Interference is one of the most critical issues in the cellular Device-to-Device (D2D) networks. The sharing of radio resources in cellular and D2D communications offers spectrum efficiency advantages for the cellular network. However, resource sharing also introduces interference, leading to a reduction in the quality of service experienced by users. In this paper, we propose an adversarial Multi-Armed Bandit learning-based transmission Power Control method called MAB-PC for both cellular and D2D transmitters. The objective of this method is to ensure the minimum service quality for users, considering the minimum spectral efficiency and maximum block error rate. Additionally, to address the conflicting objectives of D2D and cellular communications, MAB-PC is modeled as a Pareto optimization problem for D2D transmitters. MAB-PC is a distributed method that minimizes signaling overhead and relies solely on local Channel State Information (CSI). The effectiveness of the proposed method is evaluated based on reliability, total data rate, spectral efficiency, block error rate, and outage probability, demonstrating superior performance compared to its counterpart.

With the rise of massive Internet-of-Things (mIoT) applications, the IEEE 802.11ah standard is gaining more attention for implementation in wireless networks. Unbalanced distribution of the traffic load among the access points (APs) is a problem that results in performance degradation of such networks, including packet loss and delay, which are essential for some mission-critical applications. In this article, we present a novel solution for user-AP association in IEEE 802.11ah networks. Assuming that the network is carrying complex traffic, we limit the number of devices associated with each AP to keep the blocking probability under the desired value. Also, two mechanisms are proposed to limit the delay of packets by restricting the number of stations associated with an AP and to determine the transmission range of APs for covering a specific number of stations. Our simulations demonstrate that the proposed method can enhance the throughput and energy efficiency in the network and also limit the blocking probability and delay of packets in the network by restricting the number of IoT devices associated with an AP. As a result, the proposed method facilitates ultrareliable low-latency communications (URLLCs) by limiting the number of associated stations in IEEE 802.11ah networks.

Abstract Growing demands for pervasive and ubiquitous services over wireless mobile networks and evolution of such networks towards heterogeneous solutions have emphasized the necessity of more intelligent handoff decisions. The existing handoff management methods in the literature are mostly using signal strength measurements and other link quality evaluations not addressing the knowledge about context of mobile devices, users and networks. Recently, context‐aware handoff management has been considered as a novel candidate for fourth generation (4G) wireless technology. In this paper, user perceived quality of service has been considered in addition to traditional contexts such as user preferences, application requirements, network parameters and link quality for decision making. User perceived quality (UPQ) has been employed as a trigger source, in addition to link layer triggers which are emerged using media independent handover (MIH) event service. This paper presents a policy based mechanism for handoff decision making where fuzzy petri nets (FPNs) have been utilized as its evaluation algorithm. A case study has been provided by simulations to show the usability and user level satisfaction. Simulation results show superior performance in terms of UPQ, jitter and packet delivery measures. Copyright © 2009 John Wiley & Sons, Ltd.