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Mohammad Mahdi Mojahedian

Publications and source records attributed to Mohammad Mahdi Mojahedian.

12 recordsLinked to original sources
arXiv2026

Cooperative Target Localization in RIS-Enabled ISAC Systems

This paper develops a cooperative integrated sensing and communication (ISAC) framework in which a multi-antenna base station (BS) simultaneously localizes a target and serves multiple communication users, a subset of which is equipped with reconfigurable intelligent surfaces (RISs). The Fisher information matrix (FIM) for target positioning is derived, explicitly characterizing its dependence on the BS beamforming coefficients, RIS phase profiles, and bistatic sensing geometry. Under the stated scaling assumptions, coherent RIS phase alignment yields a Fisher-information gain that scales quadratically with the number of RIS elements, whereas independent random phases provide a linear gain in expectation. We formulate a sensing-centric joint active and passive beamforming problem that minimizes the position error bound (PEB) subject to per-user signal-to-interference-plus-noise ratio (SINR) and transmit-power constraints. The resulting non-convex problem is addressed through an iterative successive convex approximation (SCA) procedure that solves a sequence of convex subproblems. We further develop a target localization estimator that fuses one direct time-of-arrival (ToA) measurement, one angle-of-arrival (AoA) measurement, and multiple RIS-assisted indirect ToA measurements. Under small measurement errors and asymptotically efficient first-stage ToA/AoA estimation, the estimator covariance approaches the Cramér--Rao bound (CRB) to first order. Numerical results validate the analytical scaling laws and demonstrate the localization gains enabled by cooperative RIS-equipped users.

eess.SP↗
arXiv2026

Task-Aware Beamforming for Semantic Wireless Localization

Many semantic localization tasks require determining a contextually meaningful region or state rather than minimizing the error of a full Cartesian position estimate. In such settings, aggregate localization accuracy need not align with the accuracy of the semantic decision. This paper develops a Cramér--Rao bound (CRB)-based framework that uses the local geometry of a known semantic map to guide uplink receive beamforming. Using local boundary crossing as a surrogate for semantic misclassification, we derive normal information maximization (NIM), which minimizes the CRB of the position component along the local semantic boundary normal. Under the considered single-path line-of-sight model, an optimal receive codebook can be restricted to the subspace spanned by the matched steering vector and its angular derivative, reducing the design to an allocation of measurement resources between matched and derivative spatial modes. We derive the resulting allocation for a general smooth boundary, with geofencing and intrusion detection arising as radial and tangential limiting cases. The framework is further extended to multi-region semantic maps through a distance-normalized minimax criterion and to uncertain prior locations through worst-case and prior-weighted robust formulations. Numerical results with finite-slot implementations and observation-level Monte Carlo simulations using profile maximum-likelihood (ML) estimation show that NIM allocates measurements according to the task-relevant boundary geometry and can substantially reduce semantic error relative to the classic squared position error bound (SPEB) design in the considered scenarios. The empirical results also closely follow the local CRB-based boundary-crossing approximation in the studied operating regime.

cs.IT↗
arXiv2026

Dual-Orthogonality Waveforms for Integrated Communication and Imaging in Dynamic Multipath Channels

Dual-Orthogonality waveforms are multi-antenna signaling schemes that enforce mutual orthogonality across transmit channels and over a prescribed set of delay shifts. By relaxing strict time orthogonality to the physically admissible propagation region, they preserve full-band operation per transmit antenna while embedding communication data and maintaining stream separability. This makes them attractive for Integrated Sensing and Communications (ISAC), where reliable data transmission, high-resolution sensing, and imaging must coexist under time-varying propagation. In dynamic multipath environments, delay-Doppler dispersion across multiple paths perturbs the transmit subspaces and partially breaks the relaxed orthogonality conditions. This paper analyzes this effect and develops a multipath-aware decoding framework based on structured parameter estimation, effective-subspace reconstruction, and low-complexity linear equalization. Numerical results show communication performance comparable to OFDM-based ISAC and MIMO-OTFS baselines while improving sensing and imaging through full-band per-transmit operation. The proposed approach achieves approximately 30 cm range resolution, more than 15 dB suppression of multipath imaging artifacts with coherent SAR processing, and a favorable sensing-communication trade-off. Over-the-air experiments at 60 GHz validate multi-stream communication, the designed zero-correlation region, and accurate radar ranging. A second campaign in a highly reflective indoor environment further demonstrates multipath-aware stream equalization under strong unsuppressed reflections.

