This study evaluates the incremental benefit of selective temporal refinement beyond Static Random Forest (RF) classification and causal Majority-3 stabilization in mmWave radar human–background recognition. Static RF uses 41-D current-frame features and its intrinsic tree-probability disagreement for routing, without an additional trained router. When disagreement exceeds a development-selected threshold and consecutive three-frame (K3) history is valid, the system invokes Temporal RF with a 54-D input combining 41-D current-frame features and a 13-D temporal extension; otherwise, Static predictions are retained. All paths use the same causal Majority-3. Across eight independent test acquisitions (16,742 frames), Static, Selective, and Always produce 86, 72, and 71 final errors. Relative to Static, Selective reduces Background false positives by 14, decreasing residual errors by 16.28% while retaining two Human misses. Selective invokes Temporal RF 479 times (2.8611% of all frames), versus 15,758 times (94.1226%) for Always, reducing additional temporal calls by 96.96%. In host inference tests, Fused Selective and Optimized Always have mean latencies of 10.642 and 11.681 ms/frame, respectively, an approximately 8.9% reduction. On two independent acquisitions at an additional logistics site, Selective reduces final errors from 66 to 56. These results show that disagreement-guided selective temporal refinement provides incremental improvement beyond static classification and causal output stabilization under the evaluated conditions, with infrequent additional temporal invocation.

To address the critical requirements for electromagnetic spectrum orchestration in complex ultra-dense communication environments, this paper proposes an Expert Knowledge-Guided improved NSGA-III framework to solve large-scale frequency assignment problems efficiently. which is built upon the standard NSGA-III architecture as the algorithmic backbone. Traditional multi-objective evolutionary algorithms often struggle with slow convergence and insufficient local search capabilities when navigating high-dimensional, strongly constrained search spaces. In this study, we first introduce a Conflict Graph-based Intelligent Initialization strategy to generate high-quality initial populations by constructing an interference conflict graph based on network topology. Second, a Knowledge-Guided Mutation operator is designed to precisely identify and reconfigure conflicting communication nodes using physical layer indicators. Furthermore, a Best-individual Guided Double-Scale Mutation mechanism is incorporated to dynamically balance global exploration and local exploitation. Experimental results on complex multi-node datasets demonstrate that EKG-NSGA-III significantly outperforms the standard NSGA-III and other baseline algorithms in terms of Hypervolume and Inverted Generational Distance. Specifically, for the 200 nodes scenario, the proposed method achieves a 25.7% improvement in IGD and a 4.2% increase in HV compared to the standard NSGA-III. The proposed algorithm provides a robust and efficient solution for spectrum management in complex urban electromagnetic environments, such as future smart city infrastructures.

Dynamic spectrum reallocation must mitigate severe link degradation under limited search and frequency switching. We study a risk-triggered controller combining receiver/aggressor ranking, screened greedy repair and conditional two-coordinate refinement under cumulative co-channel and adjacent channel interference. With four frequency candidates per link and a 12-link greedy screen, each noninitial decision requires at most 104 physical evaluations and respects a hard switching limit. Component, disturbance recovery and single-factor experiments characterize the operating trade-offs. Findings from these studies motivate a separately frozen tail risk validation using 30 new seeds at each of two network sizes, yielding 240 closed-loop trajectories. Its sole primary endpoint is the trajectory-mean cross-link conditional value-at-risk of signal-to-interference-plus-noise ratio (SINR) shortfall. In the prescribed 30-link scenarios, the controller reduces this endpoint by 10.61% against ranked coordinate search and 11.97% against a literature-informed memetic reference. Both comparisons survive Holm correction. An advantage over the no-refinement ablation is not established, nor is any primary comparison at 14 links. All methods fail to attain fully feasible slots in the 30-link validation. A separate screening size sensitivity study finds lower 30-link tail risk with a 24-link screen under the same evaluation ceiling; the original 12-link choice is therefore not optimal among the tested settings. Post-review rescorings of the R4 decisions retain the 30-link comparator advantages for raw dB shortfall but not the absolute linear SINR shortfall. Thus, the observed ranking depends on the declared loss definition. The evidence therefore supports conditional mitigation of severe link degradation under that definition, not metric-independent superiority, service-level compliance or an end-to-end real-time guarantee.