arXiv subjects

Walid Saad

Publications and source records attributed to Walid Saad.

At least 163 records · Page 9Linked to original sources

arXiv2022

Neuro-Symbolic Artificial Intelligence (AI) for Intent based Semantic Communication

Intent-based networks that integrate sophisticated machine reasoning technologies will be a cornerstone of future wireless 6G systems. Intent-based communication requires the network to consider the semantics (meanings) and effectiveness (at end-user) of the data transmission. This is essential if 6G systems are to communicate reliably with fewer bits while simultaneously providing connectivity to heterogeneous users. In this paper, contrary to state of the art, which lacks explainability of data, the framework of neuro-symbolic artificial intelligence (NeSy AI) is proposed as a pillar for learning causal structure behind the observed data. In particular, the emerging concept of generative flow networks (GFlowNet) is leveraged for the first time in a wireless system to learn the probabilistic structure which generates the data. Further, a novel optimization problem for learning the optimal encoding and decoding functions is rigorously formulated with the intent of achieving higher semantic reliability. Novel analytical formulations are developed to define key metrics for semantic message transmission, including semantic distortion, semantic similarity, and semantic reliability. These semantic measure functions rely on the proposed definition of semantic content of the knowledge base and this information measure is reflective of the nodes' reasoning capabilities. Simulation results validate the ability to communicate efficiently (with less bits but same semantics) and significantly better compared to a conventional system which does not exploit the reasoning capabilities.

cs.LG↗

arXiv2022

6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities

We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, as well as a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network which should be extremely intelligent and capable of concurrently supporting hyper-fast, ultra-reliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning will play an instrumental role for advanced vehicular communication and networking. To this end, we provide an overview on the recent advances of machine learning in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies.

cs.IT↗

arXiv2022

Self-organizing Democratized Learning: Towards Large-scale Distributed Learning Systems

Emerging cross-device artificial intelligence (AI) applications require a transition from conventional centralized learning systems towards large-scale distributed AI systems that can collaboratively perform complex learning tasks. In this regard, democratized learning (Dem-AI) lays out a holistic philosophy with underlying principles for building large-scale distributed and democratized machine learning systems. The outlined principles are meant to study a generalization in distributed learning systems that goes beyond existing mechanisms such as federated learning. Moreover, such learning systems rely on hierarchical self-organization of well-connected distributed learning agents who have limited and highly personalized data and can evolve and regulate themselves based on the underlying duality of specialized and generalized processes. Inspired by Dem-AI philosophy, a novel distributed learning approach is proposed in this paper. The approach consists of a self-organizing hierarchical structuring mechanism based on agglomerative clustering, hierarchical generalization, and corresponding learning mechanism. Subsequently, hierarchical generalized learning problems in recursive forms are formulated and shown to be approximately solved using the solutions of distributed personalized learning problems and hierarchical update mechanisms. To that end, a distributed learning algorithm, namely DemLearn is proposed. Extensive experiments on benchmark MNIST, Fashion-MNIST, FE-MNIST, and CIFAR-10 datasets show that the proposed algorithms demonstrate better results in the generalization performance of learning models in agents compared to the conventional FL algorithms. The detailed analysis provides useful observations to further handle both the generalization and specialization performance of the learning models in Dem-AI systems.

cs.LG↗

arXiv2022

Positioning Using Visible Light Communications: A Perspective Arcs Approach

Visible light positioning (VLP) is an accurate indoor positioning technology that uses luminaires as transmitters. In particular, circular luminaires are a common source type for VLP, that are typically treated only as point sources for positioning, while ignoring their geometry characteristics. In this paper, the arc feature of the circular luminaire and the coordinate information obtained via visible light communication (VLC) are jointly used for VLC-enabled indoor positioning, and a novel perspective arcs approach is proposed. The proposed approach does not rely on any inertial measurement unit, and has no tilted angle limitations at the user. First, a VLC assisted perspective circle and arc algorithm (V-PCA) is proposed for a scenario in which a complete luminaire and an incomplete one can be captured by the user. Considering the cases in which parts of VLC links are blocked, an anti-occlusion VLC assisted perspective arcs algorithm (OA-V-PA) is proposed. Simulation results show that the proposed indoor positioning algorithm can achieve a 95th percentile positioning accuracy of around 10 cm. Moreover, an experimental prototype based on mobile phone is implemented, in which, a fused image processing method is proposed. Experimental results show that the average positioning accuracy is less than 5 cm.

