@article{deshpande_castellanos_khosravirad_du_viswanathan_heath jr_2023, title={A Wideband Generalization of the Near-Field Region for Extremely Large Phased-Arrays}, volume={12}, ISSN={["2162-2345"]}, DOI={10.1109/LWC.2022.3233011}, abstractNote={The narrowband and far-field assumption in conventional wireless system design leads to a mismatch with the optimal beamforming required for wideband and near-field systems. This discrepancy is exacerbated for larger apertures and bandwidths. To characterize the behavior of near-field and wideband systems, we derive the beamforming gain expression achieved by a frequency-flat phased array designed for plane-wave propagation. To determine the far-field to near-field boundary for a wideband system, we propose a frequency-selective distance metric. The proposed far-field threshold increases for frequencies away from the center frequency. The analysis results in a fundamental upper bound on the product of the array aperture and the system bandwidth. We present numerical results to illustrate how the gain threshold affects the maximum usable bandwidth for the n260 and n261 5G NR bands.}, number={3}, journal={IEEE WIRELESS COMMUNICATIONS LETTERS}, author={Deshpande, Nitish and Castellanos, Miguel R. R. and Khosravirad, Saeed R. R. and Du, Jinfeng and Viswanathan, Harish and Heath Jr, Robert W. W.}, year={2023}, month={Mar}, pages={515–519} }
 @article{kim_castellanos_heath_2023, title={Joint Relay Selection and Beam Management Based on Deep Reinforcement Learning for Millimeter Wave Vehicular Communication}, volume={72}, ISSN={["1939-9359"]}, DOI={10.1109/TVT.2023.3274763}, abstractNote={Cooperative relays improve reliability and coverage in wireless networks by providing multiple paths for data transmission. Relaying will play an essential role in vehicular networks at higher frequency bands, where mobility and frequent signal blockages cause link outages. To ensure connectivity in a relay-aided vehicular network, the relay selection policy should be designed to efficiently find unblocked relays. Inspired by recent advances in beam management in mobile millimeter wave (mmWave) networks, this article addresses the question: how can the best relay be selected with minimal overhead from beam management? In this regard, we formulate a sequential decision problem to jointly optimize relay selection and beam management. We propose a joint relay selection and beam management policy based on deep reinforcement learning (DRL) using the Markov property of beam indices and beam measurements. The proposed DRL-based algorithm learns time-varying thresholds that adapt to the dynamic channel conditions and traffic patterns. Numerical experiments demonstrate that the proposed algorithm outperforms baselines without prior channel knowledge. Moreover, the DRL-based algorithm can maintain high spectral efficiency under fast-varying channels.}, number={10}, journal={IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY}, author={Kim, Dohyun and Castellanos, Miguel R. and Heath, Robert W., Jr.}, year={2023}, month={Oct}, pages={13067–13080} }
 @article{amir_castellanos_heath_2023, title={Multi-node joint communication and radar using synchronization signals in 5G}, DOI={10.1109/CAMSAP58249.2023.10403526}, abstractNote={Joint communication and radar (JCR) provide the mutual benefits of sensing and communication with shared hardware and spectral resources. We propose a JCR system with multiple nodes that leverages the 5G communication waveform for range estimation. We analyze a multi-node scenario where each node obtains an individual range estimate and then shares the estimate with other nodes to estimate each node's distance from the target jointly. We propose a linear minimum mean squared error (LMMSE) based estimate that combines the information from all nodes. The estimate can also be adapted to account for quantized feedback. The analysis and simulations for the model show that the proposed method outperforms the baseline models for both quantized and non-quantized methods. The distributed JCR results show that the transceivers in the system can detect the target more robustly, reducing the error in detection by 75%, as compared to the conventional approach.}, journal={2023 IEEE 9TH INTERNATIONAL WORKSHOP ON COMPUTATIONAL ADVANCES IN MULTI-SENSOR ADAPTIVE PROCESSING, CAMSAP}, author={Amir, Shehla and Castellanos, Miguel R. and Heath, Robert W., Jr.}, year={2023}, pages={161–165} }
