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Accurate prediction of maximum flow latency is crucial for ensuring the efficient transport of latency-sensitive fronthaul traffic in packet-switched Xhaul networks while maintaining the reliable operation of 5G and beyond Radio Access Networks (RANs). Deterministic worst-case (WC) models provide strict latency guarantees but tend to overestimate actual delays, resulting in resource over-provisioning and inefficient network utilization. To address this limitation, this study evaluates a data-driven Quantile Regression (QR) model for latency prediction in Time-Sensitive Networking (TSN)-enabled packet-switched Xhaul networks operating under dynamic traffic conditions. The proposed QR model estimates high-percentile (tail) latency values by leveraging both deterministic and queuing-related data features. Its performance is quantitatively compared with the WC estimator across diverse network topologies and traffic load scenarios. The results demonstrate that the QR model achieves significantly higher prediction accuracy—particularly for midhaul flows—while still maintaining compliance with latency constraints. Furthermore, when applied to dynamic Xhaul network operation, QR-based latency predictions enable a reduction in active processing-node utilization compared with WC-based estimations. These findings confirm that data-driven models can effectively complement deterministic methods in supporting latency-aware optimization and adaptive operation of 5G/6G Xhaul networks.

Recent advances in mobile communication shows proliferation in networks formed by human carried devices known as the pocket switched network (PSN). Human beings are social animals. They tend to form groups and communities, and have repetitive mobility pattern which can be used to disseminate information in PSNs. In this paper, we give a deeper insight to the nature of community formation and how such information can be used to help opportunistic forwarding in mobile opportunistic networks. Using real world mobility traces, we first derive the adjacency list for each node and form the contact graph. Using tools from social network analysis we then determine various node properties like centrality and clustering coefficient and graph properties like average path length and modularity. Based on the derived graph properties, node encounter process and nature of message dissemination in PSNs, we propose two social based routing, known as the contact based routing and community aware two-hop routing. We compare the proposed routing techniques with generic epidemic and prophet routing and Bubble-Rap, a social based routing. Results show that the proposed algorithms is able to achieve better delivery ratio and lower delay than Bubble Rap, while reducing the high overhead ratio of epidemic and prophet routing.

The 5th generation mobile networks (5G) based on virtualized and centralized radio access networks will require cost-effective and flexible solutions for satisfying high-throughput and latency requirements. The next generation fronthaul interface (NGFI) architecture is one of the main candidates to achieve it. In the NGFI architecture, baseband processing is split and performed in radio (RU), distributed (DU), and central (CU) units. The mentioned entities are virtualized and performed on general-purpose processors forming a processing pool (PP) facility. Given that the location of PPs may be spread over the network and the PPs have limited capacity, it leads to the optimization problem concerning the placement of DUs and CUs. In the NGFI network scenario, the radio data between the RU, DU, CU, and a data center (DC)—in which the traffic is aggregated—are transmitted in the form of packets over a convergent packet-switched network. Because the packet transmission is nondeterministic, special attention should be put on ensuring the appropriate quality of service (QoS) levels for the latency-sensitive traffic flows. In this paper, we address the latency-aware DU and CU placement (LDCP) problem in NGFI. LDCP concerns the placement of DU/CU entities in PP nodes for a given set of demands assuming the QoS requirements of traffic flows that are related to their latency. To this end, we make use of mixed integer linear programming (MILP) in order to formulate the LDCP optimization problem and to solve it. To assure that the latency requirements are satisfied, we apply a reliable latency model, which is included in the MILP model as a set of constraints. To assess the effectiveness of the MILP method and analyze the network performance, we run a broad set of experiments in different network scenarios.

In this study, a neural network (NN) composite adaptive antidisturbance control scheme is investigated for a class of unknown pure-feedback switched nonlinear systems. First, radial basis function NNs are employed to identify unknown nonlinearities by employing a Butterworth low-pass filter to eliminate the algebraic loop problem. Subsequently, an NN composite switched state observer and an NN composite switched disturbance observer are presented by coupled design to estimate immeasurable states and compounded disturbances. Next, an improved composite control strategy is developed for the investigated problem with the help of a filtering method to avoid the “explosion of complexity” problem, and compensating signals are set up to alleviate the filter errors. By utilizing the Lyapunov stability theorem, the proposed control scheme can guarantee that all signals in the closed-loop system are bounded under a class of switching signals with the average dwell time, while the tracking error can converge to within a small neighbourhood of the origin. Simulation results are provided to demonstrate the effectiveness of the presented approach.

The aim of this work is to bring complex view on video streaming service performance within IP-based networks. Video quality as a part of multimedia technology has a crucial role nowadays due to this increase. Since architecture of IP network has not been designed for real-time services like audio or video, there are many factors that can influence the final quality of service, especially packet loss and delay variation (also known as Jitter). The research was focused on the quality of video data delivery in many scenarios included different packet loss rate and simulating of different delay variation values in the network. Performed tests were evaluated by using of video objective methods. Based on results of these measurements, an extended QoS model for estimation of triple play services was designed. The proposed model allows us to compute the estimated objective quality parameters that describe the final quality of video service as a part of triple play services.

