem; further, it is also crucial to improve the coverage and energy efficacy of UAV-assisted transmission networks. This paper presents an Enhanced Slime Mould Optimization with Deep-Learning-based Resource Allocation Approach (ESMOML-RAA) in UAV-enabled wireless networks. The presented ESMOML-RAA technique aims to efficiently accomplish computationally and energy-effective decisions. In addition, the ESMOML-RAA technique considers a UAV as a learning agent with the formation of a resource assignment decision as an action and designs a reward function with the intention of the minimization of the weighted resource consumption. For resource allocation, the presented ESMOML-RAA technique employs a highly parallelized long short-term memory (HP-LSTM) model with an ESMO algorithm as a hyperparameter optimizer. Using the ESMO algorithm helps properly tune the hyperparameters related to the HP-LSTM model. The performance validation of the ESMOML-RAA technique is tested using a series of simulations. This comparison study reports the enhanced performance of the ESMOML-RAA technique over other ML models.
... Lower power consumption models must be taken into consideration, and the performance of the mentioned approaches under imperfect CSI should be enhanced. The evaluation processes as mentioned Asia Bangladesh [61,149] China [74,75,79,99,104,105,107,109,110,118,123,126,128,131,132,142,144] Georgia [77] Hong Kong [64] India [54,62,65,96,115,120] Iran [66,73,88,89] Japan [95] Korea [55,68,72,76,103] Malaysia [47,63] Pakistan [147] Saudi Arabia [102] Taiwan [117] Turkey [98] UAE [78] Australia Australia [92,119] Europe UK [49,94,101,129] Czech Republic [50,91] France [59,69] Sweden [52,113,114] Spain [67] Georgia [77] Italy [93,100,125] Turkey [98] Greece [106] Poland [113] Germany [116] Belgium [122] Finland [143] Hungary [64,146] Ireland [150] North America USA [51,53,60,86,90,127,130,148] Canada [97,145] Chicago [121] South America Brazil [48,87,124] in this manuscript could be expanded to take into account the QoS that the UEs experience and to combat energy consumption by BS resource adaption techniques. Also, it has been observed that most of the past literature ignores the energy consumed while switching on-off BSs. ...
In today’s 5G era, the energy efficiency (EE) of cellular base stations is crucial for sustainable communication. Recognizing this, Mobile Network Operators are actively prioritizing EE for both network maintenance and environmental stewardship in future cellular networks. The paper aims to provide an outline of energy-efficient solutions for base stations of wireless cellular networks. A total of 5722 studies have been figured out by using the search string and after performing the six stages of SLR protocol, 82 studies were finalised that are published in 26 supreme journals and 19 featured conferences. EE solutions have been segregated into five primary categories: base station hardware components, sleep mode strategies, radio transmission mechanisms, network deployment and planning, and energy harvesting. The predominance of sleep mode procedures is evident in the selected survey studies. Notably, China, Korea, and the US are vigorously engaged in this field, specifically related to the 5G network. This review paper identifies the possible potential solutions for reducing the energy consumption of the networks and discusses the challenges so that more accurate and valid measures could be designed for future research.
The emergence of 5G networks has increased the demand for network resources, making efficient resource management crucial. Slice admission control (SAC) is a process that governs the creation and allocation of virtualized network environments, known as “network slices,” which can be tailored to meet specific user requirements. However, traditional SAC methods face dynamic and heterogeneous challenges in wireless networks, especially in cloud radio access networks (C‐RANs). To address this issue, machine learning (ML) techniques, particularly deep reinforcement learning (DRL), have been proposed as powerful tools for optimizing SAC. DRL‐based approaches enable SAC systems to learn from previous interactions with the network environment and dynamically adapt to changing network conditions. This review article comprehensively explains the current state‐of‐the‐art DRL‐based SAC, focusing on C‐RANs. The article identifies key challenges and future research directions and highlights the potential benefits of using DRL for SAC, including improved network performance and efficiency. However, deploying these systems in real‐world scenarios presents several challenges and trade‐offs that need to be carefully considered. Further research and development are required to address these challenges and ensure the successful deployment of DRL‐based SAC systems in wireless networks.
Due to the rapid expansion of high-speed wireless communications, the number of customers has significantly increased. Ultra-dense networks (UDN) now support a greater number of mission-critical applications and users. To overcome communication limitations in UDN caused by natural disasters, unmanned aerial vehicles (UAVs) have been introduced to assist. However, because the number of communication resources and links per unit area within the UDN is very dense, high-quality and efficient planning and resource allocation for the UDN becomes difficult. Therefore, we propose an Optimizing Resource Allocation in UAV-Assisted UDN with Optimal Ensemble SVM using the Levy flight-based EOSA to improve Energy Efficiency (EE) of the network. The developed resource allocation approach is efficiently allocates network resources to maximize Quality of Service (QoS) and network performance. To improve the population diversity and prevent the algorithm from exploring already visited solutions, the levy flight operator was incorporated into the Ebola Optimization Search Algorithm (EOSA). The levy flight-based EOSA algorithm was then used to optimize time and power transmission, thereby increasing the average throughput. To handle the dynamic and complex class of UAV-assisted ultra-dense network environments, an ensemble SVM method was used. An optimal ensemble SVM model was trained on a large dataset of relevant performance metrics and network parameters, enabling the model to accurately predict network performance for various resource allocation scenarios. The experimental results revealed that the proposed method significantly enhances EE compared to existing techniques.