 @article{loukil_castellanos_bhuyan_heath jr_2023, title={Physical Layer Security at a Point-to-Point MIMO System With 1-Bit DACs and ADCs}, volume={12}, ISSN={["2162-2345"]}, DOI={10.1109/LWC.2023.3277718}, abstractNote={Low-resolution converters reduce the power consumption of wireless devices and help enable large energy-efficient arrays. In this letter, we evaluate the secrecy rate for the extreme case of 1-bit single-user multiple-input multiple-output systems following two signaling approaches. In the first method, the system uses discrete signaling. In the second, the system combines Gaussian signaling with artificial noise alongside power allocation. In the first case, we give a closed-form expression of the secrecy rate and propose an algorithm that provides near-optimal results for low signal-to-noise ratios (SNRs). In the second scenario, depending on the channel state information, we suggest different precoding plans and derive a secrecy rate lower bound. At low SNR, power is allocated to the main signal. Only at high SNR is optimal power allocation effective. Moreover, knowing the eavesdropper channel greatly improves the system’s secrecy.}, number={8}, journal={IEEE WIRELESS COMMUNICATIONS LETTERS}, author={Loukil, Mohamed Habib and Castellanos, Miguel R. and Bhuyan, Arupjyoti and Heath Jr, Robert W. W.}, year={2023}, month={Aug}, pages={1439–1443} }
 @article{kim_castellanos_heath_2022, title={Joint Beam Management and Relay Selection Using Deep Reinforcement Learning for MmWave UAV Relay Networks}, ISSN={["2155-7578"]}, DOI={10.1109/MILCOM55135.2022.10017754}, abstractNote={Unmanned aerial vehicle (UAV) relays are useful in tactical millimeter wave (mmWave) networks to overcome blockages and improve link resilience. Getting the most benefits from relays, though, requires efficient strategies for relay selection and for beam management. In this paper, we use deep reinforcement learning (DRL) to jointly select unblocked UAV relays and to perform beam management. The proposed DRL-based algorithm uses rate feedback from the receiver to learn a policy that adapts to the dynamic channel conditions. We show with numerical evaluation that the proposed method outperforms baselines without prior channel knowledge. Moreover, the DRL-based algorithm can maintain high spectral efficiency even under frequent blockages.}, journal={2022 IEEE MILITARY COMMUNICATIONS CONFERENCE (MILCOM)}, author={Kim, Dohyun and Castellanos, Miguel R. and Heath, Robert W., Jr.}, year={2022} }
 @article{deshpande_castellanos_heath_2022, title={Nonuniform true time delay precoding in wideband MISO systems}, ISSN={["1058-6393"]}, DOI={10.1109/IEEECONF56349.2022.10051948}, abstractNote={We focus on true time delay (TTD) precoding which is a reduced hardware complexity alternative to fully-digital frequency selective beamforming in wide band systems. It is challenging to select the delays for TTD beamforming architectures for a channel with multi-path and near-field effects because of the nonuniform delay variation across the array. We design the TTD beamformer by maximizing the total energy in the filtered channel response. Based on the idea of temporal focusing, we reformulate the TTD constraint by including a sparsity regularizer penalty term in the objective. We then propose a procedure to compute the delays using the precoder response obtained after solving a sparse eigenvalue problem for maximizing the total energy. Incorporating sparsity in the problem formulation enables us to obtain a response that closely resembles the TTD precoder response in time domain. The proposed approach outperforms the time-reversal precoder based TTD design and the phase-shifter implementation in terms of the wideband mutual information.}, journal={2022 56TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS, AND COMPUTERS}, author={Deshpande, Nitish Vikas and Castellanos, Miguel R. and Heath, Robert W., Jr.}, year={2022}, pages={1233–1237} }
 @article{castellanos_peleato_love_2022, title={Position-Based Adaptive Power Back-Off for User Electromagnetic Exposure Management in Millimeter Wave Systems}, volume={11}, ISSN={["2162-2345"]}, DOI={10.1109/LWC.2021.3121005}, abstractNote={Mobile devices are subject to regulatory limits on user electromagnetic radiation exposure. User exposure is dynamic and varies as a function of the transmit signal and the device orientation. In this letter, we propose the use of a signal-level power density (PD) model to adaptively reduce the transmit power of a millimeter wave (mmWave) system as the position of the device changes. We also develop a method for efficiently calculating power back-off factors for beams in a discrete Fourier Transform (DFT) codebook. Simulation results demonstrate that position-based power reduction leads to a significant performance improvement.}, number={1}, journal={IEEE WIRELESS COMMUNICATIONS LETTERS}, author={Castellanos, Miguel R. and Peleato, Borja and Love, David J.}, year={2022}, month={Jan}, pages={86–90} }