arXiv subjects

Saeed Abdallah

Publications and source records attributed to Saeed Abdallah.

12 recordsLinked to original sources

arXiv2026

Data-Aided Channel and Doppler Estimation for mMIMO LEO SatComs with Uncompensated Doppler

This paper presents a framework for estimating and tracking massive multiple-input multiple-output (mMIMO) low-Earth-orbit (LEO) satellite channels under uncompensated Doppler. The approach begins with a pilot-based minimum mean square error (MMSE) estimate, followed by Doppler estimation and data-aided channel estimation using either a decision-directed MMSE (DD-MMSE) or an expectation-maximization (EM)-based estimator. The proposed framework achieves improved channel and Doppler estimation accuracy compared to existing methods. Results demonstrate that the DD-MMSE variant offers lower complexity, while the EM variant provides higher estimation accuracy.

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arXiv2026

Fluid-Antenna-Enabled Integrated Bistatic Sensing and Backscatter Communication Systems

This paper studies a fluid-antenna-enabled integrated bistatic sensing and backscatter communication system for future networks where connectivity, power delivery, and environmental awareness are jointly supported by the same infrastructure. A multi-antenna base station (BS) with transmitting fluid antennas serves downlink users, energizes passive tags, and illuminates radar targets, while a spatially separated multi-antenna reader decodes tag backscatter and processes radar echoes to avoid the strong self-interference that would otherwise obscure weak returns at the BS. The coexistence of tags and targets, however, induces severe near--far disparities and multi-signal interference, which can be mitigated by fluid antennas through additional spatial degrees of freedom that reshape the multi-hop channels. We formulate a transmit-power minimization problem that jointly optimizes the BS information beamformers, sensing covariance matrix, reader receive beamformers, tag reflection coefficients, and fluid-antenna (FA) positions under heterogeneous quality of service constraints for communication, backscatter, and sensing, as well as energy-harvesting and FA geometry requirements. To tackle the resulting non-convex problem, we develop an alternating-optimization block-coordinate framework that solves four tractable subproblems using semidefinite relaxation, majorization--minimization, and successive convex approximation. Numerical results show consistent transmit-power savings over fixed-position antennas and zero-forcing baselines, achieving about 13.7% and 54.5% reductions, respectively.

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arXiv2025

Channel Estimation for Full-duplex Multi-tag Ambient Backscatter Communication Systems with I/Q Imbalance

Ambient backscatter communication (AmBC) has emerged as a highly attractive paradigm for energy-efficient communication. Full-duplex multi-tag AmBC systems provide the scalability and efficient spectrum utilization essential for next generation Internet-of-Things (IoT) networks. However, the presence of multiple tags, self-interference and hardware impairments such as inphase/quadrature (I/Q) imbalance, makes accurate channel estimation indispensable for efficient interference management. The large number of channel parameters and the presence of mirror images of each signal component necessitate careful design of the channel estimation phase to prevent performance degradation. In this work, we propose a novel three-stage training protocol and pilot-based estimation scheme that ensure signal orthogonality and successfully avoid error floors. We also propose two semi-blind estimators, one based on decision-directed (DD) criterion and the other on the expectation conditional maximization (ECM) framework. By exploiting both pilots and data symbols, these two estimators achieve higher estimation accuracy than pilot-based estimation, at the cost of additional complexity. Cramer-Rao bounds (CRBs) for both types of estimation are also derived. The pilot-based estimator and the ECM estimator approach their respective CRBs, while the DD estimator performs mid-way between them. The three proposed solutions support different use cases by offering distinct tradeoffs between performance and computational complexity.

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arXiv2025

EM-Based Channel Estimation for mMIMO LEO SATCOM Under Imperfect Doppler Compensation

Massive multiple-input multiple-output low-Earth-orbit communication channels are highly time-varying due to severe Doppler shifts and propagation delays. While satellite-mobility-induced Doppler shifts can be compensated using known ephemeris data, those caused by user mobility require accurate user positioning information; the absence of such information contributes to amplified channel aging in conventional channel estimators. To address this challenge, we propose a data-aided channel estimator based on the expectation-maximization (EM) algorithm, combined with a discrete Legendre polynomial basis expansion model (DLP-BEM), to estimate the channel under imperfect Doppler compensation. The EM algorithm iteratively exploits hidden data symbols for improved channel estimation, while DLP-BEM regularizes the process by projecting the channel estimate onto a lower-dimensional subspace that mitigates estimation errors. Simulation results demonstrate the superiority of the proposed framework over existing methods in terms of normalized mean square error and symbol error rate.

