arXiv subjects

Ross Murch

Publications and source records attributed to Ross Murch.

At least 19 recordsLinked to original sources

arXiv2026

Benchmarking Regularization Methods For 3D Radio Tomographic Imaging

Integrated Sensing and Communication (ISAC) is an important technology for 6G, enabling wireless systems to perceive their physical environment. Radio Tomographic Imaging (RTI) is a potential technique for device-free sensing in ISAC, reconstructing object locations and shapes from Received Signal Strength (RSS) measurements. It can complement other techniques, such as radar and LiDAR, by providing a narrowband modality with wavelength resolution. Recently, an extended version of RTI, known as the extended phaseless Rytov approximation (x3DPRA), has been developed to enhance RTI reconstruction quality, estimate material parameters, and extend RTI from two to three dimensions (3D). However, the 3D formulation is even more ill-posed than the 2D form, as the number of measurements is far fewer than the number of unknown voxels. In this work, we systematically evaluate and compare three distinct regularization approaches (Ridge, Total Variation, and Tensor Nuclear) within the x3DPRA framework. We provide a detailed performance analysis by evaluating both reconstruction quality and computational runtime across the three methods. Our findings show that Total Variation regularization provides the best reconstruction quality but not the fastest runtime, providing guidance for developing high-resolution 3D RTI systems for future ISAC applications.

eess.IV↗

arXiv2026

A Dual-Band Reconfigurable Shared-Aperture Antenna Array With Independent Sub-6-GHz and Centimeter-Wave Beam Control

A planar dual-band reconfigurable shared-aperture antenna array is proposed for compact next-generation wireless front ends that require both sub-6-GHz and centimeter-wave (cm-wave) coverage. The array integrates a 2 by 2 sub-6-GHz microstrip dipole array and a 4 by 4 cm-wave stacked patch array within the same aperture, while providing independent beam control in the two bands without conventional T/R modules or beamforming networks. Slot-coupled feeding is employed to separate the radiating aperture from the reconfigurable RF feeding networks and DC bias circuits. PIN-diode-loaded split feeding rings first provide independent 1-bit phase reconfigurability for both bands. A compact reconfigurable $90^{\circ}$ phase shifter is then introduced as an additional phase-control stage, resulting in 2-bit phase control for sub-6 GHz elements and cm-wave subarrays. To reduce cross-band coupling in the compact shared aperture, a double-layer electromagnetic band-gap (EBG) structure is used to suppress cm-wave surface waves and higher-order sub-6-GHz modes excited by the cm-wave elements. A prototype is fabricated and measured. In the sub-6-GHz band, 11 reconfigurable radiation patterns are obtained, including two difference patterns and nine directional beams, with a peak broadside gain of 10.5 dBi. In the cm-wave band, two-dimensional beam scanning up to $\pm40^{\circ}$ is demonstrated with a peak gain of 14.6 dBi in both the E-plane and H-plane. These results show that the proposed architecture can combine dual-band shared-aperture integration and independent reconfigurable beam control in a compact antenna platform.

eess.SP↗

arXiv2026

Fluid Antenna System-Enabled Mitigation of Asynchronous Reception in Cell-Free Massive MIMO Systems

Practical distributed deployments inherently suffer from asynchronous signal arrivals, which exacerbate multi-user interference and degrade system performance, especially for coherent transmission. To natively mitigate the asynchronous reception effect, this paper proposes integrating fluid antenna systems (FASs) into distributed cell-free massive MIMO systems, exploiting their reconfigurable spatial positions to release additional spatial degrees of freedom (DoFs). We establish the FAS-enabled data transmission model with asynchronous reception, i.e., delay phases. We also derive the analytical downlink spectral efficiency (SE) performance of the proposed system under coherent and non-coherent transmissions, using low-complexity Maximum Ratio (MR) precoding to provide fundamental theoretical bounds. Specifically, we propose a novel nonmonotone accelerated projected gradient ascent algorithm to jointly optimize FAS positions and power control coefficients, maximizing the downlink sum SE. Numerical results demonstrate that while asynchronous reception severely degrades system performance for coherent transmission, the spatial DoFs unlocked by optimized FAS positions, along with efficient power control, can significantly counteract the effects of unknown delay phases and outperform traditional fixed-position antennas. For non-coherent transmission, which inherently bypasses asynchronous reception, the application of FAS leverages spatial reconfigurability to natively maximize signal strength and achieve more pronounced SE gains. Ultimately, our proposed FAS-enabled system, coupled with efficient power control, mitigates performance degradation due to asynchronous reception and outperforms traditional fixed-position antennas, paving the way for the practical deployment of FASs in robust, highly efficient 6G cell-free massive MIMO systems.

