arXiv subjects

Aditya K. Jagannatham

Publications and source records attributed to Aditya K. Jagannatham.

At least 19 recordsLinked to original sources

arXiv2026

Joint Target-Tag Estimation in Ambient Backscatter-Based ISAC

Integrated sensing and communication (ISAC) has emerged as a key paradigm for unifying radar sensing and wireless communications within a shared hardware and spectral framework. In parallel, ambient backscatter communication (AmBC) has gained attention as a low-power solution for enabling passive connectivity by modulating and reflecting existing radio-frequency signals. Motivated by these developments, this paper investigates an integrated sensing and ambient backscatter communication (ISABC) framework in which a dual-functional radar-communication (DFRC) transceiver jointly performs high-resolution target sensing and low-power tag detection in cluttered environments. Within this ISAC and AmBC setting, we develop a comprehensive system model that jointly captures the estimation of target and clutter parameters, including delay, Doppler shift, and complex reflectivity, together with the recovery of embedded AmBC tag symbols. The resulting DFRC signal model reveals strong coupling among direct-path reflections, clutter echoes, and passive backscatter signals, leading to a highly entangled estimation problem. To address this challenge, we propose a robust three-stage signal processing framework that sequentially estimates target and clutter parameters before detecting the AmBC tag message. This structured approach effectively disentangles the superimposed components, improving both sensing accuracy and communication reliability in ISAC systems. Extensive simulations, benchmarked against the Cramer-Rao lower bound (CRLB), demonstrate the superior estimation performance of the proposed DFRC-based solution. The results further highlight the fundamental trade-offs between sensing accuracy and backscatter communication efficiency in ISABC systems.

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arXiv2026

Data-Aided Bayesian Learning for CSI Estimation over Doubly-Selective DCO-OTFS MIMO VLC Channels with Affine-Precoded Superimposed Training Sequences

An orthogonal affine-precoded superimposed training sequence (AP-STS)-based framework is conceived for cyclic prefix (CP)-assisted multiple-input multiple-output (MIMO) direct-current-biased orthogonal time frequency space (DCOOTFS) visible light communication (VLC) links using arbitrary transmit-receive pulse shaping for transmission over doubly selective channels. For each light-emitting diode (LED), the pilot and data matrices are jointly affine-precoded and overlaid in the delay-Doppler (DD)-domain. Then, a unified end-to-end DD-domain input-output relationship is derived. At each photodiode (PD), orthogonal precoders are utilized to separate the pilot and data components, thereby suppressing mutual interference. Building on this model, an expectation-maximization (EM)-driven DD-domain pilot-aided Bayesian learning (DD-PBL) scheme is developed to estimate the channel state information (CSI). A DD-domain data-aided Bayesian learning (DD-DBL) procedure is then proposed for jointly refining the CSI and detecting data by exploiting the detected symbols as virtual pilots in the spirit of decision-directed channel estimation. The linear minimum mean square error (LMMSE) detector harnessed explicitly accounts for CSI uncertainty due to realistic estimation errors. In addition, Bayesian Cramer-Rao lower bounds (BCRLBs) are derived for the MIMO DCO-OTFS VLC setting considered. Numerical results confirm improved normalized mean-square-error (NMSE), reduced pilot overhead, and mitigated symbol error-rate (SER) relative to recent benchmarks.

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arXiv2026

Sparse Channel Estimation and Signal Recovery for Reduced-PAPR Visible Light Optical OFDM Systems Relying on Bayesian Learning

