arXiv subjects

Walid Saad

Publications and source records attributed to Walid Saad.

At least 73 records · Page 4Linked to original sources

arXiv2025

A Comprehensive Tutorial and Survey of O-RAN: Exploring Slicing-aware Architecture, Deployment Options, Use Cases, and Challenges

Open-radio access network (O-RAN) seeks to establish the principles of openness, programmability, automation, intelligence, and hardware-software disaggregation with interoperable and standard-compliant interfaces. It advocates for multi-vendorism and multi-stakeholderism within a cloudified and virtualized wireless infrastructure, aimed at enhancing the deployment, operation, and management of RAN architecture. These enhancements promise increased flexibility, performance optimization, service innovation, energy efficiency, and cost effectiveness across fifth-generation (5G), sixth-generation (6G), and beyond networks. A silent feature of O-RAN architecture is its support for network slicing, which entails interaction with other domains of the cellular network, notably the transport network (TN) and the core network (CN), to realize end-to-end (E2E) network slicing. The study of this feature requires exploring the stances and contributions of diverse standards development organizations (SDOs). In this context, we note that despite the ongoing industrial deployments and standardization efforts, the research and standardization communities have yet to comprehensively address network slicing in O-RAN. To address this gap, this paper provides a comprehensive exploration of network slicing in O-RAN through an in-depth review of specification documents from O-RAN Alliance and research papers from leading industry and academic institutions. The paper commences with an overview of the relevant standardization and open source contributions, subsequently delving into the latest O-RAN architecture with an emphasis on its slicing aspects. Furthermore, the paper explores O-RAN deployment scenarios, examining options for the deployment and orchestration of RAN and TN slice subnets. It also discusses the slicing of the underlying infrastructure and provides an overview of various use cases related...

cs.NI↗

arXiv2025

World Model-Based Learning for Long-Term Age of Information Minimization in Vehicular Networks

Traditional reinforcement learning (RL)-based learning approaches for wireless networks rely on expensive trial-and-error mechanisms and real-time feedback based on extensive environment interactions, which leads to low data efficiency and short-sighted policies. These limitations become particularly problematic in complex, dynamic networks with high uncertainty and long-term planning requirements. To address these limitations, in this paper, a novel world model-based learning framework is proposed to minimize packet-completeness-aware age of information (CAoI) in a vehicular network. Particularly, a challenging representative scenario is considered pertaining to a millimeter-wave (mmWave) vehicle-to-everything (V2X) communication network, which is characterized by high mobility, frequent signal blockages, and extremely short coherence time. Then, a world model framework is proposed to jointly learn a dynamic model of the mmWave V2X environment and use it to imagine trajectories for learning how to perform link scheduling. In particular, the long-term policy is learned in differentiable imagined trajectories instead of environment interactions. Moreover, owing to its imagination abilities, the world model can jointly predict time-varying wireless data and optimize link scheduling in real-world wireless and V2X networks. Thus, during intervals without actual observations, the world model remains capable of making efficient decisions. Extensive experiments are performed on a realistic simulator based on Sionna that integrates physics-based end-to-end channel modeling, ray-tracing, and scene geometries with material properties. Simulation results show that the proposed world model achieves a significant improvement in data efficiency, and achieves 26% improvement and 16% improvement in CAoI, respectively, compared to the model-based RL (MBRL) method and the model-free RL (MFRL) method.

cs.AI↗

arXiv2025

DD-JSCC: Dynamic Deep Joint Source-Channel Coding for Semantic Communications

Deep Joint Source-Channel Coding (Deep-JSCC) has emerged as a promising semantic communication approach for wireless image transmission by jointly optimizing source and channel coding using deep learning techniques. However, traditional Deep-JSCC architectures employ fixed encoder-decoder structures, limiting their adaptability to varying device capabilities, real-time performance optimization, power constraints and channel conditions. To address these limitations, we propose DD-JSCC: Dynamic Deep Joint Source-Channel Coding for Semantic Communications, a novel encoder-decoder architecture designed for semantic communication systems. Unlike traditional Deep-JSCC models, DD-JSCC is flexible for dynamically adjusting its layer structures in real-time based on transmitter and receiver capabilities, power constraints, compression ratios, and current channel conditions. This adaptability is achieved through a hierarchical layer activation mechanism combined with implicit regularization via sequential randomized training, effectively reducing combinatorial complexity, preventing overfitting, and ensuring consistent feature representations across varying configurations. Simulation results demonstrate that DD-JSCC enhances the performance of image reconstruction in semantic communications, achieving up to 2 dB improvement in Peak Signal-to-Noise Ratio (PSNR) over fixed Deep-JSCC architectures, while reducing training costs by over 40%. The proposed unified framework eliminates the need for multiple specialized models, significantly reducing training complexity and deployment overhead.