eess.SP↗
arXiv2026

TRACE: Theoretical Risk Attribution under Covariate-shift Effects

When a source-trained model $Q$ is replaced by a model $\tilde{Q}$ trained on shifted data, its performance on the source domain can change unpredictably. To address this, we study the two-model risk change, $ΔR := R_P(\tilde{Q}) - R_P(Q)$, under covariate shift. We introduce TRACE (Theoretical Risk Attribution under Covariate-shift Effects), a framework that decomposes $|ΔR|$ into an interpretable upper bound. This decomposition disentangles the risk change into four actionable factors: two generalization gaps, a model change penalty, and a covariate shift penalty, transforming the bound into a powerful diagnostic tool for understanding why performance has changed. To make TRACE a fully computable diagnostic, we instantiate each term. The covariate shift penalty is estimated via a model sensitivity factor (from high-quantile input gradients) and a data-shift measure; we use feature-space Optimal Transport (OT) by default and provide a robust alternative using Maximum Mean Discrepancy (MMD). The model change penalty is controlled by the average output distance between the two models on the target sample. Generalization gaps are estimated on held-out data. We validate our framework in an idealized linear regression setting, showing the TRACE bound correctly captures the scaling of the true risk difference with the magnitude of the shift. Across synthetic and vision benchmarks, TRACE diagnostics are valid and maintain a strong monotonic relationship with the true performance degradation. Crucially, we derive a deployment gate score that correlates strongly with $|ΔR|$ and achieves high AUROC/AUPRC for gating decisions, enabling safe, label-efficient model replacement.

cs.LG↗
arXiv2026

A Class of Subadditive Information Measures and their Applications

We introduce a two-parameter family of discrepancy measures, termed \emph{$(G,f)$-divergences}, obtained by applying a non-decreasing function $G$ to an $f$-divergence $D_f$. Building on Csiszár's formulation of mutual $f$-information, we define a corresponding $(G,f)$-information measure $ I_{G,f}(X;Y)$. A central theme of the paper is subadditivity over product distributions and product channels. We develop reduction principles showing that, for broad classes of $G$, it suffices to verify divergence subadditivity on binary alphabets. Specializing to the functions $G(x)\in\{x,\log(1+x),-\log(1-x)\}$, we derive tractable sufficient conditions on $f$ that guarantee subadditivity, covering many standard $f$-divergences. Finally, we present applications to finite-blocklength converses for channel coding, bounds in binary hypothesis testing, and an extension of the Shannon--Gallager--Berlekamp sphere-packing exponent framework to subadditive $(G,f)$-divergences.

cs.IT↗
arXiv2024

Output Statistics of Random Binning: Tsallis Divergence and Its Applications

Random binning is a widely used technique in information theory with diverse applications. In this paper, we focus on the output statistics of random binning (OSRB) using the Tsallis divergence $T_α$. We analyze all values of $α\in (0, \infty)\cup\{\infty\}$ and consider three scenarios: (i) the binned sequence is generated i.i.d., (ii) the sequence is randomly chosen from an $ε$-typical set, and (iii) the sequence originates from an $ε$-typical set and is passed through a non-memoryless virtual channel. Our proofs cover both achievability and converse results. To address the unbounded nature of $T_\infty$, we extend the OSRB framework using Rényi's divergence with order infinity, denoted $D_\infty$. As part of our exploration, we analyze a specific form of Rényi's conditional entropy and its properties. Additionally, we demonstrate the application of this framework in deriving achievability results for the wiretap channel, where Tsallis divergence serves as a security measure. The secure rate we obtain through the OSRB analysis matches the secure capacity for $α\in (0, 2]\cup\{{\infty}\}$ and serves as a potential candidate for the secure capacity when $α\in (2, \infty)$.

cs.IT↗
arXiv2024

Beyond Yao's Millionaires: Secure Multi-Party Computation of Non-Polynomial Functions

