arXiv subjects

Ian P. Roberts

Publications and source records attributed to Ian P. Roberts.

At least 19 recordsLinked to original sources

arXiv2026

A Deep Iterative Refinement Receiver for OTFS Symbol Detection in Doubly-Dispersive Channels

Orthogonal time frequency space (OTFS) modulation has emerged as a promising candidate for high-mobility wireless communication systems due to the diversity it offers across both time and frequency. Reliable OTFS detection, however, remains challenging under doubly-dispersive channels, where delay and Doppler dispersion induce structured interference between transmitted symbols and complicate symbol recovery. To address these challenges, we propose a two-stage iterative OTFS detector that integrates a physics-informed learned initializer with an iterative refinement network, enabling progressively more accurate symbol estimates in doubly-dispersive channels. The initializer incorporates the known delay-Doppler input-output relationship to produce a robust first-stage estimate, while the refinement stage iteratively suppresses residual symbol interference in the delay-Doppler domain. Simulation results demonstrate that the proposed detector achieves consistent performance gains over conventional and existing learning-based detectors across a variety of channel conditions. These results highlight the effectiveness of incorporating known channel structure into the detection process and using iterative refinement for improved and robust OTFS detection.

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arXiv2026

Rainbow Beamforming for Wideband LEO Satellite Communications: Principles, Applications, and Technical Challenges

Low Earth Orbit (LEO) satellite communications (SATCOM) has emerged as a key enabler of global connectivity for 6G networks. To overcome the significant path loss of space-to-ground links, high-gain directional beamforming (BF) is indispensable. As LEO systems evolve toward wider bandwidths to support data-intensive applications, however, they encounter a fundamental physical limitation known as the beam-squint effect, which induces frequency-dependent beam misalignment. Conventionally, the beam-squint effect has been treated as a critical performance impairment that must be mitigated. This article introduces a paradigm shift in wideband LEO satellite systems by redefining beam-squint as a valuable source of frequency-spatial diversity and presents the principles of rainbow BF. Rather than mitigating beam squint, rainbow BF deliberately exploits it to generate frequency-dependent beams, enabling different frequency components to illuminate distinct spatial directions using only a single or a small number of radio frequency chains. By supporting dynamic frequency-spatial beam allocation, rainbow BF offers enhanced flexibility and scalability for wideband LEO SATCOM. We further illustrate the benefits of rainbow BF through three representative LEO SATCOM applications: i) massive multiple access to overcome the latency and throughput bottlenecks of conventional beam hopping; ii) integrated sensing and communications for simultaneous target detection and data transmission; and iii) rapid satellite acquisition to reduce search overhead and improve link reliability. Finally, we discuss key implementation challenges and outline promising future research directions for rainbow BF in wideband LEO SATCOM.

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arXiv2026

RemoteRF: An Open-Source Platform to Democratize Access to Software-Defined Radios in Wireless Research and Education

Software-defined radios (SDRs) are powerful tools for research and education in wireless communications, but their cost and complexity put them out of reach for many universities and researchers worldwide. To address this, we introduce RemoteRF, a platform for creating large-scale testbeds of distributed SDRs that are centrally managed by a single server. These SDRs can be remotely accessed by users over the internet, allowing them to conduct wireless experiments at any time from virtually anywhere, as long as they have a network connection. When used in research, RemoteRF can be used to develop and experimentally evaluate new communication techniques or to collect real-world data to train and test machine learning models. When used in education, RemoteRF can allow students in virtually any sized class to share a handful of SDRs to complete active learning lab exercises that parallel course lectures. In an effort to democratize access to SDRs across the globe, the software powering RemoteRF has been made open-source and is extensively documented, allowing anyone to deploy their own instance today in a matter of minutes. Over the past year or so, RemoteRF has been used in both teaching and research at UCLA, where it has logged nearly 4,000 hours of use by more than 200 students and researchers to date.

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arXiv2026

Site-Specific Beamforming for Full-Duplex Massive MIMO Systems via Implicit Channel Estimation

Beamforming has proven to be valuable in enabling full-duplex massive MIMO base stations, but doing so effectively often requires knowledge of the self-interference channel matrix H. Estimating this high-dimensional channel is costly in practice, however, since it requires a prohibitive number of measurements, especially in fast-fading conditions. In this work, we overcome this dilemma by designing full-duplex beams using implicit channel knowledge gathered from a relatively small number of measurements across H. These measurements are collected by the base station using a sequence of beams tailored to both the deployment environment and the particular users being served. This is accomplished through site-specific training of a transformer-based deep learning model that learns to efficiently probe portions of H most relevant to the particular users being served by exploiting the underlying structure of the surrounding environment. The deep learning model then uses these probing measurements to design transmit and receive beams that couple low self-interference while delivering high gain to a pair of downlink and uplink users. For favorable multi-user scaling, a single set of probing measurements can be used by the model to serve several users throughout the coherence time of H by leveraging correlations across those users' channels. Simulation results using ray-tracing demonstrate that our proposed approach exceeds the best possible performance with explicit channel estimation across a wide range of scenarios, especially with large antenna arrays.