cs.NI↗

arXiv2025

Transformer based Collaborative Reinforcement Learning for Fluid Antenna System (FAS)-enabled 3D UAV Positioning

In this paper, a novel Three dimensional (3D) positioning framework of fluid antenna system (FAS)-enabled unmanned aerial vehicles (UAVs) is developed. In the proposed framework, a set of controlled UAVs cooperatively estimate the real-time 3D position of a target UAV. Here, the active UAV transmits a measurement signal to the passive UAVs via the reflection from the target UAV. Each passive UAV estimates the distance of the active-target-passive UAV link and selects an antenna port to share the distance information with the base station (BS) that calculates the real-time position of the target UAV. As the target UAV is moving due to its task operation, the controlled UAVs must optimize their trajectories and select optimal antenna port, aiming to estimate the real-time position of the target UAV. We formulate this problem as an optimization problem to minimize the target UAV positioning error via optimizing the trajectories of all controlled UAVs and antenna port selection of passive UAVs. Here, an attention-based recurrent multi-agent reinforcement learning (AR-MARL) scheme is proposed, which enables each controlled UAV to use the local Q function to determine its trajectory and antenna port while optimizing the target UAV positioning performance without knowing the trajectories and antenna port selections of other controlled UAVs. Different from current MARL methods, the proposed method uses a recurrent neural network (RNN) that incorporates historical state-action pairs of each controlled UAV, and an attention mechanism to analyze the importance of these historical state-action pairs, thus improving the global Q function approximation accuracy and the target UAV positioning accuracy. Simulation results show that the proposed AR-MARL scheme can reduce the average positioning error by up to 17.5% and 58.5% compared to the VD-MARL scheme and the proposed method without FAS.

cs.NI↗

arXiv2025

When can isotropy help adapt LLMs' next word prediction to numerical domains?

Vector representations of contextual embeddings learned by pre-trained large language models (LLMs) are effective in various downstream tasks in numerical domains such as time series forecasting. Despite their significant benefits, the tendency of LLMs to hallucinate in such domains can have severe consequences in applications such as energy, nature, finance, healthcare, retail and transportation, among others. To guarantee prediction reliability and accuracy in numerical domains, it is necessary to open the black box behind the LLM and provide performance guarantees through explanation. However, there is little theoretical understanding of when pre-trained language models help solve numerical downstream tasks. This paper seeks to bridge this gap by understanding when the next-word prediction capability of LLMs can be adapted to numerical domains through a novel analysis based on the concept of isotropy in the contextual embedding space. Specifically, a log-linear model for LLMs is considered in which numerical data can be predicted from its context through a network with softmax in the output layer of LLMs (i.e., language model head in self-attention). For this model, it is demonstrated that, in order to achieve state-of-the-art performance in numerical domains, the hidden representations of the LLM embeddings must possess a structure that accounts for the shift-invariance of the softmax function. By formulating a gradient structure of self-attention in pre-trained models, it is shown how the isotropic property of LLM embeddings in contextual embedding space preserves the underlying structure of representations, thereby resolving the shift-invariance problem and providing a performance guarantee. Experiments show that different characteristics of numerical data and model architectures have different impacts on isotropy, and this variability directly affects the performances.

cs.CL↗

arXiv2025

Sensing Safety Analysis for Vehicular Networks with Integrated Sensing and Communication (ISAC)

