This paper addresses Quality of Experience (QoE)-driven, self-optimizing storage for distributed file sharing—a field gaining increasing attention in cloud and edge systems research. We present a novel platform for secure file sharing, centered on QoE-driven optimization of the Zettabyte File System (ZFS). The proposed four-module architecture integrates ZFS with reinforcement learning (RL) to dynamically tune QoE metrics such as latency, throughput, and caching efficiency, adapting to evolving workloads and user expectations. By leveraging RL, the system continuously optimizes ZFS configurations for enhanced performance. The four-layer architecture provides a coherent end-to-end framework that links user-level QoE signals to low-level ZFS tunables, while incorporating blockchain-based traceability to ensure transparency and trust. Experimental evaluations demonstrate that the adaptive deep Q-learning strategy improves storage performance and QoE compared to static configurations, establishing a new benchmark for QoE-driven decentralized storage.

Nowadays, directional communication represents a high potential solution for Vehicle-to-Everything (V2X) communication in Beyond-Fifth-Generation (B5G) vehicular networks, using the millimeter wave (mmWave) technique. Due to the high mobility in the vehicular environment and to ensure a higher data rate to establish a reliable V2X communication link, an efficient beam alignment is needed to point the optimal direction between the moving vehicle and the mmWave base station. To deal with this issue, we propose in this paper a new beam alignment method to select the optimal beam angle of Departure (AoD) from the mmWave base station, used further to transmit data toward the moving vehicle directly. The novelty of our proposal concerns the suggestion of an original hybrid beam alignment approach, combining a 128-filters-based Convolutional Neural Network (CNN) and 4-layers based Bidirectional Long Short-Term Memory (BiLSTM). The selected angle is performed automatically for each vehicle position with the lowest error probability and the highest received signal power. The performance of our proposed CNN-BiLSTM model was evaluated using popular regression metrics such as mean squared error (MSE), mean absolute error (MAE), and root-mean-square error (RMSE). The results show that our model achieved values of at least 0.0107, 0.0765, and 0.103 for MSE, MAE, and RMSE, respectively, outperforming other machine learning algorithms such as KNN regressor, SVR, BiLSTM, and CNN-LSTM.

In this paper, we explore delivering mobile edge Virtual Reality (VR) gaming services with comprehensive and satisfactory Quality of Experience (QoE) across a distributed edge network. Our goal is to meet the QoE needs of all users, addressing both latency and visual considerations. However, the unique characteristics of edge-assisted mobile VR gaming distinguish our challenge from other distributed service provisioning issues. We introduce and address the challenge of provisioning QoE-centric mobile VR gaming services within a distributed edge environment by systematically capturing the unique attributes of mobile VR games. We demonstrate that this challenge can be formulated as a Mixed-Integer Quadratically Constrained Quadratic Programming (MIQCQP) problem. While mobile edge computing holds promise for mobile VR gaming, existing studies often overlook the need to deliver satisfactory $\mathbf{Q o E}$ to a large user base. Our paper focuses on delivering QoE-centric edge-assisted mobile VR gaming services to multiple users, comprehensively addressing visual and latency concerns.

Towards the next generation networks, low earth orbit (LEO) satellites have been considered as a promising component for beyond 5G networks. In this paper, we study downlink LEO-5G communication systems in a practical scenario, where the integrated LEO-terrestrial system is over-loaded by serving a number of terminals with high-volume traffic requests. Our goal is to optimize resource scheduling such that the amount of undelivered data and the number of unserved terminals can be minimized. Due to the inherent hardness of the formulated quadratic integer programming problem, the optimal algorithm requires unaffordable complexity. To solve the problem, we propose a near-optimal algorithm based on alternating direction method of multipliers (ADMM-HEU), which saves computational time by taking advantage of the distributed ADMM structure, and a low-complexity heuristic algorithm (LC-HEU), which is based on estimation and greedy methods. The results demonstrate the near-optimality of ADMM-HEU and the computational efficiency of LC-HEU compared to the benchmarks.

In this mymargin letter, we investigate a backbone-assisted wireless powered communication network (WPCN) under the protocol of "harvest-then-transmit" protocol, where user nodes (UNs) harvest energy from the sinks in the downlink (DL), then they use the energy to transmit signals to the sinks by a non-orthogonal multiple access in the uplink (UL). We consider joint successive interference cancellation and backbone-assisted sink cooperation to reduce the impact of co-channel interference on the throughput performance. We maximize the system throughput by jointly optimizing user scheduling and the time allocation for DL energy harvesting and UL transmission. For solving the maximum throughput problem efficiently, we propose a column generation method based on a greedy algorithm. Numerical results show that our proposed energy and information transfer design is superior to other reference methods in WPCNs.