In this paper, we present an unconditionally secure $N$-party comparison scheme based on Shamir secret sharing, utilizing the binary representation of private inputs to determine the $\max$ without disclosing any private inputs or intermediate results. Specifically, each party holds a private number and aims to ascertain the greatest number among the $N$ available private numbers without revealing its input, assuming that there are at most $T < \frac{N}{2}$ honest-but-curious parties. The proposed scheme demonstrates a lower computational complexity compared to existing schemes that can only compare two secret numbers at a time. To the best of our knowledge, our scheme is the only information-theoretically secure method for comparing $N$ private numbers without revealing either the private inputs or any intermediate results. We demonstrate that by modifying the proposed scheme, we can compute other well-known non-polynomial functions of the inputs, including the minimum, median, and rank. Additionally, in the proposed scheme, before the final reveal phase, each party possesses a share of the result, enabling the nodes to compute any polynomial function of the comparison result. We also explore various applications of the proposed comparison scheme, including federated learning.

cs.CR↗
arXiv2024

Fundamental Trade-Offs in Monostatic ISAC: A Holistic Investigation Towards 6G

This paper undertakes a holistic investigation of two fundamental trade-offs in monostatic OFDM integrated sensing and communication (ISAC) systems-namely, the time-frequency trade-off and the spatial trade-off, originating from the choice of modulation order for random data and the design of beamforming strategies, respectively. To counteract the elevated side-lobe levels induced by varying-amplitude data in high-order QAM signaling, we propose a novel linear minimum mean-squared-error (LMMSE) estimator, capable of maintaining robust sensing performance across a wide range of SNRs. Moreover, we explore spatial domain trade-offs through two ISAC transmission strategies: concurrent, employing joint beams, and time-sharing, using separate, time-non-overlapping beams for sensing and communications. Simulations demonstrate superior performance of the LMMSE estimator, especially in detecting weak targets in the presence of strong ones with high-order QAM, consistently yielding more favorable ISAC trade-offs than existing baselines under various modulation schemes, SNR conditions, RCS levels and transmission strategies. We also provide experimental results to validate the effectiveness of the LMMSE estimator in reducing side-lobe levels, based on real-world measurements.

eess.SP↗
arXiv2023

Adjustable Privacy using Autoencoder-based Learning Structure

Inference centers need more data to have a more comprehensive and beneficial learning model, and for this purpose, they need to collect data from data providers. On the other hand, data providers are cautious about delivering their datasets to inference centers in terms of privacy considerations. In this paper, by modifying the structure of the autoencoder, we present a method that manages the utility-privacy trade-off well. To be more precise, the data is first compressed using the encoder, then confidential and non-confidential features are separated and uncorrelated using the classifier. The confidential feature is appropriately combined with noise, and the non-confidential feature is enhanced, and at the end, data with the original data format is produced by the decoder. The proposed architecture also allows data providers to set the level of privacy required for confidential features. The proposed method has been examined for both image and categorical databases, and the results show a significant performance improvement compared to previous methods.

cs.LG↗
arXiv2018

A Correlation Measure Based on Vector-Valued $L_p$-Norms

In this paper, we introduce a new measure of correlation for bipartite quantum states. This measure depends on a parameter $α$, and is defined in terms of vector-valued $L_p$-norms. The measure is within a constant of the exponential of $α$-Rényi mutual information, and reduces to the trace norm (total variation distance) for $α=1$. We will prove some decoupling type theorems in terms of this measure of correlation, and present some applications in privacy amplification as well as in bounding the random coding exponents. In particular, we establish a bound on the secrecy exponent of the wiretap channel (under the total variation metric) in terms of the $α$-Rényi mutual information according to \emph{Csiszár's proposal}.

cs.IT↗
arXiv2015

Perfectly Secure Index Coding

In this paper, we investigate the index coding problem in the presence of an eavesdropper. Messages are to be sent from one transmitter to a number of legitimate receivers who have side information about the messages, and share a set of secret keys with the transmitter. We assume perfect secrecy, meaning that the eavesdropper should not be able to retrieve any information about the message set. We study the minimum key lengths for zero-error and perfectly secure index coding problem. On one hand, this problem is a generalization of the index coding problem (and thus a difficult one). On the other hand, it is a generalization of the Shannon's cipher system. We show that a generalization of Shannon's one-time pad strategy is optimal up to a multiplicative constant, meaning that it obtains the entire boundary of the cone formed by looking at the secure rate region from the origin. Finally, we consider relaxation of the perfect secrecy and zero-error constraints to weak secrecy and asymptotically vanishing probability of error, and provide a secure version of the result, obtained by Langberg and Effros, on the equivalence of zero-error and $ε$-error regions in the conventional index coding problem.

cs.IT↗