In this paper, we consider a wireless network consisting of a base station that is serving multiple real-time traffic streams forwarding information updates to their destinations in order to sustain the freshness of information for time-critical applications. Since the wireless channels may be unreliable due to the impurities of the propagation environments, such as deep fading, blockages, etc., we integrate a reconfigurable intelligent surface to the wireless system in order to mitigate the propagation-induced impairments, enhance the quality of the wireless links, and ensure that the required freshness of information is achieved for these real time applications. For this network set-up, we investigate the joint optimization of the traffic streams scheduling and the reconfigurable intelligent surface phase-shift matrix with the goal of minimizing the long-term average Age of Information. The formulated optimization problem is a mixed integer non-convex optimization problem, which is difficult to solve. To circumvent the high-coupled optimization variables, and with the aid of bi-level optimization, we decompose the original problem into an outer traffic stream scheduling problem and an inner reconfigurable intelligent surface phase-shift matrix problem. For the outer problem, owing to its complexity and stochastic nature of packet arrivals, we resort to deep reinforcement learning solution where the traffic stream scheduling is modeled as a Markov Decision Process, and Proximal Policy Optimization is invoked to solve it. Whereas, the inner problem that determines the reconfigurable intelligent surface configuration is solved through semi-definite relaxation. Finally, we show through extensive simulations that our approach evaluates the combined impact of scheduling policy and reconfigurable intelligent surface configuration on the long term average Age of Information, where we demonstrate its superiority against other baseline schemes.

This paper investigates the potential of integrating reconfigurable intelligent surface (RIS) and cooperative non-orthogonal multiple access (C-NOMA) in preserving the freshness of information in real-time Internet of Things (IoT) applications. The system model comprises one base stations (BS), one RIS, and two IoT devices (IoTDs), in an uplink setting, where the IoTD with poor channel quality is assisted by the RIS and by the IoTD with the strong quality through a full duplex (FD) device-to-device (D2D) communication. In this setup, an optimization problem has been formulated to minimize the average sum Age of Information (AoI) by optimizing the transmit power of the IoTDs and the RIS phase shift matrix, which is non-convex and is hard to solve directly. In order to resolve this issue, the formulated optimization problem is divided into a power control sub-problem and a RIS configuration sub-problem. Capitalizing on that, a closed-form solution has been derived for the power control sub-problem and the RIS configuration sub-problem is solved by resorting to difference-of-convex (DC) along with successive convex approximation (SCA). The simulation results demonstrate that the proposed RIS-empowered uplink C-NOMA scheme achieves higher AoI-reduction compared to all considered baseline schemes.

This letter investigates the benefits of integrating reconfigurable intelligent surface (RIS) on minimizing the average sum age of information in uplink NOMA-based IoT networks. A problem is formulated to optimize the RIS configuration, the transmit power of IoT devices (IoTDs) and their clustering policy. The formulated problem is a mixed-integer non-convex one, and in order to solve it, we obtain first the RIS configuration by resorting to difference-of-convex and successive convex approximation. Afterwards, the joint power allocation and clustering problem is solved using the concept of bi-level optimization and is decomposed into an outer IoTDs clustering problem and an inner power allocation problem. Optimal closed-form expressions are derived for the inner problem and one-to-one matching is employed to solve the outer one. Numerical results demonstrate the performance superiority of our scheme.

In this paper, we consider a wireless network consisting of a base station (BS) that is serving multiple real-time traffic streams forwarding information updates to their destinations in order to sustain the freshness of information. Since the wireless channels may be unreliable due to the impurities of the propagation environments, such as deep fading, blockages, etc., we integrate a reconfigurable intelligent surface (RIS) to the wireless system in order to mitigate the propagation-induced impairments, enhance the quality of the wireless links, and ensure that the required freshness of information is achieved for these real time applications. For this network set-up, we investigate the joint optimization of the traffic streams scheduling and the RIS phase-shift matrix with the goal of minimizing the sum Age of Information (AoI). In order to solve this optimization problem, we propose an efficient algorithm based on a change-of-variables with semi-definite relaxation (SDR). Finally, we perform extensive simulations to verify the effectiveness of our proposed method against other baseline schemes.

