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.

In this paper, we propose a load-balancing algorithm for small-cell integrated access and backhaul (IAB) networks operating in the millimeter wave (mmWave) band. With the help of mmWave communications, ultra-dense small cell deployment is a key technology for future networks, but it leads to high fiber installation costs. IAB has emerged as a cost-effective and flexible solution because it uses the multi-hop wireless backhaul to the core network via macro base stations (BSs). Multi-hop transmission and spectrum sharing between access and backhaul links cause an unbalanced load across BSs under IAB. To tackle the unbalanced load problem, we propose a graph-based load balancing algorithm for IAB by estimating and adapting to network load status. The weight of the graph is represented as the cost of the link between two BSs and is defined to reflect the cell load and link capacity. The proposed algorithm estimates the upcoming load and adjusts the network transmission schedule to balance the load inside the network. Through simulations, performance is evaluated in various environments. The simulation results show that the proposed algorithm not only distributes the load across small cells more evenly but also increases network throughput by 22.3% and 32.1% with 16.3% and 28.7% higher in UEs satisfaction when compared to MaxMinThroughput and Backpressure algorithms.

In this paper, we propose a battery management algorithm to maximize the lifetime of a parallel-series connected battery pack with heterogeneous states of health in a battery energy storage system. The growth of retired lithium-ion batteries from electric vehicles increases the applications for battery energy storage systems, which typically group multiple individual batteries with heterogeneous states of health in parallel and series to achieve the required voltage and capacity. However, previous work has primarily focused on either parallel or series connections of batteries due to the complexity of managing diverse battery states, such as state of charge and state of health. To address the scheduling in parallel-series connections, we propose a cooperative multi-agent deep Q network framework that leverages multi-agent deep reinforcement learning to observe multiple states within the battery energy storage system and optimize the scheduling of cells and modules in a parallel-series connected battery pack. Our approach not only balances the states of health across the cells and modules but also enhances the overall lifetime of the battery pack. Through simulation, we demonstrate that our algorithm extends the battery pack’s lifetime by up to 16.27% compared to previous work and exhibits robustness in adapting to various power demand conditions.

In this article, we model and analyze the characteristics of a blockchain-enabled wireless Internet of Things (IoT). Blockchain is considered a vital technology for the development of a secure and trusted 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.