Unmanned Aerial Vehicles (UAVs) have recently emerged as a key component for various 5G applications, offering either precise positioning or high-speed, low-latency data communication services. However, delivering both wireless positioning and data communication at the same time using UAVs remains challenging due to the different requirements of each service. While data communication demands high throughput, positioning services necessitate the establishment of multiple connections simultaneously, but achieving high throughput for all the simultaneous connections remains challenging. Pattern Division Multiple Access (PDMA), which flexibly shares Resource Elements (REs) among multiple users, presents a promising solution to this issue. In this study, we investigate the joint provisioning of communication and positioning services in a UAV airborne network using PDMA. We propose a comprehensive approach that addresses the joint problem of user-to-UAV association, RE allocation, and transmission power control. Our goal is to enhance the precision of positioning services while satisfying communication constraints. To tackle this problem, we develop both an exact solution and a Multi-Agent Deep Reinforcement Learning (MADRL) algorithm, tailored for this highly complex non-convex optimization problem. Extensive simulations demonstrate that our attentional MADDPG algorithm achieves higher positioning accuracy compared to state-of-the-art solutions and can efficiently address interferences, thereby improving both services.

Today, convolutional neural network (CNN) pruning techniques often rely on manually crafted importance criteria and pruning structures. Due to their heuristic nature, these methods may lack generality, and their performance is not guaranteed. In this paper, we propose a theoretical framework to address this challenge by leveraging the concept of $\gamma$γ-weak submodularity, based on a new efficient importance function. By deriving an upper bound on the absolute error in the layer subsequent to the pruned layer, we formulate the importance function as a $\gamma$γ-weakly submodular function. This formulation enables the development of an easy-to-implement, low-complexity, and data-free oblivious algorithm for selecting filters to be removed from a convolutional layer. Extensive experiments show that our method outperforms state-of-the-art benchmark networks across various datasets, with a computational cost comparable to the simplest pruning techniques, such as $l_{2}$l2-norm pruning. Notably, the proposed method achieves an accuracy of 76.52%, compared to 75.15% for the overall best baseline, with a 25.5% reduction in network parameters. According to our proposed resource-efficiency metric for pruning methods, the ACLI approach demonstrates orders-of-magnitude higher efficiency than the other baselines, while maintaining competitive accuracy.

Recently, companies have focused on developing new technologies for air mobility using flying cars to alleviate road congestion in urban areas. A critical aspect to consider is the seamless integration of flying cars with their ground-based counterparts in the 5G network, where ground-based cars can provide transit functions, including access to vertiports and urban amenities. Additionally, flying and ground-based cars require various services with different requirements, such as path planning, remote diagnosis, and autonomous driving/piloting. Supporting these services in 5G networks is challenging due to the high mobility and stringent network latency requirements. Network slicing can be a promising solution to meet these requirements. However, the literature lacks comprehensive research on combining flying and ground-based cars in network slicing, where resource under-provisioning can cause the violation of service requirements. We propose three-level closed-loops for sliced resource block management to satisfy the delay budget constraint of flying and ground-based cars while avoiding resource under-provisioning. We present a reward function and continual learning that links these closed-loops. Furthermore, we use Ape-X as distributed deep reinforcement learning to maximize reward and continual learning to improve resource allocation via prediction. The simulation results demonstrate that the proposed approach maximizes delay requirement satisfaction.

Mobile edge computing (MEC) has emerged as a promising technology to extend the functionality of end-users' wireless devices while prolonging their battery life by offloading computationally intensive tasks to remote edge servers. However, the inherent broadcast nature of wireless transmission during offloading introduces notable security challenges. To address this issue, we propose leveraging intelligent reflecting surface (IRS) technology to enhance physical layer security (PLS). Nevertheless, attaining high PLS for all users in dense networks with multiple malicious terminals is challenging. In this paper, we investigate the physical layer encryption (PLE) to complement the PLS in enabling secure wireless transmission. Since such encryption and decryption processes require computation resources, we aim to optimize the encryption decision, offloading decision, as well as wireless and computing resource allocations. Our objective is to minimize the maximum weighted energy consumption while satisfying practical constraints, including limited computing and wireless resources, fulfilling minimum user rate requirements, and complying with IRS conditions. To tackle the non-convex objective and constraints, we explore the utilization of bisection search and successive convex approximation (SCA) methods. Our numerical results confirm the efficiency of the proposed design in terms of energy consumption and network capacity within a secure MEC network.

Convolutional neural network (CNN) pruning has traditionally relied on heuristically designed importance criteria, often leading to limited generalizability and inconsistent performance. In this article, we propose a novel framework centered around filter replacement (FR), introducing pruning as a process of replacing selected filters with zero filters. Through a rigorous analysis, we derive an upper bound on the absolute error in the output of the subsequent layer and use this bound to define an efficient importance function. This importance function exhibits $\gamma $ -weakly submodular properties, enabling the development of a simple, low-complexity, and data-free oblivious algorithm for selecting filters to prune. In addition, we extend the FR framework to include nonzero filter alternatives, leveraging a best-approximation technique to construct optimal replacements for the pruned filters. Extensive experiments on benchmark networks and datasets validate the effectiveness of our method. The proposed approach achieves state-of-the-art results, with a complexity comparable to basic techniques such as $l_{2}$ -norm pruning. Notably, our pruning method achieves 76.52% accuracy (ACC) in ResNet-50 on the ImageNet dataset, surpassing the baseline of 75.15%, while reducing network parameters by 25.5%. Our proposed resource efficiency (RE) metric assesses that the layer interdependence-aware pruning (LIAP) method is up to $10^{11}$ times more efficient than existing techniques, setting a new standard for resource-aware CNN pruning.

Over-The-Air Programming (OTA) is an emerging framework for updating IoT devices deployed at scale and in remote areas, mitigating the need for manual intervention post-deployment. Prior research focused mainly on optimizing the update order of software components in memory to achieve energy efficiency. So far, the literature has not yet considered the major amount of energy consumed by data transmission during the update process. In this paper, we propose using multicast, multi-radio, and multi-channel communications for supporting OTA updates in component-based IoT networks, in addition to an optimized memory replacement schedule for each IoT device. We formulate an optimization model named joint multicast multi-radio multi-channel software update (M3U), considering both the data dissemination process and software replacement in memory. This model minimizes the overall energy consumption across all devices during updates. Since M3U is a combinatorial optimization problem, which is NP-hard, we propose a linearization, a relaxation, and a heuristic algorithm to find a near-optimal solution with a much lower computational time. Our experimental results demonstrate that the proposed solution outperforms prior works and approximates the optimal solution (i.e., with only 2% difference).

Despite its flexible and interoperable design with open standards and virtualization, the Open Radio Access Network (O-RAN) architecture still lacks support for advanced 5G services in the multi-cloud environment to enable efficient and customizable cellular network functions. Enabling multicasting supports in O-RAN, such as 3GPP’s 5G Multicast Broadcast Service capabilities, will unlock and improve a multitude of new O-RAN use cases. Unfortunately, such integration has not yet been clearly defined in O-RAN specifications or fully investigated in prior studies. In this paper, we jointly formulate the Multicast Service Function Chain Embedding (MSE) and O-RAN functional split optimization problems in a multi-cloud environment as an Integer Linear Programming (ILP) model. We propose a heuristic algorithm with polynomial-time complexity based on feasible search optimization and dynamic programming techniques to solve this high-complexity problem. Our experimental results demonstrate that the proposed algorithm outperforms state-of-the-art baselines, approximating the optimal solution while significantly reducing computational time.

Designing a robust, resilient, and cost-efficient Fronthaul is essential to meet the ultra-reliability and high-bandwidth demands of 5G and beyond radio access networks (RANs). State-of-the-art probabilistic backup design approaches that rely on the likelihood of link failures rather than assuming worst-case scenarios to avoid over-provisioning are realistic and cost-effective. However, tackling this design problem is challenging due to its inherent stochasticity. Traditional solutions, such as robust optimization, tend to overestimate backup requirements, leading to inflated costs. In addition, existing transport technologies deployed in mobile network operators (MNOs) Fronthaul are not flexible and scalable to new 5G requirements. To address this, we propose a novel stochastic Fronthaul backup design model that leverages the combined advantages of FlexEthernet (FlexE) and elastic optical networks (EONs) to reduce bandwidth usage, backup capacity, and overall costs. By applying Chernoff bounds, we reformulate the stochastic model into a non-convex optimization problem and develop CBFH, a successive convex approximation algorithm tailored for single-MNO scenarios. For multi-MNO environments, we introduce CBFHA, an approximation algorithm based on the facility location problem. Experimental results demonstrate that our approach reduces backup costs by at least 48.92% compared to existing state-of-the-art methods.

The integration of unmanned aerial vehicles (UAVs) and satellite technologies into modern border surveillance offers significant potential, eliminating the deployment limitations of terrestrial networks in remote and dynamic environments. These technologies ensure comprehensive coverage, and provide continuous connectivity in all areas with dynamic deployment. However, the integration presents optimization challenges in terms of resource management, communication, and energy efficiency. In this work, we propose a novel hierarchical optimization framework to address these challenges, modeling a terrestrial-non-terrestrial border surveillance (TNTBS) system in which telecom functions and surveillance services (e.g., synthetic aperture radar) are containerized and dynamically deployed into UAVs and satellites. We deal with battery, backhaul, coverage and surveillance service constraints to optimize the operational cost. To overcome the computational intractability of the large-scale problem of TNTBS, which is modeled as a mixed-integer linear programming (MILP) problem, we introduce a Stackelberg game-based approach that enables scalable and distributed decision making with multiple agents and each agent can leverage a local solver or learning model to optimize its local objective. Our extensive simulations demonstrate that our solution achieves near-optimal performance, while ensuring real-time operation and computational efficiency.

Modern cloud applications frequently reside in overlay networks and exhibit one-to-many communication patterns. The Ingress Replication (IR) approach can support overlay multicast traffic by replicating packets at the ingress node and sending a unicast copy of the packet to each interested receiver. However, IR consumes more bandwidth as multiple copies of the same packet are forwarded through the network. The tenant-routed multicast architecture addresses this drawback by leveraging IP multicast in the underlay network to transport overlay multicast application traffic. The overlay multicast traffic is encapsulated with an underlay multicast group address and then transported across the network without duplication. However, the limited number of underlay multicast groups makes it challenging to efficiently support a massive number of multicast applications, which is typical in cloud environments. In this paper, we address the problem of mapping overlay applications to underlay multicast groups and selecting Rendezvous Points (RPs) to minimize network utilization. We provide a mathematical formulation for this joint problem and design a local search-based algorithm to solve it. Extensive experimental results demonstrate that our solution can approximate the exact solution and significantly reduce network utilization compared to conventional thresholdbased methods.

Today, Warfighter Information Network-Tactical (WIN-T) systems are facing significant challenges in providing communication services with both security and quality of service (QoS). State-of-the-art security solutions for WIN-T mainly focus on a single anti-interception technique, sacrificing QoS to prevent energy-based eavesdroppers. We propose a double-layered anti-interception strategy for WIN-T, combining both passive and active defense mechanisms to address this issue. We formulate a non-convex optimization problem that jointly optimizes jamming allocation (JA), power allocation (PA), and spreading factor assignment (SA). We approximate this problem using first-order Taylor approximation and then solve it using an iterative method combined with the difference of convex (DC) functions. To further reduce the computational complexity in the high-mobility environment of ground combat vehicles, we reformulate the problem using a Multi-Agent Deep Reinforcement Learning (MADRL) framework. Simulation results demonstrate that our MADRL approach approximates the optimal solution in near real-time. It also outperforms the state-of-the-art baselines in terms of both Low Probability Interception (LPI) and throughput, promising an efficient and adaptive solution for modern WIN-T systems in real-time.

This paper addresses energy-efficient processing in the Open Radio Access Network (O-RAN) within the O-RAN Cloud (O-Cloud) infrastructure, focusing on optimal processor power scaling while scheduling packets under delay constraints. Traditionally, real-time packet scheduling tends to be suboptimal, as it prioritizes processing capacity over energy efficiency. To address this issue, we formulate a Mixed Integer Programming (MIP) model and develop a heuristic approach, along with a Convolutional Neural Network(CNN) for real-time scheduling. To enhance the CNN model, we introduce the CNN Binary Search Scheduling (CNN-BSS) algorithm, guided by the heuristic solution. Simulations show a 29.17 % reduction in energy consumption, significantly outperforming conventional methods.

This paper introduces a novel approach to AI-powered digital-twins-assisted robotic control in automated warehouses, integrating the kinetic models of robots with real-time synchronization of digital-twins. The proposed framework utilizes Ultra-Reliable Low-Latency Communication (URLLC) over 5G networks to enable seamless interaction between the physical robots and AI-driven models in the cyber twin. We formulate an optimization problem aimed at minimizing energy consumption during digital-twins-driven robotic operations, thereby enhancing both operational efficiency and energy efficiency. A Deep Reinforcement Learning (DRL)-based approach is developed for the adaptive learning of the AI models in the cyber twin, facilitating autonomous simulation and real-time decision-making for efficient robotic control. Additionally, we propose a game-theory-based resource allocation strategy to optimize the distribution of computational resources for continuous and adaptive learning within AI models. Numerical results demonstrate that the proposed game-based resource allocation scheme achieves Nash equilibrium, significantly improving performance in terms of energy consumption and resource utilization compared to the state-of-the-art DRL-based resource allocation scheme.

Internet access in rural areas should be improved to support digital inclusion and 5G services. Due to the high deployment costs of fiber optics in these areas, Fixed Wireless Access (FWA) has become a preferable alternative. Additionally, the Open Radio Access Network (O-RAN) can facilitate the interoperability of FWA elements, allowing some FWA functions to be deployed at the edge cloud. However, deploying edge clouds in rural areas can increase network and energy costs. To address these challenges, we propose a closed-loop system assisted by a Digital Twin (DT) to automate energy-aware O-RAN based FWA resource management in rural areas. We consider the FWA and edge cloud as the Physical Twin (PT) and design a closed-loop that distributes radio resources to edge cloud instances for scheduling. We develop another closed-loop for intra-slice resource allocation to houses. We design an energy model that integrates radio resource allocation and formulate ultra-small and small-timescale optimizations for the PT to maximize slice requirement satisfaction while minimizing energy costs. We then design a reinforcement learning approach and successive convex approximation to address the formulated problems. We present a DT that replicates the PT by incorporating solution experiences into future states. The results show that our approach efficiently uses radio and energy resources.