cs.IT↗

arXiv2026

A 3D Formulation of the Extended Phaseless Rytov Approximation

The extended Phaseless Rytov Approximation (xPRA) is a recently proposed device-free RF imaging technique that provides high-resolution reconstructions of the imaging region using only phaseless measurements, such as received signal strength (RSS). Because of its phaseless formulation, it can be implemented straightforwardly using existing wireless commu?nication infrastructure. It also outperforms well-known device?free phaseless RF imaging methods such as Radio Tomographic Imaging (RTI). The linear phaseless formulation used in xPRA(and RTI) makes these methods potentially useful for integrated sensing and communication (ISAC) systems in next generation wireless networks since they do not require wide bandwidths. However, so far, both xPRA and RTI have primarily been formulated in two dimensions (2D). This paper introduces a 3D extension of xPRA, which we call the extended three-dimensional phaseless Rytov approximation (x3DPRA). The novelty of our approach is that it preserves the straightforward implementation advantages of RTI and xPRA while enabling volumetric (3D) imaging. Simulation results show that x3DPRA provides good estimates of location and shape and can also reconstruct object material attenuation. We present the 3D formulation, validate it with a 2D model comparison, and report simulation results demonstrating its performance.

eess.IV↗

arXiv2026

Covariance-Aware Detection for Single-RF MIMO-OFDM Systems

We propose the use of a covariance-aware detection method for single-radio-frequency (RF) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. This detection approach enhances bit error rate (BER) performance as compared to conventional detectors. The approach is motivated by the observation that when using reconfigurable antennas (such as electronically steerable parasitic array radiators (ESPARs)) to create a single-RF MIMO system, an additional model error arising from the reconfigurable antennas is introduced. These modeling errors produce colored noise and an irreducible BER (error floor) at high signal-to-noise ratios (SNRs). Simulation results, using ESPAR as an example, validate our error floor analysis and demonstrate that the use of the proposed enhanced detection method can effectively address the error floor and improve the BER at high transmit SNRs.

cs.IT↗

arXiv2026

Fluid Antenna System Meets Low-Resolution ADCs in Energy-Efficient Cell-Free Massive MIMO

This paper proposes a novel fluid antenna system (FAS)-enabled architecture to improve energy efficiency (EE) without sacrificing capacity. Specifically, we integrate FAS into cell-free massive MIMO systems to counteract low-resolution ADCs. We establish a comprehensive uplink transmission model and derive analytical expressions for SE and EE. These expressions explicitly capture the quantization error under slow fluid antenna multiple access and quantify the benefits of low-resolution ADCs on EE. Furthermore, we formulate a joint optimization problem to maximize EE performance. To solve this, we develop an efficient alternating optimization framework. This framework leverages the Dinkelbach algorithm-based fractional programming for power control, alongside novel accelerated projected gradient ascent (APGA) algorithms to optimize both continuous FAS positions and discrete ADC bit allocations. Numerical results reveal that low-resolution ADCs aggressively compress signals to save hardware power, which inevitably degrades SE but maintains EE. However, FASs can recover this SE loss thanks to their spatial flexibility and significantly boost EE by improving the received signal prior to destructive quantization. Furthermore, optimized power control can prevent quantization-induced multi-user interference, while efficient bit allocation can reduce exponential hardware power. Ultimately, our proposed FAS-enabled system, coupled with efficient power control and bit allocation, effectively improves system performance and outperforms traditional fixed-position antennas. It establishes a highly robust and energy-efficient paradigm for 6G networks.