We consider a flow-level model for packet-switched telecommunications networks handling elastic flows with concurrent occupancy of resources, in which digital objects are transferred at a rate determined by capacity allocation on each route. The capacity of each node is dynamically allocated to the routes passing by it through a weighted proportional fair sharing policy, and the arrival request for transfer on each route is generated by N heavy-tailed On/Off sources. Under heavy-traffic, we combine state space collapse ( SSC ) and an Invariance Principle to show that when N → + ∞ the conveniently scaled workload and flow count processes converge. SSC establishes a relationship between the corresponding limits by means of a deterministic operator. In Theorem 1 we prove that assuming the other hypotheses hold, SSC is not only sufficient for the convergence, but necessary. In Theorem 2 we prove that when r → + ∞ , r being a scale parameter, the workload limit process converges to a reflected fractional Brownian motion on a polyhedral cone.

Optical packet switching (OPS) networks and its subsystems, like the burst-mode receiver, are an essential technology currently used in passive optical networks (PONs). Moreover, OPS may play a fundamental role on future hybrid optical circuit switching (OCS)/OPS networks and datacenter networks. This paper focuses on two fundamental subsystems of packetized optical networks: the digital coherent burst-mode receiver and the electro-optical switch. We describe and experimentally characterize a novel digital coherent burst-mode receiver that makes uses of the Stokes parametrization to rapidly estimate the state of polarization (SOP) and optimize the equalizer convergence time. This burst-mode receiver is suitable for optical packetized networks that make use of advanced modulation formats such as quadrature amplitude modulation (QAM). We study the suitability of (Pb,La)(Zr,La)O3 (PLZT) optical switches for amplitude-variable coherent polarization division multiplexing (PDM) 16QAM modulation format and demonstrate a switching capacity of 10.24 Tb/s/port. We demonstrate a full 2 × 2 OPS node with a control plane capable of solving packet contention by means of packet dropping or buffering with a switching capacity of 10.24 Tb/s/port. Finally, we demonstrate the operation of the 2 × 2 OPS node with a record capacity of 12.8 Tb/s/port plus 100 km of dispersion-compensated fiber transmission.

The Internet of Things (IoT) is the next big challenge for the research community where the IPv6 over low power wireless personal area network (6LoWPAN) protocol stack is a key part of the IoT. Recently, the IETF ROLL and 6LoWPAN working groups have developed new IP based protocols for 6LoWPAN networks to alleviate the challenges of connecting low memory, limited processing capability, and constrained power supply sensor nodes to the Internet. In 6LoWPAN networks, heavy network traffic causes congestion which significantly degrades network performance and impacts on quality of service aspects such as throughput, latency, energy consumption, reliability, and packet delivery. In this paper, we overview the protocol stack of 6LoWPAN networks and summarize a set of its protocols and standards. Also, we review and compare a number of popular congestion control mechanisms in wireless sensor networks (WSNs) and classify them into traffic control, resource control, and hybrid algorithms based on the congestion control strategy used. We present a comparative review of all existing congestion control approaches in 6LoWPAN networks. This paper highlights and discusses the differences between congestion control mechanisms for WSNs and 6LoWPAN networks as well as explaining the suitability and validity of WSN congestion control schemes for 6LoWPAN networks. Finally, this paper gives some potential directions for designing a novel congestion control protocol, which supports the IoT application requirements, in future work.

This paper investigates the design of a reduced‐order H∞ $H_\\infty$filter for a specific class of switched complex network systems with time‐varying delays. To handle the nonlinear components of the complex network, a T‐S fuzzy model is employed to convert them into a set of linear components. To tackle the issue of heavy network load, a memory‐based adaptive trigger mechanism is proposed. In this study, a reduced‐order H∞ $H_\\infty$state estimator is designed using the T‐S fuzzy model. To demonstrate the exponential stability of the error system, a suitable Lyapunov function is constructed based on the Lyapunov stability principle. The parameter matrix of the reduced‐order estimator is determined using the linear matrix inequality and convex linearization method. Additionally, a sufficient condition for the exponential stability of the error system at the suppression level is provided. Finally, the feasibility of the proposed scheme is validated through a simulation experiment. This paper explores the design of reduced‐order H?filter for a specific class of switched complex network systems with time‐varying delays. In this study, a reduced‐order H?state estimator is designed using the T‐S fuzzy model. A sufficient condition for the exponential stability of the error system at the suppression level is also presented.

RPL (IPv6 routing protocol for low power and lossy networks) proposed by the IETF (Internet Engineering Task Force) ROLL (routing over low-power and lossy networks) working group is a de facto standard routing protocol for IoT environments. Since the standardization was proposed, RPL has been extensively improved for diverse application scenarios and environments. Congestion control is one of the most important reasons why RPL has been improved. In an LLN (low power and lossy network), congestion may even lead to network lifetime reduction. In resource-constrained networks where end-to-end congestion control is not feasible, RPL should play a more crucial role in congestion control. In this survey, we review the RPL schemes proposed for congestion control and load-balancing and discuss future research directions.