arXiv subjects

Aamir Mahmood

Publications and source records attributed to Aamir Mahmood.

At least 19 recordsLinked to original sources

arXiv2026

React or Predict? A Spectral Rule for Wireless Threshold Detection

A wireless sensor must alert a remote monitor before a monitored process crosses a safety threshold; an alarm arriving afterward may be too late. The sensor can react to its current estimate or predict ahead and trigger earlier, but the value of such lookahead is not obvious. In some systems it creates an early-alarm opportunity unavailable to the current test, while in others it cannot cross the alarm boundary. This letter gives a practical three-stage rule for deciding when to predict. First, an algebraic spectral test decides at design time whether lookahead is structurally useful: it is redundant exactly when the threshold direction is a left-eigenvector of the dynamics with a non-negative eigenvalue. Second, a closed-form channel decomposition shows that deeper prediction becomes more valuable as the channel degrades, because longer lead windows permit more pre-crossing transmission attempts. Third, simulations show that large gains also require retained prediction magnitude; oscillatory dynamics amplify the benefit through rotation, and a two-sensor setting reveals a sensing-channel tradeoff.

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arXiv2026

Average Finite-Blocklength Packet Error Rate over Nakagami-$m$ Fading via a Logistic--Lerch Approximation

Evaluating the average packet error rate (PER) of finite-blocklength (FBL) coded transmission over fading requires integrating the block-error waterfall, given by the normal approximation, against the fading distribution, which is intractable for general Nakagami-$m$ channels. This letter approximates the conditional waterfall by a slope-matched logistic function and shows that its Nakagami-$m$ average reduces to a single Lerch-transcendent term that interpolates between the FBL waterfall and the classical outage limit, with norming constants explicit in rate and blocklength. The closed form supports non-integer fading and, composed into an effective-capacity objective, yields a quality-of-service (QoS) aware rate-selection rule. It matches the normal-approximation integral to about 1\% uniformly in $m$ over the nominal FBL operating region, while outage and linearization baselines exceed several percent at high diversity.

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arXiv2026

Predictive Triggering for Outage-Resilient Threshold Decisions over Short-Packet Links

Remote threshold decisions require more than accurate state estimates: the posterior must support reliable alarm/no-alarm decisions and, when possible, anticipate early critical decisions. We study this problem over short-packet wireless links with outage risk. We derive false-positive/false-negative feasibility conditions that define a decision-feasible region of the estimation and yield a predictive decision-update trigger. To protect predictive updates from outages, we add AoI-controlled resilience updates that both detect disruptions and maintain freshness. A two-state Markov surrogate of the thresholded process, matched to its one-step switching statistics, enables tractable long-term reliability-energy analysis. Then, we jointly optimized transmit power and AoI-controlled resilience update probabilities. Simulations show earlier, reliable decisions at competitive energy with baselines.

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arXiv2026

Lightweight Non-Line-of-Sight Channel Detection for ML-assisted Bluetooth Direction Finding

Bluetooth Low Energy (BLE) direction-finding is promising for indoor industrial localization, but its accuracy degrades in multipath environments where reflections and scattering bias angle estimates. Although line-of-sight (LOS) and non-line-of-sight (NLOS) detection is well studied for wide-band radios, BLE direction-finding still lacks narrow-band channel-feature representations, scalable kernel-based feature transformations, and dedicated datasets for data-driven, lightweight channel classification. To address this gap, the work introduces a controlled BLE measurement setup that generates labeled LOS/NLOS data in two distinct propagation environments. A quality-driven machine learning (ML)-based pipeline is then developed for BLE Constant Tone Extension (CTE) In-phase-Quadrature (IQ) features. First, robust quantile-based standardization is applied to reduce the influence of outliers and heavy-tailed effects. The standardized features are then analyzed using Principal Component Analysis (PCA) and Adaptive Kernel Density Estimation (AKDE) to verify scenario-dependent statistics and reveal LOS/NLOS separability. Next, Nystr\"om Kernel Approximation (NKA) constructs low-rank nonlinear feature maps followed by a lightweight Support Vector Classifier (SVC) head for LOS/NLOS detection. This classifier is compared with Random Forest (RF) and Multilayer Perceptron (MLP) models. Results show that NKA improves accuracy by about 7-14% relative to the raw baseline. Although the MLP achieves higher absolute accuracy, the Nystr\"om--SVC approach offers a more favorable trade-off between training complexity, inference cost, and memory footprint. Finally, several pipeline-calibrated posterior probabilities are utilized for cost-aware threshold selection and efficient real-time LOS/NLOS detection in resource-constrained localization systems.