In today's machine learning (ML), the need for vast amounts of training data has become a significant challenge. Transfer learning (TL) offers a promising solution by leveraging knowledge across different domains/tasks, effectively addressing data scarcity. However, TL encounters computational and communication challenges in resource-constrained scenarios, and negative transfer (NT) can arise from specific data distributions. This article presents a novel focus on maximizing the accuracy of instance-based TL in multisource resource-constrained environments while mitigating NT, a key concern in TL. Previous studies have overlooked the impact of resource consumption in addressing the NT problem. To address these challenges, we introduce an optimization model named multisource resource-constrained optimized TL (MSOPTL), which employs a convex combination of empirical sources and target errors while considering feasibility and resource constraints. Moreover, we enhance one of the generalization error upper bounds in domain adaptation setting by demonstrating the potential to substitute the divergence with the Kullback-Leibler (KL) divergence. We utilize this enhanced error upper bound as one of the feasibility constraints of MSOPTL. Our suggested model can be applied as a versatile framework for various ML methods. Our approach is extensively validated in a neural network (NN)-based classification problem, demonstrating the efficiency of MSOPTL in achieving the desired trade-offs between TL's benefits and associated costs. This advancement holds tremendous potential for enhancing edge artificial intelligence (AI) applications in resource-constrained environments.

Without network slicing supports, traditional Fronthaul architectures struggle to meet the demanding requirements of 5G networks, such as the ultra-low latency and high bit rate specified by the enhanced common public radio interface (eCPRI). In this paper, we design a novel Fronthaul architecture that leverages FlexEthernet (FlexE) over elastic optical network (EON) to enable Fronthaul slicing meeting 5G Fronthaul requirements. Our Fronthaul design is optimized by an integer linear programming (ILP) model, named eFFP, that minimizes the total cost of ownership (TCO). While eFFP meets the strict Fronthaul requirements by provisioning network resources based on worst-case traffic load, it tends to overestimate required bit rate as a result of the inherent uncertainty and variability in real-world traffic. To tackle this challenge, we introduce uFFP, a stochastic Fronthaul provisioning strategy tailored to accommodate uncertain traffic demands and mitigate expenditure wastage. Relying on historical data, uFFP assesses statistical characteristics of traffic patterns to better estimate Fronthaul bit rate. Subsequently, we employ chance-constrained optimization to reformulate the uFFP problem, which is approximately solved using a convex relaxation approach known as uFFPA, and optimally solved using a deep reinforcement learning (DRL) approach called uFFPL. Simulation results demonstrate that our proposed solutions achieve significant cost savings, reducing TCO by 39.79% compared to the baseline.

Today’s machine learning applications in the healthcare sector often require a vast amount of training data and lengthy training, which are not always available. Transfer learning (TL) offers a promising solution by leveraging knowledge across different domains/tasks, effectively addressing data scarcity. However, TL encounters computational and communication challenges in resource-constrained scenarios, and negative transfer (NT) can arise, posing significant challenges for sensitive tasks such as stroke prediction. This paper presents a novel method to maximize the accuracy of TL in resource-constrained environments while mitigating NT, a key concern in TL. Previous studies have overlooked simultaneously the resource consumption and the NT problem in the network-based TL era. To address this shortcoming, we formulate an optimization model named RCTL, which employs the empirical fine-tuning error of the target task as the objective while considering NT overcoming condition and resource limitations as the feasibility constraints. Our approach is extensively validated in a stroke prediction scenario, demonstrating the efficiency of RCTL in achieving the desired trade-offs between TL’s benefits and associated costs. The obtained results show the potential of enhancing edge AI applications in resource-constrained healthcare infrastructure.

Open Radio Access Network (O-RAN) aims to establish an open and intelligent RAN architecture, enhancing flexibility, scalability, and network optimization. Machine learning (ML) technologies are pivotal in realizing these objectives by facilitating intelligent decision-making, automated optimization, and proactive maintenance. However, effectively selecting and deploying ML models within O-RAN to achieve energy efficiency poses significant challenges. In this paper, we propose a novel orchestration scheme tailored for next-generation systems, building upon and extending the foundational principles of the O-RAN paradigm. Our proposed orchestration policy offers a practical solution for deploying ML applications within the ORAN framework. Through comprehensive evaluation, our scheme demonstrates a remarkable reduction of up to 72.22% in energy consumption compared to the maximum performance baseline, while maintaining an accuracy level of approximately 94.56% relative to the same baseline.

In this paper, we propose an anti-interception scheme to enhance the defence performance of frequency hopping (FH) tactical systems against the in-band full-duplex (IBFD) interception from the enemy. Our scheme utilizes an unmanned aerial vehicle (UAV)-based Intelligent Reflecting Surface (IRS) to share the interception burden with a FH system, thus mitigating jamming effects. To efficiently address IBFD interception, we jointly optimize the control of base station transmit power, FH hopping decision, and IRS phase shift adjustment. This joint optimization is mathematically formulated as a Mixed Integer Programming (MIP) non-convex optimization problem. To address the intractability of traditional optimization methods in solving this problem in modern tactical scenarios, we design a solution based on deep reinforcement learning (DRL). Extensive numerical results show that our proposed scheme significantly enhances the FH anti-interception capability and improves the QoS. Furthermore, the performance of our DRL solution is close to optimal and it is feasible to be deployed in modern practical scenarios.

Hierarchical Federated Learning (HFL) presents a promising approach for addressing communication challenges inherent in traditional FL methodologies, particularly critical in resource-constrained environments such as Smart Grids (SG). In this paper, we delve into the potential of HFL tailored to the clustered structure of typical SG use-cases such as load forecasting, particularly in scenarios characterized by high communication time and energy constraints. We propose a novel optimization model that jointly tackles topology formation and resource allocation in HFL. Unlike prior works, our model handles an unknown number of layers, allowing for flexible adaptation to different grid configurations. We solve this non-convex optimization model by an approximation method that separates clustering from resource allocation. Clustering is addressed using complete-linkage clustering and Voronoi diagrams, while resource allocation is tackled through approximations and convexification of the problem. Furthermore, we introduce a Deep Q-Network (DQN) based reinforcement learning approach that addresses the joint optimization problem in near real-time. Simulation results demonstrate that the hierarchical architecture with efficient resource allocation significantly reduces energy consumption in smart grid sensor nodes compared to traditional FL, while meeting response time requirements. In addition, our DQN solution achieves near-optimal performance, making it particularly appropriate for large-scale grids.

Time series prediction plays a crucial role in optimizing the operation of communication networks. Applications of time series prediction include traffic prediction, channel state prediction, handover prediction, etc. However, training high-quality models for these tasks requires large volumes of historical data. This requirement may not be available in some scenarios. In this case, instance-based Transfer Learning (TL) comes as a prominent solution for this problem. However, a few concerns could be raised such as: 1) the time and bandwidth resources consumed in the transfer, 2) it will be hard to specify the amount of data to be transferred, and 3) in case of transferring a subset of the data, which subset is better to transfer. To address these challenges, we propose a novel approach for TL, which is similar to, but different than, instance-based TL based on generative models. We coined the new approach as Surrogate Data Source Transfer (SDST), in which a generative model is trained on the source task. We then transfer the model to the target task (with limited historical data). Extensive experiments confirm the superior performance of the proposed approach in terms of prediction accuracy and consumed resources (time and bandwidth). Our TL approach reduced the mean absolute percentage error (MAPE) by a margin that hits 81% in some datasets. For the source code and data, we refer to the repository https://github.com/MoeR3za/Korsahy_TGAN.

The frequency hopping (FH) technique has always been crucial for anti-jamming tactical applications thanks to its advantages in avoiding the jammer's interception. However, modern tactical scenarios require FH systems to not only undertake defence missions but also meet increasingly high Quality of service (QoS) requirements. Unlike the prior works that mainly optimize FH systems by balancing anti-jamming capability and QoS performance, we propose a collaboration of FH and the intelligent reflecting surface (IRS) in an advanced anti-jamming scheme. Such approach shares the burden with the IRS and improves QoS. We formulate a joint IRS-aided FH anti-jamming problem as a Mixed Integer Programming (MIP) non-convex optimization. To address the intractability of traditional optimization methods in solving this problem in modern tactical scenarios, we design a solution based on deep reinforcement learning (DRL). The numerical results show that the performance of our solution is close to optimal, and it is scalable to be applicable in practical situations.

Deep Reinforcement Learning (DRL) solutions to 5G problems often face with communication unreliability issues due to imbalanced state-space distributions and the scarcity of rare samples. Generative Adversarial Network (GAN) is promising to improve DRL reliability. However, employing GANs in resource-constrained edge environments is very challenging due to their heavy resource consumption. Previous general resource allocation models for training neural networks do not consider GAN quality requirements such as the minimum number of training samples. We propose an architecture for sharing edge and cloud resources among multiple GANs, then formulate an optimization model, named OGAN, to maximize DRL reliability with respect to resource constraints for training GANs and fine-tuning DRLs. OGAN allocates resources for training several GANs and DRLs concurrently based on an upper bound error. Difference convex programming is then used to solve this mixed-integer non-linear model. Our experimental results show that OGAN improves the overall system reliability and performance by 23 % and 22 %, respectively, compared to baselines.

Recently, companies have increasingly developed new technologies for urban air mobility using flying cars to alleviate road congestion. Unfortunately, the seamless integration of flying cars with their ground-based counterparts in the 5G network, where ground-based cars can support flying cars in proving transit functions, has not yet been fully investigated. Flying and ground-based cars require various services, such as autonomous driving/plot, path planning, and remote di-agnosis. Supporting these services in 5G networks is challenging due to the high mobility and stringent network latency requirements. Network slicing is a promising solution. However, a comprehensive research on combining flying and ground-based cars in network slicing is still missing in the literature. Under-provisioning of radio resources can result in the violation of service requirements, while radio resource over-provisioning can cause resource under-utilization. We propose two-level closed-loops for Resource Block (RB) management to satisfy the delay budget constraint of flying and ground-based cars simultaneously while avoiding radio resource under/over provisioning. We design two closed-loops to map slices and services to Open RAN elements for radio resource scheduling and to allocate RB to cars. We propose a zero-touch RB adjustment approach and link these two closed-loops through the reward function of a deep reinforcement learning algorithm that optimizes slice resources in real time. Results show that our approach maximizes delay requirement satisfaction while preventing RB under/over-provisioning.

Using UAVs (Unmanned Aerial Vehicles) to provide either precise positioning or high speed, low latency data communication service has recently emerged for a new class of 5G applications. However, the joint optimization of data communication and wireless positioning in a UAV airborne network is challenging because each service has different requirements. Communication services demand high throughput, while positioning services require the establishment of multiple connections simultaneously. Pattern Division Multiple Access (PDMA) is an innovative communication technique based on Non-orthogonal Multiple Access (NOMA), which facilitates the sharing of REs (Resource Elements) among multiple users. In this paper, we formulate the joint problem of user-to-UAV association, RE allocation, and transmission power control, aimed to improve the precision of positioning service while meeting the communication constraints. We propose both exact and a Multi-Agent Deep Reinforcement Learning (MADRL) algorithm to solve this non-convex optimization. In addition, to address the problem of interference among users, we propose an attentional Multi-agent Deep Deterministic Policy Gradient (MADDPG) approach. Extensive simulations demonstrated that our proposed algorithm can achieve higher positioning accuracy than state-of-the-art solutions while serving more users.

Traditional FlexEthernet (FlexE) defragmentation schemes have successfully been employed to reallocate the slots of affected FlexE clients during network changes such as FlexE physical link (PHY) failures in multi-layer multidomain (MLMD) networks in the context of fixed traffic rate. In such a context, constant slots of FlexE clients are statically pre-assigned using a round-robin algorithm before the network change takes place. However, in more realistic scenarios where traffic varies over time, this static assignment requires multiple defragmentation steps, potentially violating the maximum tolerated reconfiguration time and resulting in traffic loss. Therefore, a dynamic defragmentation scheme is required to efficiently move the affected slots without disrupting unaffected traffic. This paper introduces FDL, a semi-supervised learning approach designed to efficiently address the FlexE defragmentation problem under time-varying traffic conditions. FDL leverages an autoencoder for unsupervised pre-training, particularly due to the considerable amount of unlabeled data resulting from the unsolvable high-complexity optimization problem. To optimize throughput while adhering to reconfiguration time deadlines, FDL employs a gated recurrent unit (GRU) structure to forecast the future reassignment of FlexE clients’ slots over the defragmentation steps ahead. Simulation results demonstrate that the proposed FDL achieves a throughput that is 17.18% higher than a state-of-the-art approach.

Autonomous robotics play a central role in smart logistics where robots can replace or aid humans in all kinds of tasks, such as items picking, moving, and storing. In this paper, we investigate the problem of task scheduling in automated warehouses with heterogeneous autonomous robotic (HAR) systems. We formulate a long-term non-convex queueing control optimization problem to minimize the queue length of tasks to be processed in the warehouse. Traditional task scheduling solutions based on optimization approaches are inefficient in handling the stochastic nature of the goods/tasks flow and a large number of robots in the system due to their computational cost. We propose a deep reinforcement learning (DRL) based task scheduling algorithm that employs the proximal policy optimization (PPO) method to find an optimal task scheduling policy. Due to the heterogeneity of the system, we propose a proximal weighted federated learning-based algorithm for implementing a decentralized PPO algorithm that improves the performance of the distributed PPO agents that are deployed in the workstations at the geographically distributed warehouses. The simulation results demonstrate the performance improvement of our proposed algorithm compared to the existing methods. Note to Practitioners—Task scheduling for robotic swarms in smart warehouses is substantial for e-commerce. State-of-the-art solutions have focused on efficient task scheduling for homogeneous robotic systems using machine learning techniques implemented in the warehouse management systems (WMS). However, task scheduling for a heterogeneous autonomous robotic (HAR) system has not fully been investigated so far. This article provides a comprehensive task scheduling algorithm for HAR systems that leverages innovative deep reinforcement learning and federated learning techniques. The proposed algorithm can be deployed in the geographically distributed warehouses of an e-commerce company and easily integrated into the WMS to optimally control the operation of the HAR system with stochastic goods/tasks flows in the smart warehousing.

