This paper proposes a game-theoretic approach for self-organized resource allocation in OFDMA femtocells with closed access configuration. The objective of the proposed scheme is to overcome the interference problem caused by co-channel operation of femtocells in an existing macrocell network using a self-organization capability of femto users. Inspired by the emerging cognitive radio technology, each femto user acts as an autonomous entity and attempts to select the most appropriate subset of resource blocks in a decentralized manner in order to mitigate the cross- and co-tier interference. Such a self-organized resource allocation scheme can be modeled as a potential game, which guarantees the convergence to a Nash equilibrium as long as distributed sequential play based on the best response strategy is adopted. Simulation results show that the proposed scheme improves the capacity of the femtocell network, while minimizing the performance degradation of the macrocell network.

In the present paper, a game theoretic framework of joint channel selection and power allocation for spectrum sharing in distributed cognitive radio networks is proposed. The objective of the proposed scheme is to investigate the performance of the lower- priority system in spectrum sharing where the lower-priority users utilize the spectrum by selecting the channel and transmit power while aware of the interference to the higher-priority users. The utility function that captures the cooperative behavior to minimize the interference and the satisfaction to improve the throughput is defined. The proposed joint channel selection and power allocation game can be formulated as a potential game and it is guaranteed to converge to a Nash equilibrium when the best response dynamic is performed. The simulation results verify the convergence of the proposed potential game and reveal that the proposed utility function with coefficient adjustment improves the throughput performance of the lower-priority system compared to that of without coefficient adjustment, while ensuring the Quality of Service (QoS) constraint at the higher-priority system.

Mobile edge computing (MEC) is a modern technique that has led to substantial progress in wireless networks. To address the challenge of efficient task implementation in resource-limited environments, this work strengthens system performance through resource allocation based on fairness and energy efficiency. Integration of energy-harvesting (EH) technology with MEC improves sustainability by optimizing the power consumption of mobile devices, which is crucial to the efficiency of task execution. The combination of MEC and an ultra-dense network (UDN) is essential in fifth-generation networks to fulfill the computing requirements of ultra-low-latency applications. In this study, issues related to computation offloading and resource allocation are addressed using the Lyapunov mixed-integer linear programming (MILP)-based optimal cost (LYMOC) technique. The optimization problem is solved using the Lyapunov drift-plus-penalty method. Subsequently, the MILP approach is employed to select the optimal offloading option while ensuring fairness-oriented resource allocation among users to improve overall system performance and user satisfaction. Unlike conventional approaches, which often overlook fairness in dense networks, the proposed method prioritizes fairness-oriented resource allocation, preventing service degradation and enhancing network efficiency. Overall, the results of simulation studies demonstrate that the LYMOC algorithm may considerably decrease the overall cost of system execution when compared with the Lyapunov–MILP-based short-distance complete local execution algorithm and the full offloading-computation method.

VANET (Vehicle Ad-Hoc Network) is one of the new technologies in the communication system. It supports inter-vehicle communication through the 802. 11p.Many studies have looked at improving the quality of the communication system in VANET. Optimization and effectiveness issues in VANET are still open to exploration. Some researchers have used the natural-inspired algorithm to accomplish the issues in the VANET environment. Many tools can be used as a VANET simulator. While they are helpful, only some of these tools can be used for sophisticated analysis. It can support in-depth analysis or good graphical user interface features. But they cannot be thoroughly combined now. Each simulator has a different characteristic to produce a result. It helps to make a realistic traffic model in VANET. In this paper, we provide a brief information about the VANET communication systems, simulators and models.

VANETs need electrical resources to operate their equipment, with the most significant power consumption capable of playing complex roles in the infrastructure network located in the RSU. The main resource of power for the VANET is electricity, which is currently expensive, with excess consumption capable of increasing operational and maintenance costs. Therefore, it is necessary to have an efficient method of using electric power consumption for optimal utilization. Therefore, this research aims to solve these problems by proposing a VDS method, which is capable of detecting vehicles entering the coverage of an RSU. The proposed method tends to enable RSU to run in two operating modes, namely active and sleep. The active mode serves the full connection to the vehicles, while the sleep mode is the idle state of RSU. The application of this method has been proven to reduce the electrical power consumption of RSU macrocell and microcell by 22.2%. The VDS is compelling in low traffic flows, however, it is not inferior when implemented in high vehicle traffic.