cs.IT↗

arXiv2026

Stochastic Geometry Analysis of Uplink CUMA-Enabled Cellular Networks

Uplink cellular networks are interference-dominated but interference channel state information (CSI) is rarely available at scale. The emerging fluid antenna system (FAS) concept, which provides additional spatial degrees of freedom through multi-port reconfiguration, offers a promising alternative to CSI-intensive multi-antenna processing. Building on this concept, compact ultra-massive arrays (CUMA) exploit large-scale port selection with low implementation complexity. In each uplink transmission, CUMA activates a subset of ports based on only the desired-link CSI and combines the selected ports via simple superposition, yielding coherent enhancement of the desired user signal, while inter-cell interference aggregates largely non-coherently due to the random superposition effect. Consequently, CUMA is well suited to multi-cell uplink scenarios where CSI is limited. In this paper, we analyze uplink CUMA in multi-cell cellular networks using a stochastic geometry framework. We derive a tight approximate expression for the signal-to-interference ratio (SIR) coverage probability, and further characterize the average user rate and cell sum-rate. The analysis quantifies how key design parameters impact performance and reveals the scaling behavior with network densification. Simulation results validate the accuracy of the derived expressions and show that uplink CUMA achieves competitive, and often superior, performance relative to conventional schemes under practical CSI constraints, highlighting its potential as a low-complexity, hardware-efficient uplink solution for future large-scale cellular networks.

eess.SP↗

arXiv2026

Fast Fluid Antenna Multiple Access

Fast fluid antenna multiple access (FAMA) is an idea that promises to overcome severe interference in massive access scenarios by reconfiguring the antenna's position at the receiver side on a symbol-by-symbol basis, without the need of precoding nor any other interference mitigation techniques. However, this idea is commonly studied under a \emph{genie-aided} premise: each user terminal (UT) can probe \emph{all} fluid-antenna ports in every symbol instance and ideally knows the instantaneous signal-interference split for the received signals at all the ports. Such assumption is unrealistic since it implies impractical hardware and switching limits, pilot overhead, as well as an unknown ability to determine the signal-interference split. This paper revisits the fast FAMA communication problem and asks a key question: can a UT act \emph{as if} it had full per-port interference knowledge while observing only a small fraction of ports? To this end, we propose a \emph{copula-aided FAMA} framework that learns the joint dependence structure of the complex triplets $(r_k,h_k,I_k)$ across ports, where $r_k$, $h_k$ and $I_k$ denote, respectively, the received signal, the channel coefficient and the aggregate interference signal at the $k$-th port, and uses this learned model to infer unobserved channels and interference. Concretely, we devise an attention-copula time-series model that is trained under random partial-observation masks and evaluated under both rich and finite-scattering channel models. Simulation results indicate that the reconstruction normalized mean-square-error (NMSE) for $h$, $r$, and $I$ drops to the order of $10^{-4}$ once the number of observed ports, $M$, exceeds the spatial degrees of freedom (DoF).

eess.SP↗

arXiv2026

Diffusion Fluid Antenna Systems for Resilient ISAC

Most existing integrated sensing and communication (ISAC) studies focus on enabling a base station (BS) to support sensing and communication over shared resources through advanced waveform design and power allocation. In contrast, the object-side perspective remains underexplored. For example, an object may wish to remain difficult to detect for security reasons, while another object in close proximity may generate dominant reflections that confuse the BS and impair sensing reliability for the intended target. These challenges motivate the fluid antenna system (FAS) paradigm which introduces a reconfigurable spatial degree of freedom (DoF) that can reshape sensing signatures via port selection, beyond what waveform and power control alone can provide. In this paper, we devise diffusion FAS, a generative artificial intelligence (AI)-driven framework that exploits spatial agility to steer ISAC performance over the electromagnetic fading manifold. Instead of optimizing ISAC solely in the power domain, diffusion FAS casts ISAC as a \emph{dynamic spatial selection} problem in which antenna states (i.e., ports) are chosen to shape sensing signatures while maintaining communication objectives. To work under sparse measurements, we employ a conditional denoising diffusion probabilistic model (DDPM) to reconstruct the latent spatial correlation structure from a small set of observed ports, enabling efficient exploration of the reconfigurable aperture. We demonstrate two FAS-enabled ISAC modes: (1) \emph{generative spatial stealth}, which identifies localized deep fades to suppress a target's sensing visibility by up to two orders of magnitude, and (2) \emph{target isolation}, which synthesizes spatial nulls that reject interference from adjacent objects.

