analyze link budgets

Ultra-massive MIMO (XL-MIMO) systems operating in the mid-band face a severe energy-efficiency bottleneck due to power consumption scaling with array size, limiting their practical deployment. Method: We develop the first end-to-end, hardware- and signal-processing-aware power consumption model for XL-MIMO, integrated with near-field channel modeling and closed-form throughput analysis to establish a system-level energy efficiency (EE) analytical framework. Contribution/Results: We derive, for the first time, the analytical EE scaling law under near-field conditions. Theoretical and numerical validation confirms <3% throughput estimation error and excellent agreement between analytical EE predictions and simulations. At equal spectral efficiency, mid-band XL-MIMO achieves 18–35% lower power consumption than conventional multi-antenna systems, clearly demonstrating its superior energy efficiency.

Accurately modeling energy consumption in 5G radio access networks (RANs) remains challenging due to technological heterogeneity and deployment diversity. To address this, this paper proposes a fine-grained, configurable cross-layer energy consumption model. For the first time, it jointly incorporates physical-layer algorithmic complexity and hardware implementation characteristics, using computational cycles as a unifying metric to holistically characterize energy consumption across baseband processing, user equipment access, and channel interaction. The model is calibrated via MATLAB simulations and empirical measurements on Intel platforms. Validation across diverse deployment scenarios demonstrates an average error of less than 8%, significantly outperforming existing approaches. The proposed model enables cross-application energy-efficiency benchmarking and network-level green optimization. It establishes a new paradigm for verifiable and scalable energy-efficiency assessment of 5G RANs.

This study addresses the trade-off between capacity enhancement and increased energy consumption in 6G deployments within the FR3 band (7–24 GHz) by proposing a non-co-located, user hotspot-oriented deployment strategy. Leveraging real-world 4G/5G base station locations and traffic data from China, the authors develop Giulia—a deployment-aware system-level simulation framework that departs from conventional 3GPP templates—to jointly evaluate the energy efficiency and capacity of multi-layer 6G networks. Results demonstrate that, compared to traditional co-located approaches, the proposed strategy achieves up to a 9.5× improvement in median throughput while avoiding 59% of additional energy consumption, thereby substantially enhancing throughput-per-watt efficiency. This work provides empirical evidence and a novel paradigm for green and high-performance 6G network deployment.

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 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.

This study addresses the performance degradation caused by coexistence between standard-power (SP) and low-power indoor (LPI) devices in 6 GHz Wi-Fi networks. It presents the first systematic investigation of this issue, leveraging an ns-3-based simulation platform for heterogeneous-power Wi-Fi 6E/802.11ax coexistence. The work evaluates interference and throughput under diverse deployment scenarios, channel bandwidths (20–160 MHz), and BSS coloring configurations. Results reveal that indoor SP transmissions severely suppress LPI throughput, with 20 MHz channels suffering the worst interference while 160 MHz operation mitigates it. Physical obstructions improve fairness between indoor and outdoor deployments, and BSS coloring demonstrates limited effectiveness in mixed-power environments. The study provides a reproducible methodology and uncovers key mechanisms governing coexistence performance in 6 GHz Wi-Fi.

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 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.