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arXiv2025

Novel Selection Schemes for Multi-RIS-Assisted Fluid Antenna Systems

This paper investigates the performance of a multi-reconfigurable intelligent surface (RIS)-assisted fluid antenna system (FAS). In this system, a single-antenna transmitter communicates with a receiver equipped with a planar FAS through multiple RISs in the absence of a direct link. To enhance the system performance, we propose two novel selection schemes: \textit{Max-Max} and \textit{Max-Sum}. In particular, the \textit{Max-Max} scheme selects the best combination of a single RIS and a single fluid antenna (FA) port that offers the maximum signal-to-noise ratio (SNR) at the receiver. On the other hand, the \textit{Max-Sum} scheme selects one RIS while activating all FA ports providing the highest overall SNR. We conduct a detailed performance analysis of the proposed system under Nakagami-$m$ fading channels. First, we derive the cumulative distribution function (CDF) of the SNR for both selection schemes. The derived CDF is then used to obtain approximate theoretical expressions for the outage probability (OP) and the delay outage rate (DOR). Next, a high-SNR asymptotic analysis is carried out to provide further insights into the system performance in terms of diversity and coding gains. Finally, the analytical results are validated through extensive Monte Carlo simulations, demonstrating their accuracy and providing a comprehensive understanding of the system's performance.

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arXiv2024

Semi-blind Channel Estimation for Massive MIMO LEO Satellite Communications

This letter proposes decision-directed semi-blind channel estimation for massive multiple-input multiple-output low-Earth-orbit satellite communications. Two semi-blind estimators are proposed. The first utilizes detected data symbols in addition to pilot symbols. The second, a modified semi-blind estimator, is specially designed to mitigate the channel-aging effect caused by the highly dynamic nature of low-Earth-orbit satellite communication channels -- an issue that adversely impacts the performance of pilot-based estimators. Consequently, this modified estimator outperforms an optimal pilot-based estimator in terms of normalized mean square error and achieves symbol error rate performance comparable to that of a Genie-aided (perfectly known channel) detector. The trade-offs between the proposed estimators are also examined.

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arXiv2024

Robust Secure ISAC: How RSMA and Active RIS Manage Eavesdropper's Spatial Uncertainty

Incorporating rate splitting multiple access (RSMA) into integrated sensing and communication (ISAC) presents a significant security challenge, particularly in scenarios where the location of a potential eavesdropper (Eve) is unidentified. Splitting users' messages into common and private streams exposes them to eavesdropping, with the common stream dedicated for sensing and accessible to multiple users. In response to this challenge, this paper proposes a novel approach that leverages active reconfigurable intelligent surface (RIS) aided beamforming and artificial noise (AN) to enhance the security of RSMA-enabled ISAC. Specifically, we first derive the ergodic private secrecy rate (EPSR) based on mathematical approximation of the average Eve channel gain. An optimization problem is then formulated to maximize the minimum EPSR, while satisfying the minimum required thresholds on ergodic common secrecy rate, radar sensing and RIS power budget. To address this non-convex problem, a novel optimization strategy is developed, whereby we alternatively optimize the transmit beamforming matrix for the common and private streams, rate splitting, AN, RIS reflection coefficient matrix, and radar receive beamformer. Successive convex approximation (SCA) and Majorization-Minimization (MM) are employed to convexify the beamforming and RIS sub-problems. Simulations are conducted to showcase the effectiveness of the proposed framework against established benchmarks.

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arXiv2013

EM-based Semi-blind Channel Estimation in AF Two-Way Relay Networks

We propose an expectation maximization (EM)-based algorithm for semi-blind channel estimation of reciprocal channels in amplify-and-forward (AF) two-way relay networks (TWRNs). By incorporating both data samples and pilots into the estimation, the proposed algorithm provides substantially higher accuracy than the conventional training-based approach. Furthermore, the proposed algorithm has a linear computational complexity per iteration and converges after a small number of iterations.