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arXiv2026

Integrating HAPS, LEO, and Terrestrial Networks: A Cost-Performance Study for IoT Connectivity

This work evaluates the potential of High-Altitude Platform Stations (HAPS) and Low Earth Orbit (LEO) satellites as alternative or complementary systems to enhance Internet of Things (IoT) connectivity. We first analyze the transmission erasure probability under different connectivity configurations, including only HAPS or LEO satellites, as well as hybrid architectures that integrate both aerial/spatial and terrestrial infrastructures. To make the analysis more realistic, we considered movement of LEO satellites regarding a fixed region, elevation angle between gateway and devices, and different fading models for terrestrial and non-terrestrial communication. We also analyze LR-FHSS (Long-Range Frequency Hopping Spread Spectrum) random access uplink technology as a potential use case for IoT connectivity, showing the scalability impact of the scenarios. The simulation results demonstrate that HAPS can effectively complement sparse terrestrial networks and improve the performance of satellite-based systems in specific scenarios. Furthermore, considering the deployment and operational costs, respectively, CAPEX and OPEX, the economic analysis reveals that although HAPS exhibits higher costs, these remain within a comparable order of magnitude to LEO and terrestrial deployments. In addition, specific use cases, such as natural disasters, transform HAPS into a competitive technology for conventional infrastructures.

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arXiv2026

L-Moment-Based LOS and NLOS Channel Characterization via Four-parameter Kappa Distribution for AoA BLE CTE Measurements

Bluetooth Low Energy (BLE) CTE transmissions provide in-phase and quadrature (IQ) samples whose empirical statistics are strongly governed by the propagation regime. in particular, the distributions differ markedly between line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. In NLOS, multipath-induced distortions typically degrade Angle-of-Arrivial (AoA) estimation accuracy. Existing BLE direction finding datasets rarely provide tightly controlled, IQ-level paired LOS and NLOS measurements with rigorous statistical validation, and commonly used flat-fading models can be inadequate for cluttered indoor environments exhibiting heavy-tailed power distributions. To address these limitations, we conduct a paired-geometry BLE AoA measurement campaign using an off-the-shelf module, collecting 132000 labeled CTE packets under matched anchor-tag conditions. A robust preprocessing stage removes anomalous CTEs using combined univariate and multivariate criteria. Feature-wise hypothesis tests on IQ-derived power features confirm strong LOS and NLOS separability. All mean differences are statistically significant; additionally, 92 percent of feature-wise variance differences are significant. We further compute L-moment ratios (LMRs) and analyze them in the L-moment Ratio Diagram (LMRD), showing that NLOS subsets exhibit markedly heavier tails and stronger asymmetry than LOS. Kappa-family distributions fitted from LMRs provide substantially improved dual scored L--moment goodness-of-fit (GoF), Specifically, for NLOS, which is the smallest discrepancy in the LMRD and a near-zero standardized L-kurtosis deviation. As a practice, we apply a self-supervised clustering to L-moment statistics, achieving a more separable representation, compared to product moments.

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arXiv2026

Learning-Based Sensor Scheduling for Delay-Aware and Stable Remote State Estimation

Unpredictable sensor-to-estimator delays fundamentally distort what matters for wireless remote state estimation: not just freshness, but how delay interacts with sensor informativeness and energy efficiency. In this paper, we present a unified, delay-aware framework that models this coupling explicitly and quantifies a delay-dependent information gain, motivating an information-per-joule scheduling objective beyond age of information proxies (AoI). To this end, we first introduce an efficient posterior-fusion update that incorporates delayed measurements without state augmentation, providing a consistent approximation to optimal delayed Kalman updates, and then derive tractable stability conditions ensuring that bounded estimation error is achievable under stochastic, delayed scheduling. This conditions highlight the need for unstable modes to be observable across sensors. Building on this foundation, we cast scheduling as a Markov decision process and develop a proximal policy optimization (PPO) scheduler that learns directly from interaction, requires no prior delay model, and explicitly trades off estimation accuracy, freshness, sensor heterogeneity, and transmission energy through normalized rewards. In simulations with heterogeneous sensors, realistic link-energy models, and random delays, the proposed method learns stably and consistently achieves lower estimation error at comparable energy than random scheduling and strong RL baselines (DQN, A2C), while remaining robust to variations in measurement availability and process/measurement noise.