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arXiv2025

Beyond Beam Sweeping: One-Shot Satellite Acquisition with Doppler-Aware Rainbow Beamforming

High-gain beamforming (BF) is essential for low Earth orbit (LEO) satellite communications to overcome severe path loss, but this requires acquiring precise satellite positions. Conventional satellite acquisition typically relies on time-domain beam sweeping, which incurs substantial overhead and latency. In this correspondence, we propose an efficient one-shot satellite acquisition framework that capitalizes on two phenomena traditionally regarded as impairments: i) Doppler effects and ii) beam-squint effects. Specifically, we derive a closed-form \emph{rainbow beamformer} that leverages beam-squint effects to align frequency-dependent beam directions with satellite positions inferred from their Doppler shifts. This approach enables reception from multiple satellites at once without requiring beam sweeping. To extract satellite position information, we develop three Doppler-aware angle estimation algorithms based on received signals. Simulation results demonstrate that the proposed method significantly outperforms conventional beam sweeping approaches in both acquisition accuracy and required time slots. These gains stem from the ability of the proposed rainbow BF to exploit the \emph{angle-dependent nature of Doppler shifts}, enabling full angular-domain coverage with a single pilot transmission and reception.

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arXiv2025

Random Access for LEO Satellite Communication Systems via Deep Learning

Integrating contention-based random access procedures into low Earth orbit (LEO) satellite communication (SatCom) systems poses new challenges, including long propagation delays, large Doppler shifts, and a large number of simultaneous access attempts. These factors degrade the efficiency and responsiveness of conventional random access schemes, particularly in scenarios such as satellite-based internet of things and direct-to-device services. In this paper, we propose a deep learning-based random access framework designed for LEO SatCom systems. The framework incorporates an early preamble collision classifier that uses multi-antenna correlation features and a lightweight 1D convolutional neural network to estimate the number of collided users at the earliest stage. Based on this estimate, we introduce an opportunistic transmission scheme that balances access probability and resource efficiency to improve success rates and reduce delay. Simulation results under 3GPP-compliant LEO settings confirm that the proposed framework achieves higher access success probability, lower delay, better physical uplink shared channel utilization, and reduced computational complexity compared to existing schemes.

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arXiv2025

Satellite Assignment Policy Learning for Coexistence in LEO Networks

Unlike in terrestrial cellular networks, certain frequency bands for low-earth orbit (LEO) satellite systems have thus far been allocated on a non-exclusive basis. In this context, systems that launch their satellites earlier (referred to as primary systems) are given spectrum access priority over those that launch later, known as secondary systems. For a secondary system to function, it is expected to either coordinate with primary systems or ensure that it does not cause excessive interference to primary ground users. Reliably meeting this interference constraint requires real-time knowledge of the receive beams of primary users, which in turn depends on the primary satellite-to-primary user associations. However, in practice, primary systems have thus far not publicly disclosed their satellite assignment policies; therefore, it becomes essential for secondary systems to develop methods to infer such policies. Assuming there is limited historical data indicating which primary satellites have served which primary users, we propose an end-to-end graph structure learning-based algorithm for learning highest elevation primary satellite assignment policies, that, upon deployment, can directly map the primary satellite coordinates into assignment decisions for the primary users. Simulation results show that our method can outperform the best baseline, achieving approximately a 15% improvement in prediction accuracy.

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arXiv2025

Site-Specific Beam Learning for Full-Duplex Massive MIMO Wireless Systems

Existing beamforming-based full-duplex solutions for multi-antenna wireless systems often rely on explicit estimation of the self-interference channel. The pilot overhead of such estimation, however, can be prohibitively high in millimeter-wave and massive MIMO systems, thus limiting the practicality of existing solutions, especially in fast-fading conditions. In this work, we present a novel beam learning framework that bypasses explicit self-interference channel estimation by designing beam codebooks to efficiently obtain implicit channel knowledge that can then be processed by a deep learning network to synthesize transmit and receive beams for full-duplex operation. Simulation results using ray-tracing illustrate that our proposed technique can allow a full-duplex base station to craft serving beams that couple low self-interference while delivering high SNR, with 75-97% fewer measurements than would be required for explicit estimation of the self-interference channel.