In state-of-the-art autonomous vehicles, data from the vehicle's sensors is often processed using fast and expensive onboard hardware. Such an onboard processing scheme quickly drains the vehicle's battery and consumes computing resources. Recent research proposed to offload parts of processing tasks onto cloud. However, offloading tasks to the cloud is challenging because of the low latency needed for reliable and safe autonomous driving decisions. To address this issue, we propose an Age of Processing (AoP)-aware offloading mechanism for autonomous vehicles. First, we develop a collaboration space of edge clouds to process data closely as possible to the vehicles. Second, we reveal a new communication planning model that allows the vehicle to find suitable open radio units available in route to offload tasks to edge clouds and reduce variation in offloading delay. Third, we formulate an optimization problem that minimizes AoP, i.e., elapsed time from generating tasks and getting computation results. Our AoP-based approach allows a status update to be available to the vehicle after computation. To solve the formulated non-convex problem, we apply dual decomposition and design an AoP-aware algorithm to compute the solution in near real-time. The results demonstrate that our approach meets computation deadlines while minimizing AoP.

Although multi-access edge computing (MEC) has allowed for computation offloading at the network edge, weak wireless signals in the radio access network caused by obstacles and high network load are still preventing efficient edge computation offloading, especially for user requests with stringent latency and reliability requirements. Intelligent reflective surfaces (IRS) have recently emerged as a technology capable of enhancing the quality of the signals in the radio access network, where passive reflecting elements can be tuned to improve the uplink or downlink signals. Harnessing the IRS’s potential in enhancing the performance of edge computation offloading, in this paper, we study the optimized use of a system of multi-IRS along with the design of the offloading (to an edge with multi MECs) and resource allocation parameters for the purpose of minimizing the devices’ energy consumption considering 5G services with stringent latency and reliability requirements. After presenting our non-convex mathematical problem, we propose a suboptimal solution based on alternating optimization where we divide the problem into sub-problems which are then solved separately. Specifically, the offloading decision is solved through a matching game algorithm, and then the IRS phase shifts and resource allocation optimizations are solved in an alternating fashion using the Difference of Convex approach. The obtained results demonstrate the gains both in energy and network resources and highlight the IRS’s influence on the design of the MEC parameters.

5G network provides high-rate, ultra-low latency, and high-reliability connections in support of wireless mobile robots with increased agility for factory automation. In this paper, we address the problem of swarm robotics control for mission-critical robotic applications in an automated grid-based warehouse scenario. Our goal is to maximize long-term energy efficiency while meeting the energy consumption constraint of the robots and the ultra-reliable and low latency communication (URLLC) requirements between the central controller and the swarm robotics. The problem of swarm robotics control in the URLLC regime is formulated as a nonconvex optimization problem since the achievable rate and decoding error probability with short block-length are neither convex nor concave in bandwidth and transmit power. We propose a deep reinforcement learning (DRL) based approach that employs the deep deterministic policy gradient (DDPG) method and convolutional neural network (CNN) to achieve a stationary optimal control policy that consists of a number of continuous and discrete actions. Numerical results show that our proposed multi-agent DDPG algorithm outperforms the baselines in terms of decoding error probability and energy efficiency.

Restoring traffic in multi-layer multi-domain networks (MLMD) can be inefficient and expensive due to the reconfiguration of both intra-domain and inter-domain paths under limited resources and information sharing. This often results in traffic loss and resource over-provisioning within the MLMD, leading to sub-optimal restoration throughput and high costs. In this study, we harness FlexEthernet (FlexE) on inter-domain links to maximize the restoration throughput at minimum cost. FlexE link aggregation is an effective technique to deal with the costly impact of alternative domain rerouting that allows diverting traffic over aggregated links parallel to the failed ones, without disrupting the intra-domain connections. Additionally, FlexE helps increase network reutilization by leveraging time division multiplexing (TDM) to flexibly shift affected traffic to underutilized aggregated links. However, scheduling traffic migration in FlexE is a challenging issue that has not been fully investigated in the literature. In this paper, we initially formulate the FlexE-based traffic restoration problem as a mixed integer non-linear program (MINLP) and then introduce an approximation algorithm to efficiently solve this problem in polynomial time. Furthermore, we propose a supervised learning approach to predict the optimal restoration policy for large-size instances. Experimental results show that our solution restores up to 14% more traffic than a state-of-the-art approach.

We study the dynamic joint service function chain (SFC) embedding problem in a network function virtualization (NFV)-enabled edge cloud network. Our design goal is to optimize the network throughput by maximizing the average number of SFCs successfully embedded into the network, i.e., the Grade of Service (GoS), while guaranteeing their individual stringent end-to-end delay and resource constraints over a time horizon. To this end, we proposed a deep reinforcement learning (DRL)-based framework for jointly performing VNF embedding and routing tasks for the arrival SFCs in the considered NFV-enabled network. We implemented two versions of the proposed framework, one with the Deep Q-learning (DQL) method and one with the Advantage Actor-Critic (A2C) as the core algorithms, respectively. Moreover, for training these DRL algorithms and demonstrating the performance of the proposed framework, we implement a network environment based on the real-world network topology and a service request generator for generating SFCs traffic. Numerical results show that the DQL and A2C versions of the proposed framework achieve over 95% of the average GoS and over 95% of the network throughput ratio compared to the upper bound. This performance level is comparable to that of the near-optimal optimization-based approach while having ten times shorter execution times.

The disaggregated and hierarchical architecture of Open Radio Access Network (ORAN) with openness paradigm promises to deliver the ever demanding 5G services. Meanwhile, it also faces new challenges for the efficient deployment of Machine Learning (ML) models. Although ORAN has been designed with built-in Radio Intelligent Controllers (RIC) providing the capability of training ML models, traditional centralized learning methods may be no longer appropriate for the RICs due to privacy issues, computational burden, and communication overhead. Recently, Federated Learning (FL), a powerful distributed ML training, has emerged as a new solution for training models in ORAN systems. 5G use cases such as meeting the network slice Service Level Agreement (SLA) and Key Performance Indicator (KPI) monitoring for the smart radio resource management can greatly benefit from the FL models. However, training FL models efficiently in ORAN system is a challenging issue due to the stringent deadline of ORAN control loops, expensive compute resources, and limited communication bandwidth. Moreover, to deliver Grade of Service (GoS), the trained ML models must converge with acceptable accuracy. In this paper, we propose a second order gradient descent based FL training method named MCORANFed that utilizes compression techniques to minimize the communication cost and yet converges at a faster rate than state-of-the-art FL variants. We formulate a joint optimization problem to minimize the overall resource cost and learning time, and then solve it by the decomposition method. Our experimental results prove that MCORANFed is communication efficient with respect to ORAN system, and outperforms FL methods like MFL, FedAvg, and ORANFed in terms of costs and convergence rate.

To address the explosion demand for data-intensive applications, enhancing wireless transmission capacity has become crucial for today’s networks. This paper focuses on improving the quality of service (QoS) and user satisfaction in intelligent reflecting surface (IRS)-assisted multicast multi-group systems by managing the actual transmitted data instead of sending all source data. A key challenge of this problem is determining the ergodic capacity when the signal-to-noise-plus-interference (SINR) distribution in IRS-assisted wireless systems is significantly complicated. To address this issue, we propose a deep neural network (DNN)-based framework to predict the long-term network capacity accurately. We adapt well-known zero-forcing (ZF) and block diagonalization (BD) techniques to achieve efficient and secure solutions in IRS-assisted multi-cast multi-group systems. Furthermore, we consider the system in case of imperfect channel state information (CSI). Adopting the three-phase channel estimation, we propose a two-stage learning framework to enhance the accuracy of the estimated channel. Based on predicted results, we investigate adjustment algorithms to adapt to environmental changes, thus increasing the received QoS and user satisfaction. Our numerical results confirm the efficiency of the proposed design, with the channel estimation error being significantly smaller than that of the three-phase channel estimation algorithm in the literature.

Along with the rapid increase of new applications involving multiple participants, such as metaverse, multicast traffic engineering (TE) has recently attracted significant attention. The optimal multicast TE for offered traffic can be obtained using link-state routing protocols with a proper link weight setting. However, optimizing the link weights for such link-state multicast routing protocols according to the offered traffic in real-time is a very challenging problem. In this paper, we provide an Integer Linear Programming (ILP) formulation for finding the link metrics that allow link-state multicast routing protocols to achieve optimal traffic engineering. To meet the real-time requirement, we propose a Multi-Agent Reinforcement Learning (MARL) solution to the problem of link weights adjustment to achieve efficient multicast routing in a distributed fashion. In our design, the agents collaborate and communicate with the others in the local region and learn from their experiences to determine the best action to minimize the overall network cost. Our proposed solution is evaluated on a simulation of various traffic profiles and compared with the traditional manually configured link weights and a Genetic Algorithm (GA)-based heuristic solution. Experimental results show the advantages of our solutions in reducing network cost, and suggest the potential of using MARL in achieving efficient multicast traffic engineering.

Cooperative multi-agent reinforcement learning (MARL) is a promising solution for many large-scale multi-agent system (MAS) scenarios. A MARL framework is usually based on a decentralized scheme that enables communication between all agents in a given architecture. The agents exchange information to maximize their average reward and increase the overall system performance. However, this decentralized information sharing results in high communication costs, which is a critical issue for environments with limited communication bandwidth. On the other hand, a predefined inter-agent communication architecture may limit potential cooperation. This paper addresses such issues in a vehicle-to-everything (V2X) network, a typical example of MAS with strict Quality of Service (QoS) requirements. For efficient utilization of limited network resources, a solution to the resource-sharing problem between Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) links is required. We propose a POST-Attentional Communication Actor-Critic (POST-2AC) model that learns when communication is needed and how to integrate shared information for cooperative decision-making. Our learning method uses an attention approach combined with the critic-network to label the agents local information based on its importance so that each agent learns to trade off its performance and communication cost. The simulation results show that the proposed model achieves better performance than the state-of-the-art baselines.

Open Radio Access Network (O-RAN) aims to achieve an open and intelligent RAN architecture that provides greater flexibility, scalability, and network optimization. Machine learning (ML) technologies can play a crucial role in achieving these goals by enabling intelligent decision-making, automated optimization, and proactive maintenance. In this paper, we propose an ML pipeline optimization for energy-efficient deployment of ML-based closed-loop controls (CLC) in 5G O-RAN. Specifically, we propose two ML-based CLCs for resource prediction and network slicing in Non-Realtime RIC and Near-Realtime RIC. We also propose an energy-efficient ML pipeline for dynamically deploying these two CLCs in the O-RAN architecture. Our numerical results demonstrate the effectiveness of our proposed ML pipeline deployment compared to fixed centralized and distributed deployment.

Traditional Fronthaul architectures are not efficiently provisioned for 5G due to their lack of Fronthaul slicing supports or/and inability to satisfy the very strict latency and high bandwidth requirements defined for the enhanced common public radio interface (eCPRI). In this paper, we propose a new Fronthaul architecture that leverages Flex-Ethernet (FlexE) to guarantee 5G Fronthaul quality of service (QoS) requirements without over-provisioning the Fronthaul resources. By separating the MAC and PHY layers through a time division multiplexing (TDM) shim, FlexE allows for efficient aggregation of huge traffic volume and the design of low-latency hard network slicing architectures. Unfortunately, no standard has been defined for eCPRI transmission support over FlexE. Therefore, we propose a new protocol stack in which FlexE clients are efficiently allocated to carry eCPRI data of different 5G slices. We then formulate the FlexE-based Fronthaul provisioning optimization problem as an integer linear program (ILP) model. Simulation results show the proposed solution saves 82% of CAPEX and 94% of OPEX compared to a Fronthaul baseline.

We introduce a new framework for applying machine learning to graph data. Recently, graphs have emerged for modeling complex relations between components and giving a big picture of a system. The existence of paths, in addition to nodes and edges, helps represent some non-directed relations between nodes at different levels of importance. Understanding paths and interactions between them reveals more information about the entire graph. Most representation tools in graph theory are based on neighborhoods and lack special tools to represent paths. Therefore, it is very challenging to replace neighborhoods with paths in studying a graph. In this paper, we propose a matrix representation of paths and a binary operation to get a monoid. This algebraic point of view benefits the graph neural network (GNN) and can be seen as an alternative for neighborhoods in GNN. We apply this monoidal representation of graphs to introduce a new type of GNN called Grothendieck Graph Neural Network (GGNN), inspired by the Grothendieck Topology concept [1]. To evaluate our approach, we build a model to estimate path delays in networks based on GGNN. The results (MRE=0.0004) show the eligibility of applying GGNN in this kind of problem compared with RouteNet (MRE=0.022).

In this paper, we design a jamming mitigation plan to protect a mixed radio frequency/free-space optical (RF/FSO) relay network in the context that both RF and FSO systems are simultaneously attacked by enemy jammers. Our design aims to jointly optimize the power allocation (PA) and Field-of-View (FoV) tuning strategy to maximize the RF uplink sum rate subject to practical constraints on the jamming mitigation in both FSO and RF systems. In order to address the underlying non-convex optimization problem, we first derive the closed-form expression of the optimal Fo V angle. Then, the optimal FoV angle solution is used to solve the optimization PA. Since the PA problem has a non-convex form, we use an advanced technique of first-order Taylor approximation with difference of convex functions (D.C) method to solve it. Moreover, based on the Multi-Agent Deep Reinforcement Learning (MADRL) method, we develop a MADRL-based jamming mitigation algorithm to obtain the optimized solution of PA in near real-time. The numerical results show that the performance of the proposed MADRL-based jamming mitigation algorithm with low computational complexity is close to that of the optimization method.