Integrated sensing and communication (ISAC) emerged as a key feature of next-generation 6G wireless systems, allowing them to achieve high data rates and sensing accuracy. While prior research has primarily focused on addressing communication safety in ISAC systems, the equally critical issue of sensing safety remains largely ignored. In this paper, a novel threat to the sensing safety of ISAC vehicle networks is studied, whereby a malicious reconfigurable intelligent surface (RIS) is deployed to compromise the sensing functionality of a roadside unit (RSU). Specifically, a malicious attacker dynamically adjusts the phase shifts of an RIS to spoof the sensing outcomes of a vehicular user (VU)'s echo delay, Doppler shift, and angle-of-departure (AoD). To achieve spoofing on Doppler shift estimation, a time-varying phase shift design on the RIS is proposed. Furthermore, the feasible spoofing frequency set with respect to the Doppler shift is analytical derived. Analytical results also demonstrate that the maximum likelihood estimator (MLE) of the AoD can be significantly misled under spoofed Doppler shift estimation. Simulation results validate our theoretical findings, showing that the RIS can induce a spoofed velocity estimation from 0.1 m/s to 14.9 m/s for a VU with velocity of 10 m/s, and can cause an AoD estimation error of up to 65^{\circ} with only a 5^{\circ} beam misalignment.

cs.IT↗

arXiv2025

Resilient Vehicular Communications under Imperfect Channel State Information

Cellular vehicle-to-everything (C-V2X) networks provide a promising solution to improve road safety and traffic efficiency. One key challenge in such systems lies in meeting quality-of-service (QoS) requirements of vehicular communication links given limited network resources, particularly under imperfect channel state information (CSI) conditions caused by the highly dynamic environment. In this paper, a novel two-phase framework is proposed to instill resilience into C-V2X networks under unknown imperfect CSI. The resilience of the C-V2X network is defined, quantified, and optimized the first time through two principal dimensions: absorption phase and adaptation phase. Specifically, the probability distribution function (PDF) of the imperfect CSI is estimated during the absorption phase through dedicated absorption power scheme and resource block (RB) assignment. The estimated PDF is further used to analyze the interplay and reveal the tradeoff between these two phases. Then, a novel metric named hazard rate (HR) is exploited to balance the C-V2X network's prioritization on absorption and adaptation. Finally, the estimated PDF is exploited in the adaptation phase to recover the network's QoS through a real-time power allocation optimization. Simulation results demonstrate the superior capability of the proposed framework in sustaining the QoS of the C-V2X network under imperfect CSI. Specifically, in the adaptation phase, the proposed design reduces the vehicle-tovehicle (V2V) delay that exceeds QoS requirement by 35% and 56%, and improves the average vehicle-to-infrastructure (V2I) throughput by 14% and 16% compared to the model-based and data-driven benchmarks, respectively, without compromising the network's QoS in the absorption phase.

cs.IT↗

arXiv2025

Joint Holographic Beamforming and User Scheduling with Individual QoS Constraints

Reconfigurable holographic surfaces (RHS) have emerged as a transformative material technology, enabling dynamic control of electromagnetic waves to generate versatile holographic beam patterns. This paper addresses the problem of joint hybrid holographic beamforming and user scheduling under per-user minimum quality-of-service (QoS) constraints, a critical challenge in resource-constrained networks. However, such a problem results in mixed-integer non-convex optimization, making it difficult to identify feasible solutions efficiently. To overcome this challenge, we propose a novel iterative optimization framework that jointly solves the problem to maximize the RHS-assisted network sum-rate, efficiently managing holographic beamforming patterns, dynamically scheduling users, and ensuring the minimum QoS requirements for each scheduled user. The proposed framework relies on zero-forcing digital beamforming, gradient-ascent-based holographic beamformer optimization, and a greedy user selection principle. Our extensive simulation results validate the effectiveness of the proposed scheme, demonstrating their superior performance compared to the benchmark algorithms in terms of sum-rate performance, while meeting the minimum per-user QoS constraints

eess.SP↗

arXiv2025

Joint Beamforming and 3D Location Optimization for Multi-User Holographic UAV Communications

