Mixture-of-Experts Deep Reinforcement Learning for Reliability-Constrained Energy-Efficient PDCCH Monitoring in Internet of Thing Device

The continuous monitoring of the physical downlink control channel (PDCCH) is a major source of energy consumption in fifth-generation (5G) Internet of thing device (IoT-D), since the UE has to blindly detect downlink control information even when no valid scheduling grant is present. Although predictive dynamic power management can reduce unnecessary receiver activity by skipping PDCCH monitoring in grant-free slots, aggressive sleeping may lead to missed grants and degrade reception reliability. To address this tradeoff, this paper formulates UE-side PDCCH monitoring as a reliability-constrained long-term energy minimization problem. Specifically, the IoT-D determines, before observing the actual scheduling outcome, whether to monitor the PDCCH or switch the receiver chain into a low-power state. The objective is to minimize the long-term average energy consumption, including receiver operating energy, component switching energy, and prediction-related computational energy, while ensuring that the false negative rate of scheduling-grant detection remains below a prescribed threshold. The resulting problem is non-convex due to the bursty and temporally correlated nature of grant arrivals, and the binary monitoring decisions coupled by a long-term reliability constraint. To solve this problem, we propose a mixture-of-experts input-output hidden Markov model (MoE-IOHMM)-based predictive monitoring scheme, where multiple IO-HMM experts capture heterogeneous grant-arrival patterns and a gating network adaptively combines their predictions. Simulation results show that the proposed scheme effectively reduces IoT-D-side energy consumption compared with always-on PDCCH monitoring and conventional predictive baselines, while maintaining the false negative rate below the prescribed reliability threshold.

Geometry-Aware Cross-Height Channel Knowledge Map Prediction for UAV-Assisted Communications With Uncertainty-Guided 3D Sensing

Low-altitude Unmanned Aerial Vehicles (UAVs) often need to infer channel knowledge across a range of heights from only sparse observations collected at a few altitude layers. To address this challenge, this paper studies height-conditioned cross-height channel knowledge map (CKM) prediction for UAV-assisted communications in geometry-rich urban environments. We develop a geometry-aware conditional prediction framework that combines urban scene priors, sparse multi-altitude observations, and target-height descriptors to reconstruct dense CKMs at unobserved target heights. An uncertainty head is further introduced to characterize prediction confidence and to support cost-aware online UAV sensing under motion and safety constraints. Experiments on a layered aerial CKM benchmark show that the proposed Feature Pyramid Network (FPN)-Transformer achieves the best overall performance under both unseen-scene zero-shot and legacy patch-random protocols, reducing the Root Mean Square Error (RMSE) to 5.347dB and 1.111dB, respectively, compared with 6.937dB and 1.221dB for the strongest baseline 3D-RadioDiff. Moreover, after applying our unseen-scene few-shot adaptation, the RMSE further decreases from 5.347dB in zero-shot prediction to 3.518dB with 10-shot two-height support, while the uncertainty-guided cost-aware sensing policy improves active reconstruction from 6.94dB at initialization to 4.79dB at sensing budget 40, outperforming uncertainty-only sensing at 5.08dB and random aerial sampling at 5.84dB.

Robust Secure Beamforming for Movable Antenna Enhanced Integrated Sensing and Communications

In this letter, we investigate robust beamforming design for a movable antenna (MA)-enhanced secure integrated sensing and communications (ISAC) system with imperfect eaves?dropping channel state information (CSI). To improve radar sensing performance, we formulate a radar signal-to-interference?plus-noise ratio (SINR) maximization problem by jointly opti?mizing the transmit beamforming and antenna placement while ensuring communication data security. However, the resulting op?timization problem is inherently intractable due to the nonlinea mapping from antenna positions to channel coefficients, as well as the eavesdropper (Eve) channel uncertainty. To handle these challenges, we propose a block coordinate descent (BCD)-based algorithm incorporating successive convex approximation (SCA) and fractional programming (FP) techniques. Simulation results show that our proposed algorithm exhibits fast convergence and achieves a significant improvement in the radar SINR while guaranteeing communication security.

