arXiv subjects

Prathapasinghe Dharmawansa

Publications and source records attributed to Prathapasinghe Dharmawansa.

At least 19 recordsLinked to original sources

arXiv2026

Robust Near-Field Beam Focusing Under Imperfect Localization

The transition to 6G-and-beyond wireless systems with large-scale antenna arrays and high-frequency deployments significantly extends the near-field region, where channels exhibit a strong dependence on user location. While this enables location-based beam focusing as a low-overhead alternative to conventional channel estimation, its performance is highly sensitive to localization errors. In this paper, we study robust near-field beam focusing under imperfect user localization. We explicitly characterize the impact of localization errors on the line-of-sight-dominated channel by deriving a tractable uncertainty model via a first-order Taylor approximation, which captures the coupled effects of distance and angle in near-field propagation. Building on this model, we formulate a max-min signal-to-interference-plus-noise ratio optimization problem that guarantees performance under worst-case channel realizations induced by bounded localization errors. The resulting problem is reformulated into a feasibility problem using semidefinite relaxation. Numerical results demonstrate that the proposed robust design significantly improves the worst-user rate compared to non-robust beam focusing, particularly under high total transmit power levels and large localization error ranges.

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arXiv2026

Rethinking Passive RIS: Finite Blocklength Reliability Analysis Under Thermal Noise

Short-packet communication alters the fundamental performance limits of reconfigurable intelligent surface (RIS)-assisted systems, making conventional analyses based on the infinite blocklength regime insufficient. This work investigates RIS-assisted transmission in the finite blocklength (FBL) regime while explicitly incorporating thermal noise generated by passive RIS elements, an effect commonly neglected in existing models. A unified analytical framework is developed to characterize the block-error rate (BLER), its asymptotic behavior, and the resulting goodput under both uniform and non-uniform RIS reflection coefficients. Our results show that ignoring RIS thermal noise leads to a pronounced overestimation of reliability with the mismatch increasing as the number of reflecting elements grows. Furthermore, increasing the RIS size does not always improve performance, particularly in the low transmit power regime where accumulated noise becomes dominant. Overall, the results highlight fundamental limitations of idealized RIS models and demonstrate the need for incorporating thermal noise for accurate system evaluation.

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arXiv2026

Impact of CSIR, SIC, and Hardware Impairments on the Ergodic Rate of Downlink RSMA

This work investigates the ergodic rate performance analysis of rate-splitting multiple access (RSMA) in a downlink communication system under practical impairments. Closed-form expressions are derived for key performance metrics such as ergodic rate, energy efficiency, sum-rate, and Jains fairness index, capturing the joint effects of imperfect channel state information at the receiver (CSIR), imperfect successive interference cancellation (SIC), and hardware impairments. Numerical simulations validate the accuracy of the analytical expressions and reveal several insightful trends. At low transmit powers, imperfect CSIR is the dominant performance-limiting factor, followed by hardware impairments and imperfect SIC. However, as the transmit power increases, hardware impairments become the primary bottleneck, with the impact of imperfect CSIR gradually diminishing, and imperfect SIC becoming a more prominent bottleneck. Moreover, RSMA consistently outperforms non-orthogonal multiple access (NOMA) in terms of ergodic rate, fairness, and sum-rate, even under severe non-idealities. These findings underscore the importance of incorporating fairness as a core design objective alongside rate and energy efficiency, positioning RSMA as a robust and strong multiple access candidate for next-generation wireless networks.

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arXiv2026

Passive RIS Is Not Silent: Revisiting Performance Limits Under Thermal Noise

Reconfigurable intelligent surfaces (RISs) have emerged as a promising solution for enabling energy-efficient and flexible spectrum usage in wireless communication, particularly in the context of sixth-generation (6G) networks. While passive RIS architectures are widely regarded as virtually noiseless due to the lack of active components, this idealized assumption can lead to misleading performance evaluations. In this paper, we revisit this assumption and demonstrate that the thermal noise generated by passive RIS elements, though often neglected, can significantly affect system performance. We propose a tractable approximated analytical framework that incorporates RIS-induced thermal noise into the system and derive closed-form expressions for key performance metrics, such as outage probability and throughput. Simulation results validate our approximated analysis and highlight the substantial performance discrepancies that arise when RIS thermal noise is ignored. Our results offer valuable insights into the trade-offs between receiver and RIS noise, guiding the development of robust and efficient 6G communication systems.