eess.SP↗

arXiv2026

Beyond Covariance: Generative Spatial Correlation Modeling and Channel Interpolation for Fluid Antenna Systems

Fluid antenna systems (FAS) enable unprecedented spatial diversity within a compact form factor by flexibly switching among high-density antenna ports. To activate this capability, channel state information (CSI) over the ports is required, which implies high estimation overhead because the number of ports is usually very large. Conventional estimation schemes tend to first estimate the CSI for a small number of ports and then infer the CSI for the remaining antenna ports by interpolation exploiting correlation characteristics. However, existing correlation-based techniques lack generalization ability, and the fundamental limits of interpolating the CSI from sparse observations remain poorly understood. This paper adopts a generative modeling framework for characterizing the channel correlation among the FAS ports that departs fundamentally from covariance-descriptive models. Specifically, we represent the spatially sampled channel as a $p$th-order autoregressive (AR) Gauss-Markov process, which provides a principled and tunable tradeoff between model complexity and approximation accuracy via the AR order. In so doing, we can characterize the limits of channel interpolation by deriving the globally optimal minimum mean-square error (MMSE) estimator and establishing a tight lower bound on the minimum number of observations required to meet a prescribed reconstruction error. To reduce the complexity of MMSE estimation, we then exploit the state-space structure due to the ${\rm AR}(p)$ model and develop a Kalman filtering/smoothing-based interpolation algorithm. The resulting method attains the optimal MMSE performance with strictly linear complexity $\mathcal{O}(N)$ with $N$ denoting the number of ports, resulting in a scalable, efficient, and theoretically grounded framework for practical FAS channel reconstruction.

cs.IT↗

arXiv2026

SAR/ISAR Imaging in 6G Network

Imaging is a crucial sensing function that finds wide applications in environmental reconstruction, autonomous driving, etc. However, the signal processing methods for existing radio imaging techniques, such as millimeter wave (mmWave) imaging, require high-resolution range estimation enabled by Gigahertz-level or even Terahertz-level bandwidth, and cannot be applied in 6G integrated sensing and communication (ISAC) network with Megahertz-level bandwidth. This paper proposes two novel high-resolution radio imaging schemes that can work on the 6G signals with limited bandwidth - bandwidth-independent synthetic aperture radar (BI-SAR), where the movable base station (BS) revolves along the static targets by 360 degrees; as well as bandwidth-independent inverse synthetic aperture radar (BI-ISAR), where the BS is static and the targets revolve along an axis by 360 degrees. Different from conventional SAR and ISAR counterparts that rely on range estimation, our proposed imaging schemes solely utilize Doppler information to perform imaging without any range information. The main technical challenge of our schemes lies in the anisotropic scattering functions over different directions, which hinder the coherent synthesis of the backscattered signals from all directions. We design an iterative adaptive approach-based Doppler association (IAA-DA) algorithm to tackle the above issue. Moreover, we also derive the imaging resolution to characterize the reconstruction quality. Real-world experiments are provided to show the feasibility and the effectiveness of our proposed 6G imaging schemes.

eess.SP↗

arXiv2026

Optimizing Antenna Coding for Pixel Antenna Empowered SISO-OFDM Systems

This work investigates antenna coding optimization to enhance the channel capacity of single-input single-output orthogonal frequency division multiplexing (SISO-OFDM) systems empowered by highly reconfigurable pixel antennas. We first introduce the model for pixel antenna empowered SISO-OFDM systems using a beamspace channel representation. We next formulate the problem to maximize the channel capacity through jointly optimizing antenna coding and the power allocation across subcarriers and solve it by Successive Exhaustive Boolean Optimization (SEBO) and water-filling (WF) algorithm. To reduce computational complexity, a codebook-based approach is also proposed for antenna coding optimization. Simulation results show that the channel capacity of SISO-OFDM system across all signal-to-noise-ratio (SNR) regions considered can be enhanced through leveraging pixel antennas as compared to using conventional antenna with fixed configuration. This result demonstrates the effectiveness of antenna coding technology empowered by pixel antenna in enhancing SISO-OFDM systems.

