arXiv subjects

Robert W. Heath Jr

Publications and source records attributed to Robert W. Heath Jr.

At least 19 recordsLinked to original sources

arXiv2026

Energy Efficiency Optimization for 5G NR PDSCH: A Cross-Layer Link Abstraction Framework

Energy efficiency (EE) optimization for cellular links often assumes Shannon-capacity transmission. The 3GPP New Radio (NR) physical downlink shared channel (PDSCH), however, delivers a transport block whose size is fixed by the modulation and coding scheme (MCS), the rank, and the resource allocation chosen by the scheduler. The transport block must also meet a target block error rate (BLER). In this paper, we optimize EE for the NR PDSCH with the standard transport block size as the throughput and the target BLER as the constraint. Exponential effective signal-to-noise ratio (SNR) mapping (EESM) turns the BLER constraint into a convex per-subcarrier power constraint. We derive a closed-form waterfilling-like power allocation and an EE-maximizing MCS selection rule. With a single calibration parameter per MCS, fitted offline over 3GPP tapped delay line C (TDL-C) channels, EESM predicts the BLER without per-realization link-level simulation. Numerical results show that a Shannon-capacity baseline overestimates the EE of equal-power transmission by 1.5 to 1.7 times at high SNR within the transmit power budget.

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arXiv2026

Precoding Design for Limited-Feedback MIMO Systems via Character-Polynomial Codes

This paper presents a precoding codebook design for limited-feedback multiple-input multiple-output (MIMO) systems under the equal-gain transmission (EGT) constraint. In particular, we demonstrate that character--polynomial (CP) codes provide a structured solution that achieves constant-envelope transmission, low storage complexity, and Grassmannian packing without dependence on array geometry or channel statistics. In contrast to geometry-dependent discrete Fourier transform (DFT) codebooks used in current 5G systems and unstructured Grassmannian codebooks with high storage complexity, CP codebooks combine practical implementation advantages with near-optimal packing performance. For multiple-input single-output (MISO) systems, we derive an upper bound on the mean squared quantization error and show that the distortion relative to the EGT baseline vanishes asymptotically as the number of transmit antennas increases and the code rate approaches one. For MIMO systems with two receive antennas, we develop an iterative method to establish the EGT baseline. Simulation results under Rayleigh, correlated, and clustered delay line (CDL) channel models show that CP codebooks approach the EGT baseline across all channel conditions while outperforming phase shift keying (PSK) and 5G DFT codebooks in several operating regimes, and, in the Rayleigh fading case, incur negligible packing loss relative to numerically optimized Grassmannian codebooks obtained via the alternating projection (AP) method.

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arXiv2026

Channel2World: A Wireless Foundation Model for RF Environment Representation

Wireless channels are commonly treated as link-specific observations, although their multipath structure is governed by the surrounding radio-frequency (RF) environment. In this paper, we propose Channel2World, a wireless foundation model that learns a reusable environment-level representation from multiple-input multiple-output (MIMO) channel-position observations. The model aggregates channels collected within the same base-station-centered environment into a wireless world embedding using a Transformer-based encoder. The encoder is pretrained through context-query prediction, where context channels condition user equipment (UE) position and relative path-gain prediction for disjoint query channels. After pretraining, the encoder is frozen and used as a task-agnostic environment-conditioning module for downstream wireless models, enabling adaptation to unseen environments without site-specific fine-tuning. To learn an environment-level latent space that generalizes across deployments, we pretrain Channel2World using ray-tracing data from 26,000 environments, with approximately 5,000 channel measurements per environment. Evaluations on UE localization, beam-domain channel state information (CSI) reconstruction, and RF-observable geometry reconstruction show that the learned embeddings provide effective conditioning in unseen environments. For localization and CSI reconstruction tasks, embedding-based conditioning outperforms or remains competitive with site-specific fine-tuning, although fine-tuning requires task-specific labeled data and additional gradient-based adaptation. The embeddings also support the reconstruction of dominant reflector structures, indicating their utility as reusable environmental priors across tasks.