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arXiv2012

Exact Cramer-Rao Bounds for Semi-blind Channel Estimation in Amplify-and-Forward Two-Way Relay Networks

In this paper, we derive for the first time the exact Cramer-Rao bounds (CRBs) on semi-blind channel estimation for amplify-and-forward two-way relay networks. The bounds cover a wide range of modulation schemes that satisfy a certain symmetry condition. In particular, the important classes of PSK and square QAM are covered. For the case square QAM, we also provide simplified expressions that lend themselves more easily to numerical implementation. The derived bounds are used to show that the semi-blind approach, which exploits both the transmitted pilots and the transmitted data symbols, can provide substantial improvements in estimation accuracy over the training-based approach which only uses pilot symbols to estimate the channel parameters. We also derive the more tractable modified CRB which accurately approximates the exact CRB at high SNR for low modulation orders.

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arXiv2012

Blind Channel Estimation for Amplify-and-Forward Two-Way Relay Networks Employing M-PSK Modulation

We consider the problem of channel estimation for amplify-and-forward (AF) two-way relay networks (TWRNs). Most works on this problem focus on pilot-based approaches which impose a significant training overhead that reduces the spectral efficiency of the system. To avoid such losses, this work proposes blind channel estimation algorithms for AF TWRNs that employ constant-modulus (CM) signaling. Our main algorithm is based on the deterministic maximum likelihood (DML) approach. Assuming M-PSK modulation, we show that the resulting estimator is consistent and approaches the true channel with high probability at high SNR for modulation orders higher than 2. For BPSK, however, the DML performs poorly and we propose an alternative algorithm that performs much better by taking into account the BPSK structure of the data symbols. For comparative purposes, we also investigate the Gaussian maximum-likelihood (GML) approach which treats the data symbols as Gaussian-distributed nuisance parameters. We derive the Cramer-Rao bound and use Monte-Carlo simulations to investigate the mean squared error (MSE) performance of the proposed algorithms. We also compare the symbol-error rate (SER) performance of the DML algorithm with that of the training-based least-squares (LS) algorithm and demonstrate that the DML offers a superior tradeoff between accuracy and spectral efficiency.

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arXiv2012

Partially-blind Estimation of Reciprocal Channels for AF Two-Way Relay Networks Employing M-PSK Modulation

We consider the problem of channel estimation for amplify-and-forward two-way relays assuming channel reciprocity and M-PSK modulation. In an earlier work, a partially-blind maximum-likelihood estimator was derived by treating the data as deterministic unknowns. We prove that this estimator approaches the true channel with high probability at high signal-to-noise ratio (SNR) but is not consistent. We then propose an alternative estimator which is consistent and has similarly favorable high SNR performance. We also derive the Cramer-Rao bound on the variance of unbiased estimators.

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arXiv2011

Widely Linear vs. Conventional Subspace-Based Estimation of SIMO Flat-Fading Channels: Mean-Squared Error Analysis

We analyze the mean-squared error (MSE) performance of widely linear (WL) and conventional subspace-based channel estimation for single-input multiple-output (SIMO) flat-fading channels employing binary phase-shift-keying (BPSK) modulation when the covariance matrix is estimated using a finite number of samples. The conventional estimator suffers from a phase ambiguity that reduces to a sign ambiguity for the WL estimator. We derive closed-form expressions for the MSE of the two estimators under four different ambiguity resolution scenarios. The first scenario is optimal resolution, which minimizes the Euclidean distance between the channel estimate and the actual channel. The second scenario assumes that a randomly chosen coefficient of the actual channel is known and the third assumes that the one with the largest magnitude is known. The fourth scenario is the more realistic case where pilot symbols are used to resolve the ambiguities. Our work demonstrates that there is a strong relationship between the accuracy of ambiguity resolution and the relative performance of WL and conventional subspace-based estimators, and shows that the less information available about the actual channel for ambiguity resolution, or the lower the accuracy of this information, the higher the performance gap in favor of the WL estimator.

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