For digital inclusion, Internet quality in Low-Density and Rural Areas (LDRAs) should be enhanced to satisfy QoS requirements of various services and applications. Due to the high operating costs of fiber optic deployment in LDRAs, 5G Fixed Wireless Access (5G FWA) is becoming a more attractive solution. Furthermore, 5G services require edge cloud deployment for proximity computation, which increases both required network and energy resources. Therefore, we propose closed-loops assisted by Digital Twin (DT) for energy-efficient Open RAN-based FWA provisioning in LDRAs. We consider a 5G FWA and edge cloud system as Physical Twin (PT) and design a closed-loop that distributes radio resources to edge cloud instances that manage network slices for scheduling purposes. We propose another closed-loop for intra-slice resource allocation to LDRAs. We develop an energy model and join radio resource allocation with the energy model. Then, we design reinforcement learning and optimization approaches to maximize delay requirement satisfaction while minimizing energy cost. Finally, we present DT replicating PT by incorporating solution experiences into future states. The results show that our approach uses energy resources efficiently while satisfying delay requirements of slices.

Artificial neural networks (NNs) are widely used in many modern applications, including signal processing and communication systems. Conventionally, NNs are trained by different forms of stochastic gradient descent (GD) algorithm. However, since NN optimization cost functions are non-convex, training NNs with GD has fundamental common shortcoming of all non-convex problems. To address this issue, in this paper, we take advantage of the difference of convex (DC) programming as an innovative approach for smooth/non-smooth non-convex optimizations. We model the training of NN as a DC problem and propose DC programming as an alternative optimization technique to find NN parameters. Furthermore, we obtains the convex components of the DC function. In particular, we efficiently compute convex components of regression and binary classification cost functions by means of convex analysis tools. We verify our proposed model by comparing its result with the conventional gradient descent optimizer. Simulation results confirm that the superiority of the proposed DC programming approach over GD.

The vehicular communication technology has enabled new services for drivers and passengers with different Quality of Service (QoS) demands. Due to network resource limitation, a cooperative resource allocation scheme between Vehicle to Infrastructure (V2I) links and Vehicle to Vehicle (V2V) links is needed. In literature, the resource allocation problem can be solved using a cooperative multi-agent reinforcement learning (MARL) framework. Such framework can be implemented in a central controller that receives observations and rewards of all agents, and then calculates the action for each agent accordingly. However, such a central controller may not be realistic for a vehicular network which is highly flexible and requires real-time decision making. Therefore, decentralized schemes where the agents exchange messages to maximize their average rewards would be more appropriate. Nevertheless, decentralized training increases communication costs among the agents, which is a challenging issue for a vehicular network with limited communication bandwidth. This paper proposes an Attentional Double Hierarchical Advantage Actor-Critic (ADHA2C) to address this issue. Specifically, ADHA2C adopts an attention mechanism added to the actor part to classify the important messages sent from other agents in the network. Our extensive experiments and analysis show that the proposed method approximates the performance of the upper bound model, and disturbance in the learning phase can be avoided through our proposed attention mechanism.

In this paper, we consider a UAV-assisted cloud robotic network in which a set of robots is deployed to perform specific missions, e.g., surveillance and rescue, in an area where the communication condition is unfavorable for the robots. Data collected by a robot can be either offloaded to a MEC server or to a remote cloud through the UAVs or to a nearby robot for computation. We formulate this offloading problem as a combinatorial nonconvex problem. A joint scheme for offloading decision-making, robot-UAV association, and computational resource allocation is proposed using KKT conditions, Lagrangian dual decomposition, and the Proximal Policy Optimization method to obtain the solution to the formulated problem. The simulation results show our proposed algorithm achieves a solution close to the optimal solution and outperforms the baselines.

5G intends to use network slicing to support multiple vertical industries such as the power grid. 5G network slicing can provide different levels of physical resources and virtual resources for various applications/services in vertical domains to meet their diversified communication requirements. These heterogeneous Service Level Agreements (SLAs) make the network highly dynamic in nature and challenging to operate and manage efficiently. In this paper, we formulate the SLA-based closed-loop automation network slicing management problem for 5G smart grid services in Open Radio Access Network (O-RAN). The resource scheduling problem is non-convex combinatorial while the resource reservation is a long-term mean-square-error minimization which is difficult to solve. We propose a collaborative game theory and deep learning solution that overcomes the complexity difficulty of the formulated problems. The proposed network slicing mechanism comprises three closed-loop control: closed-loop 1 resource request at the service layer, closed-loop 2 resource scheduling at the radio access layer, and closed-loop 3 resource reservation at the network layer. Simulation results show that the proposed slicing framework is more efficient than the baselines regarding fairness and network throughput.

We investigate the problem of dual protection for Warfighter Information Network-Tactical (WIN-T) of high-mobility ground combat vehicles (GCVs) against simultaneous energy-based and correlation-based interceptions. We design a joint resource optimization strategy in which the power allocation (PA) scheme controls transmit power, avoiding energy interception, and at the same time, the spreading factor assignment (SA) scheme manages correlation signal peaks to protect the network against the correlation analysis. We mathematically formulate this dual anti-interception resource allocation problem as a non-convex optimization model. We decompose this intractable optimization problem into two sub-problems, then solve the first sub-problem using an iterative method. To handle the non-convex form of the second sub-problem, we combine first-order Taylor approximation with the difference of convex functions (D.C) method. To obtain the optimized solution in near real-time, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) approach. The numerical results show that the performance of the low computational complexity MADRL is close to that of the optimization method. Thus, the MADRL method has the potential to be applicable in high-complexity military scenarios.

The imperfection of channel state information (CSI) estimation in intelligent reflecting surface (IRS)-assisted multi-user systems may heavily reduce the network capacity. Therefore, in this paper, we first investigate the two-stage learning channel estimation (2S-CE) framework to enhance the accuracy of the three-phase channel estimation (3P-CE) algorithm in the literature. Then, we manage the actual transmitted data instead of sending all source data in IRSs-assisted multi-cast multi-group (IRS-MC-MG) systems to achieve high quality of service (QoS) and user satisfaction. Well-known zero-forcing (ZF) and block diagonalization (BD) techniques are adapted to achieve high-efficient solutions in IRS-MC-MG systems. Finally, we investigate adjustment algorithms to adapt to the environmental change and the channel estimation (CE) imperfection, thus can increase the received QoS and user satisfaction. Numerical results show our proposed framework decreases more than 38 times of error compared with the literature method, i.e., the 3P-CE algorithm.

Recently, IoT, SDN and NFV have emerged as significant technological enablers for telemedicine. Because of specific characteristics of telemedicine services, reliability is one of the critical elements to guarantee the quality of services. To maintain high availability of services, existing backup solutions focus on resources constraints where backup instances are placed at the same node (vertical backup) or distributively deployed at different nodes (horizontal backup). While they put more effort to satisfy resource requirements, routing issues are often neglected such as end-to-end latency, multi-path routing, and synchronization in a multi-path scenario. Such aspects are key requirements to deploy high reliability telemedicine services. Therefore, we investigate the dynamic backup mechanism for a telemedicine system. We aim to optimize the reliability of telemedicine service function chains (TSFCs) where a joint horizontal/vertical backup (JHVB) optimization problem is first formulated. Since JHVB is a combinatorial optimization problem, which is NP-Hard, we then solve this problem in both offline and online fashions using Block Successive Upper Bound Minimization (BSUM) and Multi-Armed Bandit (MAB) frameworks. We compare our methods to the benchmarks via intensive simulations based on the Nano Datacenter solution that is used for enabling telemedicine services. The results demonstrates an outstanding performance in terms of failure awareness and service reliability.

During the COVID-19 pandemic, social distancing has been applied worldwide to reduce the risk of infection. In hospitals, this requires a new strategy for movement management in the corridors to avoid cross-overs and satisfy social distancing requirements. In this paper, we study the problem of routing and path finding for two flows of patients (COVID-19 and Non-COVID) in the hospital during the pandemic using as many disjoint paths as possible. We present two scenarios based respectively on an offline and an online method to model the routing and labeling of the hospital paths and compare them using a simulation tool. The simulation result shows the proposed solutions outperform the baseline which is based on the Dijkstra algorithm. This outcome can help increase safety by considering the guideline for COVID-19 and Non-COVID patients in healthcare centers during the pandemic.

The orchestrator coordinates multiple layers, such as IP, OTN, and DWDM, in a Multi-Layer Network (MLN), in which each layer is a domain. Accurately updating the orchestrator's network view is a key challenge in MLNs. Due to the hierarchical structure of MLNs, a single failure in an underlying layer can propagate to the upper layers, hence generating many alarms in each of these layers. These alarms may send incorrect updates of the root cause of the failure, which results in incorrect decisions of the orchestrator for routing and resource allocation. Therefore, setting up the order of updating the orchestrator by different layers is crucial in MLN to avoid confusion in the orchestrator's network view. This task is challenging, due to the flexible mapping of links between different layers, and also to the failure propagation time from the underlying layers to the upper layers. In this paper, we propose a method to update the orchestrator to ensure that the root cause is reported correctly, taking into account the dependency among different layers and failure propagation time. Moreover, to compute the optimal frequency of sending update messages from layers to the orchestrator. Our proposed method can be implemented in the Topology Server (TS) of the orchestrator. We formulate an integer nonlinear optimization problem for updating the orchestrator and then propose an algorithm to approximate the optimal failure probability for updating the orchestrator. Simulation results show that our algorithm can obtain a near-optimal which is, on average 11.3% different from the global minimum.

Unmanned aerial vehicles (UAVs) are projected to be utilized in a variety of unexpected applications, including agriculture, firefighting, emergency response, intelligent transportation, and so on. Wireless communication is one of the primary facilitators in bringing UAVs into a new phase in such applications. To realize the vision in fifth-generation (5G) networks, we propose a 5G-integration of the flexible multi-UAV system and the Open Radio Access Network (O-RAN) architecture, named U-ORAN. Although different studies have been proposed to optimize the UAV trajectory and resource allocation in the radio access network (RAN), our work is the first study to investigate the benefits of adopting UAVs in the O-RAN architecture. In U-ORAN, we consider a flying base station system and propose a joint optimization problem of multi-UAV trajectory and offloading tasks (UTOT) in which UTOT can optimize the routes from users to the core network as well as resource allocation to process offloading tasks. We decompose UTOT into two sub-problems and provide learning solutions based on the multi-agent reinforcement learning and online learning methodologies, both of which are well supported by the O-RAN architecture. Our intensive numerical simulations show that the proposed approaches outperform in a variety of settings and validation scenarios.

A fine power control policy and beam alignment is required between the base station (BS) and user equipment (UE) to achieve the promising performance of massive multiple input multiple output (MIMO) in millimeter wave (mmWave) communications. However, obtaining the channel state information (CSI) of mmWave - massive MIMO systems is challenging. In this paper, the beam-steering technique is used to estimate the signal strength from the BS to the user. We propose a novel learning framework to determine the suitable beam for a specific user and the transmit power for minimizing the cost including the transmit power and the unsatisfied rate when the channel is unknown. In addition, we address the missing data problem, and then employ the long-short term memory (LSTM) on the temporal processed inputs to select the suitable beam. Furthermore, we design a learning agent to predict the proper transmit power from the transmitted SSBs taking into account the required transmission rate. We then validate the proposed learning framework on the Deep MIMO dataset constructed based on accurate ray-tracing channels. Numerical results show our proposed framework outperforms the state-of-the-art prediction strategies, and approximates the best performance which is obtained when the CSI is available.

The fifth-generation (5G) wireless network provides high-rate, ultra-low latency, and high-reliability connections that can meet the Industrial Internet of Things (IIoT) requirements in factory automation, especially for robot motion control. In this paper, we address 5G service provisioning in an automated warehouse scenario, where swarm robotics is controlled by an industrial controller that provides routing and job instructions over the 5G network. Leveraging the coordinated multipoint (CoMP), we formulate a time-varying joint CoMP clustering and 5G ultra-reliable low-latency communication (URLLC) beamforming design problem to control the robots that move around the automated warehouse for goods storage with the planned reference tracks. Traditional iterative optimization approaches are impractical in such a dynamic wireless environment due to high computational time. We propose a game-theoretic CoMP clustering algorithm combined with the Proximal Policy Optimization method to obtain a stationary solution closed to that of the exhaustive search algorithm considered as the global optimal solution.

Flying and ground-based cars require various services such as autonomous driving, remote piloting, infotainment, and remote diagnosis. Each service requires specific Quality of Service (QoS) and network features. Therefore, network slicing can be a solution to fulfill the requirements of various services. Some services, such as infotainment, may have similar requirements to serve flying and ground-based cars. Therefore, some slices can serve both kinds of cars. However, when network slice resource sharing is too aggressive, slices can not meet QoS requirements, where resource under-provisioning causes the violation of QoS, and resource over-provisioning causes resources under-utilization. We propose two closed loops for managing RAN slice resources for cars to address these challenges. First, we present an auction mechanism for allocating Resource Block (RB) to the tenants who provide services to the cars using slices. Second, we design one closed loop that maps slices and services of tenants to Open Distributed Units (vO-DUs) and assigns RB to vO-DUs for management purposes. Third, we design another closed loop for intra-slices RB scheduling to serve cars. Fourth, we present a reward function that interconnects these two closed loops to satisfy the time-varying demands of cars at each slice while meeting QoS requirements in terms of delay. Finally, we design distributed deep reinforcement learning approach to maximize the formulated reward function. The simulation results show that our approach satisfies more than 90% vODUs resource constraints and network slice requirements.