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arXiv2026

Geometry-Aware DRL for Multi-Subband Scheduling in Satellite-Assisted UAM Networks

In this paper, we investigate downlink scheduling for urban air mobility (UAM) in a cooperative space-air-ground integrated network. Multiple ground stations (GSs) employ narrow three-dimensional beams and share spectrum across multiple subbands, while a satellite provides an orthogonal-band service option. Rapidly time-varying geometry and directional interference require joint decisions on base station association, GS subband assignment, and transmit powers. We formulate a finite-horizon mixed discrete-continuous problem that maximizes sum rate while penalizing handovers and GS overload, using only UAM positions and velocities. To address the combinatorial scheduling problem, we propose GeoSetPPO, a geometry-aware set-attention proximal policy optimization (PPO) method that outputs per-UAM discrete association and subband decisions with permutation-invariant representations. Conditioned on each schedule, GS powers are computed by a per-slot successive convex approximation (SCA) module under per-GS power budgets and minimum signal-to-interference-plus-noise ratio (SINR) constraints. To reduce training cost and improve stability, we adopt a two-stage training strategy that transitions reward evaluation from uniform power to SCA-based power allocation. Simulations demonstrate stable convergence, higher returns than multi-layer perceptron (MLP)- and Transformer-based PPO under the considered training setting, and favorable reward and schedule-feasibility performance relative to algorithm-based and distance-based schedulers. In the larger evaluated network, GeoSetPPO also reduces the scheduling latency from 40.84 ms to 2.90 ms relative to the previous algorithm-based method.

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arXiv2026

Variance-Reduced Q-Learning over Static and Time-Varying Networks

We investigate a decentralized reinforcement learning problem involving multiple agents that interact with the same Markov Decision Process (MDP). The agents can exchange information over a network to collectively learn the optimal state-action value function. For this setting, we introduce a novel epoch-based distributed $Q$-learning algorithm called VRDQ, where within each epoch, agents locally estimate the Bellman optimality operator and diffuse information using a consensus-based protocol. For both static and time-varying networks, we establish high-probability finite-time convergence rates for VRDQ that enjoy linear speedups from collaboration. Crucially, we prove that such speedups in sample-complexity require only $\tilde{O}(1)$ communication, substantially improving upon the communication costs in prior work.

cs.LG↗

arXiv2026

Energy Efficiency Optimization in Distributed MIMO vRAN via Cross-Layer Link Abstraction

Virtualized radio access networks (vRAN) run the compute-intensive multiple-input multiple-output (MIMO) baseband as software on shared servers, which makes energy efficiency (EE) a primary design objective. Distributed MIMO vRAN consumes power across virtualized distributed unit (vDU) baseband, fronthaul transport, and per-radio-unit operation. We build a power model that resolves these three components. We then develop a framework that jointly selects modulation, transmission rank, and per-subcarrier power to maximize system EE. Exponential effective SNR mapping induces a convex per-subcarrier power constraint, which yields a convex power minimization problem with a closed-form waterfilling-like solution. We show that radio frequency-only models underestimate the spectral efficiency range where single-input multiple-output (SIMO) transmission saves power, and our power model extends this range by 24%. We further extend the framework to a traffic-aware setting with realistic user trajectories from the multi-agent transport simulator. We propose a traffic-aware strategy that switches each radio unit among MIMO, SIMO, and sleep modes based on demand. Simulation results over 3GPP NR compliant fading channels show that, after a one-time offline calibration, the framework predicts link performance without further link-level simulation. The proposed framework achieves higher average EE than a traffic-agnostic always-on MIMO baseline, while maintaining comparable throughput at peak hours.

