arXiv subjects

Muhammad Mahtab Alam

Publications and source records attributed to Muhammad Mahtab Alam.

8 recordsLinked to original sources

arXiv2026

Empowering Embodied AI in 6G Networks: Architecture, Enablers, and Open Challenges

Embodied artificial intelligence (AI) is emerging as a key driver of the sixth-generation (6G) wireless networks by enabling agents that continuously perceive, communicate, and act in dynamic physical environments. Unlike conventional AI systems that process disembodied data, embodied agents such as robots, autonomous vehicles, and extended reality (XR) devices operate through closed-loop perception-communication-action (PCA) interactions, where communication performance directly affects physical behavior, control stability, and task success. However, existing AI-native wireless architectures remain largely connectivity-centric and are not designed to support task-driven embodied intelligence at large scale. Therefore, we present a holistic framework for embodied AI-native 6G systems, in which communication, sensing, computation, and control are jointly designed as a unified closed-loop infrastructure. We introduce a system-level PCA architecture, discuss key enabling technologies and representative applications, and highlight major open challenges in multimodal intelligence, edge-aware deployment, evaluation, trustworthiness, and practical implementation. Our central argument is that future 6G systems must evolve from intelligent communication platforms into active enablers of embodied physical intelligence.

cs.NI↗

arXiv2026

Integrated Sensing, User Location and Orientation Estimation in RIS-Assisted Dynamic Rich Scattering Environment

This paper investigates an uplink user equipment (UE) location and orientation estimation problem in an indoor rich-scattering environment (RSE) for a multiple-input-multiple-output (MIMO) narrowband reconfigurable intelligent surfaces (RIS)-assisted communication system. The localization problem in RSE is challenging as the uplink pilot signal undergoes multiple interactions with the RIS and dynamic scattering objects (SOs). This paper proposes an approach where base station (BS) adaptively senses the environment with the help of RIS. Based on this sensing, it sequentially designs RIS configuration, BS beamforming and UE beamforming vectors, using the sequence of pilot transmissions from the UE to the BS, with an objective of progressively focusing them onto the UE. Towards this end, we train a bidirectional long-short term memory (biLSTM) network based controller to capture the temporal dependencies between measurements to first adaptively sense the RSE and then design RIS, BS and UE beamforming vectors to localize the UE. We evaluate the proposed approach under various RSE conditions such as various distributed RIS installations, varying number of randomly moving SOs and sensing RIS elements. Simulation results illustrate that it effectively enables adaptive sensing to achieve low localization error with robustness in various RSEs.

eess.SP↗

arXiv2026

Adaptive RIS Configuration Design with Environmental Sensing for User Localization in Dynamic Rich Scattering Environment

This paper addresses the problem of adaptive reconfigurable intelligent surfaces (RIS) configuration design for user localization in rich-scattering environment (RSE), where electromagnetic waves undergo multiple interactions with dynamic scatterers and RIS elements. We propose an adaptive learning-based localization approach for a distributed RIS-assisted network in a RSE using a bidirectional long-short term memory (biLSTM) model that captures temporal correlations between observations. The proposed approach actively senses the environment using sequential pilot transmissions from the base station (BS), accounting for scattering effects, and adaptively updates the RIS configuration based on prior measurements to eventually accurately estimate and minimize the user localization error. The proposed model comprises two neural sub-networks: Scattering Estimation Network (Bi-SEN), for estimation of scattering in the environment, and Adaptive RIS-Assisted User Localization Network (Bi-ARULN), for RIS configuration and localization. Bayesian optimization is used for hyperparameter tuning of the model. The simulation results demonstrate the effectiveness of the proposed approach, achieving significantly lower localization root mean squared error (RMSE compared to random configuration, prestored codebook look-ups, and adaptive baselines in both single-input-single-output (SISO) and multiple-input-multiple output(MIMO) RIS-assisted networks in RSE. The design is generalized across configurations and scales with RIS size and network dimensions. The results highlight the strong potential of RIS deployment and of the proposed approach to enable reliable location services in RSE.

eess.SP↗

arXiv2026

CoBA: Integrated Deep Learning Model for Reliable Low-Altitude UAV Classification in mmWave Radio Networks

Uncrewed Aerial Vehicles (UAVs) are increasingly used in civilian and industrial applications, making secure low-altitude operations crucial. In dense mmWave environments, accurately classifying low-altitude UAVs as either inside authorized or restricted airspaces remains challenging, requiring models that handle complex propagation and signal variability. This paper proposes a deep learning model, referred to as CoBA, which stands for integrated Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Attention which leverages Fifth Generation (5G) millimeter-wave (mmWave) radio measurements to classify UAV operations in authorized and restricted airspaces at low altitude. The proposed CoBA model integrates convolutional, bidirectional recurrent, and attention layers to capture both spatial and temporal patterns in UAV radio measurements. To validate the model, a dedicated dataset is collected using the 5G mmWave network at TalTech, with controlled low altitude UAV flights in authorized and restricted scenarios. The model is evaluated against conventional ML models and a fingerprinting-based benchmark. Experimental results show that CoBA achieves superior accuracy, significantly outperforming all baseline models and demonstrating its potential for reliable and regulated UAV airspace monitoring.