Offloading computation-intensive tasks to edge clouds has become an efficient way to support resource constraint edge devices. However, task offloading delay is an issue largely due to the networks with limited capacities between edge clouds and edge devices. In this paper, we consider task offloading in Open Radio Access Network (O-RAN), which is a new 5G RAN architecture allowing Open Central Unit (O-CU) to be co-located with Open Distributed Unit (DU) at the edge cloud for low-latency services. O-RAN relies on fronthaul network to connect O-RAN Radio Units (O-RUs) and edge clouds that host O-DUs. Consequently, tasks are offloaded onto the edge clouds via wireless and fronthaul networks, which requires routing. Since edge clouds do not have the same available computation resources and tasks’ computation deadlines are different, we need a task distribution approach to multiple edge clouds. Prior work has never addressed this joint problem of task offloading, fronthaul routing, and edge computing. To this end, using segment routing, O-RAN intelligent controllers, and multiple edge clouds, we formulate an optimization problem to minimize offloading, fronthaul routing, and computation delays in O-RAN. To determine the solution of this NP-hard problem, we use Deep Q-Learning assisted by federated learning with a reward function that reduces the Cost of Delay (CoD). The simulation results show that our solution maximizes the reward in minimizing CoD.

Transfer learning (TL) reduces the training overheads by transferring knowledge across domains/tasks. However, the advantages of TL come with computation and communication costs. Therefore, the decision to transfer knowledge between learners should be optimized while at the same time avoiding negative transfer (NT), i.e. when the source information does not improve but rather degrades the learning performance in the target. In this paper, we propose a new notion namely, regret of learner (RoL) as a quantitative measure for the learner's performance, computation costs and communication resources of TL. Then, we use a convex combination of the empirical source and target errors with respect to the feasibility and resource constraints to design an optimization model called OPTL that deploys a TL model in a resource-constrained environment to avoid NT. This model can be employed as a general framework for different ML methods and various communication scenarios and use cases by changing the unification parameters. To validate our approach, we use OPTL for optimized TL in a deep learning (DL)-based classification problem. Extensive experiments confirm the efficiency of our proposed method.

In this paper, we investigate the problem of task scheduling in automated warehouses with hetero-geneous autonomous robotic systems. We formulate the task scheduling for a heterogeneous autonomous robots (HAR) system in each warehouse as a queueing control optimization problem in which we aim to minimize the queue length of tasks that are waiting to be processed. We propose a deep reinforcement learning (DRL) based approach that employs the proximal policy optimization (PPO) to achieve an optimal task scheduling policy. We then propose a federated learning based algorithm to improve the performance of the PPO agents. The simulation results fully demonstrate the performance improvement of our proposed algorithm in terms of average queue length compared to the distributed learning algorithm.

Seeing the poor wireless conditions caused by obstacles and deep fading that often face the access network, intelligent reflective surfaces (IRS) have been recently studied for enhancing the quality of the wireless signals using a set of passive reflecting elements. Due to the channel quality issue severely impacting the performance of edge computation offloading, the IRS technology can be applied to enhance the edge offloading performance, especially for devices with strict requirements. In this paper, we study the optimized use of the IRS along with the design of the offloading and resource allocation parameters for maximizing the UEs’ sum of offloaded bits, considering 5G services with stringent latency and reliability requirements. After presenting our non-convex mathematical problem, we propose a sub-optimal solution based on the alternating optimization technique. The offloading decision is solved through a customized matching game algorithm, and then the IRS phase shifts and resources allocation are optimized through in alternating fashion using the Difference of Convex approach. Finally, numerical results demonstrate the improvement in the offloading performance provided by the optimized use of the IRSs, and highlights on the IRSs’ influence on the design of the MEC parameters.

Unmanned aerial vehicles (UAVs) are increasingly deployed as flying base stations to serve various applications, such as smart agriculture, emergency healthcare system, smart transportation, etc, thanks to their advantages of flexible movement, strong wireless communication, and heavy payload capability. To facilitate the deployment of the fifth-generation (5G) networks, the Open Radio Access Network (O-RAN) has presented a distributed architecture for terrestrial and non-terrestrial networks. Unfortunately, O-RAN architecture for wireless sensor networks is still in development. In this paper, we investigate a 5G integration of multi-flying base stations in a wireless sensor network using O-RAN. Specifically, we formulate a joint optimization problem of UAV trajectory and resource allocations to process sensing data, named UTRA. We use decomposition to address it based on two solvable sub-problems and provide learning methodologies solve UTRA based on the multi-agent reinforcement learning and online learning methods, both of which are well supported by the O-RAN architecture. Our extensive numerical simulations show that our proposed approaches are efficient in a variety of settings and validation scenarios.

Open Radio Access Network (O-RAN) is an innovative RAN architecture designed to revolutionize 5G-and-beyond mobile networks. O-RAN virtualizes the fronthaul network functions into Open Centralized Unit (O-CU), Open Distributed Unit (O-DU) and Open Radio Unit (O-RU). Unfortunately, there is no standard data communication mechanism to disaggregate Quality of Service (QoS) flow traffic into multiple routes to access O-DUs to leverage the distributed computing capability. Furthermore, there is no centralized scheduler to coordinate processors that are processing O-DU functions efficiently to meet fifth generation (5G) QoS services. Therefore, O-RAN performance is still questionable. This paper investigates an optimized solution for joint Routing and Packet Scheduling (RPS) which is implemented in the O-DU pool to replace individual O-DUs. We formulate two joint RPS problems to coordinate multiple routes and multiple parallel processors in the centralized O-DU pool to accommodate the Ultra-Reliable Low Latency Communications (URLLC) and enhanced Mobile Broadband (eMBB) services. We propose a greedy algorithm and a Min-Max algorithm to approximate the optimal result. Numerical results show that our proposed solution improves significantly system processing delay compared with a scheme of individual O-DUs which are selfishly maximized.

Network slicing is among the most wanted features of the 5G to address the diverse requirements of different classes of services, such as IoT applications. However, bringing the slicing to Wi-Fi networks is very challenging due to the lack of wireless virtualization supports in hardware. This paper proposes a new approach to achieve network slicing in Enterprise WiFi without virtualization techniques. Our approach is based on the dynamic association of user equipment to Wi-Fi access points to meet the requirements of differentiated IoT services. We model this technique as an optimization problem with the objective of maximizing the total throughput of the network while meeting the differentiated IoT service requirements. A heuristic algorithm based on the stable matching mechanism has been proposed to solve the optimization problem in near real-time. Furthermore, to enable a practical implementation, we advocate an online algorithm based on Reinforcement Learning which can mitigate the calculation in each time slot as done by the matching algorithm. Simulation results show that our solutions achieve the total throughput approximately to the optimum one and outperform the traditional RSSI method while guaranteeing the IoT slicing service requirements.

Recently, Deep Reinforcement Learning (DRL) has increasingly been used to solve complex problems in mobile networks. There are two main types of DRL models: off-policy and on-policy. Both of them have been shown to have advantages. While off-policy models can improve sample efficiency, on-policy models are generally easy to implement and have stable performance. Therefore, it becomes hard to decide the appropriate model in a given scenario. In this paper, we compare an on-policy model: Proximal Policy Optimization (PPO) with an off-policy model: Sample Efficient Actor-Critic with Experience Replay (ACER) in solving a resource allocation problem for a stringent Quality of Service (QoS) application. Results show that for an Open Radio Access Network (O-RAN) with latency-sensitive and latency-tolerant users, both DRL models outperform a greedy algorithm. We also point out that the on-policy model can guarantee a good trade-off between energy consumption and users latency, while the off-policy model provides a faster convergence.

The fifth-generation (5G) wireless net-work provides high-rate, ultra-low latency, and high-reliability connections that can meet the industrial IoT requirements in factory automation especially for swarm robotics communication. In this paper, we address 5G service provisioning in an automated warehouse scenario where swarm robotics is controlled by an industrial controller that provides routing and job instructions over the 5G network. Leveraging the co-ordinated multipoint (CoMP), we formulate a joint CoMP clustering and 5G ultra-reliable low-latency communication (URLLC) beamforming design problem to control the robots that move around the automated warehouse for goods storage with the planed reference tracks. Traditional iterative optimization approaches are impractical in such dynamic wireless environments due to high computational time. We propose a game-theoretic CoMP clustering algorithm combined with the Proximal Policy Optimization method to obtain a stationary solution closed to that of the exhaustive search algorithm considered as the global optimal solution.

In a multiple-input multiple-output frequency-division duplexing (MIMO-FDD) system, the user equipment (UE) sends the downlink channel state information (CSI) to the base station to report link status. Due to the complexity of MIMO systems, the overhead incurred in sending this information negatively affects the system bandwidth. Although this problem has been widely considered in the literature, prior work generally assumes an ideal feedback channel. In this paper, we introduce PRVNet, a neural network architecture inspired by variational autoencoders (VAE) to compress the CSI matrix before sending it back to the base station under noisy channel conditions. Moreover, we propose a customized loss function that best suits the special characteristics of the problem being addressed. We also introduce an additional regularization hyperparameter for the learning objective, which is crucial for achieving competitive performance. In addition, we provide an efficient way to tune this hyperparameter using KL-annealing. Experimental results show the proposed model outperforms the benchmark models including two deep learning-based models in a noise-free feedback channel assumption. In addition, the proposed model achieves an outstanding performance under different noise levels for additive white Gaussian noise feedback channels.

Recently, Federated Learning (FL) has been applied in various research domains specially because of its privacy preserving and decentralized approach of model training. However, very few FL applications have been developed for the Radio Access Network (RAN) due to the lack of efficient deployment models. Open RAN (O-RAN) promises a high standard of meeting 5G services through its disaggregated, hierarchical, and distributed network function processing framework. Moreover, it comes with built-in intelligent controllers to instill smart decision making ability into RAN. In this paper, we propose a framework named O-RANFed to deploy and optimize FL tasks in O-RAN to provide 5G slicing services. To improve the performance of FL we formulate a joint mathematical optimization model of local learners selection and resource allocation to perform model training in every iteration. We solve this non-convex problem using the decomposition method. First, we propose a slicing based and deadline aware client selection algorithm. Then, we solve the reduced resource allocation problem by using successive convex approximation (SCA) method. Our simulation results show the proposed model outperforms the state-of-the-art FL methods such as FedAvg and FedProx in terms of convergence, learning time, and resource costs.

The Open Radio Access Network (O-RAN) architecture offers flexible association of distributed network elements, i.e., O-RAN elements, for establishing on-demand radio access network (RAN) stacks [1]. In such as distributed architecture, content caching may help relieve network traffic congestion and end-to-end latency. Leveraging the strengths of both O-RAN architecture and content caching is thus essential for reducing network traffic while meeting user’s quality of experience (QoE) requirements. However, integrating content caching into O-RAN architecture is not straightforward as the content placement policy and the O-RAN element association are tightly coupled. In this paper, we investigate the problem of joint O-RAN element association and content placement in the O-RAN architecture, aiming at minimizing the average long-term traffic incurred in the xHaul transport networks. To this end, a line-search based algorithm is proposed to find efficient O-RAN element association solution; meanwhile, a deep reinforcement learning (DRL) based algorithm is proposed to obtain an efficient content placement policy while being able to deal with large action space issue. The efficacy of our proposed framework is confirmed through extensive numerical results.

Recently, wireless service provisioning via Unmanned Aerial Vehicles (UAVs) has emerged in 5G and beyond mobile networks. Due to the limited capacity of UAV batteries, tethered UAVs (TUAVs), which are powered from ground, are increasingly deployed in worldwide projects. However, the deployment of TUAVs in mobile networks requires high spectral efficiency, particularly in dense areas. Non-orthogonal Multiple Access (NOMA), serving users with strong channels and weak channels in the same Resource Blocks (RBs), helps overcome this issue. Due to the dynamic and massive deployment of TUAVs, inter-cell interference becomes critical. To alleviate the submerging of signals between TUAVs, we introduce a new parameter named Channel Gain Plus Interference (CGPI), reputing the interference as channel characteristics. Then, we formulate the joint optimization of power, altitude and user association. To solve this high-complexity problem, we design an algorithm, called NOMA SIC-Aware TUAV Base Station Control (NSATC) based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG). The experiment shows our proposed algorithm presents a performance enhancement between 18.8% and 121.77% of throughput and 23.76% and 51.62% of serving users than the greedy algorithm.

Recently, the exponential growth of the Internet-of-Things (IoT) services with heterogeneous requirements becomes a burden to the traditional cloud/data center platform. Edge computing is an emerging solution to gain business value of IoT services where real-time demands become satisfied by moving computing resources close to data sources. Nevertheless, edge resources are still limited to be able to fulfill all demands at the same time. Among new approaches, resource sharing between edge/cloud service providers has been considered as a promising mechanism to address resource scarcity and pursue cost reduction. In this article, we propose an allocation and sharing model in the edge cloud network where providers team up to efficiently utilize resources, named the share-to-run IoT services (SRIS). In particular, we formulate a resource allocation and sharing optimization model to implement IoT services of multiple edge/cloud providers that can maximize the providers’ utility while satisfying service constraints. We relax SRIS into a tractable form that can be solved efficiently using well-known distributed convex frameworks, such as the dual decomposition and alternating direction method of multipliers. Finally, we evaluate our methods by providing several simulation cases, in which our proposed mechanisms show outstanding outcomes by obtaining a faster convergence, increasing by 6.9% of utilization, and 16% of acceptance rate compared to the nonoptimal approach.

Today, vehicles use smart sensors to collect data from the road environment. This data is often processed onboard of the vehicles, using expensive hardware. Such onboard processing increases the vehicle’s cost, quickly drains its battery, and exhausts its computing resources. Therefore, offloading tasks onto the cloud is required. Still, data offloading is challenging due to low latency requirements for safe and reliable vehicle driving decisions. Moreover, age of processing was not considered in prior research dealing with low-latency offloading for autonomous vehicles. This paper proposes an age of processing-based offloading approach for autonomous vehicles using unsupervised machine learning, Multi-Radio Access Technologies (multi-RATs), and Edge Computing in Open Radio Access Network (O-RAN). We design a collaboration space of edge clouds to process data in proximity to autonomous vehicles. To reduce the variation in offloading delay, we propose a new communication planning approach that enables the vehicle to optimally preselect the available RATs such as Wi-Fi, LTE, or 5G to offload tasks to edge clouds when its local resources are insufficient. We formulate an optimization problem for age-based offloading that minimizes elapsed time from generating tasks and receiving computation output. To handle this non-convex problem, we develop a surrogate problem. Then, we use the Lagrangian method to transform the surrogate problem to unconstrained optimization problem and apply the dual decomposition method. The simulation results show that our approach significantly minimizes the age of processing in data offloading with 90.34% improvement over similar method.