This paper pioneers the field of multi-user holographic unmanned aerial vehicle (UAV) communications, laying a solid foundation for future innovations in next-generation aerial wireless networks. The study focuses on the challenging problem of jointly optimizing hybrid holographic beamforming and 3D UAV positioning in scenarios where the UAV is equipped with a reconfigurable holographic surface (RHS) instead of conventional phased array antennas. Using the unique capabilities of RHSs, the system dynamically adjusts both the position of the UAV and its hybrid beamforming properties to maximize the sum rate of the network. To address this complex optimization problem, we propose an iterative algorithm combining zero-forcing digital beamforming and a gradient ascent approach for the holographic patterns and the 3D position optimization, while ensuring practical feasibility constraints. The algorithm is designed to effectively balance the trade-offs between power, beamforming, and UAV trajectory constraints, enabling adaptive and efficient communications, while assuring a monotonic increase in the sum-rate performance. Our numerical investigations demonstrate that the significant performance improvements with the proposed approach over the benchmark methods, showcasing enhanced sum rate and system adaptability under varying conditions.

eess.SP↗

arXiv2025

R-MTLLMF: Resilient Multi-Task Large Language Model Fusion at the Wireless Edge

Multi-task large language models (MTLLMs) are important for many applications at the wireless edge, where users demand specialized models to handle multiple tasks efficiently. However, training MTLLMs is complex and exhaustive, particularly when tasks are subject to change. Recently, the concept of model fusion via task vectors has emerged as an efficient approach for combining fine-tuning parameters to produce an MTLLM. In this paper, the problem of enabling edge users to collaboratively craft such MTLMs via tasks vectors is studied, under the assumption of worst-case adversarial attacks. To this end, first the influence of adversarial noise to multi-task model fusion is investigated and a relationship between the so-called weight disentanglement error and the mean squared error (MSE) is derived. Using hypothesis testing, it is directly shown that the MSE increases interference between task vectors, thereby rendering model fusion ineffective. Then, a novel resilient MTLLM fusion (R-MTLLMF) is proposed, which leverages insights about the LLM architecture and fine-tuning process to safeguard task vector aggregation under adversarial noise by realigning the MTLLM. The proposed R-MTLLMF is then compared for both worst-case and ideal transmission scenarios to study the impact of the wireless channel. Extensive model fusion experiments with vision LLMs demonstrate R-MTLLMF's effectiveness, achieving close-to-baseline performance across eight different tasks in ideal noise scenarios and significantly outperforming unprotected model fusion in worst-case scenarios. The results further advocate for additional physical layer protection for a holistic approach to resilience, from both a wireless and LLM perspective.

eess.SP↗

arXiv2025

eQMARL: Entangled Quantum Multi-Agent Reinforcement Learning for Distributed Cooperation over Quantum Channels

Collaboration is a key challenge in distributed multi-agent reinforcement learning (MARL) environments. Learning frameworks for these decentralized systems must weigh the benefits of explicit player coordination against the communication overhead and computational cost of sharing local observations and environmental data. Quantum computing has sparked a potential synergy between quantum entanglement and cooperation in multi-agent environments, which could enable more efficient distributed collaboration with minimal information sharing. This relationship is largely unexplored, however, as current state-of-the-art quantum MARL (QMARL) implementations rely on classical information sharing rather than entanglement over a quantum channel as a coordination medium. In contrast, in this paper, a novel framework dubbed entangled QMARL (eQMARL) is proposed. The proposed eQMARL is a distributed actor-critic framework that facilitates cooperation over a quantum channel and eliminates local observation sharing via a quantum entangled split critic. Introducing a quantum critic uniquely spread across the agents allows coupling of local observation encoders through entangled input qubits over a quantum channel, which requires no explicit sharing of local observations and reduces classical communication overhead. Further, agent policies are tuned through joint observation-value function estimation via joint quantum measurements, thereby reducing the centralized computational burden. Experimental results show that eQMARL with $Ψ^{+}$ entanglement converges to a cooperative strategy up to $17.8\%$ faster and with a higher overall score compared to split classical and fully centralized classical and quantum baselines. The results also show that eQMARL achieves this performance with a constant factor of $25$-times fewer centralized parameters compared to the split classical baseline.

quant-ph↗

arXiv2025

Placement, Orientation, and Resource Allocation Optimization for Cell-Free OIRS-aided OWC Network

