Temporal knowledge graph reasoning (TKGR) is crucial for capturing dynamic relational dependencies and inferring evolving facts. However, existing embedding-based and diffusion-based methods primarily rely on Euclidean point embeddings and face two limitations: (1) sparse entities may suffer from embedding crowding and norm-sensitive scoring, especially under limited supervision in long-tail temporal knowledge graphs; (2) uniform noise schedules in diffusion models ignore temporal heterogeneity and instance-level prediction difficulty. To address these challenges, we propose SimDiff-DNS (Simplex Diffusion with Dynamic Noise Scheduling), a simplex-anchored logit diffusion framework for TKGR. First, we represent entity states as probability distributions on the simplex and obtain continuous embeddings as convex combinations of learnable entity basis vectors. This simplex-coordinate parameterization provides a probabilistic view of uncertainty and alleviates norm-sensitive ranking behavior for sparse entities. Second, we introduce a difficulty-aware dynamic noise scheduling strategy for the training corruption process, which adjusts the noise applied to target-entity logits according to a bounded difficulty score. Extensive experiments on three TKGR datasets show that SimDiff-DNS consistently improves over the diffusion-based baseline (improving MRR by 3.25 points on ICEWS14) and achieves competitive or state-of-the-art results on several metrics. Additional diagnostic analyses further demonstrate its benefits for long-tail entities, calibration, and ranking stability.