This paper investigates the benefits of integrating multiple reconfigurable intelligent surfaces (RISs) in enhancing the timeliness performance of uplink Internet-of-Things (IoT) network, where IoT devices (IoTDs) upload their time-stamped status update information to a base station (BS) using non-orthogonal multiple access (NOMA). Accounting to the potential unreliable wireless channels due to the impurities of the propagation environments, such as deep fading, blockages, etc., multiple RISs are deployed in the considered IoT network to mitigate the propagation-induced impairments, to enhance the quality of the wireless links, and to ensure that the required freshness of information is achieved. In this setup, an optimization problem has been formulated to minimize the average sum Age of Information (AoI) by optimizing the transmit power of the IoTDs, the IoTDs clustering policy, and the RISs configurations. The formulated problem ends up to be a mixed-integer non-convex problem. In order to tackle this challenge, the RISs configurations are first obtained by adopting a semi-definite relaxation (SDR) approach. Then, the joint power allocation and user-clustering problem is solved using the concept of bi-level optimization, where the original problem is decomposed into an outer IoTDs clustering problem and an inner power allocation problem. Optimal closed-form expressions are derived for the inner problem and the Hungarian method is invoked to solve the outer problem. Numerical results demonstrate that our proposed approach achieves lowest AoI compared to the other baseline approaches.

This paper investigates the benefits of integrating reconfigurable intelligent surface (RIS) on minimizing the average sum age of information (AoI) in uplink non-orthogonal multiple access-based Internet-of-Things (IoT) networks. In this setup, an optimization problem is formulated to optimize the RIS configuration, the transmit power per IoT device and the clustering policy of IoT devices. The formulated problem is a mixed-integer non-convex one, and in order to solve it we obtain first the RIS configuration by adopting a semi-definite relaxation (SDR) approach. Afterwards, the joint power allocation and user-clustering problem is solved using the concept of bi-level optimization and is decomposed into an outer user clustering problem and an inner power allocation problem. Optimal closed-form expressions are derived for the inner problem and the Hungarian method is employed to solve the outer one. Numerical results demonstrate the performance superiority of our approach.

Internet of Things (IoT) applications, such as augmented/virtual reality, tactile Internet, immersive gaming, etc., are currently experiencing an unprecedented growth in their demand. IoT devices are constrained by limited computation and power features and might experience excessive computational latency to support resource-intensive tasks. Multiaccess edge computing (MEC) appears to be a promising solution in this regard to expedite the computations of resource-intensive tasks by offloading them to the edge of the network. This article considers a scenario where a base station (BS) serves traffic streams from multiple IoT devices. The packets from each stream arrive at the BS (following a stochastic process) and then forwarded to their respective destinations after they are processed by the MEC node. The scheduling decisions are aimed to keep the information fresh at the destination. The information freshness is captured by Age of Information (AoI) metric. We aim to minimize the expected sum AoI for the MEC-assisted IoT network and provide mathematically traceable expressions for the AoI. First, an optimization problem is formulated to find the optimal scheduling policy in order to minimize the expected sum AoI. The optimization problem is an integer linear programming (LP) problem, which is generally difficult to solve. Hence, we provide a simpler formulation of the problem and derive a more traceable expression for the expected sum AoI. With this approach, the joint impact of stochastic arrivals, scheduling policy, and unreliable channel conditions on the AoI is assessed. We also propose low-complexity algorithms to obtain results for larger networks. Finally, through extensive simulations, we demonstrate the effectiveness of our proposed methods as compared to other existing strategies in terms of achievable AoI.

