Rapid signal fluctuations due to blockage effects cause severe risks of handover failures (HOF) and ping pongs (PPs). By reconfiguring line-of-sight (LoS) Links through passive reflections, intelligent reflecting surface (IRS) has the potential to maintain link stability and resolve this issue. However, existing handover (HO) process analyses have not introduced both blockage effects and IRS reflections, thus fail to explore the potential of IRS in this aspect.This paper analyzes the IRS-aided HO process by tracking the Line-of-Sight (LoS) state of moving users, where effects of LoS state transitions are characterized by modifying the state transition probability of HO process. Specifically, LoS states involve LoS, non-LoS, and IRS-reconfigured LoS, and the state transition probabilities are obtained through exact blockage modeling and IRS reflection analysis, taking into account the correlation of adjacent moments. In addition, Markov Chain-based analytical models are designed for the process of HO, HOF, and PP considering HO parameters (Time-to-Trigger and HO margin), whose transition probabilities are integrated with all LoS states. The results indicate that under severe blocking effects, the trade-off of HO parameters becomes ineffective, with HOF and PP probabilities remaining high. However, after introducing IRS, a viable range of HO parameters emerges.

Group Re-Identification (Group ReID) aims matching groups of pedestrians across non-overlapping cameras. Unlike single person ReID, Group ReID focuses more on the changes in group structure, emphasizing the number of members and their spatial arrangement. However, most methods rely on certainty-based models, which consider only the specific group structures in the group images, often failing to match unseen group configurations. To this end, we propose a novel Group-CLIP Uncertainty Modeling (GCUM) approach that adapts group text descriptions to undetermined accommodate member and layout variations. Specifically, we design a Member Variant Simulation (MVS) module that simulates member exclusions using a Bernoulli distribution and a Group Layout Adaptation (GLA) module that generates uncertain group text descriptions with identity-specific tokens. In addition, we design a Group Relationship Construction Encoder (GRCE) that uses group features to refine individual features, and employ cross-modal contrastive loss to obtain generalizable knowledge from group text descriptions. It is worth noting that we are the first to employ CLIP to Group ReID, and extensive experiments show that GCUM significantly outperforms state-of-the-art Group ReID methods.

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 46% but exacerbate HOFs by 91% under regular parameters. Optimal parameters are mined ensuring probabilities of HOF and PP are both less than 0.1%.

Signal fluctuations caused by blockage effects induce numerous handover failures (HOFs) and ping-pongs (PPs). Intelligent reflecting surfaces (IRSs) have the potential to address this issue by constructing indirect Line-of-Sight (LoS) links. However, this potential remains underexploited since existing works lack dedicated handover (HO) parameter optimization approaches. This paper proposes an HO boundary generation model for optimizing the HO parameters of IRS-aided networks, where the locations of HO events (HO triggers, HOFs, and PPs) are explicitly modeled as geometric boundaries reshaped by signal blocking and IRS reflection to evaluate and minimize HOFs and PPs. Specifically, initial boundaries are generated by projecting received signal strength trigger conditions for HO events onto the geometric elements across all considered propagation conditions. Exact boundaries are then determined by preserving parts of the geometric elements where the propagation condition holds. By evaluating the distance between the boundaries of HO events, the upper bounds on HOF and PP probabilities are derived and minimized under the worst-case criterion to determine the optimal HO parameters (HO offset and time-to-trigger). Simulations and IRS prototype-based experimental verifications are conducted to validate the proposed algorithm. Field measurements in a typical corridor corner scenario confirm that the IRS inherently reduces HOFs and PPs, and the proposed algorithm further reduces incorrect HOs by at least 42.2% compared to existing algorithms.

Due to the time-varying channels with line-of-sight (LoS) and non-LoS (NLOS) components, the handover (HO) trigger locations of unmanned aerial vehicle (UAV)-assisted networks are irregular and fluctuating, which causes frequent HOs and HO failures (HOFs). However, existing works only consider path loss and cannot obtain sound HO parameters. In this paper, the HO events trigger locations are explicitly modeled as time-varying boundaries, where the UAV altitude and LoS/NLoS components are projected to geometric boundaries satisfying the conditions of HO events, and the fluctuation of boundaries is introduced by modeling their radii as random variables with fast fading coefficients. Based on specific received signal ratios of HO events, the distributions of HO event boundaries are derived via the projection criterion. Specifically, the HO trigger location is analyzed through the HO boundary considering the measurement period, then HO status during Time-to-Trigger is analyzed via HO event boundaries and user-UAV geometric relationship. Expressions of HO events in UAV-assisted networks, including HO, HOF, and ping-pong (PP), are obtained with and without fast fading. The results show more HOs, HOFs, and PPs occur with fast fading, which increase by 1.7×, 1.4×, and 3.1× when densities of the MBS and UAV are 10/km2and 100/km2.

Featured by flexible deployment, unmanned aerial vehicles (UAVs) can form multi-tier networks in 3D space, enhancing the capacity, but this heterogeneity causes more unnecessary handovers (HOs), i.e., ping-pongs (PPs) and handover failures (HOFs). However, existing studies have not modeled the HO process in multi-tier UAV networks. This paper proposes an equivalent model for the HO process in$K$-tier UAV networks, where nonlinear mapping rules are established from the altitude and transmit power to equivalent horizontal distances specific to HO events, then HO status during time-to-trigger (TTT) is analyzed via the mapping rules. Specifically, each trigger condition of the HO events is transformed into a circular boundary centered on the projection of the target UAV with the corresponding trigger radii. Considering the practical HO process, the HO trigger location is tractably modeled as a random variable of the path length from the start, and the signal-to-interference ratio during TTT and the residence time are analyzed through equivalent geometric relations. The results show that there exists an altitude ratio that causes the most frequent HO, which increases by 25% to 150% under different configurations. Trade-offs between HOF and PP exist in the altitude ratio and transmit power ratio, besides the HO parameters.

User‐centric network (UCN) is regarded as a promising candidate to approach the challenges of more radio link failures (RLFs) due to the ultradense deployment of small base stations (SBSs) and meet the requirements of ultrahigh throughput, ultrahigh reliability, and ultralow latency for the 6G system. In this paper, soft mobility is proposed for UCN with the split of control and user plane (C/U‐plane) and shared physical cell identifier (PCI) to achieve the goal of zero handover failure (HOF) probability, where transparent handover (HO) within a cell is realized with user configuration duplication and measurement enhancement. Specifically, the cell is composed of several SBSs around the user, where one anchor SBS is selected for controlling, and others act as slave SBSs for transmission with duplicated UE configuration from the anchor SBS. Based on the proposed architecture, the user measures downlink channel quality for cells and SBSs distinguishingly, via SS/PBCH Block (SSB) and channel‐state information‐reference signal (CSI‐RS), respectively, and then makes the HO decision. Results show that soft mobility can reduce the number of HOF by about 50% over the current system, and the HOF probability is lower than 1% for TTT = 40 ms and offset = −1 dB.