Learning-Based Signal Recovery in Nonlinear Systems with Spectrally Separated Interference

Upper Mid-Band (FR3, 7-24 GHz) receivers for 6G must operate over wide bandwidths in dense spectral environments, making them particularly vulnerable to strong adjacent-band interference and front-end nonlinearities. While conventional linear receivers can suppress spectrally separated interferers under ideal hardware assumptions, receiver saturation and finite-resolution quantization cause nonlinear spectral leakage that severely degrades performance in practical wideband radios. We study the recovery of a desired signal from nonlinear receiver observations corrupted by a high-power out-of-band interferer. The receiver front-end is modeled as a smooth, memoryless nonlinearity followed by additive noise and optional quantization. To mitigate these nonlinear and quantization-induced distortions, we propose a learned multi-layer Vector Approximate Message Passing (LMLVAMP) algorithm that incorporates spectral priors with neural network based denoising. Simulation results demonstrate significant performance gains over conventional methods, particularly in high-interference regimes representative of FR3 coexistence scenarios.

Interpolation Techniques for Fast Channel Estimation in Ray Tracing

Ray tracing is increasingly utilized in wireless system simulations to estimate channel paths. In large-scale simulations with complex environments, ray tracing at high resolution can be computationally demanding. To reduce the computation, this paper presents a novel method for conducting ray tracing at a coarse set of reference points and interpolating the channels at other locations. The key insight is to interpolate the images of reflected points. In addition to the computational savings, the method directly captures the spherical nature of each wavefront enabling fast and accurate computation of channels using line-of-sight MIMO and other wide aperture techniques. Through empirical validation and comparison with exhaustive ray tracing, we demonstrate the efficacy and practicality of our approach in achieving high-fidelity channel predictions with reduced computational resources.

Distributed Uplink Anti-Jamming in LEO Mega-Constellations via Game-Theoretic Beamforming

Low-Earth-Orbit (LEO) satellite constellations have become vital in emerging commercial and defense Non-Terrestrial Networks (NTNs). However, their predictable orbital dynamics and exposed geometries make them highly susceptible to ground-based jamming. Traditional single-satellite interference mitigation techniques struggle to spatially separate desired uplink signals from nearby jammers, even with large antenna arrays. This paper explores a distributed multi-satellite anti-jamming strategy leveraging the dense connectivity and high-speed inter-satellite links of modern LEO mega-constellations. We model the uplink interference scenario as a convex-concave game between a desired terrestrial transmitter and a jammer, each optimizing their spatial covariance matrices to maximize or minimize achievable rate. We propose an efficient min-max solver combining alternating best-response updates with projected gradient descent, achieving fast convergence of the beamforming strategy to the Nash equilibrium. Using realistic Starlink orbital geometries and Sionna ray-tracing simulations, we demonstrate that while close-proximity jammers can cripple single-satellite links, distributed satellite cooperation significantly enhances resilience, shifting the capacity distribution upward under strong interference.

Scalable Long-Term Beamforming for Massive Multi-User MIMO

Fully digital massive MIMO systems with large numbers (1000+) of antennas offer dramatically increased capacity gains from spatial multiplexing and beamforming. Designing digital receivers that can scale to these array dimensions presents significant challenges regarding both channel estimation overhead and digital computation. This paper presents a computationally efficient and low-overhead receiver design based on long-term beamforming. The method combines finding a low-rank projection from the spatial covariance estimate with a fast polynomial matrix inverse. Ray tracing simulations show minimal loss relative to complete instantaneous beamforming while offering significant overhead and computational gains.

Traffic Adaptive Moving-window Service Patrolling for Real-time Incident Management during High-impact Events

This paper presents the Traffic Adaptive Moving-window Patrolling Algorithm (TAMPA), designed to improve real-time incident management during major events like sports tournaments and concerts. Such events significantly stress transportation networks, requiring efficient and adaptive patrol solutions. TAMPA integrates predictive traffic modeling and real-time complaint estimation, dynamically optimizing patrol deployment. Using dynamic programming, the algorithm continuously adjusts patrol strategies within short planning windows, effectively balancing immediate response and efficient routing. Leveraging the Dvoretzky-Kiefer-Wolfowitz inequality, TAMPA detects significant shifts in complaint patterns, triggering proactive adjustments in patrol routes. Theoretical analyses ensure performance remains closely aligned with optimal solutions. Simulation results from an urban traffic network demonstrate TAMPA's superior performance, showing improvements of approximately 87.5\% over stationary methods and 114.2\% over random strategies. Future work includes enhancing adaptability and incorporating digital twin technology for improved predictive accuracy, particularly relevant for events like the 2026 FIFA World Cup at MetLife Stadium.