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arXiv2026

The Effect of Noise Correlation on MMSE Channel Estimation in One-Bit Quantized Systems

This paper analyzes the impact of spatially correlated additive noise on the minimum mean-square error (MMSE) estimation of multiple-input multiple-output (MIMO) channels from one-bit quantized observations. Although additive noise can be correlated in practical scenarios, e.g., due to jamming, clutter, or other external disturbances, the effect of such correlation on the MMSE channel estimator in this setting remains unexplored in prior work. Against this backdrop, we derive a novel analytical expression for the general MIMO MMSE channel estimator, which is inherently nonlinear in one-bit observations, and accommodates arbitrary channel and noise correlation structures. To further characterize the impact of noise correlation, we subsequently specialize the general MMSE expression to certain tractable multi antenna configurations in which both the channel and the noise assume single-parameter constant correlation structures. Our analyses reveal nontrivial, noise-correlation-induced scenarios in which the estimator remains linear despite non-zero channel and noise correlation parameters. Moreover, the results indicate that, at low-to-medium signal-to-noise ratio, noise correlation improves the MMSE performance when channels are uncorrelated, but degrades performance when channels are strongly correlated.

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arXiv2025

LoS+NLoS Holographic MIMO: Analysis and Application of Wavenumber-Division Multiplexing

Holographic multiple-input multiple-output (MIMO) enables electrically large continuous apertures, overcoming the physical scaling limits of conventional MIMO architectures with half-wavelength spacing. Their near-field operating regime requires channel models that jointly capture line-of-sight (LoS) and non-line-of-sight (NLoS) components in a physically consistent manner. Existing studies typically treat these components separately or rely on environment-specific multipath models. In this work, we develop a unified LoS+NLoS channel representation for holographic lines that integrates spatial-sampling-based and expansion-based formulations. Building on this model, we extend the wavenumber-division multiplexing (WDM) framework, originally introduced for purely LoS channels, to the LoS+NLoS scenario. Applying WDM to the NLoS component yields its angular-domain representation, enabling direct characterization through the power spectral factor and power spectral density. We further derive closed-form characterizations for isotropic and non-isotropic scattering, with the former recovering Jakes' isotropic model. Lastly, we evaluate the resulting degrees of freedom and ergodic capacity, showing that incorporating the NLoS component substantially improves the performance relative to the purely LoS case.

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arXiv2025

Wavenumber-Division Multiplexing in Holographic MIMO with NLoS Channels

Wavenumber-division multiplexing (WDM) was introduced as a counterpart of orthogonal frequency-division multiplexing in the spatial-frequency domain for line-of-sight holographic multiple-input multiple-output (MIMO) systems. In this paper, we extend WDM to holographic MIMO channels with non-line-of-sight (NLoS) propagation. We show that applying WDM to the NLoS channel yields the corresponding angular-domain representation, which we characterize through the power spectral factor and power spectral density. We further obtain a closed-form characterization for the case of isotropic scattering, recovering Jakes' isotropic model. The analysis is complemented by numerical results evaluating the degrees of freedom and ergodic capacity under both isotropic and non-isotropic scattering.