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arXiv2026

Symbol Distributions in Semantic Communications: A Source-Channel Equilibrium Perspective

Semantic communication systems often use end-to-end neural networks to map input data into continuous symbols. These symbols, which are essentially neural network features, have fixed dimensions and often exhibit heavy-tailed distributions. However, the mechanism behind this distributional shape remains underexplored due to the end-to-end nature of encoder training, hindering systematic analysis and design. In this paper, we propose a parametric model for semantic symbol distributions. We model end-to-end training as inducing two coupled pressures on the symbol distribution: a source pressure that favors power allocation minimizing the average description cost, and a channel pressure that favors distributions with higher channel utilization. Under surrogate objectives that capture these effects, we obtain a Student's t-distribution as a model for the semantic symbols. Experiments on image-based semantic systems show that the model closely predicts how the shape parameter varies with (i) explicit symbol rate control and (ii) dataset entropy variability. Furthermore, enforcing a target symbol distribution via regularization (e.g., a Gaussian prior) improves training convergence, which is consistent with our hypothesis.

cs.IT↗

arXiv2026

Bridging Neural Networks and Wireless Systems with MIMO-OFDM Semantic Communications

Semantic communications aim to enhance transmission efficiency by jointly optimizing source coding, channel coding, and modulation. While prior research has demonstrated promising performance in simulations, real-world implementations often face significant challenges, including noise variability and nonlinear distortions, leading to performance gaps. This article investigates these challenges in a multiple-input multiple-output (MIMO) and orthogonal frequency-division multiplexing (OFDM)-based semantic communication system, focusing on the practical impacts of power amplifier (PA) nonlinearity and peak-to-average power ratio (PAPR) variations. Our analysis identifies frequency selectivity of the actual channel as a critical factor in performance degradation and demonstrates that targeted mitigation strategies can enable semantic systems to approach theoretical performance. By addressing key limitations in existing designs, we provide actionable insights for advancing semantic communications in practical wireless environments. This work establishes a foundation for bridging the gap between theoretical models and real-world deployment, highlighting essential considerations for system design and optimization.

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arXiv2026

Switch-DFT: Adaptive Waveform and MIMO Switching for Energy-Efficient Base Stations

Energy efficiency has emerged as a critical challenge in modern base stations (BSs), as the power amplifier (PA) consumes a substantial portion of the total power due to its limited efficiency. We investigate waveform and mode adaptation to enhance the energy efficiency of BSs. We propose Switch-DFT, an adaptive switching framework that selects between cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) and discrete Fourier transform-spread-OFDM (DFT-s-OFDM) waveforms, as well as between single-input multiple-output (SIMO) and multiple-input multiple-output (MIMO) modes. Switch-DFT improves efficiency by reducing PA backoff with DFT-s-OFDM and achieves the target rate at lower power by leveraging higher MIMO throughput. This results in superior energy efficiency over a wide range of the spectral efficiencies compared with static configurations.

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arXiv2026

Accelerating vRAN and O-RAN with SIMD: Architectural Perspectives and Performance Evaluation

The evolution of radio access networks (RANs) toward virtualization and openness creates new opportunities for flexible, cost-effective, and high-performance deployments. Achieving real-time and energy-efficient baseband processing on commercial off-the-shelf platforms, however, remains a critical challenge. This article explores how single instruction multiple data (SIMD) architectures can accelerate RAN workloads. We first outline why key physical-layer functions, such as channel estimation, multiple-input multiple-output (MIMO) detection, and forward error correction, are well aligned with SIMD's data-level parallelism. We then present practical design guidelines and prototype results, showing significant improvements in throughput and energy efficiency compared to conventional CPU-only processing, while retaining programmability and ease of integration. Finally, we discuss open challenges in workload balancing and hardware heterogeneity, and highlight the role of SIMD as an enabling technology for flexible, efficient, and sustainable 6G-ready RANs.

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arXiv2026

Power consumption and spectral efficiency analysis for uplink analog radio-over-fiber

Radio-over-fiber centralizes radio access networks by using a low-loss optical fiber link between the remote radio head and the central unit. Analog radio-over-fiber (A-RoF) transmits RF signals modulated directly onto an optical carrier, avoiding digitization and digital signal processing at the remote radio head. In this way, A-RoF shifts power-hungry processing from the antenna to the baseband unit. This paper outlines a mathematical framework to analyze the effect of fiber nonlinearity in an uplink wireless system supported by A-RoF. We model an input/output relationship that incorporates the wireless channel, thermal noise, and impairments encountered in the optical fiber channel: chromatic dispersion, electrical-to-optical conversion loss, amplification noise, and fiber nonlinear interference. We compare A-RoF with DSP-assisted A-RoF and digital radio receivers. Our results show that A-RoF achieves higher energy efficiency as compared to digital receivers with 8- and 16-bit analog-to-digital converters and DSP-assisted A-RoF. We further characterize the trade-offs among transmit power, nonlinear interference, and spectral efficiency, demonstrating that nonlinear effects fundamentally limit achievable rates. These results identify the linear operating regions where A-RoF is most effective for uplink wireless communication.