A multipath CIR estimator is proposed, followed by sparse frequency-domain (FD) signal detection, which harnesses the simultaneous-sparsity innate in the multipath CIR and FD signals across measurement vectors, conceived for optical OFDM (O-OFDM) utilized for visible light communication (VLC) systems having reduced peak-to-average power ratio. At the outset, we derive the input-output relationships for the asymmetrically clipped O-OFDM (ACO-OFDM) as well as for the direct current-biased O-OFDM (DCO-OFDM) systems. Next, traditional benchmarking methods are introduced for channel estimation (CE), including both the LMMSE and LS methods, followed by an orthogonal matching pursuit (OMP)-based technique capable of exploiting sparsity in the multipath CIR of the VLC system. Subsequently, an CE technique based on a novel group sparse OMP (GOMP) concept is proposed, which capitalizes on the group-sparsity in the delay domain of the CIR across measurement vectors, attributed to the multipath characteristics inherent in the NLoS components of the VLC channel model. Additionally, an advanced group-sparse CE technique is put forth based on the group-sparse Bayesian learning (GBL) approach, which considerably reduces the pilot overhead. A low complexity version of GBL, termed LCGBL, is also developed that reduces the computational cost of GBL significantly. Consequently, the GOMP and GBL frameworks are also extended to the data detection of the sparse FD O-OFDM symbols, which utilizes the group-sparsity of the FD O-OFDM symbols across measurement vectors. The Bayesian Cramer-Rao lower bound (BCRLB) is computed to assess the estimation performance of the proposed CE techniques. Our simulations demonstrate that, despite its reduced pilot overhead, the proposed GBL technique outperforms the other CE schemes in terms of its BER, outage probability, and NMSE, quantitatively reinforcing its superiority.

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arXiv2026

Statistical Inference-Based Channel Estimation for LD-Driven Visible Light O-OFDM Systems in the Presence of Relative Intensity and Input-Signal-Dependent Shot Noise

Laser diode (LD)-based luminaires are gaining increasing attention in automotive applications and are expected to extend to residential and commercial environments, creating opportunities for high-bandwidth visible light communication (VLC) systems. However, practical LD-based VLC links are impaired by input-signal-dependent shot noise (ISDSN), relative intensity noise (RIN), and thermal noise, which affect reliable channel estimation (CE). This work investigates their joint impact on receiver-side CE in a single-input single-output (SISO) optical orthogonal frequency division multiplexing (OOFDM) VLC system under a statistically random channel model. A statistical inference framework is developed in which the receiver exploits observed signal variations to estimate the channel under optical impairments. Closed-form expressions are derived for least squares (LS), maximum likelihood (ML), maximum a posteriori probability (MAP), minimum mean square error (MMSE), and linear MMSE (LMMSE) estimators. In addition, the Bayesian Cramer-Rao lower bound (BCRLB) is derived to benchmark mean square error (MSE) performance. Monte Carlo simulations for direct current-biased O-OFDM (DCO-OFDM) and asymmetrically clipped O-OFDM (ACO-OFDM) validate the analysis. Results show substantial CE degradation under the joint presence of ISDSN and RIN, while the MMSE estimator consistently achieves the lowest MSE, demonstrating strong potential for robust and adaptive receiver operation in practical VLC systems.

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arXiv2026

Data-Aided Variational Bayesian Inference for CSI Estimation over Doubly-Selective DCO-OTFS MIMO VLC Systems with Affine-Precoded Superimposed Training Sequences

An orthogonal affine-precoded superimposed training sequences (AP-STS)-based architecture is developed for the cyclic prefix (CP)-aided multiple input multiple output (MIMO) direct-current-biased orthogonal time frequency space (DCO-OTFS) visible light communication (VLC) systems relying on arbitrary transmitter-receiver pulse shaping. The data and pilot symbol matrices are affine-precoded (AP) and superimposed in the delay-Doppler (DD)-domain for each transmit light-emitting diode (LED), followed by the development of an end-to-end DD-domain relationship for the input-output symbols. At the receiver for each receiver photodiode (PD), the decoupled pilot and data symbol are extracted by employing orthogonal precoder matrices, which eliminates the mutual interference. Furthermore, a novel pilot-aided (PA) variational Bayesian inference (PA-VBI) technique is conceived for the channel state information (CSI) estimation of MIMO DCO-OTFS VLC systems based on the expectation-maximization (EM) technique. Subsequently, a data-aided (DA) variational Bayesian inference (DA-VBI)-based joint CSI estimation and data detection technique is proposed, which beneficially harnesses the estimated data symbols for improved CSI estimation. Moreover, the Bayesian Cramer-Rao lower bounds (BCRLBs) are also derived for MIMO DCO-OTFS VLC systems. Finally, simulation results demonstrate that the proposed method yields superior performance in terms of normalized mean-square-error (NMSE), pilot overhead, and symbol error-rate (SER).