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arXiv2025

Autonomous Task Offloading of Vehicular Edge Computing with Parallel Computation Queues

This work considers a parallel task execution strategy in vehicular edge computing (VEC) networks, where edge servers are deployed along the roadside to process offloaded computational tasks of vehicular users. To minimize the overall waiting delay among vehicular users, a novel task offloading solution is implemented based on the network cooperation balancing resource under-utilization and load congestion. Dual evaluation through theoretical and numerical ways shows that the developed solution achieves a globally optimal delay reduction performance compared to existing methods, which is also validated by the feasibility test over a real-map virtual environment. The in-depth analysis reveals that predicting the instantaneous processing power of edge servers facilitates the identification of overloaded servers, which is critical for determining network delay. By considering discrete variables of the queue, the proposed technique's precise estimation can effectively address these combinatorial challenges to achieve optimal performance.

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arXiv2025

Space-Time Beamforming for LEO Satellite Communications

Inter-beam interference poses a significant challenge in low Earth orbit (LEO) satellite communications due to dense satellite constellations. To address this issue, we introduce spacetime beamforming, a novel paradigm that leverages the spacetime channel vector, uniquely determined by the angle of arrival (AoA) and relative Doppler shift, to optimize beamforming between a moving satellite transmitter and a ground station user. We propose two space-time beamforming techniques: spacetime zero-forcing (ST-ZF) and space-time signal-to-leakage-plus-noise ratio (ST-SLNR) maximization. In a partially connected interference channel, ST-ZF achieves a 3dB SNR gain over the conventional interference avoidance method using maximum ratio transmission beamforming. Moreover, in general interference networks, ST-SLNR beamforming significantly enhances sum spectral efficiency compared to conventional interference management approaches. These results demonstrate the effectiveness of space-time beamforming in improving spectral efficiency and interference mitigation for next-generation LEO satellite networks.

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arXiv2025

Satellite Selection for In-Band Coexistence of Dense LEO Networks

We study spectrum sharing between two dense low-earth orbit (LEO) satellite constellations, an incumbent primary system and a secondary system that must respect interference protection constraints on the primary system. In particular, we propose a secondary satellite selection framework and algorithm that maximizes capacity while guaranteeing that the time-average interference and absolute interference inflicted upon each primary ground user never exceeds specified thresholds. We solve this NP-hard constrained, combinatorial satellite selection problem through Lagrangian relaxation to decompose it into simpler problems which can then be solved through subgradient methods. A high-fidelity simulation is developed based on public FCC filings and technical specifications of the Starlink and Kuiper systems. We use this case study to illustrate the effectiveness of our approach and that explicit protection is indeed necessary for healthy coexistence. We further demonstrate that deep learning models can be used to predict the primary satellite system associations, which helps the secondary system avoid inflicting excessive interference and maximize its own capacity.

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arXiv2025

Can TDD Be Employed in LEO SatCom Systems? Challenges and Potential Approaches

Frequency-division duplexing (FDD) remains the de facto standard in modern low Earth orbit (LEO) satellite communication (SatCom) systems, such as SpaceX's Starlink, OneWeb, and Amazon's Project Kuiper. While time-division duplexing (TDD) is often regarded as superior in today's terrestrial networks, its viability in future LEO SatCom systems remains unclear. This article details how the long propagation delays and high orbital velocities exhibited by LEO SatCom systems impedes the adoption of TDD, due to challenges involving the frame structure and synchronization. We then present potential approaches to overcome these challenges, which vary in terms of resource efficiency and operational/device complexity and thus would likely be application-specific. We conclude by assessing the performance of these proposed approaches, putting into perspective the tradeoff between complexity and performance gains over FDD. Overall, this article aims to motivate future investigation into the prospects of TDD in LEO SatCom systems and solutions to enable such, with the goal of enhancing future systems and unifying them with terrestrial networks.

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arXiv2024

Active Beam Learning for Full-Duplex Wireless Systems

In this paper, we present a novel active beam learning method for in-band full-duplex wireless systems, that aims to design transmit and receive beams which suppress self-interference and maximize the sum spectral efficiency. Rather than rely on explicit estimation of the downlink, uplink, and/or self-interference channels like in most existing work, our method instead actively probes all three channels through measurements of SNR and INR over a fixed number of time slots. Then, once this probing concludes, all collected probing measurements are used to design transmit and receive beams which serve downlink and uplink in a full-duplex fashion. We realize this active beam learning scheme through a network of LSTMs and DNNs, which learns to design each probing beam pair and subsequently extract and record valuable information from each probing measurement such that near-optimal serving beams can be designed following the probing stage. Simulation indicates that our method reliably suppresses self-interference while delivering near-maximal SNR on the downlink and uplink with merely 3-10 probing time slots, while exhibiting robustness to measurement noise and the structure of the self-interference channel.

