analyze channel ordering

This paper addresses beam misalignment in analog beamforming for 3GPP millimeter-wave NR systems, modeling long-term misalignment under the coupled effects of user mobility, SSB periodicity, TDD frame structure, and deployment parameters. Method: It innovatively incorporates practical NR constraints—such as SSB overhead, timing restrictions, and feasible beam count—into a Poisson process modeling framework. Contribution/Results: The work derives, for the first time, closed-form expressions for misalignment duration, misalignment ratio, and beamforming gain loss. The analytical model uncovers fundamental trade-offs among beam count, user velocity, and SSB configuration, providing theoretical design guidelines for robust beam management. Numerical evaluation using 3GPP-standard parameters validates model accuracy and quantifies parameter sensitivity and optimization boundaries.

This study addresses the challenges in joint communication and control design, where transmission delay and control steady-state variance are difficult to co-optimize and system reliability lacks a unified evaluation metric. To this end, the authors develop an integrated communication-control analysis framework and, for the first time, derive the Pareto boundary characterizing the trade-off between these two performance measures. They further propose a Joint Decoding and Control Completion (JDCC) outage probability to quantify system reliability. Leveraging maximum-ratio transmission (MRT) and zero-forcing (ZF) beamforming, combined with stochastic process and information-theoretic techniques, closed-form expressions for both the achievable performance region and the JDCC outage probability are obtained. Theoretical analysis and simulations confirm the tightness of the derived Pareto boundary, reveal the coupling mechanisms between communication and control in uplink–downlink closed-loop systems, and establish fundamental performance limits for JDCC-based systems.

This study addresses the unclear mechanisms by which configuration parameters influence topology quality and performance in tactical wireless networks. It systematically investigates the sensitivity of three parameter categories—structural constraints, technology choices, and modeling assumptions—by generating optimized topologies using a tabu search metaheuristic and assessing statistical significance through Friedman and Wilcoxon non-parametric tests. The findings reveal a fundamental distinction between parameters that substantially reshape network topology and those that merely modulate performance magnitude. Moreover, the work identifies scale-dependent technological transition phenomena and threshold effects induced by structural constraints. These insights yield actionable design principles for parameter tuning and topology optimization in mission-critical tactical networks.

This paper addresses dynamic user scheduling in a single-cell millimeter-wave (mmWave) downlink under constrained base station beam resources, aiming to minimize the long-term average cost—comprising queue holding and beam activation costs. We formulate the problem as a Restless Multi-Armed Bandit (RMAB) and, for the first time, rigorously prove its Whittle indexability. Leveraging this property, we derive a closed-form, analytically tractable, and computationally efficient expression for the Whittle index, and design a real-time scheduling policy based on the “lowest-index-first” principle. Our approach jointly incorporates mmWave channel state awareness and stochastic queue analysis. Simulation results demonstrate that the proposed method reduces average cost by over 30%, decreases end-to-end latency by more than 25%, and significantly improves energy efficiency compared to state-of-the-art baselines. Key contributions include: (i) the first theoretical proof of Whittle indexability for mmWave beam scheduling; (ii) an efficient closed-form Whittle index computation; and (iii) a low-complexity online scheduling policy with provable performance guarantees.

To address the joint scheduling challenge of communication, radar search, and tracking tasks under QoS constraints in multi-cell integrated sensing and communication (ISAC) networks, this paper proposes an interference-aware medium access control (MAC) framework. Methodologically, it jointly optimizes radar scanning patterns and inter-cell task scheduling, formulating a QoS-driven multi-task resource reuse model and designing a low-complexity algorithm for dynamic sensing-communication resource coordination. The key contribution lies in the first explicit incorporation of radar scanning degrees of freedom—such as azimuth/elevation angular resolution and revisit interval—into MAC-layer scheduling decisions, thereby enabling deep coupling between physical-layer sensing characteristics and link-layer task orchestration. Simulation results demonstrate that the proposed scheme achieves a 23.7% gain in spectral efficiency and an 18.4% improvement in radar target detection probability, while strictly satisfying latency and reliability QoS requirements—significantly outperforming conventional orthogonal scheduling and static scanning baselines.

This study addresses the limitations of conventional optimization methods in the joint communication and control co-design for B6G networks, particularly regarding modular representation, requirements traceability, and design space analysis. To overcome these challenges, this work proposes a composition-driven methodology grounded in formal co-design theory. By introducing a compositional perspective, the proposed approach circumvents the bottlenecks inherent in joint optimization, thereby enabling the modular modeling of complex interacting subsystems and systematic exploration of the design space. The effectiveness of this methodology is validated through a wireless-assisted robotic control case study. Furthermore, this paper elucidates its complementary relationship with optimization-driven approaches. Ultimately, this research establishes a novel paradigm for cross-domain co-design within B6G scenarios, offering a rigorous framework to facilitate scalable and verifiable system integration.

This study addresses a critical gap in communication-aware robotic planning, where existing approaches commonly rely on channel-level metrics to predict end-to-end 5G throughput—a practice lacking empirical validation in private 5G deployments. Conducted in a shielded underground industrial environment, the work integrates commercial ray-tracing simulations, Gaussian process regression with a rational quadratic kernel, a mobile robotic platform, and off-the-shelf 5G user equipment to perform real-world measurements. It reveals for the first time that dynamic adaptation of MIMO spatial layers is the primary cause of systematic overestimation of throughput by conventional channel models, with ray tracing significantly overpredicting performance even in line-of-sight conditions. In contrast, a data-driven approach that directly learns end-to-end throughput reduces prediction error by approximately two-thirds and exhibits near-zero bias, demonstrating clear superiority over traditional channel-centric modeling.

This work addresses the joint wireless resource management challenge in uplink hybrid beamforming systems, where constraints on the number of radio-frequency chains and per-user power-time allocation complicate system optimization. To tackle this, the paper proposes a low-complexity heuristic algorithm that jointly optimizes, for each time slot, analog beam selection, user scheduling, power allocation, modulation and coding scheme, and digital zero-forcing beamforming. Leveraging codebook-based analog beamforming combined with zero-forcing digital processing, the proposed method achieves near-optimal performance while reducing computational complexity by two orders of magnitude and enabling scalability to large numbers of users. Experimental results demonstrate that the online algorithm closely approaches the theoretical performance upper bound and provide insights into the practical impact of key system parameters.

This work addresses energy efficiency and latency optimization in integrated sensing and communication (ISAC) systems under imperfect information. The authors jointly optimize time-slot allocation, beamforming adaptation, functionality selection, and user–target pairing to minimize energy consumption while prioritizing time savings, accounting for uncertainties arising from target dynamics, quantization errors, feedback delays, and hardware constraints. The problem is innovatively formulated as a semi-infinite nonconvex mixed-integer nonlinear program. By exploiting hidden convexity, the authors develop a structure-aware exact reformulation that equivalently transforms the problem into a globally solvable mixed-integer semidefinite program (MISDP). Simulations demonstrate that the proposed approach achieves up to 88% resource savings compared to baseline schemes and reveals strong coupling among the various resource management components.

本文提出了一种基于干扰驱动的聚类优化框架，用于大规模FM频谱协调，通过识别主要干扰源并利用GPU加速计算来减少优化复杂性和运行时间。