Computationally Efficient Signal Detection with Unknown Bandwidths

Signal detection in environments with unknown signal bandwidth and time intervals is a basic problem in adversarial and spectrum-sharing scenarios. This paper addresses the problem of detecting signals occupying unknown degrees of freedom from non-coherent power measurements where the signal is constrained to an interval in one dimension or hypercube in multiple dimensions. A Generalized Likelihood Ratio Test (GLRT) is derived, resulting in a straightforward metric involving normalized average signal energy on each candidate signal set. We present bounds on false alarm and missed detection probabilities, demonstrating their dependence on signal-to-noise ratios (SNR) and signal set sizes. To overcome the inherent computational complexity of exhaustive searches, we propose a computationally efficient binary search method, reducing the complexity from O(N2) to O(N) for one-dimensional cases. Simulations indicate that the method maintains performance near exhaustive searches and achieves asymptotic consistency, with interval-of-overlap converging to one under constant SNR as measurement size increases. The simulation studies also demonstrate superior performance and reduced complexity compared to contemporary neural network-based approaches, specifically outperforming custom-trained U-Net models in spectrum detection tasks.

Upper Mid-Band Spectrum for 6G: Vision, Opportunity and Challenges

Driven by the pursuit of gigabit-per-second data speeds for future 6G mobile networks, in addition to the support of sensing and artificial intelligence applications, the industry is expanding beyond crowded sub-6 GHz bands with innovative new spectrum allocations. In this paper, we chart a compelling vision for 6G within the frequency range 3 (FR3) spectrum, i.e. $7.125$-$24.25$ $\GHz$, by delving into its key enablers and addressing the multifaceted challenges that lie ahead for these new frequency bands. Here we highlight the physical properties of this \textcolor{black}{never-before} used spectrum by reviewing recent channel measurements for outdoor and indoor environments, including path loss, delay and angular spreads, and material penetration loss, all which offer insights that underpin future 5G/6G wireless communication designs. Building on the fundamental knowledge of the channel properties, we explore FR3 spectrum agility strategies that balance coverage and capacity (e.g. data rate) tradeoffs, while also examining coexistence with incumbent systems, such as satellites, radio astronomy, and earth exploration. Moreover, we discuss the potential of massive multiple-input multiple-output, compact and digital architectures, and evaluate the potential of multiband sensing for FR3 integrated sensing and communications. Finally, we outline 6G standardization features that are likely to emerge from 3GPP radio frame innovations and open radio access network developments.

Near-Field Measurement System for the Upper Mid-Band

The upper mid-band (or FR3, spanning 6-24 GHz) is a crucial frequency range for next-generation mobile networks, offering a favorable balance between coverage and spectrum efficiency. From another perspective, the systems operating in the near-field in both indoor environment and outdoor environments can support line-of-sight multiple input multiple output (MIMO) communications and be beneficial from the FR3 bands. In this paper, a novel method is proposed to measure the near-field parameters leveraging a recently developed reflection model where the near-field paths can be described by their image points. We show that these image points can be accurately estimated via triangulation from multiple measurements with a small number of antennas in each measurement, thus affording a low-cost procedure for near-field multi-path parameter extraction. A preliminary experimental apparatus is presented comprising 2 transmit and 2 receive antennas mounted on a linear track to measure the 2x2 MIMO channel at various displacements. The system uses a recently-developed wideband radio frequency (RF) transceiver board with fast frequency switching, an FPGA for fast baseband processing, and a new parameter extraction method to recover paths and spherical characteristics from the multiple 2x2 measurements.

An Experimental Multi-Band Channel Characterization in the Upper Mid-Band

The following paper provides a multi-band channel measurement analysis on the frequency range (FR)3. This study focuses on the FR3 low frequencies 6.5 GHz and 8.75 GHz with a setup tailored to the context of integrated sensing and communication (ISAC), where the data are collected with and without the presence of a target. A method based on multiple signal classification (MUSIC) is used to refine the delays of the channel impulse response estimates. The results reveal that the channel at the lower frequency 6.5 GHz has additional distinguishable multipath components in the presence of the target, while the one associated with the higher frequency 8.75 GHz has more blockage. The set of results reported in this paper serves as a benchmark for future multi-band studies in the FR3 spectrum.

Channel Modeling for FR3 Upper Mid-band via Generative Adversarial Networks

The upper mid-band (FR3) has been recently attracting interest for new generation of mobile networks, as it provides a promising balance between spectrum availability and coverage, which are inherent limitations of the sub 6GHz and millimeter wave bands, respectively. In order to efficiently design and optimize the network, channel modeling plays a key role since FR3 systems are expected to operate at multiple frequency bands. Data-driven methods, especially generative adversarial networks (GANs), can capture the intricate relationships among data samples, and provide an appropriate tool for FR3 channel modeling. In this work, we present the architecture, link state model, and path generative network of GAN-based FR3 channel modeling. The comparison of our model greatly matches the ray-tracing simulated data.