eess.SP↗

arXiv2026

Antenna Coding Optimization for Pixel Antenna Empowered MIMO Wireless Power Transfer

We investigate antenna coding utilizing pixel antennas as a new degree of freedom for enhancing multiple-input multiple-output (MIMO) wireless power transfer (WPT) systems. The objective is to enhance the output direct current (DC) power under RF combining and DC combining schemes by jointly exploiting gains from antenna coding, beamforming, and rectenna nonlinearity. We first propose the MIMO WPT system model with binary and continuous antenna coding using the beamspace channel model and formulate the joint antenna coding and beamforming optimization using a nonlinear rectenna model. We propose two efficient closed-form successive convex approximation algorithms to efficiently optimize the beamforming. To further reduce the computational complexity, we propose codebook-based antenna coding designs for output DC power maximization based on K-means clustering. Results show that the proposed pixel antenna empowered MIMO WPT system with binary antenna coding increases output DC power by more than 15 dB compared with conventional systems with fixed antenna configuration. With continuous antenna coding, the performance improves another 6 dB. Moreover, the proposed codebook design outperforms previous designs by up to 40% and shows good performance with reduced computational complexity. Overall, the significant improvement in output DC power verifies the potential of leveraging antenna coding utilizing pixel antennas to enhance WPT systems.

eess.SP↗

arXiv2025

Downlink Power Allocation for STAR-RIS-Assisted Cell-Free Massive MIMO with Multi-antenna Users

This paper investigates the downlink power allocation of the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted cell-free massive multiple-input multiple-output (MIMO) system with multi-antenna users. We introduce downlink spectral efficiency (SE) and derive novel closed-form SE expressions using linear minimum mean squared error (MMSE) detectors. We also address the downlink power allocation via a sum SE maximization problem framed within an alternating direction method of multipliers (ADMM)-based fractional programming (FP) algorithm. Numerical results demonstrate that systems utilizing multi-antenna users significantly enhance SE, achieving at least a 20% SE increase as the number of antennas increases from one to six. Additionally, our proposed ADMM-based FP algorithm outperforms existing fractional power control approaches, yielding a more than 20% SE increase. These results highlight the necessity for adopting multi-antenna users and efficient power allocation algorithms in STAR-RIS-assisted cell-free massive MIMO systems.

cs.IT↗

arXiv2025

Antenna Coding Optimization Based on Pixel Antennas for MIMO Wireless Power Transfer with DC Combining

This paper investigates antenna coding based on pixel antennas as a new degree of freedom for enhancing multiple-input multiple-output (MIMO) wireless power transfer (WPT) systems. Antenna coding is closely related to the Fluid Antenna System (FAS) concept and further generalizes the radiation pattern reconfigurability. We first introduce a beamspace channel model to demonstrate reconfigurable radiation patterns enabled by antenna coders. By jointly optimizing the antenna coding and transmit beamforming with perfect channel state information (CSI), we exploit gains from antenna coding, transmit beamforming, and rectenna nonlinearity to maximize the output DC power. We adopt an alternating optimization approach with the quasi-Newton method and Successive Exhaustive Boolean Optimization (SEBO) method with warm-start to handle the transmit beamforming design and antenna coding design respectively. Finally, simulation results show that the proposed MIMO WPT system with pixel antennas achieves up to 15 dB gain in average output DC power compared with a conventional system with fixed antenna configuration, highlighting the potential of pixel antennas for boosting the WPT efficiency.

eess.SP↗

arXiv2025

Exploiting Spatial Multiplexing Based on Pixel Antennas: An Antenna Coding Approach