To meet the needs of high throughput, low delay and high resolution video services, the free space optical network (FSO) has recently received increasing attention due to its large bandwidth and high transmission rate. However, atmospheric turbulence is a major challenge, which can lead to severe BER performance degradation or even make the link ineffective. To mitigate the significant degradation of the video streaming quality on FSO networks, in this paper, an unequal error protection (UEP) scheme for scalable video coding is designed to minimize the video distortion. By adaptively choosing the number of video layers and the redundancy rate of the channel coding, unequal error protection of Scalable video coding is achieved. In addition, to solve the optimization model, a heuristic-based fast algorithm is proposed. A large number of simulation experiments in an NS3 network simulator show that the proposed scheme offers significantly higher quality in the reconstructed video sequences.

In this paper, we developed a theoretical and experimental framework for the mapping of obstacles using WiFi, based on a small number of wireless channel samples. This is very challenging due to the numerous channel coefficients to be estimated over the time-varying channel and the channel estimation of a wireless signal transmission to be considered for compressive sampling. In a typical communication system, the signal is sampled at least twice at the highest frequency contained in the signal. However, this limits efficient ways to compress the signal, as it places a huge burden on sampling the entire signal while only a small number of the transform coefficients are needed to represent the signal. To tackle this problem, we will focused on a mathematical optimization problem for the most efficient compressed sensing method called $\ell_1$-norm, known as Basis Pursuit. Before optimizing the problem, the noise was removed from the signal, namely, multipath fading. Our experimental results show the improved performance in the number of iterations for obtaining a framework for the mapping of obstacles.

Traffic calming measures, such as speed bumps and elevated crossing points, are used to reduce speed, to prevent overtaking and generally contribute to a safer traffic situation. However, they might also cause increased response times for rescue vehicles (e.g. ambulances or fire trucks). An alternative to the conventional traffic calming measures is so-called smart traffic calming measures. These can determine when a vehicle approaches, whose journey should not be hindered, and adjust to allow for free passage for this vehicle.

This report gives an overview of the problem, and some examples of smart  traffic calming measures are discussed. Special focus is put on the wireless communication necessary to detect emergency vehicles. Furthermore, existing challenges and possible solutions for traffic calming measures and the communication needed to make them smart are discussed.

Trafiklugnande åtgärder, såsom vägbulor och förhöjda övergångsställen, används för att minska hastigheten, hindra farliga omkörningar och generellt bidra till en säkrare trafiksituation. Dock kan de också bidra till att insatstiderna för räddningsfordon (t.ex. ambulanser eller räddningstjänstens släckbilar) förlängs. Ett alternativ till de konventionella trafiklugnande åtgärderna, är s.k. smarta trafiklugnade åtgärder. Dessa kan avgöra när ett fordon närmar sig, vars färd inte bör hindras, och anpassa sig så att fri färd för detta fordon tillåts.

I denna rapport ges en översikt av problemet, och några exempel på smarta trafiklugnade åtgärder diskuteras, med fokus på sådana som hämtar information och styrs med hjälp av trådlös kommunikation. Vidare diskuteras existerande utmaningar och möjliga lösningar för trafiklugnande åtgärder och den kommunikation som krävs för att göra dem smarta.

The 4G standard Long Term Evolution-Advanced(LTE-A) has been deployed in many countries. Now, technologyis evolving towards the 5G standard since it is expecting tostart its service in 2020. The 5G cellular networks will mainlycontain in cloud computing and primarily Quality of Service(QoS) parameters (e.g. delay, loss rate, etc.) influence thecloud network performance. The impact of user perceivedQuality of Experience (QoE) using multimedia services, andapplication significantly relies on the QoS parameters. The keychallenge of 5G technology is to reduce the delay less thanone millisecond. In this paper, we have described a methodthat minimizes the overall network delay for multimediaservices; which are constant bit rate (VoIP) and variablebit rate (video) traffic model. We also proposed a methodthat measures the user’s QoE for video streaming trafficusing the network QoS parameters, i.e. delay and packet lossrate. The performance of proposed QoE method is comparedwith QoV method, and our proposed QoE method performsbest by carefully handle the impact of QoS parameters. Theresults show that our described method successfully reduces theoverall network delays, which result to maximize the user’s QoE.

The fastest growing of multimedia services overfuture wireless communication system demand more networkresources, efficient delivery of multimedia service with highusers satisfaction, and power optimization of User Equipments(UEs). The resources and power optimization are significant infuture mobile computing systems, because emerging multimediaservices consume more resources and power. The 4G standard ofLTE-A wireless system has adopted the Discontinuous Reception(DRX) method to extend and optimize the UE battery life,while there is no standard scheduling method to distribute theradio resources among the UE. This paper presents a downlinkscheduler, i.e. Quality of Experience (QoE) Power EfficientMethod (QEPEM) for LTE-A, which efficiently allocates theradio resources and optimizes the use of UE power using theDRX mechanism. We investigates how the different durationof DRX Light and Deep Sleep cycle influences the QoS andQoE of end users, using VoIP over the LTE-A. The QEPEMis evaluated with the traditional methods, in terms of SystemThroughput, Fairness Index, Packet Loss Rate, and PacketDelay. The QEPEM measures the user’s QoE, and feedback toeNodeB for scheduling decisions along with other importantparameters. Our proposed method reduces the packet delay,packet loss, and increases the fairness and UE’s power savingwith high users satisfaction.