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arXiv2025

A Robust 5G Terrestrial Positioning System with Sensor Fusion in GNSS-denied Scenarios

This paper presents a terrestrial localization system based on 5G infrastructure as a viable alternative to GNSS, particularly in scenarios where GNSS signals are obstructed or unavailable. It discusses network planning aimed at enabling positioning as a primary service, in contrast to the traditional focus on communication services in terrestrial networks. Building on a network infrastructure optimized for positioning, the paper proposes a system that leverages carrier phase (CP) ranging in combination with trilateration to localize the user within the network when at least three base stations (BSs) provide line-of-sight (LOS) conditions. Achieving accurate CP-based positioning requires addressing three key challenges: integer ambiguity resolution, LOS/NLOS link identification, and localization under obstructed LOS conditions. To this end, the system employs a multi-carrier CP approach, which eliminates the need for explicit integer ambiguity estimation. Additionally, a deep learning model is developed to identify NLOS links and exclude them from the trilateration process. In cases where LOS is obstructed and CP ranging becomes unreliable, the system incorporates an error-state extended Kalman filter to fuse complementary data from other sensors, such as inertial measurement units (IMUs) and cameras. This hybrid approach enables robust tracking of moving users across diverse channel conditions. The performance of the proposed terrestrial positioning system is evaluated using the real-world KITTI dataset, featuring a moving vehicle in an urban environment. Simulation results show that the system can achieve a positioning error of less than 5 meters in the KITTI urban scenario--comparable to that of public commercial GNSS services--highlighting its potential as a resilient and accurate solution for GNSS-denied environments.

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arXiv2025

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning

6G networks are composed of subnetworks expected to meet ultra-reliable low-latency communication (URLLC) requirements for mission-critical applications such as industrial control and automation. An often-ignored aspect in URLLC is consecutive packet outages, which can destabilize control loops and compromise safety in in-factory environments. Hence, the current work proposes a link adaptation framework to support extreme reliability requirements using the soft actor-critic (SAC)-based deep reinforcement learning (DRL) algorithm that jointly optimizes energy efficiency (EE) and reliability under dynamic channel and interference conditions. Unlike prior work focusing on average reliability, our method explicitly targets reducing burst/consecutive outages through adaptive control of transmit power and blocklength based solely on the observed signal-to-interference-plus-noise ratio (SINR). The joint optimization problem is formulated under finite blocklength and quality of service constraints, balancing reliability and EE. Simulation results show that the proposed method significantly outperforms the baseline algorithms, reducing outage bursts while consuming only 18\% of the transmission cost required by a full/maximum resource allocation policy in the evaluated scenario. The framework also supports flexible trade-off tuning between EE and reliability by adjusting reward weights, making it adaptable to diverse industrial requirements.

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arXiv2025

On Energy-Efficient Passive Beamforming Design of RIS-Assisted CoMP-NOMA Networks

This paper investigates the synergistic potential of reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA) to enhance the energy efficiency and performance of next-generation wireless networks. We delve into the design of energy-efficient passive beamforming (PBF) strategies within RIS-assisted coordinated multi-point (CoMP)-NOMA networks. Two distinct RIS configurations, namely, enhancement-only PBF (EO) and enhancement & cancellation PBF (EC), are proposed and analyzed. Our findings demonstrate that RIS-assisted CoMP-NOMA networks offer significant efficiency gains compared to traditional CoMP-NOMA systems. Furthermore, we formulate a PBF design problem to optimize the RIS phase shifts for maximizing energy efficiency. Our results reveal that the optimal PBF design is contingent upon several factors, including the number of cooperating base stations (BSs), the number of RIS elements deployed, and the RIS configuration. This study underscores the potential of RIS-assisted CoMP-NOMA networks as a promising solution for achieving superior energy efficiency and overall performance in future wireless networks.

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arXiv2025

Efficient Multi-Source Localization in Near-Field Using only Angular Domain MUSIC

The localization of multiple signal sources using sensor arrays has been a long-standing research challenge. While numerous solutions have been developed, signal space methods like MUSIC and ESPRIT have gained widespread popularity. As sensor arrays grow in size, sources are frequently located in the near-field region. The standard MUSIC algorithm can be adapted to locate these sources by performing a 3D search over both the distance and the angles of arrival (AOA), including azimuth and elevation, though this comes with significant computational complexity. To address this, a modified version of MUSIC has been developed to decouple the AoA and distance, enabling sequential estimation of these parameters and reducing computational demands. However, this approach suffers from reduced accuracy. To maintain the accuracy of MUSIC while minimizing complexity, this paper proposes a novel method that exploits angular variation across the array aperture, eliminating the need for a grid search over distance. The proposed method divides the large aperture into smaller sections, with each focusing on estimating the angles of arrival. These angles are then triangulated to localize the sources in the near-field of the large aperture. Numerical simulations show that this approach not only surpasses the Modified MUSIC algorithm in terms of mean absolute error but also achieves accuracy comparable to standard MUSIC, all while greatly reducing computational complexity-370 times in our simulation scenario.

