Extreme Network Conditions Research Articles

Refiner GAN Algorithmically Enabled Deep-RL for Guaranteed Traffic Packets in Real-Time URLLC B5G Communication Systems

Ultra-reliable and Low-latency Communications (URLLC) is expected to be one of the most critical characteristics Beyond fifth-Generation (B5G) cellular networks with stringent low latency and high-reliability requirements. The Deep Reinforcement Learning (deep-RL) framework has been applied to predict the optimization of a Resource Block (RB) and minimize Power Allocation (PA) to guarantee a high End-to-End (E2E) reliability and low E2E latency under rate constraints. This paper proposes a novel Policy Gradient-based Actor-Critic Learning (PGACL) algorithm to optimize the policy gradient for optimal rate allocation to solve the RB, minimize power, and guarantee a solution for URLLC scheduling. The purpose of a PGACL algorithm is to provide a good policy with a closer convergence rate and a low computational cost depending on the reduced action space for every user. URLLC systems need to operate in highly reliable systems and account for extreme network conditions. Therefore, we proposed the refiner Generative Adversarial Networks (GANs) that apply enough extreme events for the deep-RL agent to generate synthetic data with high reliability similar to real data based on the regulated number of extreme events in the dataset. This refiner GAN method enables a deep-RL approach to generate large amounts of data practically used in real-time operations. Simulation results showed that the proposed deep-RL for refiner-GAN can omit the transient training time and develop deep learning based on a controlled set of unlabeled real traffic at a relatively short time. Furthermore, the refiner GAN demonstrated 99.9999% reliability and E2E latency of less than 1.4ms.

Experienced Deep Reinforcement Learning With Generative Adversarial Networks (GANs) for Model-Free Ultra Reliable Low Latency Communication

In this paper, a novel experienced deep reinforcement learning (deep-RL) framework is proposed to provide model-free resource allocation for ultra reliable low latency communication (URLLC-6G) in the downlink of a wireless network. The goal is to guarantee high end-to-end reliability and low end-to-end latency, under explicit data rate constraints, for each wireless user without any models of or assumptions on the users' traffic. In particular, in order to enable the deep-RL framework to account for extreme network conditions and operate in highly reliable systems, a new approach based on generative adversarial networks (GANs) is proposed. This GAN approach is used to pre-train the deep-RL framework using a mix of real and synthetic data, thus creating an experienced deep-RL framework that has been exposed to a broad range of network conditions. The proposed deep-RL framework is particularly applied to a multi-user orthogonal frequency division multiple access (OFDMA) resource allocation system. Formally, this URLLC-6G resource allocation problem in OFDMA systems is posed as a power minimization problem under reliability, latency, and rate constraints. To solve this problem using experienced deep-RL, first, the rate of each user is determined. Then, these rates are mapped to the resource block and power allocation vectors of the studied wireless system. Finally, the end-to-end reliability and latency of each user are used as feedback to the deep-RL framework. It is then shown that at the fixed-point of the deep-RL algorithm, the reliability and latency of the users are near-optimal. Moreover, for the proposed GAN approach, a theoretical limit for the generator output is analytically derived. Simulation results show how the proposed approach can achieve near-optimal performance within the rate-reliability-latency region, depending on the network and service requirements. The results also show that the proposed experienced deep-RL framework is able to remove the transient training time that makes conventional deep-RL methods unsuitable for URLLC-6G. Moreover, during extreme conditions, it is shown that the proposed, experienced deep-RL agent can recover instantly while a conventional deep-RL agent takes several epochs to adapt to new extreme conditions.

Optimal Reallocation of Generators using Line Utilization Factor and L-Index with UPFC

In the electrical industry, globalization and privatization have greatly increased competition. Therefore, it has become completely necessary to make full use of existing power assets. In this paper, a technique was proposed in the presence of UPFC to optimize the sizing of generators with Krill Herd algorithm. The UPFC is based on an index incorporating both the L-index and the LUF index. For tuning the generators, a multi-objective function has been selected. The multi-objective feature consists of deviation of voltage, cost of active generation of power and loss of transmission line. This approach was tested and implemented for regular loading and extreme network conditions due to line failure on an IEEE 30 bus system.