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arXiv2025

Performance Analysis for Multi-User Holographic MIMO Downlink with Matched Filter Precoding

Holographic MIMO (HMIMO) has emerged as a promising solution for future wireless systems by enabling ultra-dense, spatially continuous antenna deployments. While prior studies have primarily focused on electromagnetic (EM) modeling or simulation-based performance analysis, a rigorous communication-theoretic framework remains largely unexplored. This paper presents the first analytical performance study of a multi-user HMIMO downlink system with matched filter (MF) precoding - a low-complexity baseline scheme. By incorporating multipath propagation, mutual coupling, and element excitation, we derive a novel closed-form expression for the MF signal-to-interference-plus-noise ratio (SINR) using an equivalent random variable model. Leveraging bivariate gamma distributions, we then develop tractable throughput approximations under full, partial, and no channel state information (CSI) scenarios. Additionally, we formulate a max-min beamforming problem to benchmark optimal user fairness performance. Numerical results validate the accuracy of the proposed framework and reveal that MF precoding achieves competitive performance with strong robustness to low SINR and CSI uncertainty.

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arXiv2025

Localization-Based Beam Focusing in Near-Field Communications

Shifting 6G-and-beyond wireless systems to higher frequency bands and the utilization of massive multiple-input multiple-output arrays will extend the near-field region, affecting beamforming and user localization schemes. In this paper, we propose a localization-based beam-focusing design, in which the receive combiners are directly constructed from the steering vectors corresponding to the estimated user locations. To support this approach, we analyze the 2D-MUSIC algorithm by examining its spectrum in simplified, tractable setups with minimal numbers of antennas and users. Lastly, we compare the proposed localization-based beam focusing, with locations estimated via 2D-MUSIC, with pilot-based zero forcing in terms of uplink sum spectral efficiency. Our results show significant gains under dominant line-of-sight propagation, short coherence blocks, and high noise power typical of high-frequency systems.

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arXiv2025

Eigenvalue-Based Detection in MIMO Systems for Integrated Sensing and Communication

This paper considers a MIMO Integrated Sensing and Communication (ISAC) system, where a base station simultaneously serves a MIMO communication user and a remote MIMO sensing receiver, without channel state information (CSI) at the transmitter. Existing MIMO ISAC literature often prioritizes communication rate or detection probability, typically under constant false-alarm rate (CFAR) assumptions, without jointly analyzing detection reliability and communication constraints. To address this gap, we adopt an eigenvalue-based detector for robust sensing and use a performance metric, the total detection error, that jointly captures false-alarm and missed-detection probabilities. We derive novel closed-form expressions for both probabilities under the eigenvalue detector, enabling rigorous sensing analysis. Using these expressions, we formulate and solve a joint power allocation and threshold optimization problem that minimizes total detection error while meeting a minimum communication rate requirement. Simulation results demonstrate that the proposed joint design substantially outperforms conventional CFAR-based schemes, highlighting the benefits of power- and threshold-aware optimization in MIMO ISAC systems.

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arXiv2025

Max-Min Fairness for Stacked Intelligent Metasurface-Assisted Multi-User MISO Systems

Stacked intelligent metasurface (SIM) is an emerging technology that uses multiple reconfigurable surface layers to enable flexible wave-based beamforming. In this paper, we focus on an \ac{SIM}-assisted multi-user multiple-input single-output system, where it is essential to ensure that all users receive a fair and reliable service level. To this end, we develop two max-min fairness algorithms based on instantaneous channel state information (CSI) and statistical CSI. For the instantaneous CSI case, we propose an alternating optimization algorithm that jointly optimizes power allocation using geometric programming and wave-based beamforming coefficients using the gradient descent-ascent method. For the statistical CSI case, since deriving an exact expression for the average minimum achievable rate is analytically intractable, we derive a tight upper bound and thereby formulate a stochastic optimization problem. This problem is then solved, capitalizing on an alternating approach combining geometric programming and gradient descent algorithms, to obtain the optimal policies. Our numerical results show significant improvements in the minimum achievable rate compared to the benchmark schemes. In particular, for the instantaneous CSI scenario, the individual impact of the optimal wave-based beamforming is significantly higher than that of the power allocation strategy. Moreover, the proposed upper bound is shown to be tight in the low signal-to-noise ratio regime under the statistical CSI.