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arXiv2026

Signal Processing Foundations of Reconfigurable Antennas in the Tri-Hybrid MIMO Architecture

To enable larger apertures in multipleinput multipleoutput MIMO systems the trihybrid MIMO architecture offers a promising lowcost and lowpower solution by introducing reconfigurable antennas as a third layer of precoding on top of conventional digital and analog processing In this paper we develop a unified signal processing framework for trihybrid MIMO that explicitly captures the electromagnetic EM characteristics of diverse reconfigurable antenna technologies We first propose a generic inputoutput model that incorporates the reconfigurable antenna layer into an effective channel representation revealing a fundamental coupling between the channel precoder and radiated power Building on this model we formulate a general optimization problem that jointly accounts for digital analog and antennadomain precoding under hardware and power constraints We then instantiate this framework across seven representative reconfigurable antenna architectures including parasitic arrays dynamic metasurface antennas fluidpixel antennas polarizationreconfigurable antennas stacked intelligent metasurfaces pinching antenna systems and nonradiating wires To systematically compare these heterogeneous architectures we introduce a new metric the reconfigurability efficiency factor REF which quantifies the performance gains achievable through antenna reconfiguration under realistic constraints Numerical results demonstrate the tradeoffs among aperture size power consumption hardware complexity and spectral efficiency Our results establish that EMlevel reconfiguration reshapes the signal processing design space highlighting the need for new architectures and algorithms that jointly optimize across digital analog and electromagnetic domains This work reveals that electromagnetic reconfiguration couples the channel and precoder

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arXiv2026

Fixed-wing UAV relay optimization for coverage hole recovery

Unmanned aerial vehicles (UAVs) fill coverage holes as wireless relays during emergency situations. Fixed-wing UAVs offer longer flight duration and larger coverage in such situations than rotary-wing counterparts. Maximizing the effectiveness of fixed-wing UAV relay systems requires careful tuning of system and flight parameters. This process is challenging because factors including flight trajectory, timeshare, and user scheduling are not easily optimized. In this paper, we propose an optimization for UAV-based wireless relaying networks based on a setup which is applicable to arbitrary spatial user positions. In the setup, a fixed-wing UAV flies over a circular trajectory and relays data from ground users in a coverage hole to a distant base station (BS). Our optimization iteratively maximizes the average achievable spectral efficiency (SE) for the UAV trajectory, user scheduling, and relay timeshare. The simulation results show that our optimization is effective for varying user distributions and that it performs especially well on distributions with a high standard deviation.

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arXiv2026

Heterogeneity-agnostic AI/ML-assisted beam selection for multi-panel arrays

AI/ML-based beam selection methods coupled with location information effectively reduce beam training overhead. Unfortunately, heterogeneous antenna hardware with varying dimensions, orientations, codebooks, element patterns, and polarization angles limits their feasibility and generalization. This challenge requires either a heterogeneity-agnostic model functional under these variations, or developing many models for each configuration, which is infeasible and expensive in practice. In this paper, we propose a unifying AI/ML-based beam selection algorithm supporting antenna heterogeneity by predicting wireless propagation characteristics independent of antenna configuration. We derive a reference signal received power (RSRP) model that decouples propagation characteristics from antenna configuration. We propose an optimization framework to extract propagation variables consisting of angle-of-arrival (AoA), angle-of-departure (AoD), and a matrix incorporating path gain and channel depolarization from beamformed RSRP measurements. We develop a three-stage autoregressive network to predict these variables from user location, enabling RSRP calculation and beam selection for arbitrary antenna configurations without retraining or having a separate model for each configuration. Simulation results show our heterogeneity-agnostic method provides spectral efficiency close to that of genie-aided selection both with and without antenna heterogeneity.