Movable Antenna Enhanced Dual-Functional Radar-Communication: A Symbol-Level Precoding Approach

This letter investigates a symbol-level precoder design for movable antenna (MA)-enhanced dual-functional radar-communication (DFRC) systems. To enhance radar sensing capabilities, we formulate an optimization problem aimed at maximizing the minimum radar signal-to-interference-plus-noise ratio (SINR) across multiple targets in a cluttered environment. Our approach jointly designs the space-time transmitted waveforms, receiving filters, and antenna placement. However, the resulting problem is intractable to solve due to practical waveform constraints and the non-linear mapping from antenna positions to the corresponding channel coefficients. To address these challenges, we develop a bi-level optimization framework by leveraging deep reinforcement learning (DRL). Specifically, the twin delayed deep deterministic policy gradient (TD3) algorithm is employed in the outer layer to optimize antenna placement, while penalty convex-concave procedure (CCP) and majorization-minimization (MM) techniques are incorporated in the inner layer for regularizing waveform design. Simulation results demonstrate that the proposed method significantly improves radar SINR and achieves a superior sensing-communication trade-off compared to benchmark schemes.

Flexible Cylindrical Array-Aided Secure Wireless Communications

Flexible-geometry arrays based on movable antennas have shown considerable potential for improving wireless communication performance. In this letter, we investigate a multiuser multiple-input single-output (MU-MISO) downlink secure communication system aided by a flexible cylindrical array (FCLA) and artificial noise (AN), where each antenna element rotates along circular tracks while the circular slices move along a vertical axis. To guarantee transmission security, we aim to maximize the achievable sum rate at multiple legitimate information receivers by jointly optimizing transmit beamforming, AN covariance matrix, and antenna placement under secrecy constraints for an eavesdropper. While the resulting problem is intractable to solve, we develop a block coordinate descent (BCD)-based framework that combines the Lagrangian dual transform, tight semidefinite relaxation (SDR), and Nesterov-accelerated projected gradient descent (PGD). Numerical results show that the proposed algorithm converges rapidly and achieves significant sum-rate gains over benchmark schemes by exploiting the geometry flexibility of the array.

Heterogeneous Mixture-of-Experts for Energy-Efficient Multimodal ISAC in Highly Mobile Networks

The integration of multimodal sensing and millimeter-wave (mmWave) communications is a key enabler for highly mobile vehicle-to-infrastructure (V2I) networks. However, continuous high-resolution visual sensing incurs prohibitive computational energy, while delayed sensing information worsens beam misalignment. In this paper, we establish a physics-aware multimodel integrated sensing and communication (M-ISAC) framework that quantifies the mathematical trade-off between sensing energy and communication reliability using the semantic age of information (AoI). To address the coupled challenges of temporal AoI evolution and instantaneous non-convex constant modulus constraints, we propose a novel reinforcement learning approach empowered by a heterogeneous mixture-of-experts (RL-H-MoE) architecture. By strictly decoupling the temporal scheduling and spatial phase mapping, the RL-H-MoE avoids prevalent gradient conflicts in multi-task learning. Extensive simulations demonstrate that the proposed architecture achieves an optimal event-triggered sensing policy, significantly minimizing the long-term system cost while guaranteeing ultra-low sensing errors and reliable physical-layer link connectivity.

Sparse Gain Radio Map Reconstruction With Geometry Priors and Uncertainty-Guided Measurement Selection

Radio maps are important for environment-aware wireless communication, network planning, and radio resource optimization. However, dense radio map construction remains challenging when only a limited number of measurements are available, especially in complex urban environments with strong blockages, irregular geometry, and restricted sensing accessibility. Existing methods have explored interpolation, low-rank cartography, deep completion, and channel knowledge map (CKM) construction, but many of these methods insufficiently exploit explicit geometric priors or overlook the value of predictive uncertainty for subsequent sensing. In this paper, we study sparse gain radio map reconstruction from a geometry-aware and active sensing perspective. We first construct \textbf{UrbanRT-RM}, a controllable ray-tracing benchmark with diverse urban layouts, multiple base-station deployments, and multiple sparse sampling modes. We then propose \textbf{GeoUQ-GFNet}, a lightweight network that jointly predicts a dense gain radio map and a spatial uncertainty map from sparse measurements and structured scene priors. The predicted uncertainty is further used to guide active measurement selection under limited sensing budgets. Extensive experiments show that our proposed GeoUQ-GFNet method achieves strong and consistent reconstruction performance across different scenes and transmitter placements generated using UrbanRT-RM. Moreover, uncertainty-guided querying provides more effective reconstruction improvement than non-adaptive sampling under the same additional measurement budget. These results demonstrate the effectiveness of combining geometry-aware learning, uncertainty estimation, and benchmark-driven evaluation for sparse radio map reconstruction in complex urban environments.