The emergence of optical intelligent reflecting surface (OIRS) technologies marks a milestone in optical wireless communication (OWC) systems, enabling enhanced control over light propagation in indoor environments. This capability allows for the customization of channel conditions to achieve specific performance goals. This paper presents an enhancement in downlink cell-free OWC networks through the integration of OIRS. The key focus is on fine-tuning crucial parameters, including transmit power, receiver orientations, OIRS elements allocation, and strategic placement. In particular, a multi-objective optimization problem (MOOP) aimed at simultaneously improving the network's spectral efficiency (SE) and energy efficiency (EE) while adhering to the network's quality of service (QoS) constraints is formulated. The problem is solved by employing the $ε$-constraint method to convert the MOOP into a single-objective optimization problem and solving it through successive convex approximation. Simulation results show the significant impact of OIRS on SE and EE, confirming its effectiveness in improving OWC network performance.

eess.SP↗

arXiv2024

SpaFL: Communication-Efficient Federated Learning with Sparse Models and Low computational Overhead

The large communication and computation overhead of federated learning (FL) is one of the main challenges facing its practical deployment over resource-constrained clients and systems. In this work, SpaFL: a communication-efficient FL framework is proposed to optimize sparse model structures with low computational overhead. In SpaFL, a trainable threshold is defined for each filter/neuron to prune its all connected parameters, thereby leading to structured sparsity. To optimize the pruning process itself, only thresholds are communicated between a server and clients instead of parameters, thereby learning how to prune. Further, global thresholds are used to update model parameters by extracting aggregated parameter importance. The generalization bound of SpaFL is also derived, thereby proving key insights on the relation between sparsity and performance. Experimental results show that SpaFL improves accuracy while requiring much less communication and computing resources compared to sparse baselines. The code is available at https://github.com/news-vt/SpaFL_NeruIPS_2024

cs.LG↗

arXiv2024

Resilience and Criticality: Brothers in Arms for 6G

In this paper, we develop the first comprehensive tutorial on designing future 6G networks that synergistically integrate notions of resilience and criticality from the ground up. While resilience refers to the ability to absorb, adapt to, and recover from adversarial or challenging conditions, criticality indicates the degree of importance or urgency assigned to a particular service or component. Despite a spiking interest in designing resilient wireless networks, most prior works do not provide a unified resilience definition, nor harness the intricate interplay between resilience and criticality. In order to fill this gap, in this paper, we highlight the importance of a criticality-aware approach as a key enabler for providing reliable and resilient service functionality. Moreover, we delve into the unique challenges and opportunities of the envisioned 6G features pertaining to resilience and (mixed) criticality. After reviewing resilience definitions, we present a core resilience strategy, a unified resilience metric, different criteria for service criticality, and prioritization frameworks, that augment the 6G resilience prospects. Afterwards, we explore the opportunities presented by promising technologies that enable a resilient 6G network design from a radio access network protocol stack perspective. We briefly revisit state-of-the-art network architectures, establish a rough connection to the Open-RAN Alliance vision, and discuss opportunities, existing techniques, and promising enabling mechanisms for 6G at each layer. Finally, the article discusses important research directions and open problems concerning resilience and criticality in 6G.

cs.IT↗

arXiv2024

A Resilience Perspective on C-V2X Communication Networks under Imperfect CSI

Cellular vehicle-to-everything (C-V2X) networks provide a promising solution to improve road safety and traffic efficiency. One key challenge in such systems lies in meeting different quality-of-service (QoS) requirements of coexisting vehicular communication links, particularly under imperfect channel state information (CSI) conditions caused by the highly dynamic environment. In this paper, a novel analytical framework for examining the resilience of C-V2X networks in face of imperfect CSI is proposed. In this framework, the adaptation phase of the C-V2X network is studied, in which an adaptation power scheme is employed and the probability distribution function (PDF) of the imperfect CSI is estimated. Then, the resilience of C-V2X networks is studied through two principal dimensions: remediation capability and adaptation performance, both of which are defined, quantified, and analyzed for the first time. Particularly, an upper bound on the estimation's mean square error (MSE) is explicitly derived to capture the C-V2X's remediation capability, and a novel metric named hazard rate (HR) is exploited to evaluate the C-V2X's adaptation performance. Afterwards, the impact of the adaptation power scheme on the C-V2X's resilience is examined, revealing a tradeoff between the C-V2X's remediation capability and adaptation performance. Simulation results validate the framework's superiority in capturing the interplay between adaptation and remediation, as well as the effectiveness of the two proposed metrics in guiding the design of the adaptation power scheme to enhance the system's resilience.