cs.LG↗

arXiv2024

Reconfigurable Intelligent Surfaces in Dynamic Rich Scattering Environments: BiLSTM-Based Optimization for Accurate User Localization

The integration of reconfigurable intelligent surfaces (RIS) in wireless environments offers channel programmability and dynamic control over propagation channels, which is expected to play a crucial role in sixth generation (6G) networks. The majority of RIS-related research has focused on simpler, quasi-free-space conditions, where wireless channels are typically modeled analytically. However, many practical localization scenarios unfold in environments characterized by rich scattering that also change over time. These dynamic and complex conditions pose significant challenges in determining the optimal RIS configuration to maximize localization accuracy. In this paper, we present our approach to overcoming this challenge. This paper introduces a novel approach that leverages a bidirectional long-short term memory (biLSTM) network, trained with a simulator that accurately reflects wave physics, to capture the relationship between wireless channels and the RIS configuration under dynamic, rich-scattering conditions. We use this approach to optimize RIS configurations for enhanced user equipment (UE) localization, measured by mean squared error (MSE). Through extensive simulations, we demonstrate that our approach adapts RIS configurations to significantly improve localization accuracy in such dynamically changing rich scattering environments.

eess.SP↗

arXiv2023

Reconfigurable Intelligent Surfaces in 6G Radio Localization: A Survey of Recent Developments, Opportunities, and Challenges

In this survey paper, we present an extensive review of the use of RIS in 6G radio localization, highlighting their pivotal role as a low-cost, energy-efficient technology that reshapes wireless communication and localization landscapes. Investigating the versatile capabilities of RIS, we explore their dynamic control over electromagnetic wave manipulation, including reflection, refraction, and transmission, which opens new horizons in diverse applications ranging from IOT connectivity to advanced mobile communication, and various innovative applications in Industry 4.0. Our comprehensive review provides an overview of RIS use in 6G radio localization, highlighting recent progress in RIS technology assisted localization. It focuses on key aspects, including network scenarios, transmission bands, deployment environments, and near-field operations. We discuss studies to examine the state-of-the-art RIS-assisted localization and optimization techniques and their performance evaluation matrices. In addition, we present a detailed taxonomy of RIS-assisted radio localization, emphasizing the rapid evolution and potential of RIS technology in non-line-of-sight scenarios as an alternative to traditional base stations. Based on the careful investigation of the reviewed studies, the survey also sheds light on future research directions, technical challenges, and limitations, offering a clear perspective on the integration and optimization of RIS in 6G networks for enhanced localization capabilities.

eess.SP↗

arXiv2017

Behavior of Wireless Body-to-Body Networks Routing Strategies for Public Protection and Disaster Relief

Critical and public safety operations require real-time data transfer from the incident area(s) to the distant operations command center going through the evacuation and medical support areas. Any delay in communication may cause significant loss. In some cases, it is anticipated that the existing communication infrastructures can be damaged or out-of-service. It is thus required to deploy tactical ad-hoc networks to cover the operation zones. Routing data over the deployed network is a significant challenge with consideration to the operations conditions. In this paper we evaluate the performance of mutli-hop routing protocols while using different wireless technologies in an urban critical and emergency scenario. Using a realistic mobility model, Mobile Ad hoc, geographic based and data-centric routing protocols are evaluated with different communication technologies (i.e. WiFi IEEE 802.11; WSN IEEE 802.15.4; WBAN IEEE 802.15.6). It is concluded that, WiFi IEEE 802.11 is the best wireless technology with consideration to the packet reception rate and the energy consumption. Whereas, in terms of delay, WBAN IEEE 802.15.6 is the most efficient. With regards to the routing protocols, assuming that the location information is available, geographical based routing protocol with WiFi IEEE 802.11 performed much better compared to the others routing protocols. In case where the location information is unavailable, gradient based routing protocol with WBAN IEEE 802.15.6 seems the best combination.

cs.NI↗

arXiv2017

Data Dissemination Strategies for Emerging Wireless Body-to-Body Networks based Internet of Humans

With the recent advent of Internet of Humans (IoH), wireless body-to-body networks (WBBNs) are emerging as the fundamental part of this new paradigm. In particular with reference to newly emerging applications, the research trends on data routing and dissemination strategies have gained a great interest in WBBN. In this paper, we present the performance evaluation of the clustered and distributed data dissemination approaches in tactical WBBN. We used a realistic radio-link and biomechanical mobility model for on-body motions, and group mobility model for WBBN to effectively realize rescue and emergency management application scenario. In this regard, we are using the newly proposed IEEE 802.15.6 standard targeted for body area networks. Extensive (IEEE 802.15.6 standard compliance) network level, packet oriented simulations are conducted in WSNet simulator. During the simulations, various payloads, frequencies (narrow-band) and modulation techniques are exploited. We based our performance evaluation on relevant metrics according to the operational requirements for tactical networks such as packet reception ratio, latency, energy consumption and hop count. The results showed a trade-offs between clustered-based and distributed-based dissemination approaches. With regards to packet delay, distributed approach provided the best performance. However, in terms of average packet reception ratio (PRR), clustered-based approach achieves up to 97% reception and remained the best strategy. Whereas, the results of the hop count and energy consumption are almost comparable in both schemes.

cs.DC↗