Future wireless communication networks are envisioned to revolutionize the digital world by offering superlow latency, unrivalled speed and reliable communication. Consequently, a myriad of propitious applications such as augmented/virtual reality, industry 4.0, etc., is anticipated to flourish. These applications rely on real-time information to make critical decisions and hence the temporal value of information generation and dissemination carries a paramount importance. One of the acute challenges restraining to unleash the full potentials of these applications is the time-varying wireless communication environment implying the unpredictable fading effects. In this paper, we consider a wireless network consisting of a base stations (BS) that is serving multiple traffic streams to forward their information updates to the destinations over an unreliable wireless channel. We study the benefits of utilizing reconfigurable intelligent surface (RIS) to mitigate the propagation-induced impairments of the wireless environment, enhance the link quality and ensure that the required freshness of information is achieved for the real time applications. To quantify the freshness of information at each destination, we utilize the concept of Age of Information (AoI). AoI is determined by the time for a fresh update to arrive to its queue at the BS till it is successfully received at the destination. A joint RIS phase shift and scheduling optimization problem is formulated with the goal of minimizing the AoI. In order to solve this optimization problem, we propose an efficient algorithm based on semi-definite relaxation (SDR). We then extend our proposed system model and studied a use-case of real-time edge video analytics utilizing multi-access edge computing (MEC) system. Finally, we perform extensive simulations to verify the effectiveness of our proposed methods against other approaches.

Telecommunication networks are currently realizing more-huge-than-ever data demands from subscribers all over the world. Due to the ongoing pandemic, nearly all businesses have adapted working models with remote operations. People engaged with major industries, e.g., academia, health and municipalities are utilizing online platforms to carryout their routine tasks. This indeed shifts the attention from one-to-one (unicast) communication to one-to-many (multicast) and many-to-many (multi-source multi-destination) communications. Network operators are facing increased pressure to provide quick responses in order to satisfy the bandwidth hungry and time sensitive user demands. This can only be done by enhancing deployability as well as manageability of the services. Network Function Virtualization (NFV) provides a transformation of traditional proprietary network designs to a more agile and software based environment in order to achieve flexible deployments, reduced setup costs and less-time-to-market for the new services which is very much needed in the current scenarios. Previous studies on NFV-enabled multicast problem either proposed Integer Linear Program (ILP) models, that are pretty unscalable, or heuristic-based techniques that do not guarantee good quality of the solutions obtained. In this article, we propose an NFV multicast resource optimization model exploiting the use of multiple sources and considering the end-to-end delay and bandwidth requirements. Herein, we propose a novel Dantzig-Wolfe (DW) decomposition model that tackles the complexity of the problem by breaking it down into a master problem and several pricing problems. We compare the DW approach with the ILP and heuristic methods and demonstrate that our approach achieves near to optimal solution (in comparison to heuristic based methods) much faster than ILP. We also study the dynamic admission of NFV-enabled multicast requests by solving the problem in an online manner using the batch processing of requests. We then evaluate the performance of the proposed algorithms through extensive simulations and demonstrate that proposed algorithms are promising and outperform existing solutions.

Network Function Virtualization (NFV) is a transformation of traditional proprietary network designs to a more agile and software based environment. NFV architecture is considered as a key enabler for 5G as it offers the flexible deployment, reduced setup costs and less-time-to-market for the new services. Current studies on NFV in unicast transmission case can not be extended to multicast. Owing to the recent popularity and growing interest for live video streaming applications, efficient multicast solutions in NFV-enabled networks are needed. In this paper, we propose an NFV multicast resource optimization model as a Mixed Integer Linear Program (MILP) exploiting the use of multiple sources and considering the end-to-end delay and bandwidth requirements along with two heuristics algorithms. We evaluate the performance of the proposed algorithms on different network topologies. Simulation results prove that the proposed algorithms outperform the existing solution in terms of reduced bandwidth consumption and the delay values.

Telecommunication networks are comprised of interconnected network elements which provide communication services to end users. The port-to-port connectivity of these network elements is referred to as the network topology. Due to the ever growing demand, these networks undergo frequent changes in their topology as new fiber optic links, nodes and circuits are regularly provisioned and upgraded.Topology discovery of legacy optical networks, though currently a challenge for network operators, has not been studied much in the literature. It is a difficult problem, that resembles a puzzle, as very often, some network information is missing or erroneous. We propose a new algorithm for the physical ( link) layer network topology. We define the concept of port signature with the combination of four different network parameters, and then a weighted matching model in order to identify the pairs of connected ports. Confidence coefficients are assigned to the discovered port pairs to express their connectivity likeliness.Numerical results are presented on a data set coming from a customer of Ciena Corporation. We discuss the validation of the results, and the performance of the proposed tools for topology discovery.