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arXiv2025

Uniform Planar Array Based Weighted Cooperative Spectrum Sensing for Cognitive Radio Networks

Cooperative spectrum sensing (CSS) is essential for improving the spectrum efficiency and reliability of cognitive radio applications. Next-generation wireless communication networks increasingly employ uniform planar arrays (UPA) due to their ability to steer beamformers towards desired directions, mitigating interference and eavesdropping. However, the application of UPA-based CSS in cognitive radio remains largely unexplored. This paper proposes a multi-beam UPA-based weighted CSS (WCSS) framework to enhance detection reliability, applicable to various cognitive radio networks, including cellular, vehicular, and satellite communications. We first propose a weighting factor for commonly used energy detection (ED) and eigenvalue detection (EVD) techniques, based on the spatial variation of signal strengths resulting from UPA antenna beamforming. We then analytically characterize the performance of both weighted ED and weighted EVD by deriving closed-form expressions for false alarm and detection probabilities. Our numerical results, considering both static and dynamic user behaviors, demonstrate the superiority of WCSS in enhancing sensing performance compared to uniformly weighted detectors.

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arXiv2024

Detection of Signals in Colored Noise: Roy's Largest Root Test for Non-central $F$-matrices

This paper investigates the signal detection problem in colored noise with an unknown covariance matrix. In particular, we focus on detecting a non-random signal by capitalizing on the leading eigenvalue (a.k.a. Roy's largest root) of the whitened sample covariance matrix as the test statistic. To this end, the whitened sample covariance matrix is constructed via $m$-dimensional $p $ plausible signal-bearing samples and $m$-dimensional $n $ noise-only samples. Since the signal is non-random, the whitened sample covariance matrix turns out to have a {\it non-central} $F$-distribution with a rank-one non-centrality parameter. Therefore, the performance of the test entails the statistical characterization of the leading eigenvalue of the non-central $F$-matrix, which we address by deriving its cumulative distribution function (c.d.f.) in closed-form by leveraging the powerful orthogonal polynomial approach in random matrix theory. This new c.d.f. has been instrumental in analyzing the receiver operating characteristic (ROC) of the detector. We also extend our analysis into the high dimensional regime in which $m,n$, and $p$ diverge such that $m/n$ and $m/p$ remain fixed. It turns out that, when $m=n$ and fixed, the power of the test improves if the signal-to-noise ratio (SNR) is of at least $O(p)$, whereas the corresponding SNR in the high dimensional regime is of at least $O(p^2)$. Nevertheless, more intriguingly, for $m<n$ with the SNR of order $O(p)$, the leading eigenvalue does not have power to detect {\it weak} signals in the high dimensional regime.

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arXiv2024

Standard Condition Number-Based Robust Signal Detection with Whitening under Uncertainty

Robust signal detection in colored noise with unknown covariance is essential in radar, cognitive radio, integrated sensing and communication (ISAC), and quantum sensing applications. This paper develops a unified analytical framework for the Standard Condition Number (SCN) detector, which employs the ratio of the largest to smallest eigenvalues of the whitened sample covariance matrix. The framework jointly covers both ideal conditions in which the training and sensing noise statistics are identical and disturbed conditions in which interference or jamming alters the sensing covariance. Despite the SCN's practical relevance, its finite-sample false-alarm and detection behavior has not been analytically characterized. Using random matrix theory (RMT), we derive general expressions for these probabilities, provide closed-form results for special cases, and show that the SCN preserves the Constant False Alarm Rate (CFAR) property under covariance mismatch. Analytical and simulation results confirm that the proposed unified framework delivers consistent detection performance and greater robustness than conventional eigenvalue- and LRT-based detectors.