Multi-access edge computing (MEC) is the key enabling technology that supports compute-intensive applications in 5G networks. By deploying powerful servers at the edge of wireless networks, MEC can extend the computational capacity of the mobile devices by migrating compute-intensive tasks to the MEC servers. In this paper, we consider a multi-user MEC wireless network in which multiple mobile devices can associate and perform computation offloading via wireless channels to MEC servers attached to the base stations (BSs). The decision whether the computation task is executed locally at the user device or to be offloaded for MEC server execution should be adaptive to the time-varying network dynamics. Taking into account the dynamic of the environment, we propose a deep reinforcement learning (DRL) based approach to solve the formulated nonconvex problem of minimizing computation cost in terms of total delay. However, real-world networks tend to have a large number of users and MEC servers involving large numbers of different actions (continuous and discrete), where evaluating the combination of every possible action becomes impractical. Therefore, conventional DRL methods may be difficult or even impossible to directly apply to the proposed model. Based on the recursive decomposition of the action space available to each state, we propose a DRL-based algorithm for joint server selection, cooperative offloading, and handover in a multi-access edge wireless network. Numerical results show that the proposed DRL based algorithm significantly outperforms the traditional Q-learning method and local computation in terms of task success rate and total delay.

With the capability of decoupling the control plane and the data plane of networks, Software-Defined Network (SDN) enables flexible and efficient implementations in networks. In addition, Network Function Virtualization (NFV) with Virtual Network Function (VNF) service chain capabilities provides high-performance networks with greater scalability, elasticity, and adaptability. Such an elastic deployment of service chains results in different Service Level Agreements (SLA) and resource requirements on the control plane. In this work, we illustrate the impact of service chains on the control plane and formulate the dynamic controller/switch mapping (DCSM) problem in NFV networks in order to reduce the operational cost. We address the combinatorial optimization problem, DCSM, by designing a novel mechanism to relax DCSM into a tractable problem based on the Penalty Successive Upper Bound Minimization (PSUM) method. In doing so, we conduct several simulation scenarios to evaluate the performance. The experimental results show that our proposed algorithms can achieve a near-optimal result and reduce the operational cost up to 31.7% and 28.3% compared to K-Mean and the matching game-based approaches, respectively.

Today, Machine Learning (ML) techniques are increasingly used to detect abnormal behaviours of industrial applications. Since many of these applications are moving to the cloud environments, classical ML approaches are facing new challenges in accurately identifying abnormal behaviours due to the highly dynamic and heterogeneous nature of the cloud. In this paper, we propose a novel framework, DistAppGaurd, for profiling simultaneously the behaviour of all microservice components of a distributed application in the cloud. The framework can therefore, detect complex attacks that are not observable by monitoring a single process or a single microservice. DistAppGaurd utilizes the system calls executed by all the processes of an application to build a graph consisting of data exchanges among different application entities (e.g., processes and files) representing the behaviour of the application. This representation is then used by our novel miroservice-aware Autoencoder model to perform anomaly detection at runtime. The efficiency and feasibility of our approach is shown by implementing several different real-world attacks, which yields high detection rates (94%-97%) at 0.01% false alarm rate.

Recently, transfer learning (TL) has emerged as a powerful machine learning method in distributed environments. Transferring the knowledge between distributed agents helps reduce both learning time and computing costs. However, in a communication system, the advantage of TL comes with communication costs. To make an optimal decision of transfer between two agents, we try to answer three key questions: i) which information should be transferred from a source to a target?, ii) how this transferred information will be adapted to the target? and iii) when should TL be triggered to optimize the costs?. To this end, we introduce a new concept of similarity based on the Best Approximation Theory and a general transfer rule. Then, we propose a model to evaluate the feasibility and optimality of TL. We verify our proposed model in the context of the wireless channel selection problem using contextual multi-armed bandits. Experimental results show optimal TL decisions can be made, and Extra Action is an efficient technique for TL in channel selection.

In IoT applications, the sensors usually have limited bandwidth and power resources. Therefore, the sensed data should be mapped to a low-bit representation by means of compression and quantization before being transmitted to a central node, called the fusion center (FC). At the FC, a global decision is inferred from this data. In many cases, this data is intended for machine consumption, not for human perception. However, the compression techniques are mainly designed for reconstruction fidelity. The accuracy of the inferred decision at the FC is less considered. In this work, we present an end-to-end framework for learning a 1-bit representation of correlated-sensors data. We also propose a novel loss function and a three-stage training algorithm for learning discriminative binary features at each sensor. Extensive experiments show the proposed framework achieves high compression ratios with a marginal loss in the inferred decision accuracy. Comparatively, the obtained results outperform other benchmark models in the literature.

A key challenge facing any channel selection technique is the dynamic nature of wireless channels. To address this issue, reinforcement learning techniques have widely been used, e.g., contextual multi-armed bandit (CMAB) theory. In fact, prior works solved the problem at each individual node. However, they did not consider the cooperative learning techniques, e.g., transfer learning. In communication systems, the advantage of transfer learning comes with computation and communication costs. Therefore, the decision of transferring the knowledge between agents should be optimized. In this paper, we develop a model to evaluate the feasibility and optimality of transfer learning for CMAB-based channel selection in communication systems. To this end, we introduce a utility model for evaluating these economical aspects. Leveraging Best Approximation Theory, we propose a new similarity concept and a transfer rule applied in the context of channel selection. Experimental results show that Extra Action is an efficient technique for transfer learning in a channel selection regime. More importantly, our proposed utility and optimization model is shown to be a powerful framework for deciding when transfer learning is feasible, and when it is optimal.

Unmanned aerial vehicle (UAV)-assisted communications have several promising advantages, such as the ability to facilitate on-demand deployment, high flexibility in network reconfiguration, and high chance of having line-of-sight (LoS) communication links. In this paper, we aim to optimize the UAV control for maximizing the UAV's energy efficiency, in which both aerodynamic energy and communication energy are considered while ensuring the communication requirements for each ground terminal (GT) and backhaul link between the UAV and the terrestrial base station (BS). The mobility of the UAV and GTs lead to time-varying channel conditions that make the environment dynamic. We formulate a nonconvex optimization for controlling the UAV considering the practical angle-dependent Rician fading channels between the UAV and GTs, and between the UAV and the terrestrial BS. Traditional optimization approaches are not able to handle the dynamic environment and high complexity of the problem in real-time. We propose to use the Trust Region Policy Optimization (TRPO) method that can improve the performance of the UAV compared to the Deep Deterministic Policy Gradient (DDPG) method in such a dynamic environment as in this paper.

In this paper, we investigate the problem of robot swarm control in 5G mission-critical robotic applications, i.e., in an automated grid-based warehouse scenario. Such application requires both the kinematic energy consumption of the robots and the ultra-reliable and low latency communication (URLLC) between the central controller and the robot swarm to be jointly optimized in real-time. The problem is formulated as a nonconvex optimization problem since the achievable rate and decoding error probability with short block-length are neither convex nor concave in bandwidth and transmit power. We propose a deep reinforcement learning (DRL) based approach that employs the deep deterministic policy gradient (DDPG) method and convolutional neural network (CNN) to achieve a stationary optimal control policy that consists of a number of continuous and discrete actions. Numerical results show that our proposed multi-agent DDPG algorithm achieves a performance close to the optimal baseline and outperforms the single-agent DDPG in terms of decoding error probability and energy efficiency.

Recently, cell-free massive multiple-input multiple-output (mMIMO), a distributed version of mMIMO, has increasingly been deployed to increase the spectral and energy efficiency of communication systems. In this paper, we investigate the ability of using cell-free mMIMO to reduce the jamming attacks which is a critical issue for communication. We propose a full-stack framework from detecting to suppressing jamming attacks in cell-free mMIMO system. At first, we exploit the unused pilots to design a jammer detector based on the likelihood functions of the measured signals. Then, we propose a jamming suppression method including two tasks: jamming estimation and access point (AP) selection. In the first task, we estimate the phase and amplitude when projecting the received signals onto the unused pilots, and then use them to estimate the jamming signal. In the second task, we employ the neural network-contextual multi-armed bandit (NN-CMAB) for online selection of APs which can provide the multi-user spatial diversity considering the existence of the jammers. Furthermore, we also propose a power control strategy for managing the minimum rate requirement in multi-user settings. Numerical results confirm the advantages of proposed designs over conventional jamming-ignorant-LMMSE strategy in spectral efficiency.

Open Radio Access Network (O-RAN) is an innovative RAN architecture designed to revolutionize 5G and beyond mobile networks. O-RAN virtualizes the fronthaul network functions into O-RAN Centralized Unit (O-CU), O-RAN Distributed Unit (O-DU) and O-RAN Radio Unit (O-RU). Unfortunately, no standard data communication mechanism has been defined for the communication between these elements. Therefore, O-DUs may not work efficiently in O-DU pool, limiting the RAN performance. This paper investigates an optimized solution for routing and packet scheduling, allowing multiple O-DU pools to communicate with their O-RUs meeting the requirements of different 5G classes of service. We propose an O-DU pool architecture and formulate the problem of optimal routing and packet scheduling to forward Orthogonal Frequency-Division Multiplexing (OFDM) symbols over the optimal routes and map UDP packet sizes to fragment OFDM symbols. Numerical results show our solution can select the optimal routes and packet sizes to carry requested traffic. Moreover, in the multiple O-DU pools coexisting, we use the Dynamic Programming (DP) algorithm to find out the optimal global solution and a greedy algorithm to approximate the solution in near real-time.

The number of Internet of Things (IoT) devices and applications is growing rapidly, as well as the amount of data generated by these devices. To afford various user requirements in data transmission, and reduce bottlenecks, multi-interface intelligent radio devices have increasingly been deployed to provide the flexibility of simultaneous access to several heterogeneous access networks. In this article, we propose a mechanism to dynamically assign the flows of these devices to the appropriate gateway interfaces while satisfying their QoS, and simultaneously increase the valid data accepted by the gateways of our IoT network. We formulate the problem of optimal assignment of data flows coming from IoT devices having multiple interfaces to several multi-interface IoT gateways (OFAP-MIG) as a mixed Integer Non-Linear Programming model (MINLP), and propose a solution based on deep reinforcement learning (DRL) approach to solve this NP-hard problem. The agents deployed in the IoT gateways and the central cloud employ Deep Neural Network (DNN) to find the optimal solution based on a state space, an action space and a reward function. The simulation results shows the proposed solution outperforms the centralized one, which has been presented in prior work, in terms of the total amount data transferred by the gateways, and the acceptance rate.

In this paper, we propose a deep reinforcement learning (DRL) based approach to solving the problem of joint server selection, task offloading and handover in a multi-access edge computing (MEC) wireless network. The 5G networks tend to have a large number of users and MEC servers involving large numbers of different states and actions (both continuous and discrete), in which evaluating every possible combination becomes very challenging for traditional DRL methods. In addition, user mobility in 5G requires multiple handover decisions to be made in real-time, adding a new level of complexity to this already hard problem. Based on the recursive decomposition of the action space available to each state, we propose a deep Q-network (DQN) based online algorithm for this high-complexity problem. Numerical results show the proposed algorithm significantly outperforms the traditional Q-learning method and local computation in terms of task success rate and total delay.

The problem of selecting the modulation and coding scheme (MCS) that maximizes the system throughput, known as link adaptation, has been investigated extensively, especially for IEEE 802.11 (WiFi) standards. Recently, deep learning has widely been adopted as an efficient solution to this problem. However, in failure cases, predicting a higher-rate MCS can result in a failed transmission. In this case, a retransmission is required, which largely degrades the system throughput. To address this issue, we model the adaptive modulation and coding (AMC) problem as a multi-label multi-class classification problem. The proposed modeling allows more control over what the model predicts in failure cases. We also design a simple, yet powerful, loss function to reduce the number of retransmissions due to higher-rate MCS classification errors. Since wireless channels change significantly due to the surrounding environment, a huge dataset has been generated to cover all possible propagation conditions. However, to reduce training complexity, we train the CNN model using part of the dataset. The effect of different subdataset selection criteria on the classification accuracy is studied. The proposed model adapts the IEEE 802.11ax communications standard in outdoor scenarios. The simulation results show the proposed loss function reduces up to 50% of retransmissions compared to traditional loss functions.

Recently, Flexible Ethernet (FlexE) has emerged as a new transmission technology allowing the flexible utilization of optical transport. This flexibility helps improve network ability against failures. FlexE recovers from a physical link (PHY) failure by migrating traffic to a new PHY. However, this task is costly, especially for critical failures or when the network is under high utilization. In this paper, we investigate the FlexE Traffic Restoration (FTR) problem that aims to maintain high network utilization by the fast recovery of FlexE clients with the minimum cost using the spare capacity in the already deployed PHYs. High network utilization can be obtained by rerouting of FlexE subgroups, moving clients to another subgroup, and shifting the clients’ slots in the same subgroup without traffic disruption. We formulate the FTR optimization problem and solve it in polynomial time using learning theory and approximation. Experiments carried out in a real testbed show the proposed solution recovers 63% more traffic than baseline restoration schemes.

Internet of Things (IoT) services have been implemented for several network applications from smart cities to rural areas. However, there are many barriers to provide an efficient solution for the IoT service deployment underlying innovation SDN/NFV-based technologies. First, though an IoT service can flexibly deploy via virtual network functions (VNFs), a deployment scheme needs to solve the joint routing and resource allocation problem, which becomes more difficult than the traditional centralized cloud/datacenter solution due to distributed resources in the edge-cloud network. In addition, due to uncertain workloads in IoT services, static optimization solutions may not deal with uncompleted knowledge of the entire input, which is often given by assumptions, but unrealistic in current provisioning approaches. Aiming to address these issues, we model an online mechanism for the dynamic IoT service chain deployment to optimize the operational cost in a finite horizon. We propose a JOint Routing and Placement problem for IoT service chain (JORP) that can dynamically scale in/out the number of VNF instances. We then propose a learning method to efficiently solve JORP based on branch-and-bound (BnB). Our proposed learning mechanism can intelligently imitate the branching/pruning actions of BnB, and remove unlikely solutions in the search space based on the deep neural network model to improve the performance. In that respect, we take an intensive simulation that illustrates the promising result of our proposed deep learning method compared to BnB and the greedy baseline in terms of the performance of the algorithm and the operational cost reduction.