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arXiv2024

Adaptive Cell Range Expansion in Multi-Band UAV Communication Networks

This paper leverages stochastic geometry to model, analyze, and optimize multi-band unmanned aerial vehicle (UAV) communication networks operating across low-frequency and millimeter-wave (mmWave) bands. We introduce a novel approach to modeling mmWave antenna gain in such networks, which allows us to better capture and account for interference in our analysis and optimization. We then propose a simple yet effective user-UAV association policy, which strategically biases users towards mmWave UAVs to take advantage of lower interference and wider bandwidths compared to low-frequency UAVs. Under this scheme, we analytically derive the corresponding association probability, coverage probability, and spectral efficiency. We conclude by assessing our proposed association policy through simulation and analysis, demonstrating its effectiveness based on coverage probability and per-user data rates, as well as the alignment between analytical and simulation results.

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arXiv2024

Splitting Messages in the Dark- Rate-Splitting Multiple Access for FDD Massive MIMO Without CSI Feedback

A critical hindrance in realizing frequency division duplex (FDD) massive multi-input multi-output (MIMO) systems is the overhead associated with the downlink (DL) channel state information at the transmitter (CSIT) acquisition. To address this, we propose a novel framework that eliminates the need for CSI feedback, while achieving robust sum spectral efficiency (SE). Specifically, by leveraging partial frequency invariance of channel parameters, we reconstruct the DL CSIT using uplink (UL) pilots with the 2D-Newtonized orthogonal matching pursuit (2D-NOMP) algorithm. Due to discrepancies between the two disjoint bands, however, perfect DL CSIT acquisition is infeasible; resulting in multi-user interference (MUI). To account for this, we reformulate the sum SE maximization problem using the reconstructed channel and its error covariance matrix (ECM). Then, we propose an ECM estimation method based on the observed Fisher information matrix and introduce a precoder optimization technique with rate-splitting multiple access (RSMA). Our simulation results verify the validity of the proposed framework in the practical FDD massive MIMO scenarios, highlighting the essential role of ECM estimation in mitigating MUI to attain RSMA gains.

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arXiv2024

Beam Tracking for Full-Duplex User Terminals in Low Earth Orbit Satellite Communication Systems

This paper introduces a novel beam tracking scheme for full-duplex ground user terminals aiming to transmit uplink and receive downlink from two low Earth orbit (LEO) satellites at the same time and same frequency. Our proposed technique leverages observed phenomena from a recent measurement campaign to strategically select transmit and receive beams which couple low self-interference across the satellites' trajectories, thereby enabling in-band full-duplex operation. Our scheme takes a measurement-driven approach, meaning it does not rely on explicit knowledge of the self-interference channel and can inherently account for hardware impairments or other nonidealities. We show that our proposed scheme reliably selects beams which spatially cancel self-interference to below the noise floor, circumventing the need for digital/analog cancellation. Simulation results using satellite and orbital parameters published in 3GPP and FCC filings show that this substantial reduction in self-interference does not prohibitively compromise beamforming gain, allowing the user terminal to attain near-maximal SINRs, thus unlocking full-duplex operation.

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arXiv2024

Analog Beamforming Codebooks for Wideband Full-Duplex Millimeter-Wave Systems

In full-duplex millimeter-wave (mmWave) systems, the effects of beam squint and the frequency-selectivity of self-interference exacerbate over wide bandwidths. This complicates the use of beamforming to cancel self-interference when communicating over bandwidths on the order of gigahertz. In this work, we present the first analog beamforming codebooks tailored to wideband full-duplex mmWave systems, designed to both combat beam squint and cancel frequency-selective self-interference. Our proposed design constructs such codebooks by minimizing self-interference across the entire band of interest while constraining the coverage provided by these codebooks across that same band. Simulation results using computational electromagnetics to model self-interference suggest that a full-duplex 60 GHz system with our design enjoys lower self-interference and delivers better coverage across bandwidths as wide as 6 GHz, when compared to similar codebook designs that ignore beam squint and/or frequency-selectivity. This allows our design to sustain higher SINRs and spectral efficiencies across wide bandwidths, unlocking the potentials of wideband full-duplex mmWave systems.

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arXiv2024

A Survey on Advancements in THz Technology for 6G: Systems, Circuits, Antennas, and Experiments

Terahertz (THz) carrier frequencies (100 GHz to 10 THz) have been touted as a source for unprecedented wireless connectivity and high-precision sensing, courtesy of their wide bandwidth availability and small wavelengths, but noteworthy implementation challenges remain to make this a reality. In this paper, we survey recent advancements in THz technology and its role in future 6G wireless networks, with a particular emphasis on the 200-400 GHz frequency range and the IEEE 802.15.3d standard. We provide a comprehensive overview of THz systems, circuits, device technology, and antennas, while also highlighting recent experimental demonstrations of THz technology. Throughout the paper, we review the state-of-the-art and call attention to open problems, future prospects, and areas of further improvement to fully realize the potential of THz communication in next-generation wireless connectivity.

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