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arXiv2026

Sub-Sampling for Positioning Privacy in ISAC: Deception by Aliasing via Sparse Arrays and Pilots

Integrated sensing and communications (ISAC) enables simultaneous communication and sensing using shared spectrum and hardware resources in wireless systems. However, securing the sensing functionality against unauthorized receivers remains a fundamental challenge. In this paper, we propose a sub-sampling based sensing-privacy framework for communication-centric (CC)-ISAC systems that jointly exploits sparse arrays and sparse pilot allocations to induce controlled aliasing in the spatial and frequency domains, respectively. By interpreting antenna arrays and pilot subcarriers as spatial and frequency sampling mechanisms, respectively, we show that spatial-frequency undersampling naturally distorts the range-angle multiple-input multiple-output (MIMO) ambiguity function (AF) observed by an unauthorized receiver. To this end, we first derive a closed-form expression for the range-angle MIMO-AF, and subsequently characterize the ghost targets that arise due to spatial and frequency-domain aliasing. Next, we establish a sufficient condition under which these ambiguities jointly translate into positioning ambiguity and show that, for sufficiently large spatial and frequency sub-sampling factors, an unauthorized receiver inevitably positions a target at incorrect ghost positions. Finally, we show that the proposed sub-sampling framework preserves the native legitimate ISAC performance without introducing additional trade-offs. Numerical results verify the analysis and show that sparse arrays and sparse pilots naturally enable sensing and positioning privacy through deception by aliasing.

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arXiv2026

Technical Supplement Report on Full-Duplex FBMC/QAM MIMO Systems: Transceiver Design and Optimization

This technical report presents the design and analysis of filter bank multicarrier (FBMC)/QAM multi-user MISO systems. We describe the complete uplink and downlink signal processing chains and characterize the end-to-end effective channel, including inter-carrier interference, inter-symbol interference, intrinsic interference, and residual self-interference. We compare FBMC/QAM with CP-OFDM and FBMC/OQAM through the lens of the Balian-Low theorem, and analyze prototype filter choices (PHYDYAS, Type-I, and Type-II), including the interference power breakdown under MRT and ZF precoding. Furthermore, we present an online stochastic successive convex approximation framework for ergodic sum-rate maximization with closed-form power updates, and contrast it with offline Monte Carlo-based approaches. Simulation results demonstrate the BER and network spectral efficiency advantages of FBMC/QAM over CP-OFDM under residual carrier frequency offset.

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arXiv2026

SEMIKHORN: Globally balanced affinities for mmWave Localization in MU mMIMO systems

This work conceives SEMIKHORN, a semisupervised channel charting (CC) framework for mmWave localization, which leverages t-SNEkhorn, a doubly stochastic variant of t-distributed Stochastic Neighbor Embedding (t-SNE) that utilizes entropic optimal transport to construct pairwise similarities. Unlike standard t-SNE, which normalizes affinities independently for each data point, t-SNEkhorn generates globally balanced similarities ensuring consistent neighborhood representation. We consider wireless networks with distributed base stations (BSs) equipped with multiple antennas, where each BS constructs a local dissimilarity matrix from the channel state information (CSI). These local dissimilarity matrices are then fused to obtain a single global dissimilarity matrix, which is processed through manifold learning to embed users onto a geometric map. The performance is evaluated in a simulated outdoor environment, and Bayesian optimization is employed on the framework hyperparameters to minimize the mean localization error (MLE). Experimental results demonstrate that the proposed framework achieves an MLE of 6.86% in a circular vicinity of radius 100m, requiring less than 15% of labeled CSI samples.

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arXiv2026

Variational Bayes Estimation for Affine-Precoded Superimposed Pilots in Partially Connected Dual-Wideband Tera-Hertz MU-MIMO Systems

This work conceives two affine precoding based system models, common precoding with joint channel estimation (CP-JCE) and user-specific precoding for decoupled channel estimation (USPDCE). Considering a dual-wideband effected partially connected architecture, we rigorously model the terahertz (THz) multiple input multiple output (MIMO) channel for each subarray corresponding to each user by incorporating the absorption, reflection, and freespace losses. Next, to address the significant bandwidth overhead associated with conventional pilot-based channel estimation, we employ superimposed pilots. Building on this, we formulate a structured sparse channel model and develop a variational Bayesian inference algorithm that jointly estimates the channel coefficients and learns the underlying sparsity structure through hyperparameter inference, thereby enabling robust and high-precision superimposed pilotbased channel estimation under severe model uncertainty. Lastly, we compare our results for both systems and provide a trade-off analysis between them.