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arXiv2025

Ultra-High Reliability by Predictive Interference Management Using Extreme Value Theory

Ultra-reliable low-latency communications (URLLC) require innovative approaches to modeling channel and interference dynamics, extending beyond traditional average estimates to encompass entire statistical distributions, including rare and extreme events that challenge achieving ultra-reliability performance regions. In this paper, we propose a risk-sensitive approach based on extreme value theory (EVT) to predict the signal-to-interference-plus-noise ratio (SINR) for efficient resource allocation in URLLC systems. We employ EVT to estimate the statistics of rare and extreme interference values, and kernel density estimation (KDE) to model the distribution of non-extreme events. Using a mixture model, we develop an interference prediction algorithm based on quantile prediction, introducing a confidence level parameter to balance reliability and resource usage. While accounting for the risk sensitivity of interference estimates, the prediction outcome is then used for appropriate resource allocation of a URLLC transmission under link outage constraints. Simulation results demonstrate that the proposed method outperforms the state-of-the-art first-order discrete-time Markov chain (DTMC) approach by reducing outage rates up to 100-fold, achieving target outage probabilities as low as $10^{-7}$. Simultaneously, it minimizes radio resource usage $\simnot15 \%$ compared to DTMC, while remaining only $\simnot20 \%$ above the optimal case with perfect interference knowledge, resulting in significantly higher prediction accuracy. Additionally, the method is sample-efficient, able to predict interference effectively with minimal training data.

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arXiv2024

Deep Reinforcement Learning for Trajectory and Phase Shift Optimization of Aerial RIS in CoMP-NOMA Networks

This paper explores the potential of aerial reconfigurable intelligent surfaces (ARIS) to enhance coordinated multi-point non-orthogonal multiple access (CoMP-NOMA) networks. We consider a system model where a UAV-mounted RIS assists in serving multiple users through NOMA while coordinating with multiple base stations. The optimization of UAV trajectory, RIS phase shifts, and NOMA power control constitutes a complex problem due to the hybrid nature of the parameters, involving both continuous and discrete values. To tackle this challenge, we propose a novel framework utilizing the multi-output proximal policy optimization (MO-PPO) algorithm. MO-PPO effectively handles the diverse nature of these optimization parameters, and through extensive simulations, we demonstrate its effectiveness in achieving near-optimal performance and adapting to dynamic environments. Our findings highlight the benefits of integrating ARIS in CoMP-NOMA networks for improved spectral efficiency and coverage in future wireless networks.

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arXiv2024

Balancing AoI and Rate for Mission-Critical and eMBB Coexistence with Puncturing, NOMA,and RSMA in Cellular Uplink

Through the lens of average and peak age-of-information (AoI), this paper takes a fresh look into the uplink medium access solutions for mission-critical (MC) communication coexisting with enhanced mobile broadband (eMBB) service. Considering the stochastic packet arrivals from an MC user, we study three access schemes: orthogonal multiple access (OMA) with eMBB preemption (puncturing), non-orthogonal multiple access (NOMA), and rate-splitting multiple access (RSMA), the latter two both with concurrent eMBB transmissions. Puncturing is found to reduce both average AoI and peak AoI (PAoI) violation probability but at the expense of decreased eMBB user rates and increased signaling complexity. Conversely, NOMA and RSMA offer higher eMBB rates but may lead to MC packet loss and AoI degradation. The paper systematically investigates the conditions under which NOMA or RSMA can closely match the average AoI and PAoI violation performance of puncturing while maintaining data rate gains. Closed-form expressions for average AoI and PAoI violation probability are derived, and conditions on the eMBB and MC channel gain difference with respect to the base station are analyzed. Additionally, optimal power and rate splitting factors in RSMA are determined through an exhaustive search to minimize MC outage probability. Notably, our results indicate that with a small loss in the average AoI and PAoI violation probability the eMBB rate in NOMA and RSMA can be approximately five times higher than that achieved through puncturing.