eess.SP↗

arXiv2022

Edge Continual Learning for Dynamic Digital Twins over Wireless Networks

Digital twins (DTs) constitute a critical link between the real-world and the metaverse. To guarantee a robust connection between these two worlds, DTs should maintain accurate representations of the physical applications, while preserving synchronization between real and digital entities. In this paper, a novel edge continual learning framework is proposed to accurately model the evolving affinity between a physical twin (PT) and its corresponding cyber twin (CT) while maintaining their utmost synchronization. In particular, a CT is simulated as a deep neural network (DNN) at the wireless network edge to model an autonomous vehicle traversing an episodically dynamic environment. As the vehicular PT updates its driving policy in each episode, the CT is required to concurrently adapt its DNN model to the PT, which gives rise to a de-synchronization gap. Considering the history-aware nature of DTs, the model update process is posed a dual objective optimization problem whose goal is to jointly minimize the loss function over all encountered episodes and the corresponding de-synchronization time. As the de-synchronization time continues to increase over sequential episodes, an elastic weight consolidation (EWC) technique that regularizes the DT history is proposed to limit de-synchronization time. Furthermore, to address the plasticity-stability tradeoff accompanying the progressive growth of the EWC regularization terms, a modified EWC method that considers fair execution between the historical episodes of the DTs is adopted. Ultimately, the proposed framework achieves a simultaneously accurate and synchronous CT model that is robust to catastrophic forgetting. Simulation results show that the proposed solution can achieve an accuracy of 90 % while guaranteeing a minimal desynchronization time.

cs.LG↗

arXiv2022

Meta-Reinforcement Learning for Reliable Communication in THz/VLC Wireless VR Networks

In this paper, the problem of enhancing the quality of virtual reality (VR) services is studied for an indoor terahertz (THz)/visible light communication (VLC) wireless network. In the studied model, small base stations (SBSs) transmit high-quality VR images to VR users over THz bands and light-emitting diodes (LEDs) provide accurate indoor positioning services for them using VLC. Here, VR users move in real time and their movement patterns change over time according to their applications, where both THz and VLC links can be blocked by the bodies of VR users. To control the energy consumption of the studied THz/VLC wireless VR network, VLC access points (VAPs) must be selectively turned on so as to ensure accurate and extensive positioning for VR users. Based on the user positions, each SBS must generate corresponding VR images and establish THz links without body blockage to transmit the VR content. The problem is formulated as an optimization problem whose goal is to maximize the reliability of the VR network by selecting the appropriate VAPs to be turned on and controlling the user association with SBSs. To solve this problem, a policy gradient-based reinforcement learning (RL) algorithm that adopts a meta-learning approach is proposed. The proposed meta policy gradient (MPG) algorithm enables the trained policy to quickly adapt to new user movement patterns. In order to solve the problem of maximizing the average number of successfully served users for VR scenarios with a large number of users, a dual method based MPG algorithm (D-MPG) with a low complexity is proposed. Simulation results demonstrate that, compared to the trust region policy optimization algorithm (TRPO), the proposed MPG and D-MPG algorithms yield up to 26.8% and 21.9% improvement in the reliability as well as 81.2% and 87.5% gains in the convergence speed, respectively.