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arXiv2026

Subarray based Wideband Beamforming and Variational Sparse CSI Estimation for Low-Resolution MU THz MIMO Systems

This work conceives a unified channel estimation and beamforming framework, formulated within the principles of variational Bayesian inference. Recognizing the limitations imposed by hardware constraints, frequency-dependent propagation effects, and the structural restrictions of partially connected architectures in the Terahertz (THz) band, we formulate a dual-wideband channel model incorporating root raised cosine (RRC) pulse shape to account its band-limited nature. To further address the nonlinear distortions introduced by low-resolution ADCs, Bussgang decomposition is employed, enabling a tractable linearized inference process. Unlike conventional techniques, the proposed method accommodates both on-grid and off-grid angular domains, capturing spatial sparsity with improved resolution and robustness. The multi-user (MU) Bayesian Cramér-Rao lower bound is also derived to benchmark the performance of the proposed estimator. Moreover, the framework incorporates a true time delay (TTD)-based hybrid transceiver design that inherently compensates for the beam-squint effect; a frequency-dependent angular deviation that arises due to the fixedphase nature of the conventional beamformer in wideband systems, thereby ensuring accurate directional alignment across all subcarriers. Extensive simulation results validate the effectiveness of the proposed variational Bayesian inference-based estimator and the TTD-enabled beamforming architecture, highlighting their robustness and performance gains under practical wideband THz system.

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arXiv2026

Two-Stage Hybrid Transceiver Design Relying on Low-Resolution ADCs in Partially Connected MU Terahertz (THz) MIMO Systems

A two-stage hybrid transceiver is designed by considering a partially connected architecture at the base station (BS) for a low-resolution multi-user (MU) THz massive multiple input multiple output (MIMO) system. Due to its high bandwidth coupled with a high number of antennas, the THz band suffers from the deleterious spatial-wideband and frequency-wideband effects jointly termed as the dual-wideband effect. To address this undesired phenomenon, we rigorously model the THz MIMO channel at each subarray corresponding to each user by incorporating the absorption, reflection, and free-space losses. Subsequently, a novel beamforming technique is proposed that employs only a few true time delay (TTD) lines for eliminating the beam-split effect, which is the manifestation of the spatial-wideband effect in the frequency domain. Our simulation results demonstrate a performance improvement of around 13% in terms of spectral efficiency over the existing state-of-the-art techniques.

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arXiv2026

Amalgamated CHIRP and OFDM for ISAC

Integrated Sensing and Communication (ISAC) requires the development of a waveform capable of efficiently supporting both communication and sensing functionalities. This paper proposes a novel waveform that combines the benefits of both the orthogonal frequency division multiplexing (OFDM) and the chirp waveforms to improve both the communication and sensing performance within an ISAC framework. Hence, a new architecture is proposed that utilizes the conventional communication framework while leveraging the parameters sensed at the receiver (Rx) for enhancing the communication performance. We demonstrate that the affine addition of OFDM and chirp signals results in a near constant-envelope OFDM waveform, which effectively reduces the peak-to-average power ratio (PAPR), a key limitation of traditional OFDM systems. Using the OFDM framework for sensing in the conventional fashion requires the allocation of some resources for sensing, which in turn reduces communication performance. As a remedy, the proposed affine amalgam facilitates sensing through the chirp waveform without consuming communication resources, thereby preserving communication efficiency. Furthermore, a novel technique of integrating the chirp signal into the OFDM framework at the slot-level is proposed to enhance the accuracy of range estimation. The results show that the OFDM signal incorporated with chirp has better autocorrelation properties, improved root mean square error (RMSE) of range and velocity, and lower PAPR. Finally, we characterize the trade-off between communications and sensing performance.