Hybrid electrical/optical (E/O) switching data center network (DCN) has recently emerged as a promising paradigm for future DCN architectures. However, there exist two major challenges: 1) the traffic is a mixture of both stable and burst components due to the diverse and heterogeneous user demands; 2) current scheduling algorithms are mostly static and not designed for the complex structure of hybrid E/O switching DCN, provoking frequent burst traffic congestion and performance degradation. This article endeavors to overcome the above challenges as follows. We first construct an error feedback-based spiking neural network (SNN) framework with high accuracy burst traffic prediction. We then design a prediction-assisted scheduling algorithm to handle the worst-case burst traffic. On the one hand, the error feedback-based SNN framework can significantly enhance the extraction of burst traffic features by mimicking the biological neuron system. On the other hand, prediction-assisted scheduling arranges the well-predicted traffic using a global evaluation factor and a traffic scaling factor. The simulation results reveal that our approach can efficiently integrate a spiking neural network into the traffic scheduling scheme and achieve satisfying performance with affordable computational complexity.

In the recent literature, Machine Learning (ML) techniques are increasingly used to detect the abnormal behaviour for different applications. Recently, these applications have moved to the cloud and virtualized environments due to the unique benefits such as deployment agility, scalability, flexibility and resiliency. However, those benefits pose a new challenge for classical ML approaches to accurately identify abnormal behaviours due to their highly dynamic and heterogeneous nature. In this paper, we propose a new approach Malchain for profiling virtual applications based on using a new concept: microservice role. The roles are used to provide a consistent view of the virtual application addressing the mentioned new challenges. The microservice data exchange graph built using this consistent view is then used to extract features providing the appropriate measures to profile the aggregated behaviour of the microservices comprising a virtual application. We show the efficiency and feasibility of our approach by implementing several different real-world attacks, and measuring high detection rates (86%-99%) for those attacks.

Recently, fog computing, which can be done in proximity to data sources, has emerged as a solution to provide lowlatency Quality-of-Service (QoS) for IoT services in complement to centralized cloud with unlimited computing resources. Optimized service deployment on both cloud and fog environments is challenging due to their heterogeneity. Prior works mainly focus on mapping service functions and dependencies directly to physical network. In this paper, we propose a multi-layer mapping mechanism that efficiently deploys multiple IoT services to the appropriate virtual networks in physical infrastructure. We design greedy-based algorithms for solving this NP-hard problem with two phases executed sequentially. Experimental results show our proposed solution can reduce upto 80% of the total service cost compared to the state-of-the-art solutions.

The following topics are dealt with: learning (artificial intelligence); optimisation; telecommunication computing; resource allocation; wireless channels; Internet of Things; telecommunication traffic; deep learning (artificial intelligence); mobile computing; cellular radio.

As an emerging solution to latency requirements of Internet of Things (IoT) services, edge computing can bring powerful processing capacity closer to data sources. However, with the limited resources at edge nodes, a major challenge is finding optimal resources in distributed edges to reduce the operational costs of service deployment. Prior works focus mainly on static optimization which may not work efficiently with the time-varying workloads and resource constraints. In this paper, we, therefore, consider a dynamic allocation framework in the edge-cloud network over the long run with uncertainty workloads. In such a system, we introduce a JOint Routing and Placement problem for IoT services, called JORP, that dynamically assigns resources according to workload demand in order to reduce the operational costs in long term. Inspired from the well-known algorithm, branch-and-bound (BnB), for solving the mixed-integer non linear problems (MINLPs) like JORP, we bring the learning concept to address the high complexity of BnB when the search space is huge. Particularly, we design a deep neural network (DNN) and train it under the imitation learning to mimic branching behaviors in BnB for searching the optimal solution. Finally, simulations show our solution outperforms baselines in terms of convergence and operational cost.

Optimized routing in multi-layer multi-domain (MLMD) IP-optical networks is challenging due to different technologies and policies in different domains. In this paper, we investigate the problem of using the hierarchical path computation engine (PCE) to leverage the performance of FlexE-the new flexible Ethernet technology which is used to map traffic between different layers and different domains. Our proposed PCE can be implemented in MLMD orchestration platforms to optimize network utilization while meeting delay constraints. We formulate the optimization problems of traffic routing and physical slot assignment for both FlexE-Aware and FlexE-Unaware modes with respect to QoS requirements, intra-domain information privacy and FlexE constraints. To solve the problem, we propose new algorithms that jointly optimize the routing and FlexE client assignment in polynomial time. To deal with the issue of missing intra-domain information, we use a novel implicit routing strategy to collect the intra-domain information from the child PCEs. Simulation results show the proposed solution achieves 90.1% higher efficiency than the state-of-the-art solutions.

Electric vehicle (EV) charging station integrated into the photovoltaic-based microgrid (MG) is emerging as a promising alternative energy storage solution for MG connected to the low-voltage distribution network. However, the lack of commitment from EV owners to share their vehicle storage capability while parking, challenges to ensure the economic operation of the system. In this paper, we propose a dynamic pricing scheme that is constructed by varying charging price and vehicle-to-grid rewards to encourage the participation of EV battery reserve to minimize operating costs for the MG and de-stress the distribution network while satisfying all physical and operating constraints. A PSO technique is applied to search for the optimal dynamic price and reward, then the model is reformulated as a MILP problem. The numerical simulations investigate three different scenarios of the arrival/departure period of the EV fleets to demonstrate the effective impacts of integrating EV into the PV-based MG and distribution network. Furthermore, the proposed method outperforms when scheduling the dynamic pricing and optimal energy trajectory for 24 hours ahead with the 1-hour interval.

A main component of the Multi-Layer Multi-domain (MLMD) orchestration is the end-to-end path computation over the packet and optical layers. Routing in MLMD networks is complex and requires special computational elements and cooperation between different layers and domains. The goal is to optimize the utilization of Wide Area Networks (WANs) leveraging on FlexE - the new Flexible Ethernet technology which couldn’t be fully achieved from local resource allocation in a single domain. We present MLMD-PCE, a path computation engine for MLMD networks that achieves optimal routes through a hierarchical path computation. We formulate an optimization problem of traffic routing and resource assignment for FlexE-Aware and FlexE-Unaware modes and propose an approximation algorithm that runs in polynomial time. Another issue of multi-domain routing is the lack of visibility over the intra-domain typologies in the parent-PCE. To solve this problem, we use a novel mechanism to gather the intra-domain information from the child-PCEs while keeping the domain privacy. Simulation results show that MLMD-PCE carries 77% more traffic than the current Hierarchical-PCE.

Emergence of Internet of Things (IoT) and its affiliated services brought new challenges, such as diversity of service requirements, massive connectivity, security and isolating concerns, and integration issues in networks and business. Network slicing is a powerful tool to address these challenges leveraging technologies such as Software Defined Networking (SDN) and Network Function Virtualization (NFV). In this article, we propose an architecture to address diverse requirements and resource provisioning challenges of IoT in WiFi networks. The difference between this work and the prior work is on the combination of IoT slicing with WiFi. We utilize the WiFi slicing paradigm to leverage SDN capabilities, such as service classification based on user demands and elastic resource allocation, to map different services into IoT slices. We translate Quality of Service (QoS) requirements into different IoT slices and formulate system requirements as an optimization problem with the objective of maximizing the total throughput of the WiFi system while meeting the IoT slices' constraints. Based on the numerical results, our proposed architecture not only guarantees all slicing service demands but also improves the total throughput of a WiFi network by 8%.

This paper investigates the problem of resource allocation for multiple providers on a heterogeneous edge-cloud environment. Unlike prior work that tried to solve this problem using traditional optimization, we model the problem as a non-cooperative game and show it is closely similar to a transportation game. We chose this method to deal with our multi-objective function problem which is not suitable with traditional one as the latter generates a single optimal solution. We propose a solution to the game based on Removal of Strictly Dominated Strategy (RSDS) and Best Response Methods (BRM). The goal is to seek the Nash equilibrium over the generated set of equilibria. Our simulation result shows that our proposed solution can find a set of minimized equilibria and an equilibrium point in most of the executions, which outperforms other methods using only RSDS.

Smart home gateway has to process different types of network traffic generated from several devices in an optimal way to meet their Quality-of-Service (QoS) requirements. However, the fluctuation of network traffic distributions results in packets concurrency. Current QoS-aware scheduling methods in smart home networks do not consider concurrent traffic in their scheduling solutions. This article presents an analytic model for a QoS-aware scheduling optimization of concurrent smart home network traffic with mixed arrival distributions and using probabilistic queuing disciplines. We formulate a hybrid QoS-aware scheduling problem for concurrent traffic in smart home network, propose an innovative queuing design based on the auction economic model of the game theory to provide a fair multiple access over different communication channels/ports, and design an applicable model to implement auction game on both sides; traffic sources and the home gateway, without changing the structure of the IEEE 802.11 standard. Our experiments show the proposed solution achieves an improvement of 14% of packets that meet their required delay and 57% of delay for different number of concurrent flows in the system.

This paper investigates an efficient placement and chaining of Virtual Network Functions (VNFs) to provide cloud based IoT services with minimal resource usage cost. We take into account bandwidth capacity and link delay of network connection between clouds where VNFs are allocated and underlying IoT networks where sensors and IoT gateways are deployed. Regarding the constantly changing network dynamics, input traffic of service components is considered at the lower granularity level of messages based on the communication between each VNF and corresponding sensors via IoT gateways. From the algorithm perspective, the specific topology of multiple edge clouds is leveraged to improve the solution. In this paper, we present an NFV-based high-level architecture for a system that enables the deployment of IoT services across multiple edges and clouds. We formulate the VNF placement problem using a non-convex Integer Programming model. Taking into account different IoT topologies, we devise two algorithms for small- and large-scale networks to find the near optimal solution: i) a customized Markov approximation with two techniques, i.e., multi-start and batching, and a node ranking-based heuristic. Simulation and experimental results show that the proposed solution improves the cost up to 21% compared to state-of-the-art schemes.

Smart home gateway has to process different types of network traffic generated from several devices in an optimal way to meet their QoS requirements. However, the fluctuation of network traffic distributions results in packets concurrency. Current QoS-aware scheduling methods in the smart home networks do not consider concurrent traffic in their scheduling solutions. This paper presents an analytic model for a QoS-aware scheduling optimization of concurrent smart home network traffic with mixed arrival distributions and using probabilistic queuing disciplines. We formulate a hybrid QoS-aware scheduling problem for concurrent traffics in smart home network, and propose an innovative queuing design based on the auction economic model of game theory to provide a fair multiple access over different communication channels/ports. Our experiments show the proposed solution achieves an improvement of 14% of packets that meet their required delay and 57% of delay for different number of concurrent flows in the system.

Software in IoT devices needs to be improved regularly to adapt security issues and new user requirements. In advanced IoT networks, devices employ the component-based software architecture in which components can be updated at run-time, such devices can download software components from neighbors, enabling fast distribution of updates in the entire network. One of the most energy consuming operations in the update process is flash re-writing in which the order of re-writing components into the flash memory is decisive for energy consumption. In this paper, we propose a mechanism that schedules updates in an entire IoT network to minimize the energy consumption, while satisfying the deadline constraint for updating all the devices. We mathematically formulate the problem of energy efficient update scheduling as an optimization problem with a novel energy model of the update process, then propose an algorithm to approximate the optimal schedule for updating all devices in the network. We examine the proposed algorithm in three different network instances including a tree, a partial mesh and a full mesh topology. Simulation results illustrate that our algorithm can obtain a near optimum which is, in the best case, only 3.2% different from the minimum.

The emerging paradigm of Software Defined Network (SDN) and virtualization technology promises an efficient solution for network providers to deploy services. Adopting them not only facilitates network management but also helps reduce the cost of maintaining network infrastructure. However, despite these advantages, there are still obstacles that must be overcome before SDN and virtualization can advance to reality in industrial deployments. In this paper, we focus on two well-researched issues, namely controller-switch assignment and Virtual Network Function (VNF) placement. Unlike prior works, our purpose is to jointly solve these two problems, accounting for the complex and counter-intuitive manner they are related to each other. We present a service chain aware framework (SACO) that enables the controller-switch association in a multi-controller network regarding the relationship of switches via their connected VNFs that implement service components of the chain. We also propose a model and formulate the joint optimization problem of dynamic controller-switch mapping and VNF allocation. We apply the Lyapunov optimization framework to transform a long-term optimization problem into a series of real-time problem and employ the Markov approximation method to find a near-optimal solution. Simulation results show that our service chain aware approach improves the system cost up to 10 ~ 43% compared to the state-of-the-art solutions.

Due to security issues and incremental user requirements, software in IoT devices needs to be changed frequently. Recently, advanced IoT devices employ the component-based software architecture in which components can be updated at run-time. In such IoT networks, devices can download updated components from neighbor nodes, enabling quick deployment of updates in the entire network. A key operation which consumes a significant amount of energy in the update process is flash re-writing, in which the order of re-writing components into the memory is decisive for energy consumption. In this paper, we propose a mechanism that schedules updates on all devices in an IoT network to minimize the energy consumption, taking into account the deadline constraint for updating the entire network. We introduce a novel energy model of the update process, then propose an algorithm to approximate the optimal schedule for updating all devices in the network. Simulation results show that our algorithm can obtain a near optimal which is, on average, 7.1% different from the global minimum.