cs.NI↗

arXiv2022

Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networks

Collaborative deep reinforcement learning (CDRL) algorithms in which multiple agents can coordinate over a wireless network is a promising approach to enable future intelligent and autonomous systems that rely on real-time decision-making in complex dynamic environments. Nonetheless, in practical scenarios, CDRL faces many challenges due to the heterogeneity of agents and their learning tasks, different environments, time constraints of the learning, and resource limitations of wireless networks. To address these challenges, in this paper, a novel semantic-aware CDRL method is proposed to enable a group of heterogeneous untrained agents with semantically-linked DRL tasks to collaborate efficiently across a resource-constrained wireless cellular network. To this end, a new heterogeneous federated DRL (HFDRL) algorithm is proposed to select the best subset of semantically relevant DRL agents for collaboration. The proposed approach then jointly optimizes the training loss and wireless bandwidth allocation for the cooperating selected agents in order to train each agent within the time limit of its real-time task. Simulation results show the superior performance of the proposed algorithm compared to state-of-the-art baselines.

cs.IT↗

arXiv2022

Common Language for Goal-Oriented Semantic Communications: A Curriculum Learning Framework

Semantic communications will play a critical role in enabling goal-oriented services over next-generation wireless systems. However, most prior art in this domain is restricted to specific applications (e.g., text or image), and it does not enable goal-oriented communications in which the effectiveness of the transmitted information must be considered along with the semantics so as to execute a certain task. In this paper, a comprehensive semantic communications framework is proposed for enabling goal-oriented task execution. To capture the semantics between a speaker and a listener, a common language is defined using the concept of beliefs to enable the speaker to describe the environment observations to the listener. Then, an optimization problem is posed to choose the minimum set of beliefs that perfectly describes the observation while minimizing the task execution time and transmission cost. A novel top-down framework that combines curriculum learning (CL) and reinforcement learning (RL) is proposed to solve this problem. Simulation results show that the proposed CL method outperforms traditional RL in terms of convergence time, task execution time, and transmission cost during training.

cs.NI↗

arXiv2022

3TO: THz-Enabled Throughput and Trajectory Optimization of UAVs in 6G Networks by Proximal Policy Optimization Deep Reinforcement Learning

Next-generation networks need to meet ubiquitous and high data-rate demand. Therefore, this paper considers the throughput and trajectory optimization of terahertz (THz)-enabled unmanned aerial vehicles (UAVs) in the sixth-generation (6G) communication networks. In the considered scenario, multiple UAVs must provide on-demand terabits per second (TB/s) services to an urban area along with existing terrestrial networks. However, THz-empowered UAVs pose some new constraints, e.g., dynamic THz-channel conditions for ground users (GUs) association and UAV trajectory optimization to fulfill GU's throughput demands. Thus, a framework is proposed to address these challenges, where a joint UAVs-GUs association, transmit power, and the trajectory optimization problem is studied. The formulated problem is mixed-integer non-linear programming (MINLP), which is NP-hard to solve. Consequently, an iterative algorithm is proposed to solve three sub-problems iteratively, i.e., UAVs-GUs association, transmit power, and trajectory optimization. Simulation results demonstrate that the proposed algorithm increased the throughput by up to 10%, 68.9%, and 69.1% respectively compared to baseline algorithms.