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arXiv2025

Bayesian Learning Aided Simultaneous Sparse Estimation of Dual-Wideband THz Channels in Multi-User Hybrid MIMO Systems

This work conceives the Bayesian Group-Sparse Regression (BGSR) for the estimation of a spatial and frequency wideband, i.e., a dual wideband channel in Multi-User (MU) THz hybrid MIMO scenarios. We develop a practical dual wideband THz channel model that incorporates absorption losses, reflection losses, diffused ray modeling and angles of arrival/departure (AoAs/AoDs) using a Gaussian Mixture Model (GMM). Furthermore, a low-resolution analog-to-digital converter (ADC) is employed at each RF chain, which is crucial for wideband THz massive MIMO systems to reduce power consumption and hardware complexity, given the high sampling rates and large number of antennas involved. The quantized MU THz MIMO model is linearized using the popular Bussgang decomposition followed by BGSR based channel learning framework that results in sparsity across different subcarriers, where each subcarrier has its unique dictionary matrix. Next, the Bayesian Cramér Rao Bound (BCRB) is devised for bounding the normalized mean square error (NMSE) performance. Extensive simulations were performed to assess the performance improvements achieved by the proposed BGSR method compared to other sparse estimation techniques. The metrics considered for quantifying the performance improvements include the NMSE and bit error rate (BER).

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arXiv2025

Joint Angle and Velocity-Estimation for Target Localization in Bistatic mmWave MIMO Radar in the Presence of Clutter

Sparse Bayesian learning (SBL)-aided target localization is conceived for a bistatic mmWave MIMO radar system in the presence of unknown clutter, followed by the development of an angle-Doppler (AD)-domain representation of the target-plus-clutter echo model for accurate target parameter estimation. The proposed algorithm exploits the three-dimensional (3D) sparsity arising in the AD domain of the scattering scene and employs the powerful SBL framework for the estimation of target parameters, such as the angle-of-departure (AoD), angle-of-arrival (AoA) and velocity. To handle a practical scenario where the actual target parameters typically deviate from their finite-resolution grid, a super-resolution-based improved off-grid SBL framework is developed for recursively updating the parameter grid, thereby progressively refining the estimates. We also determine the Cramér-Rao bound (CRB) and Bayesian CRB for target parameter estimation in order to benchmark the estimation performance. Our simulation results corroborate the superior performance of the proposed approach in comparison to the existing algorithms, and also their ability to approach the bounds derived.

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arXiv2025

Spectral Efficiency Maximization for mmWave MIMO-Aided Integrated Sensing and Communication Under Practical Constraints

A hybrid transmit precoder (TPC) and receive combiner (RC) pair is conceived for millimeter wave (mmWave) multiple input multiple output (MIMO) integrated sensing and communication (ISAC) systems. The proposed design considers a practical mean squared error (MSE) constraint between the desired and the achieved beampatterns constructed for identifying radar targets (RTs). To achieve optimal performance, we formulate an optimization problem relying on sum spectral efficiency (SE) maximization of the communication users (CUs), while satisfying certain radar beampattern similarity (RBPS), total transmit power, and constant modulus constraints, where the latter are attributed to the hybrid mmWave MIMO architecture. Since the aforementioned problem is non-convex and intractable, a sequential approach is proposed wherein the TPCs are designed first, followed by the RCs. To deal with the non-convex MSE and constant modulus constraints in the TPC design problem, we propose a majorization and minimization (MM) based Riemannian conjugate gradient (RCG) method, which restricts the tolerable MSE of the beampattern to within a predefined limit. Moreover, the least squares and the zero-forcing methods are adopted for maximizing the sum-SE and for mitigating the multiuser interference (MUI), respectively. Furthermore, to design the RC at each CU, we propose a linear MM-based blind combiner (LMBC) scheme that does not rely on the knowledge of the TPC at the CUs and has a low complexity. To achieve user fairness, we further extend the proposed sequential approach for maximizing the geometric mean (GM) of the CU's rate. Simulation results are presented, which show the superior performance of the proposed hybrid TPC and RC in comparison to the state-of-the-art designs in the mmWave MIMO ISAC systems under consideration.