ShardMemo: Masked MoE Routing for Sharded Agentic LLM Memory

Agentic large language model (LLM) systems rely on external memory for long-horizon state and concurrent multi-agent execution, but centralized indexes and heuristic partitions become bottlenecks as memory volume and parallel access grow. We present ShardMemo, a budgeted tiered memory service with Tier A per-agent working state, Tier B sharded evidence with shard-local approximate nearest neighbor (ANN) indexes, and Tier C, a versioned skill library. Tier B enforces scope-before-routing: structured eligibility constraints mask ineligible shards before routing or ANN search. We cast shard probing as masked mixture-of-experts (MoE) routing over eligible shards, probing up to $B_{\mathrm{probe}}$ shards via Top-$B_{\mathrm{probe}}$ or adaptive Top-$P$, and use cost-aware gating over profile/observation/session shard families; the router is trained from evidence-to-shard supervision. On LoCoMo, ShardMemo improves over the strongest baseline (GAM) by +5.11 to +6.82 F1 across question categories. Under a fixed-budget routing setting ($B_{\mathrm{probe}}=3$), ShardMemo improves over cosine-to-prototype shard routing by +6.87 F1 while reducing retrieval work (VecScan 521->414, -20.5%) and p95 latency (95->76 ms). On long-context HotpotQA, ShardMemo achieves 63.41/61.88/57.95 F1 at 56K/224K/448K tokens. On ToolBench, Tier C reaches 0.97 Precision@3 and 1.94 StepRed (+10.2% and +7.2% over embedding-similarity retrieval).

Movable Antenna Empowered Covert Dual-Functional Radar-Communication

Movable antenna (MA) has emerged as a promising technology to flexibly reconfigure wireless channels by adjusting antenna placement. In this paper, we study a secured dual-functional radar-communication (DFRC) system aided by movable antennas. To enhance the communication security, we aim to maximize the achievable sum rate by jointly optimizing the transmitter beamforming vectors, receiving filter, and antenna placement, subject to radar signal-to-noise ratio (SINR) and transmission covertness constraints. We consider multiple Willies operating in both non-colluding and colluding modes. For noncolluding Willies, we first employ a Lagrangian dual transformation procedure to reformulate the challenging optimization problem into a more tractable form. Subsequently, we develop an efficient block coordinate descent (BCD) algorithm that integrates semidefinite relaxation (SDR), projected gradient descent (PGD), Dinkelbach transformation, and successive convex approximation (SCA) techniques to tackle the resulting problem. For colluding Willies, we first derive the minimum detection error probability (DEP) by characterizing the optimal detection statistic, which is proven to follow the generalized Erlang distribution. Then, we develop a minimum mean square error (MMSE)-based algorithm to address the colluding detection problem. We further provide a comprehensive complexity analysis on the unified design framework. Simulation results demonstrate that the proposed method can significantly improve the covert sum rate, and achieve a superior balance between communication and radar performance compared with existing benchmark schemes.

PhyG-MoE: A Physics-Guided Mixture-of-Experts Framework for Energy-Efficient GNSS Interference Recognition

Complex electromagnetic interference increasingly compromises Global Navigation Satellite Systems (GNSS), threatening the reliability of Space-Air-Ground Integrated Networks (SAGIN). Although deep learning has advanced interference recognition, current static models suffer from a \textbf{fundamental limitation}: they impose a fixed computational topology regardless of the input's physical entropy. This rigidity leads to severe resource mismatch, where simple primitives consume the same processing cost as chaotic, saturated mixtures. To resolve this, this paper introduces PhyG-MoE (Physics-Guided Mixture-of-Experts), a framework designed to \textbf{dynamically align model capacity with signal complexity}. Unlike static architectures, the proposed system employs a spectrum-based gating mechanism that routes signals based on their spectral feature entanglement. A high-capacity TransNeXt expert is activated on-demand to disentangle complex features in saturated scenarios, while lightweight experts handle fundamental signals to minimize latency. Evaluations on 21 jamming categories demonstrate that PhyG-MoE achieves an overall accuracy of 97.58\%. By resolving the intrinsic conflict between static computing and dynamic electromagnetic environments, the proposed framework significantly reduces computational overhead without performance degradation, offering a viable solution for resource-constrained cognitive receivers.