Owning to the reflection gain and double path loss featured by intelligent reflecting surface (IRS) channels, handover (HO) locations become irregular and the signal strength fluctuates sharply with variations in IRS connections during HO, the risk of HO failures (HOFs) is exacerbated and thus HO parameters require reconfiguration. However, existing HO models only assume monotonic negative exponential path loss and cannot obtain sound HO parameters. This paper proposes a discrete-time model to explicitly track the HO process with variations in IRS connections, where IRS connections and HO process are discretized as finite states by measurement intervals, and transitions between states are modeled as stochastic processes. Specifically, to capture signal fluctuations during HO, IRS connection state-dependent distributions of the user-IRS distance are modified by the correlation between measurement intervals. In addition, states of the HO process are formed with Time-to-Trigger and HO margin whose transition probabilities are integrated concerning all IRS connection states. Trigger location distributions and probabilities of HO, HOF, and ping-pong (PP) are obtained by tracing user HO states. Results show IRSs mitigate PPs by 48% but exacerbate HOFs by 90% under regular parameters. Optimal parameters are mined ensuring probabilities of HOF and PP are both less than 0.1%.

翻译：由于智能反射面信道具有反射增益与双路径损耗特性，切换位置呈现不规则性且切换期间信号强度随IRS连接变化剧烈波动，导致切换失败风险加剧，因此切换参数需重新配置。然而现有切换模型仅假设单调负指数路径损耗，无法获得合理的切换参数。本文提出一种离散时间模型，通过显式追踪IRS连接变化下的切换过程，将IRS连接状态与切换过程按测量间隔离散化为有限状态，并将状态间转移建模为随机过程。具体而言，为捕获切换期间的信号波动，通过测量间隔间的相关性修正了基于用户-IRS距离的IRS连接状态依赖分布。此外，切换过程的状态由触发时间与切换余量构成，其转移概率通过整合所有IRS连接状态得出。通过追踪用户切换状态，获得了触发位置分布以及切换、切换失败与乒乓的概率。结果表明，在常规参数下，智能反射面可使乒乓概率降低48%，但切换失败概率增加90%。通过优化参数挖掘，可确保切换失败与乒乓概率均低于0.1%。