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arXiv2025

Optimal Hybrid Transmit Beamforming for mm-Wave Integrated Sensing and Communication

A hybrid beamformer (HBF) is designed for integrated sensing and communication (ISAC)-aided millimeter wave (mmWave) systems. The ISAC base station (BS), relying on a limited number of radio frequency (RF) chains, supports multiple communication users (CUs) and simultaneously detects the radar target (RT). To maximize the probability of detection (PD) of the RT, and achieve rate fairness among the CUs, we formulate two problems for the optimization of the RF and baseband (BB) transmit precoders (TPCs): PD-maximization (PD-max) and geometric mean rate-maximization (GMR-max), while ensuring the quality of services (QoS) of the RT and CUs. Both problems are highly non-convex due to the intractable expressions of the PD and GMR and also due to the non-convex unity magnitude constraints imposed on each element of the RF TPC. To solve these problems, we first transform the intractable expressions into their tractable counterparts and propose a power-efficient bisection search and majorization and minimization-based alternating algorithms for the PD-max and GMR-max problems, respectively. Furthermore, both algorithms optimize the BB TPC and RF TPCs in an alternating fashion via the successive convex approximation (SCA) and penalty-based Riemannian conjugate gradient (PRCG) techniques, respectively. Specifically, in the PRCG method, we initially add all the constraints except for the unity magnitude constraint to the objective function as a penalty term and subsequently employ the RCG method for optimizing the RF TPC. Finally, we present our simulation results and compare them to the benchmarks for demonstrating the efficacy of the proposed algorithms.

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arXiv2024

Multi-Beam Object-Localization for Millimeter-Wave ISAC-Aided Connected Autonomous Vehicles

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems capable of integrated sensing and communication (ISAC) constitute a key technology for connected autonomous vehicles (CAVs). In this context, we propose a multi-beam object-localization (MBOL) model for enhancing the sensing beampattern (SBP) gain of adjacent objects in CAV scenarios. Given the ultra-narrow beams of mmWave MIMO systems, a single pencil beam is unsuitable for closely located objects, which tend to require multiple beams. Hence, we formulate the SBP gain maximization problem, considering also the constraints on the signal-to-interference and noise ratio (SINR) of the communication users (CUs), on the transmit power, and the constant modulus of the phase-shifters in the mmWave hybrid transceiver. To solve this non-convex problem, we propose a penalty-based triple alternating optimization algorithm to design the hybrid beamformer. Finally, simulation results are provided for demonstrating the efficacy of the proposed model.

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arXiv2024

Joint Hybrid Transceiver and Reflection Matrix Design for RIS-Aided mmWave MIMO Cognitive Radio Systems

In this work, a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) multiple-input multiple-output (MIMO) cognitive radio (CR) downlink operating in the underlay mode is investigated. The cognitive base station (CBS) communicates with multiple secondary users (SUs), each having multiple RF chains in the presence of a primary user (PU). We conceive a joint hybrid transmit precoder (TPC), receiver combiner (RC), and RIS reflection matrix (RM) design, which maximizes the sum spectral efficiency (SE) of the secondary system while maintaining the interference induced at the PU below a specified threshold. To this end, we formulate the sum-SE maximization problem considering the total transmit power (TP), the interference power (IP), and the non-convex unity modulus constraints of the RF TPC, RF RC, and RM. To solve this highly non-convex problem, we propose a two-stage hybrid transceiver design in conjunction with a novel block coordinate descent (BCD)-successive Riemannian conjugate gradient (SRCG) algorithm. We initially decompose the RF TPC, RC, and RM optimization problem into a series of sub-problems and subsequently design pairs of RF TPC and RC vectors, followed by successively optimizing the elements of the RM using the iterative BCD-SRCG algorithm. Furthermore, based on the effective baseband (BB) channel, the BB TPC and BB RC are designed using the proposed direct singular value decomposition (D-SVD) and projection based SVD (P-SVD) methods. Subsequently, the proportional water-filling solution is proposed for optimizing the power, which maximizes the weighted sum-SE of the system. Finally, simulation results are provided to compare our proposed schemes to several benchmarks and quantify the impact of other parameters on the sum-SE of the system.

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