The last few years have witnessed a significant increase in the deployment of heterogeneous Internet of Things (IoT) networks. IoT devices send data with different requirements such as tolerated delay and data rates. Emerging multi-interface IoT devices bring the flexibility of connecting to multiple heterogeneous access networks, which thus improves the network capacity. However, each network interface has its own constraints in terms of network coverage, capacity, packet loss rates, etc. An efficient utilization of the available multiple interfaces in IoT gateways would improve the network performance. Therefore, it is crucial to design a flow assignment mechanism to select the appropriate interface that best satisfies the flow's requirements and maximizes the amount of data accepted by an IoT gateway. In this work, we model and formulate the optimized flow assignment problem (OFAP) in a multi-interface IoT gateway. Then, we develop two heuristic algorithms to find a feasible solution for OFAP. The first algorithm is based on the greedy approach and the second uses dynamic programming to assign flows to interfaces. We provide simulation results that show the effectiveness of our algorithms.

Smart home gateways have to forward multi-sourced network traffic generated with different distributions and with different Quality of Service (QoS) requirements. Most of the current QoS-aware scheduling methods consider only the conventional priority metrics based on the IP Type of Service (ToS) field to make decision for bandwidth allocation. Such priority-based scheduling methods are not optimal to provide both QoS and QoE (quality of experience) since higher-priority traffic do not necessary require higher stringent delay than lower-priority traffic. To solve the gaps between QoS and QoE, we propose a new queuing model for QoS-level Pair traffic with mixed arrival distributions in Smart Home network (QP-SH) to make a dynamic QoS-aware scheduling decision which meets delay requirements of all traffic while preserves their degrees of criticality. A new metric which combines, the ToS field and the maximum number of packets that can be processed by the system's service during the maximum required delay, is defined. Our experiments show the proposed solution provides an improvement regarding the number of packets that meet their priorities and their maximum delays as well as the mean number of packets in the system.

Adaptive Link Rate (ALR) is widely used to save energy consumption of network by adjusting the link rate according to the carried traffic through a network-level optimization of the flow allocation process. Existing ALR solution is mainly reactive, in which link speed is changed only when new traffic demand is requested. Also, they focus on energy consumption, and do not consider the cost of changes in the network (e.g., change in traffic routes, and link rates). Once bandwidth has been allocated for a demand, the link rate remains constant during the entire session. Therefore, this solution may result in sub-optimal schemes and requires multiple re-optimizations as traffic flows are fluctuating during the session, hence reducing the overall network performance. In this paper, we improve the ALR with a multiple-step-ahead method to optimize link rates based on forecasting traffic demand predictively. We formulate the proposed Predictive ALR (PALR) as an Integer Linear Programming (ILP) model and then design a heuristic simulated annealing (SA) -based algorithm to solve it. Our experimental results show our approach provides energy saving while it decreases on average 18% of link state transition and 11% of the flow reroutings compared to the original ALR.

Raising density and ever-increasing traffic demand within future 5G Heterogeneous Networks (HetNets) will result in huge deployment, expansion and operating costs for upcoming Mobile Backhaul (MBH) networks. Multi-tenancy and network slicing based on virtualized resources are promising solutions to satisfy MBH network greediness while reducing related expenditures. Nevertheless, there is no appropriate model that fairly distributes costs over multiple Mobile Network Operators (MNO), and also optimizes physical resource planning. In this paper, we introduce a new model of 5G multi-tenant MBH costs (CapEx and OpEx). Then, we drive a novel pay-as-you-grow and optimization model called Virtual-Backhaul-as-a-Service (VBaaS) as a planning tool optimizing the Project Profit Margin (PPM) while considering the Total-Cost-of-Ownership (TCO) and the yearly generated Return-on-Investment (ROI). We also formulate an MNO pricing game (MPG) for TCO optimization to calculate the optimal Pareto-Equilibrium pricing strategy for offered Tenant Service Instances (TSI). Finally, we compare the PPM for a specific use-case known in the industry as CORD project using Traditional MBH (T-MBH) versus Virtualized MBH (V-MBH) as well as using randomized versus Pareto-Equilibrium pricing strategies. Numerical results show more than three times increase in network profitability using our proposed solutions compared with Traditional MBH (T-MBH).

This paper investigates an efficient placing mechanism for placing Virtual Network Function (VNF) on the cloud networks to enable the construction of Internet of Things (IoT) service chains with minimal resource usage cost. In particular, we propose a model taking IoT network infrastructure into account. Such the network composed of numerous sensors with constrained resource, dynamic connectivity toward multi-homing IoT gateways makes the problem of optimally placing VNFs with expected Quality of Service (QoS) more challenging and has not been considered in prior works. From the model, we formulate a non-convex Integer Programming (IP) placement problem and devise a batching Markov approximation placement (BMAP) algorithm to find the optimal solution. Simulation results show that the proposed approach improves the cost compared to those that do not consider the IoT network.

Accelerated Software Defined Networking (SDN) adoption makes SDN paradigm emerging in the state of the art. Especially, the combination of SDN and network functions virtualization (NFV) becomes a promising trend in deploying virtual network services for network operators. Although SDN can decouple networks into the control plane and the data plane to obtain flexible operation and programmability, there are many open issues that need to be addressed for SDN deployments, such as i) where to place SDN controllers in a given network, ii) how to assign connections from controllers to switches in terms of satisfying multiple objectives (e.g., resource utilization, failure, quality of services, etc.). In this work, we focus on the efficient assignment between SDN controllers and switches to guarantee a low operational cost, quality of services, and fault tolerance, in which the complexity of virtual links in network services, an omitted factor in current works, is considered and addressed. We formulate an optimization problem for dynamic controller/switch mapping (DCSM) in the network virtualization. We then proposed approximation algorithms in terms of relaxing the binary variables to solve the NP-hard problem, DCSM, in both centralized and distributed mechanisms. We also create various simulation schemes to evaluate our methods where they outperform state-of-the-art methods.

The emerging paradigm of network function virtualization (NFV) technology promises an efficient solution for optimized service deployment in the cloud computing environment thanks to its ability to dynamically add or remove virtual resources when there is a change in workload. Nevertheless, telecom providers are still facing a challenging issue in efficiently adopting NFV to deploy Web real-time communication (WebRTC) service on top of IP multimedia subsystem (IMS). Providing WebRTC service increases the inherent complexity of the IMS system in terms of the number of service nodes as virtual network functions (VNFs) and the way they interact, both of which play significant roles in the problem of optimally allocating resources. This paper proposes a virtualized interworking system between IMS and WebRTC called NFV-based interworking architecture, and describes the mechanism for VNFs to exchange messages with each other. We present an analytic system model considering the constraints of resources, quality of service (QoS), and service costs. A real-time Markov approximation-based resource allocation algorithm (RIDRA) is then designed allowing a provisioned resource at service nodes to be reconfigured in time to meet performance requirements. The proposed solution is evaluated on the large scale by simulation and on the small scale by our developed testbed. Experimental results reveal that our algorithm effectively responds to fluctuating service demands with a service cost reduced by 19% via efficiently allocating virtual resources while maintaining QoS requirement.

Although Internet of Things (IoT) has been growing rapidly in recent years, the deployment of large-scale IoT solutions are facing several challenges, in particular resource optimization. This is due to high requirements of IoT applications which are usually composed of numerous services each of which could be provided by multiple providers with various specifications. Since an IoT service is often micro-services, dynamically opting appropriate micro-services to guarantee the quality of delivered services to end-users is challenging because conventional network paradigm was not designed to support removal or insertion of modular services on the fly. In this paper, we investigate a cloud-based IoT service orchestration architecture to facilitate the IoT service deployment and propose algorithms to optimize service chaining process. Experimental results show our algorithms effectively orchestrating microservices by reducing total service cost up to 36% while maintaining real-time requirement.

Among the innovative approaches to reduce the greenhouse gas (GHG) emissions of data centres during their use phase, electrical power from renewable sources appears promising. However, renewable electricity is often intermittent due to meteorological conditions. Consequently, the regional availability of renewable power varies constantly over time. This created the opportunity to deploy cloud computing systems relying on data centres located in different regions. Cloud computing technology enables real-time load migration to a data centre in the region where the GHG emissions per kWh are the lowest. While this approach is becoming popular to manage distributed data centres, there is still room for improvement in its implementation. Indeed, the consequences of data centre power demand migrations across electric networks and the resulting GHG emissions are usually neglected. In this project, we developed a novel GHG emission factor based on the sources of electricity affected by the server load migrations. Then, we used this emission factor in a simulation of distributed data centres to minimize their GHG emissions. Results show, the use of the novel emission factor enables an extra reduction of 23% of GHG emissions as compared to the usual approach.

Diversity, dynamicity, and the huge volume of traffic in the network between datacenters has risen network administrators concerns on how to efficiently visualize their system in real-time. To deal with these challenges, we present in this paper a visualization framework based on advanced machine learning, traffic characterization, sampling, and graphical visualization algorithms, which aims to efficiently support inter-datacenter network monitoring. Experimental results show the framework is able to process real-time big flows and provides human-friendly interactive graphical representations.

Despite recent efforts and important results already achieved, the reduction of energy consumption and carbon emissions by Information and Communication Technologies is still far from the expected goals. As the annual growth in traffic is doubling every two years with more and more connections to the Internet, to be energy and carbon-aware it is paramount to implement a Monitoring and Measurement System which supports green strategies in a geographically distributed environment. Such an environment has some specific challenges that must be taken into account, such as the WAN connection, security and latency concerns. On the other hand, it also provides opportunities to reduce operational costs and emissions, improve reliability and resources management etc. This work proposes a framework which is capable of supporting green metrics in network monitoring. The framework comprises temporally differentiated data on emission factors and provides ground information able to support different applications. We have implemented the framework in a nationwide testbed and our experiments show the framework is able to provide the ground information for customizable green metrics, like power/energy, traffic, and carbon equivalent emissions. This framework can be used as a support for a variety of applications which depend on energy and emissions metrics.

Evolving nature of network traffic challenges existing models to fit and predict its behavior. In particular, real traffic modeling requires more flexible design that can adapt to long-range and short-range dependent traffic with dynamic patterns. Unfortunately, existing models cannot handle such requirements because various traffic behaviors such as periodic and self-similar are not taken into account. In this paper, Gaussian process regression (GPR) is adapted for traffic modeling and prediction. The connection between self-similarity as a traffic characteristic and GPR parameters has been driven and exerted to build of a new Hurst estimation method based on machine learning techniques. This led to propose self-similar covariance functions for enhancing prediction accuracy of GPR. The proposed GPR model has been applied for Hurst estimation as well as for traffic prediction on real traffic traces at different time-scales. The experimental results show the employment of self-similar covariance functions increases generalization ability of GPR for traffic modeling and prediction.

Environmental footprint resulting from datacenters activities can be reduced by both energy efficiency and renewable energy in a complementary fashion thanks to cloud computing paradigms. In a cloud hosting multi-tenant applications, virtual service providers can be provided with real-time recommendation techniques to allocate their virtual resources in edge, core, or access layers in an optimal way to minimize costs and footprint. Such a dynamic technique requires a flexible and optimized networking scheme to enable elastic virtual tenants spanning multiple physical nodes. In this paper, we investigate an environment-aware paradigm for virtual slices that allows improving energy efficiency and dealing with intermittent renewable power sources. A virtual slice consists of optimal flows assigned to virtual machines (VMs) in a virtual data center taking into account traffic requirements, VM locations, physical network capacity, and renewable energy availability. Considering various cloud consolidation schemes, we formulate and then propose an optimal solution for virtual slice assignment problem. Simulations on the GSN showed that the proposed model achieves better performance than the existing methods with respect to network footprint reductions.

Data center energy efficiency and carbon footprint reduction have attracted a great deal of attention across the world for some years now, and recently more than ever. Live Virtual Machine (VM) migration is a prominent solution for achieving server consolidation in Local Area Network (LAN) environments. With the introduction of live Wide Area Network (WAN) VM migration, however, the challenge of energy efficiency extends from a single data center to a network of data centers. In this paper, intelligent live migration of VMs within a WAN is used as a reallocation tool to minimize the overall carbon footprint of the network. We provide a formulation to calculate carbon footprint and energy consumption for the whole network and its components, which will be helpful for customers of a provider of cleaner energy cloud services. Simulation results show that using the proposed Genetic Algorithm (GA)-based method for live VM migration can significantly reduce the carbon footprint of a cloud network compared to the consolidation of individual data center servers. In addition, the WAN data center consolidation results show that an optimum solution for carbon reduction is not necessarily optimal for energy consumption, and vice versa. Also, the simulation platform was tested under heavy and light VM loads, the results showing the levels of improvement in carbon reduction under different loads.

In this paper, we present our research aiming at building a petabit router model for next generation networks. Considering the increasing traffic requirements on the Internet, current gigabit and terabit speed routers will soon not be able to meet user demand. One of the promising trends of router evolution is to build next generation routers with enhanced memory capacity and computing resources, distributed across a very high speed switching fabric. The main limitation of the current routing and signaling software modules, traditionally designed in a centralized manner, is that they do not scale in order to fully exploit such an advanced distributed hardware architecture. This paper discusses an implementation for an control plane for next generation routers integrating several protocol dedicated distributed architectures, aiming at increasing the scalability and resiliency. The proposed architecture distributes the processing functions on router cards, i.e., on both control and line cards. Therefore, it reduces the bottlenecks and improves both the overall performance and the resiliency in the presence of faults. Scalability is estimated with respect to the CPU utilization and memory requirements.

The growing traffic on the core Internet entails new requirements related to scalability and resiliency of the routers. One of the promising trends of router evolution is to build next generation routers with enhanced memory capacity and computing resources, distributed across a very high speed switching fabric. The main limitation of the current routing and signaling software modules, traditionally designed in a centralized manner, is that they do not scale in order to fully exploit such an advanced distributed hardware architecture. This paper discusses an implementation for an OSPF architecture for next generation routers, aiming at increasing the scalability and resiliency. The proposed architecture distributes the OSPF processing functions on router cards, i.e., on both control and line cards. Therefore, it reduces the bottlenecks and improves both the overall performance and the resiliency in the presence of faults. Scalability is estimated with respect to the CPU utilization and memory requirements.