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arXiv2024

Indoor Positioning in 5G-Advanced: Challenges and Solution towards Centimeter-level Accuracy with Carrier Phase Enhancements

After robust connectivity, precise positioning is evolving into an innovative component of 5G service offerings for industrial use-cases and verticals with challenging indoor radio environments. In this direction, the 3GPP Rel-16 standard has been a tipping point in specifying critical innovations, followed by enhancements in Rel-17 and Rel-18. In this article, we elaborate on the 5G positioning framework, measurements, and procedures before shifting the focus mainly to recently identified carrier-phase (CP) measurements in Rel-18 as a complementary measure for time- and angular-based positioning methods. We discuss the associated challenges and potential solutions for exploiting CP, including integer ambiguity, multipath sensitivity, and signaling aspects. Furthermore, we study how phase-continuous reference signaling can counter noisy phase measurements using realistic simulations to achieve centimeter-level accuracy in indoor factory (InF) scenarios.

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arXiv2022

Industry 5.0 is Coming: A Survey on Intelligent NextG Wireless Networks as Technological Enablers

Industry 5.0 vision, a step toward the next industrial revolution and enhancement to Industry 4.0, envisioned the new goals of resilient, sustainable, and human-centric approaches in diverse emerging applications, e.g., factories-of-the-future, digital society. The vision seeks to leverage human intelligence and creativity in nexus with intelligent, efficient, and reliable cognitive collaborating robots (cobots) to achieve zero waste, zerodefect, and mass customization-based manufacturing solutions. However, the vision requires the merging of cyber-physical worlds through utilizing Industry 5.0 technological enablers, e.g., cognitive cobots, person-centric artificial intelligence (AI), cyberphysical systems, digital twins, hyperconverged data storage and computing, communication infrastructure, and others. In this regard, the convergence of the emerging computational intelligence (CI) paradigm and next-generation wireless networks (NGWNs) can fulfill the stringent communication and computation requirements of the technological enablers in the Industry 5.0 vision, which is the aim of this survey-based tutorial. In this article, we address this issue by reviewing and analyzing current emerging concepts and technologies, e.g., CI tools and frameworks, network-in-box architecture, open radio access networks, softwarized service architectures, potential enabling services, and others, essential for designing the objectives of CINGWNs to fulfill the Industry 5.0 vision requirements. Finally, we provide a list of lessons learned from our detailed review, research challenges, and open issues that should be addressed in CI-NGWNs to realize Industry 5.0.

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arXiv2021

Edge Intelligence in Softwarized 6G: Deep Learning-enabled Network Traffic Predictions

The 6G vision is envisaged to enable agile network expansion and rapid deployment of new on-demand microservices (e.g., visibility services for data traffic management, mobile edge computing services) closer to the network's edge IoT devices. However, providing one of the critical features of network visibility services, i.e., data flow prediction in the network, is challenging at the edge devices within a dynamic cloud-native environment as the traffic flow characteristics are random and sporadic. To provide the AI-native services for the 6G vision, we propose a novel edge-native framework to provide an intelligent prognosis technique for data traffic management in this paper. The prognosis model uses long short-term memory (LSTM)-based encoder-decoder deep learning, which we train on real time-series multivariate data records collected from the edge $μ$-boxes of a selected testbed network. Our result accurately predicts the statistical characteristics of data traffic and verifies the trained model against the ground truth observations. Moreover, we validate our novel framework with two performance metrics for each feature of the multivariate data.

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arXiv2021

Industrial Digital Twins at the Nexus of NextG Wireless Networks and Computational Intelligence: A Survey

By amalgamating recent communication and control technologies, computing and data analytics techniques, and modular manufacturing, Industry~4.0 promotes integrating cyber-physical worlds through cyber-physical systems (CPS) and digital twin (DT) for monitoring, optimization, and prognostics of industrial processes. A DT is an emerging but conceptually different construct than CPS. Like CPS, DT relies on communication to create a highly-consistent, synchronized digital mirror image of the objects or physical processes. DT, in addition, uses built-in models on this precise image to simulate, analyze, predict, and optimize their real-time operation using feedback. DT is rapidly diffusing in the industries with recent advances in the industrial Internet of things (IIoT), edge and cloud computing, machine learning, artificial intelligence, and advanced data analytics. However, the existing literature lacks in identifying and discussing the role and requirements of these technologies in DT-enabled industries from the communication and computing perspective. In this article, we first present the functional aspects, appeal, and innovative use of DT in smart industries. Then, we elaborate on this perspective by systematically reviewing and reflecting on recent research in next-generation (NextG) wireless technologies (e.g., 5G and beyond networks), various tools (e.g., age of information, federated learning, data analytics), and other promising trends in networked computing (e.g., edge and cloud computing). Moreover, we discuss the DT deployment strategies at different industrial communication layers to meet the monitoring and control requirements of industrial applications. We also outline several key reflections and future research challenges and directions to facilitate industrial DT's adoption.

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