cs.NI↗

arXiv2022

Blue Data Computation Maximization in 6G Space-Air-Sea Non-Terrestrial Networks

Non-terrestrial networks (NTN), encompassing space and air platforms, are a key component of the upcoming sixth-generation (6G) cellular network. Meanwhile, maritime network traffic has grown significantly in recent years due to sea transportation used for national defense, research, recreational activities, domestic and international trade. In this paper, the seamless and reliable demand for communication and computation in maritime wireless networks is investigated. Two types of marine user equipment (UEs), i.e., low-antenna gain and high-antenna gain UEs, are considered. A joint task computation and time allocation problem for weighted sum-rate maximization is formulated as mixed-integer linear programming (MILP). The goal is to design an algorithm that enables the network to efficiently provide backhaul resources to an unmanned aerial vehicle (UAV) and offload HUEs tasks to LEO satellite for blue data (i.e., marine user's data). To solve this MILP, a solution based on the Bender and primal decomposition is proposed. The Bender decomposes MILP into the master problem for binary task decision and subproblem for continuous-time resource allocation. Moreover, primal decomposition deals with a coupling constraint in the subproblem. Finally, numerical results demonstrate that the proposed algorithm provides the maritime UEs coverage demand in polynomial time computational complexity and achieves a near-optimal solution.

cs.NI↗

arXiv2022

Digital Twin of Wireless Systems: Overview, Taxonomy, Challenges, and Opportunities

Future wireless services must be focused on improving the quality of life by enabling various applications, such as extended reality, brain-computer interaction, and healthcare. These applications have diverse performance requirements (e.g., user-defined quality of experience metrics, latency, and reliability) that are challenging to be fulfilled by existing wireless systems. To meet the diverse requirements of the emerging applications, the concept of a digital twin has been recently proposed. A digital twin uses a virtual representation along with security-related technologies (e.g., blockchain), communication technologies (e.g., 6G), computing technologies (e.g., edge computing), and machine learning, so as to enable the smart applications. In this tutorial, we present a comprehensive overview on digital twins for wireless systems. First, we present an overview of fundamental concepts (i.e., design aspects, high-level architecture, and frameworks) of digital twin of wireless systems. Second, a comprehensive taxonomy is devised for both different aspects. These aspects are twins for wireless and wireless for twins. For the twins for wireless aspect, we consider parameters, such as twin objects design, prototyping, deployment trends, physical devices design, interface design, incentive mechanism, twins isolation, and decoupling. On the other hand, for wireless for twins, parameters such as, twin objects access aspects, security and privacy, and air interface design are considered. Finally, open research challenges and opportunities are presented along with causes and possible solutions.

cs.NI↗

arXiv2022

A Joint Learning and Communications Framework for Federated Learning over Wireless Networks

In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that will generate a global FL model and send it back to the users. Since all training parameters are transmitted over wireless links, the quality of the training will be affected by wireless factors such as packet errors and the availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS must select an appropriate subset of users to execute the FL algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm. To address this problem, a closed-form expression for the expected convergence rate of the FL algorithm is first derived to quantify the impact of wireless factors on FL. Then, based on the expected convergence rate of the FL algorithm, the optimal transmit power for each user is derived, under a given user selection and uplink resource block (RB) allocation scheme. Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function. Simulation results show that the proposed joint federated learning and communication framework can reduce the FL loss function value by up to 10% and 16%, respectively, compared to: 1) An optimal user selection algorithm with random resource allocation and 2) a standard FL algorithm with random user selection and resource allocation.

cs.NI↗

arXiv2022

Seamless and Energy Efficient Maritime Coverage in Coordinated 6G Space-Air-Sea Non-Terrestrial Networks

Non-terrestrial networks (NTNs), which integrate space and aerial networks with terrestrial systems, are a key area in the emerging sixth-generation (6G) wireless networks. As part of 6G, NTNs must provide pervasive connectivity to a wide range of devices, including smartphones, vehicles, sensors, robots, and maritime users. However, due to the high mobility and deployment of NTNs, managing the space-air-sea (SAS) NTN resources, i.e., energy, power, and channel allocation, is a major challenge. The design of a SAS-NTN for energy-efficient resource allocation is investigated in this study. The goal is to maximize system energy efficiency (EE) by collaboratively optimizing user equipment (UE) association, power control, and unmanned aerial vehicle (UAV) deployment. Given the limited payloads of UAVs, this work focuses on minimizing the total energy cost of UAVs (trajectory and transmission) while meeting EE requirements. A mixed-integer nonlinear programming problem is proposed, followed by the development of an algorithm to decompose, and solve each problem distributedly. The binary (UE association) and continuous (power, deployment) variables are separated using the Bender decomposition (BD), and then the Dinkelbach algorithm (DA) is used to convert fractional programming into an equivalent solvable form in the subproblem. A standard optimization solver is utilized to deal with the complexity of the master problem for binary variables. The alternating direction method of multipliers (ADMM) algorithm is used to solve the subproblem for the continuous variables. Our proposed algorithm provides a suboptimal solution, and simulation results demonstrate that the proposed algorithm achieves better EE than baselines.

