5G and the next generation networks are expected to support diverse quality of service (QoS) requirements while ensuring network energy efficiency in the face of increasing mobile data traffic. However, wireless networks are resource-constrained, and ensuring QoS guarantees and maintaining energy efficiency is a challenging task. Rapid fluctuation of signal quality due to the mobility of the user equipment (UE) makes the task more complicated. Nevertheless, user mobility is not entirely random (e.g., UE with vehicle speed traveling along a highway) and results in channel gain that significantly varies with time. Since mobility factors (speed, location, direction of movement) and received signal strength are correlated, user mobility can be exploited to enhance resource allocation decisions and thus improve both UE QoS and network energy efficiency. In this paper, we propose a deep reinforcement learning (DRL)-based mobility and QoS-aware downlink scheduling algorithm that takes advantage of user mobility information to minimize network energy consumption. Using reference signal received power (RSRP) measurement reports, the proposed algorithm incorporates mobility awareness into the scheduling rule to decide whether to serve UE in the current time slot or future slots, within the packet delay budget. By jointly considering packet remaining due-time and mobility information, the algorithm schedules transmissions of packets at lower power, improving energy efficiency while meeting QoS requirements. Simulation results demonstrate that the proposed approach reduces energy consumption by 18.2% and improves QoS satisfaction compared to existing methods.

In this paper, we model and analyze the characteristics of a blockchain enable wireless Internet of Things. Blockchain is considered a vital technology for the development of a secure and trusted Internet of Things (IoT) in next-generation networks. However, the current blockchain architecture usually requires extensive computing power and storage capacity, which hinders its deployment in a wireless IoT network. Taking into account these limitations, we establish an analytical model for a lightweight blockchain architecture tailored to wireless IoT networks, and investigate the transmission success probability of the proposed model. We then capture the relationship between transmission link condition (e.g., the transmission success probability) and the success rate of Raft consensus inside the blockchain network. Our analysis shows that the Raft consensus success rate is a function of the transmission link condition and the number of network nodes. Furthermore, when we increase the number of nodes, the consensus success rate displays different tendencies with regard to uplink and downlink transmission link conditions. In particular, consensus success increases when the combination of transmission link probabilities on uplink and downlink is greater than 1/2, and decreases in the opposite scenario. The authenticity of our analysis is verified via simulations.

In this paper, we analyze vertical handovers in heterogeneous networks. Vertical handovers are an important ramification of user mobility in a heterogeneous wireless network since they directly impact network performance, such as throughput, packet delay, or call blocking probability. However, the study of vertical handovers faces many difficulties when it comes to analysis, including network modeling which is extremely complicated due to topology diversity. To tackle such analytic challenges, we propose a stochastic geometric analysis scheme on user mobility and small cell networks, in order to capture the impacts of different topologies. We derive a theoretical expression for the expected number of handovers that occur and a corresponding approximation. Our analysis shows that the number of vertical handovers is determined as a concave function of small cell radius r when given the small cell density. Via simulations our analysis is verified in various wireless environments.