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arXiv2024

A Sample-Deficient Analysis of the Leading Generalized Eigenvalue for the Detection of Signals in Colored Gaussian Noise

This paper investigates the signal detection problem in colored Gaussian noise with an unknown covariance matrix. To be specific, we consider a sample deficient scenario in which the number of signal bearing samples ($n$) is strictly smaller than the dimensionality of the signal space ($m$). Our test statistic is the leading generalized eigenvalue of the whitened sample covariance matrix (a.k.a. $F$-matrix) which is constructed by whitening the signal bearing sample covariance matrix with noise-only sample covariance matrix. The whitening operation along with the observation model induces a single spiked covariance structure on the $F$-matrix. Moreover, the sample deficiency (i.e., $m>n$) in turn makes this $F$-matrix rank deficient, thereby {\it singular}. Therefore, a simple exact statistical characterization of the leading generalized eigenvalue (l.g.e.) of a complex correlated {\it singular} $F$-matrix with a single spiked associated covariance is of paramount importance to assess the performance of the detector (i.e., the receiver operating characteristics (ROC)). To this end, we adopt the powerful orthogonal polynomial technique in random matrix theory to derive a new finite dimensional c.d.f. expression for the l.g.e. of this particular $F$-matrix. It turns out that when the noise only sample covariance matrix is nearly rank deficient and the signal-to-noise ratio is $O(m)$, the ROC profile converges to a remarkably simple limiting profile.

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arXiv2024

Detection of Signals in Colored Noise: Leading Eigenvalue Test for Non-central $F$-matrices

This paper investigates the signal detection problem in colored noise with an unknown covariance matrix. In particular, we focus on detecting an unknown non-random signal by capitalizing on the leading eigenvalue of the whitened sample covariance matrix as the test statistic (a.k.a. Roy's largest root test). Since the unknown signal is non-random, the whitened sample covariance matrix turns out to have a non-central $F$-distribution. This distribution assumes a singular or non-singular form depending on whether the number of observations $p\lessgtr$ the system dimensionality $m$. Therefore, we statistically characterize the leading eigenvalue of the singular and non-singular $F$-matrices by deriving their cumulative distribution functions (c.d.f.). Subsequently, they have been utilized in deriving the corresponding receiver operating characteristic (ROC) profiles. We also extend our analysis into the high dimensional domain. It turns out that, when the signal is sufficiently strong, the maximum eigenvalue can reliably detect it in this regime. Nevertheless, weak signals cannot be detected in the high dimensional regime with the leading eigenvalue.

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arXiv2023

On the Convergence of Inexact Gradient Descent with Controlled Synchronization Steps

We develop a gradient-like algorithm to minimize a sum of peer objective functions based on coordination through a peer interconnection network. The coordination admits two stages: the first is to constitute a gradient, possibly with errors, for updating locally replicated decision variables at each peer and the second is used for error-free averaging for synchronizing local replicas. Unlike many related algorithms, the errors permitted in our algorithm can cover a wide range of inexactnesses, as long as they are bounded. Moreover, we do not impose any gradient boundedness conditions for the objective functions. Furthermore, the second stage is not conducted in a periodic manner, like many related algorithms. Instead, a locally verifiable criterion is devised to dynamically trigger the peer-to-peer coordination at the second stage, so that expensive communication overhead for error-free averaging can significantly be reduced. Finally, the convergence of the algorithm is established under mild conditions.

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arXiv2023

Flex-Net: A Graph Neural Network Approach to Resource Management in Flexible Duplex Networks

Flexible duplex networks allow users to dynamically employ uplink and downlink channels without static time scheduling, thereby utilizing the network resources efficiently. This work investigates the sum-rate maximization of flexible duplex networks. In particular, we consider a network with pairwise-fixed communication links. Corresponding combinatorial optimization is a non-deterministic polynomial (NP)-hard without a closed-form solution. In this respect, the existing heuristics entail high computational complexity, raising a scalability issue in large networks. Motivated by the recent success of Graph Neural Networks (GNNs) in solving NP-hard wireless resource management problems, we propose a novel GNN architecture, named Flex-Net, to jointly optimize the communication direction and transmission power. The proposed GNN produces near-optimal performance meanwhile maintaining a low computational complexity compared to the most commonly used techniques. Furthermore, our numerical results shed light on the advantages of using GNNs in terms of sample complexity, scalability, and generalization capability.

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