cs.NI↗

arXiv2022

Learning from Images: Proactive Caching with Parallel Convolutional Neural Networks

With the continuous trend of data explosion, delivering packets from data servers to end users causes increased stress on both the fronthaul and backhaul traffic of mobile networks. To mitigate this problem, caching popular content closer to the end-users has emerged as an effective method for reducing network congestion and improving user experience. To find the optimal locations for content caching, many conventional approaches construct various mixed integer linear programming (MILP) models. However, such methods may fail to support online decision making due to the inherent curse of dimensionality. In this paper, a novel framework for proactive caching is proposed. This framework merges model-based optimization with data-driven techniques by transforming an optimization problem into a grayscale image. For parallel training and simple design purposes, the proposed MILP model is first decomposed into a number of sub-problems and, then, convolutional neural networks (CNNs) are trained to predict content caching locations of these sub-problems. Furthermore, since the MILP model decomposition neglects the internal effects among sub-problems, the CNNs' outputs have the risk to be infeasible solutions. Therefore, two algorithms are provided: the first uses predictions from CNNs as an extra constraint to reduce the number of decision variables; the second employs CNNs' outputs to accelerate local search. Numerical results show that the proposed scheme can reduce 71.6% computation time with only 0.8% additional performance cost compared to the MILP solution, which provides high quality decision making in real-time.

cs.NI↗

arXiv2021

Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM)

To meet the growing mobility needs in intra-city transportation, the concept of urban air mobility (UAM) has been proposed in which vertical takeoff and landing (VTOL) aircraft are used to provide a ride-hailing service. In UAM, aircraft can operate in designated air spaces known as corridors, that link the aerodromes. A reliable communication network between GBSs and aircraft enables UAM to adequately utilize the airspace and create a fast, efficient, and safe transportation system. In this paper, to characterize the wireless connectivity performance for UAM, a spatial model is proposed. For this setup, the distribution of the distance between an arbitrarily selected GBS and its associated aircraft and the Laplace transform of the interference experienced by the GBS are derived. Using these results, the signal-to-interference ratio (SIR)-based connectivity probability is determined to capture the connectivity performance of the UAM aircraft-to-ground communication network. Then, leveraging these connectivity results, a wireless-enabled asynchronous federated learning (AFL) framework that uses a Fourier neural network is proposed to tackle the challenging problem of turbulence prediction during UAM operations. For this AFL scheme, a staleness-aware global aggregation scheme is introduced to expedite the convergence to the optimal turbulence prediction model used by UAM aircraft. Simulation results validate the theoretical derivations for the UAM wireless connectivity. The results also demonstrate that the proposed AFL framework converges to the optimal turbulence prediction model faster than the synchronous federated learning baselines and a staleness-free AFL approach. Furthermore, the results characterize the performance of wireless connectivity and convergence of the aircraft's turbulence model under different parameter settings, offering useful UAM design guidelines.

eess.SP↗

arXiv2021

Ensuring Reliable Connectivity to Cellular-Connected UAVs with Up-tilted Antennas and Interference Coordination