An antenna coding approach for exploiting the spatial multiplexing capability of pixel antennas is proposed. This approach can leverage additional degrees of freedom in the beamspace domain to transmit more information streams. Pixel antennas are a general reconfigurable antenna design where a radiating structure with arbitrary shape and size can be discretized into sub-wavelength elements called pixels which are connected by radio frequency switches. By controlling the switch states, the pixel antenna topology can be flexibly adjusted so that the resulting radiation pattern can be reconfigured for beamspace spatial multiplexing. In this work, we introduce the antenna coder and pattern coder for pixel antennas, provide a multiple-input multiple-output (MIMO) communication system model with antenna coding in the beamspace domain, and derive the spectral efficiency. Utilizing the antenna coder, the radiation pattern of the pixel antenna is analyzed and efficient optimization algorithms are provided for antenna coding design. Numerical simulation results show that the proposed technique using pixel antennas can enhance spectral efficiency of 4-by-4 MIMO by up to 12 bits/s/Hz or equivalently reduce the required transmit power by up to 90% when compared to conventional MIMO, demonstrating the effectiveness of the antenna coding technique in spectral efficiency enhancement and its promise for future sixth generation (6G) wireless communication.

eess.SP↗

arXiv2025

Pixel-based Reconfigurable Beamforming Networks Emulating Physical Movement in FAS

The concept of Fluid Antenna Systems (FAS) has emerged as an attractive new system technology for use in sixth-generation (6G) wireless systems. However, most FAS implementations rely on mechanical antenna movement and thus are too slow to be useful. In this paper, a novel pixel-based reconfigurable beamforming network (PRBFN) is used to emulate movement in Fluid Antenna Systems (FASs). Using the insight that changing an antenna's physical position is equivalent to changing radiation patterns that satisfy the desired pattern correlation, the PRBFN is used to control the excitation current vectors of a multi-port antenna, thereby governing the pattern correlation. Key novelties of our work involve the selection of current vectors, and the methodology for scaling the PRBFN to realize large-aperture FAS. Results are provided for our PRBFN combined with an FAS (denoted as a PRBFN-FAS) when the equivalent physical movement is set to 1.5 wavelengths. Measurements demonstrate that the PRBFN-FAS provides the desired spatial correlation, including the Bessel function relation from Clarke's model across a 5\% bandwidth, satisfying FAS requirements. System-level experiments confirm the viability of the PRBFN-FAS in communication scenarios.

eess.SP↗

arXiv2025

Multiport Analytical Pixel Electromagnetic Simulator (MAPES) for AI-assisted RFIC and Microwave Circuit Design

This paper proposes a novel analytical framework, denoted the Multiport Analytical Pixel Electromagnetic Simulator (MAPES). MAPES enables efficient and accurate prediction of the electromagnetic (EM) performance of arbitrary pixel-based microwave (MW) and RFIC structures. Unlike the Internal Multiport Method (IMPM), which optimizes only connecting elements within a fixed, gap-separated pixel skeleton, MAPES operates directly on the all-pixel presence/absence formulation used in recent MW/RFIC design. This is enabled by diagonal virtual pixels, an occupancy-to-load mapping, and a multi-layer/via port-level formulation that have no counterpart in IMPM. By introducing virtual pixels and diagonal virtual pixels and inserting virtual ports at critical positions, MAPES captures all horizontal, vertical, and diagonal electromagnetic couplings within a single multiport impedance matrix. Only a small set of full-wave simulations (typically about 1% of the datasets required by AI-assisted EM emulators) is needed to construct this matrix. Subsequently, any arbitrary pixel configuration can be evaluated analytically using a closed-form multiport relation without additional full-wave calculations. The proposed approach eliminates data-driven overfitting and ensures accurate results across all design variations. Using MAPES, comprehensive examples for single- and double-layer PCBs and CMOS processes (180 nm and 65 nm) confirm that high prediction accuracy with 600-2000$\times$ speed improvement is achieved compared to CST simulations. Owing to its efficiency, scalability, and reliability, MAPES provides a practical and versatile tool for AI-assisted MW circuit and RFIC design across diverse fabrication technologies.

eess.SP↗