cs.IT↗

arXiv2024

Internet of Federated Digital Twins (IoFDT): Connecting Twins Beyond Borders for Society 5.0

The concept of digital twin (DT), which enables the creation of a programmable, digital representation of physical systems, is expected to revolutionize future industries and will lie at the heart of the vision of a future smart society, namely, Society 5.0, in which high integration between cyber (digital) and physical spaces is exploited to bring economic and societal advancements. However, the success of such a DT-driven Society 5.0 requires a synergistic convergence of artificial intelligence and networking technologies into an integrated, programmable system that can coordinate DT networks to effectively deliver diverse Society 5.0 services. Prior works remain restricted to either qualitative study, simple analysis or software implementations of a single DT, and thus, they cannot provide the highly synergistic integration of digital and physical spaces as required by Society 5.0. In contrast, this paper envisions a novel concept of an Internet of Federated Digital Twins (IoFDT) that holistically integrates heterogeneous and physically separated DTs representing different Society 5.0 services within a single framework and system. For this concept of IoFDT, we first introduce a hierarchical architecture that integrates federated DTs through horizontal and vertical interactions, bridging cyber and physical spaces to unlock new possibilities. Then, we discuss challenges of realizing IoFDT, highlighting the intricacies across communication, computing, and AI-native networks while also underscoring potential innovative solutions. Subsequently, we elaborate on the importance of the implementation of a unified IoFDT platform that integrates all technical components and orchestrates their interactions, emphasizing the necessity of practical experimental platforms with a focus on real-world applications in areas like smart mobility.

cs.AI↗

arXiv2024

Augmented Intelligence in Smart Intersections: Local Digital Twins-Assisted Hybrid Autonomous Driving

Vehicle-road collaboration is a promising approach for enhancing the safety and efficiency of autonomous driving by extending the intelligence of onboard systems to smart roadside infrastructures. The introduction of digital twins (DTs), particularly local DTs (LDTs) at the edge, in smart mobility presents a new embodiment of augmented intelligence, which could enhance information exchange and extract human driving expertise to improve onboard intelligence. This paper presents a novel LDT-assisted hybrid autonomous driving system for improving safety and efficiency in traffic intersections. By leveraging roadside units (RSUs) equipped with sensory and computing capabilities, the proposed system continuously monitors traffic, extracts human driving knowledge, and generates intersection-specific local driving agents through an offline reinforcement learning (RL) framework. When connected and automated vehicles (CAVs) pass through RSU-equipped intersections, RSUs can provide local agents to support safe and efficient driving in local areas. Meanwhile, they provide real-time cooperative perception (CP) to broaden onboard sensory horizons. The proposed LDT-assisted hybrid system is implemented with state-of-the-art products, e.g., CAVs and RSUs, and technologies, e.g., millimeter-wave (mmWave) communications. Hardware-in-the-loop (HiL) simulations and proof-of-concept (PoC) tests validate system performance from two standpoints: (i) The peak latency for CP and local agent downloading are 8.51 ms and 146 ms, respectively, aligning with 3GPP requirements for vehicle-to-everything (V2X) and model transfer use cases. Moreover, (ii) local driving agents can improve safety measures by 10% and reduce travel time by 15% compared with conventional onboard systems. The implemented prototype also demonstrates reliable real-time performance, fulfilling the targets of the proposed system design.

eess.SY↗

arXiv2024

Induced Covariance for Causal Discovery in Linear Sparse Structures

Causal models seek to unravel the cause-effect relationships among variables from observed data, as opposed to mere mappings among them, as traditional regression models do. This paper introduces a novel causal discovery algorithm designed for settings in which variables exhibit linearly sparse relationships. In such scenarios, the causal links represented by directed acyclic graphs (DAGs) can be encapsulated in a structural matrix. The proposed approach leverages the structural matrix's ability to reconstruct data and the statistical properties it imposes on the data to identify the correct structural matrix. This method does not rely on independence tests or graph fitting procedures, making it suitable for scenarios with limited training data. Simulation results demonstrate that the proposed method outperforms the well-known PC, GES, BIC exact search, and LINGAM-based methods in recovering linearly sparse causal structures.

cs.LG↗