To integrate unmanned aerial vehicles (UAVs) in future large-scale deployments, a new wireless communication paradigm, namely, the cellular-connected UAV has recently attracted interest. However, the line-of-sight dominant air-to-ground channels along with the antenna pattern of the cellular ground base stations (GBSs) introduce critical interference issues in cellular-connected UAV communications. In particular, the complex antenna pattern and the ground reflection (GR) from the down-tilted antennas create both coverage holes and patchy coverage for the UAVs in the sky, which leads to unreliable connectivity from the underlying cellular network. To overcome these challenges, in this paper, we propose a new cellular architecture that employs an extra set of co-channel antennas oriented towards the sky to support UAVs on top of the existing down-tilted antennas for ground user equipment (GUE). To model the GR stemming from the down-tilted antennas, we propose a path-loss model, which takes both antenna radiation pattern and configuration into account. Next, we formulate an optimization problem to maximize the minimum signal-to-interference ratio (SIR) of the UAVs by tuning the up-tilt (UT) angles of the up-tilted antennas. Since this is an NP-hard problem, we propose a genetic algorithm (GA) based heuristic method to optimize the UT angles of these antennas. After obtaining the optimal UT angles, we integrate the 3GPP Release-10 specified enhanced inter-cell interference coordination (eICIC) to reduce the interference stemming from the down-tilted antennas. Our simulation results based on the hexagonal cell layout show that the proposed interference mitigation method can ensure higher minimum SIRs for the UAVs over baseline methods while creating minimal impact on the SIR of GUEs.

eess.SP↗

arXiv2021

Jamming Pattern Recognition over Multi-Channel Networks: A Deep Learning Approach

With the advent of intelligent jammers, jamming attacks have become a more severe threat to the performance of wireless systems. An intelligent jammer is able to change its policy to minimize the probability of being traced by legitimate nodes. Thus, an anti-jamming mechanism capable of constantly adjusting to the jamming policy is required to combat such a jammer. Remarkably, existing anti-jamming methods are not applicable here because they mainly focus on mitigating jamming attacks with an invariant jamming policy, and they rarely consider an intelligent jammer as an adversary. Therefore, in this paper, to employ a jamming type recognition technique working alongside an anti-jamming technique is proposed. The proposed recognition method employs a recurrent neural network that takes the jammer's occupied channels as inputs and outputs the jammer type. Under this scheme, the real-time jammer policy is first identified, and, then, the most appropriate countermeasure is chosen. Consequently, any changes to the jammer policy can be instantly detected with the proposed recognition technique allowing for a rapid switch to a new anti-jamming method fitted to the new jamming policy. To evaluate the performance of the proposed recognition method, the accuracy of the detection is derived as a function of the jammer policy switching time. Simulation results show the detection accuracy for all the considered users numbers is greater than 70% when the jammer switches its policy every 5 time slots and the accuracy raises to 90% when the jammer policy switching time is 45.

cs.CR↗

arXiv2021

Joint Sensing and Communication for Situational Awareness in Wireless THz Systems

Next-generation wireless systems are rapidly evolving from communication-only systems to multi-modal systems with integrated sensing and communications. In this paper a novel joint sensing and communication framework is proposed for enabling wireless extended reality (XR) at terahertz (THz) bands. To gather rich sensing information and a higher line-of-sight (LoS) availability, THz-operated reconfigurable intelligent surfaces (RISs) acting as base stations are deployed. The sensing parameters are extracted by leveraging THz's quasi-opticality and opportunistically utilizing uplink communication waveforms. This enables the use of the same waveform, spectrum, and hardware for both sensing and communication purposes. The environmental sensing parameters are then derived by exploiting the sparsity of THz channels via tensor decomposition. Hence, a high-resolution indoor mapping is derived so as to characterize the spatial availability of communications and the mobility of users. Simulation results show that in the proposed framework, the resolution and data rate of the overall system are positively correlated, thus allowing a joint optimization between these metrics with no tradeoffs. Results also show that the proposed framework improves the system reliability in static and mobile systems. In particular, the highest reliability gains of 10% in reliability are achieved in a walking speed mobile environment compared to communication only systems with beam tracking.

cs.IT↗