wireless systems design

To address physical-layer modeling distortions and the inability to accurately capture deep-fading mitigation effects in mid-band digital wireless communication system simulations, this paper proposes a comprehensive end-to-end physical-layer modeling framework for the mid-band. Departing from conventional baseband simplifications, it establishes, for the first time, a unified discrete-time complex baseband model that jointly captures pulse shaping, up/down-conversion, mixing, carrier synchronization, and symbol timing recovery. Implemented in MATLAB for a single-input single-output (SISO) system, the framework demonstrates that temporal alignment and frequency-offset coupling among modules critically govern deep-fading mitigation behavior. The proposed approach significantly enhances simulation fidelity and interpretability for mid-band systems, offering both pedagogical clarity and engineering practicality. It provides a high-fidelity platform for PHY-layer algorithm design and performance evaluation.

The absence of a standardized system-level simulation framework hinders rigorous performance evaluation of Reconfigurable Intelligent Surfaces (RIS) in 6G multi-RIS, multi-base-station networks. Method: This work establishes the first 3GPP-compliant system-level simulator for RIS-aided networks. It integrates geometrically random RIS deployment, configurable panel density and size, and—novelly within a standardized framework—jointly models key non-ideal hardware effects: near-field propagation, inter-panel interference, phase quantization error, and unit failure. These factors are rigorously incorporated into path loss and large-scale fading modeling for both RIS-reflected and direct links. Results: Simulation results demonstrate that strategic RIS deployment significantly enhances Reference Signal Received Power (RSRP), Signal-to-Interference-plus-Noise Ratio (SINR), spectral efficiency, and cell coverage. The platform provides a reproducible, quantitative performance benchmark and design guidelines to support standardization of RIS technology in 6G systems.

This study addresses reliability challenges in maritime wireless communications under shared-spectrum conditions, focusing on signal degradation induced by dynamic obstructions and complex marine environments. We extend laboratory-based electromagnetic propagation measurement methodologies—previously confined to controlled settings—to real-world deployments on an electric research vessel, conducting systematic indoor–outdoor comparative experiments. These include line-of-sight obstruction tests, multi-location received signal strength (RSS) measurements, and empirical signal attenuation analysis. We quantitatively characterize the impact of obstacles (e.g., lab objects and ship structures), transmission distance, and device mounting height on path loss. Results reveal that obstructions significantly exacerbate path loss; distance and antenna height exhibit nonlinear effects on link margin; and typical ship structures introduce up to 20 dB additional attenuation. The findings provide reproducible empirical evidence and actionable design guidelines for robust maritime wireless network deployment.

To address the stringent requirements of 6G—namely, ultra-high throughput, ultra-low latency, and seamless connectivity—conventional digital baseband processing faces fundamental bottlenecks in hardware complexity and computational latency. This paper proposes stacked intelligent metasurfaces (SIMs), which exploit intrinsic electromagnetic wave physics to perform signal processing directly in the wave domain. We innovatively design a joint wave-domain transmit beamforming and semantic coding architecture, and introduce, for the first time, an SIM-assisted channel estimation paradigm, supported by a novel near-field channel model and corresponding estimation algorithm. The proposed approach drastically reduces hardware overhead and enables nanosecond-scale wave-domain computation, thereby breaking through the latency and complexity limits of digital baseband processing. Experimental results demonstrate that this technology supports deployable, novel air-interface architectures, offering a performance- and implementation-aware enabling solution for 6G.

Wireless communication beginners face high barriers to AI/ML practice and difficulty reproducing simulations. Method: This paper proposes a lightweight, end-to-end MIMO-OFDM simulation prototype framework implemented in Python at the single-OFDM-symbol level. It integrates canonical AI/ML use cases—such as supervised-learning-based channel estimation—and open-sources the custom deepwireless library, enabling efficient deployment and experimental reproducibility on low-cost hardware. Contribution/Results: It introduces the novel pedagogical paradigm of “lecture notes as code,” directly transforming theoretical courses (e.g., EESC 7v86) into extensible, modular, open-source simulation frameworks. The framework significantly reduces both research and teaching overhead for intelligent wireless algorithms, fostering a reproducible, lightweight, and accessible experimental ecosystem for AI-driven communications.

This study reveals that reconfigurable intelligent surfaces (RIS), while optimizing a primary communication link, can induce significant unintended interference on nearby secondary links—even when the two are spatially separated and operate on distinct carrier frequencies. Through real-world experiments conducted in the FR1 band using the CorteXlab platform and Greenerwave RIS hardware, the work systematically evaluates the impact of RIS configurations on the received power and channel phase of secondary links under representative coexistence scenarios, including both co-channel and inter-channel conditions. The experiments provide the first empirical evidence that conventional assumptions relying on frequency-domain isolation to mitigate interference no longer hold in RIS-enabled environments, thereby underscoring the critical need for cross-link compatibility considerations in RIS deployment strategies.

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.

Conventional single-layer reconfigurable intelligent surfaces (RISs) offer limited electromagnetic control, insufficient to meet 6G’s demand for high-dimensional and flexible signal processing. This work presents a systematic review of stacked intelligent metasurfaces (SIMs), establishing— for the first time—a theoretical framework that positions SIMs as programmable electromagnetic processors. It introduces a novel wave-domain signal processing paradigm grounded in cascaded wave–matter interactions. By leveraging cascaded operator modeling, multi-port impedance analysis, and learning-driven control strategies, the study reveals the potential of SIMs in near-field communications, broadband transmission, and integrated sensing and communication. Furthermore, it identifies key research directions, including cross-layer co-design and network-level integration, thereby providing a comprehensive technical roadmap for programmable electromagnetic front-ends in 6G systems.