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arXiv2026

Rethinking Beam Management: Generalization Limits Under Hardware Heterogeneity

Hardware heterogeneity across diverse user devices poses new challenges for beam-based communication in 5G and beyond. This heterogeneity limits the applicability of machine learning (ML)-based algorithms. This article highlights the critical need to treat hardware heterogeneity as a first-class design concern in ML-aided beam management. We analyze key failure modes in the presence of heterogeneity and present case studies demonstrating their performance impact. Finally, we discuss potential strategies to improve generalization in beam management.

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arXiv2025

Quantum-Accelerated Wireless Communications: Concepts, Connections, and Implications

Quantum computing is poised to redefine the algorithmic foundations of communication systems. While quantum superposition and entanglement enable quadratic or exponential speedups for specific problems, identifying use cases where these advantages yield engineering benefits is still nontrivial. This article presents the fundamentals of quantum computing in a style familiar to the communications society, outlining the current limits of fault-tolerant quantum computing and clarifying a mathematical harmony between quantum and wireless systems, which makes the topic more enticing to wireless researchers. Based on a systematic review of pioneering and state-of-the-art studies indicating speedup opportunities, we distill common design trends for the research and development of quantum-accelerated communication systems and highlight lessons learned. The key insight is that quantum algorithms, including their gate-level realizations, can benefit from the design intuition applied in communication engineering. This article aims to catalyze interdisciplinary research at the frontier of quantum information processing and future communication systems.

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arXiv2025

Target Detection with Tightly-coupled Antennas: Analysis for Unknown Wideband Signals

This paper presents analysis for target detection using tightly-coupled antenna (TCA) arrays with high mutual coupling (MC). We show that the wide operational bandwidth of TCAs is advantageous for target detection. We assume a sensing receiver equipped with a TCA array that collects joint time and frequency samples of the target's echo signals. Echoes are assumed to be unknown wideband signals, and noise at the TCA array follows a frequency-varying correlation model due to MC. We also assume that the echo signals are time varying, with no assumption on the temporal variation. We consider three regimes in frequency as constant, slowly or rapidly varying, to capture all possible spectral dynamics of the echoes. We propose a novel detector for the slowly-varying regime, and derive detectors based on maximum likelihood estimation (MLE) for the other regimes. For the rapidly-varying regime, we derive an extended energy detector for correlated noise with frequency and time samples. We analyze the performance of all the detectors. We also derive and analyze an ideal detector giving an upper bound on performance. We validate our analysis with simulations and demonstrate that our proposed detector outperforms the MLE-based detectors in terms of robustness to frequency variation. Also, we highlight that TCA arrays offer clear advantages over weakly-coupled antenna arrays in target detection.

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arXiv2025

Wideband dynamic metasurface antenna performance with practical design characteristics

Dynamic metasurface antennas (DMA) provide low-power beamforming through reconfigurable radiative slots. Each slot has a tunable component that consumes low power compared to typical analog components like phase shifters. This makes DMAs a potential candidate to minimize the power consumption of multiple-input multiple-output (MIMO) antenna arrays. In this paper, we investigate the use of DMAs in a wideband communication setting with practical DMA design characteristics. We develop approximations for the DMA beamforming gain that account for the effects of waveguide attenuation, element frequency-selectivity, and limited reconfigurability of the tunable components as a function of the signal bandwidth. The approximations allow for key insights into the wideband performance of DMAs in terms of different design variables. We develop a simple successive beamforming algorithm to improve the wideband performance of DMAs by sequentially configuring each DMA element. Simulation results for a line-of-sight (LOS) wideband system show the accuracy of the approximations with the simulated DMA model in terms of spectral efficiency. We also find that the proposed successive beamforming algorithm increases the overall spectral efficiency of the DMA-based wideband system compared with a baseline DMA beamforming method.

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