Strategic Position and Tuning of UPFC using Multiple Indices and Flower pollination Algorithm for Contingency Management

In this paper, a technique was proposed in the presence of UPFC to optimize the sizing of generators with Flower Pollination algorithm. The UPFC is based on an index incorporating both the L-index and the LUF index. For tuning the generators, a multi objective function has been selected. The multi-objective feature consists of deviation of voltage, cost of active generation of power and loss of transmission line. This approach was tested and implemented for regular loading and extreme network conditions due to line failure (contingency situation) on an IEEE 30 test bus system

IoT Services and Virtual Objects Management in Hyperconnected Things Network

In recent past, Internet of Things- (IoT-) based applications have experienced tremendous growth in various domains, and billions of devices are expected to be connected to the Internet in near future. The first step for development of IoT-based applications is to virtualize the physical devices by abstracting device properties in virtual objects. Later, these virtual objects can be combined to compose different services for diverse applications. Many existing systems provide virtualization service for physical devices and service composition. But, with the growth of the network, when too many devices and services are added in the IoT network, its management will become a cumbersome task. This paper presents an architecture of IoT services and virtual objects management in hyperconnected things network to facilitate the management tasks. We also have implemented a Service and Virtual Objects Management (SVOM) system prototype to effectively organize and monitor the physical devices through corresponding virtual objects and services composed in the IoT environment. The proposed system also provides interface for user interaction to perform supported control operations on selected device and check device operational and fault status. For scalability analysis of the proposed system, we have performed simulation in the OMNeT++ simulator to study impact of the IoT network size on key performance measures like response time, throughput, and packet delivery ratio. Simulation results reveal that with the growing network size, the gateway nodes become the performance bottleneck. We have also performed resources requirement analysis for virtual objects and control overhead analysis of the proposed management system. Simulation results reveal that control overhead is insignificant in normal scenarios; however, in extreme network conditions, we may have to sacrifice fewer bits which is, in fact, worth nothing when compared to the flexibility and control offered by the proposed management system.

Link Layer Correction Techniques and Impact on TCP’s Performance in IEEE 802.11 Wireless Networks

TCP performance degrades when end-to-end connections extend over wireless links which are characterized by high Bit Error Rate and intermittent connectivity. Such degradation is mainly accounted for TCP’s unnecessary congestion control actions while attempting TCP loss recovery. Several independent link loss recovery approaches are proposed by researchers to reduce number of losses visible at TCP. In this paper we first presented a survey of loss mitigation techniques at wireless link layer. Secondly performance evaluation for TCP through Type 0 Automatic Retransmission Request mechanism in erroneous Wireless LAN is presented. In particular, simulations are performed taking into account the wireless errors introduced over IEEE 802.11 link using a well-established 2-State Markov model. TCP performance is evaluated under different settings for maximum link retransmissions allowed for each frame. Simulation results show that, link retransmission improves TCP performance by reducing losses perceived at TCP sender. However, such improvement is often associated with adverse effect on other TCP parameters that may cost a lot in return under extreme network conditions. In this paper an attempt is made to observe impact of link retransmissions on the performance of multiple TCP flows competing with each other. The analysis presented in this paper signifies the scope for maximizing TCP’s throughput at the least possible cost.

Modeling of Audiofrequency Track Circuits for Validation, Tuning, and Conducted Interference Prediction

The modeling of track circuit (TC) signaling systems is considered to be a valuable tool for their validation (check of operation for exceptional and extreme network conditions and configurations), compatibility assessment (with respect to sources of conducted interference), and pretuning (before on-site tuning). The model can replace a series of preliminary measurements and speed up the on-site tuning procedure. The results of model verification and validation on the Torino-Novara trunk of the Italian High Speed Railway Line are presented. The accuracy and reliability are quite good from an absolute viewpoint and are compared with the traditional on-site TC tuning; the time saving is of an order of magnitude.

Power Efficient Video Multipath Transmission over Wireless Multimedia Sensor Networks

This paper proposes a power efficient multipath video packet scheduling scheme for minimum video distortion transmission (optimised Video QoS) over wireless multimedia sensor networks. The transmission of video packets over multiple paths in a wireless sensor network improves the aggregate data rate of the network and minimizes the traffic load handled by each node. However, due to the lossy behavior of the wireless channel the aggregate transmission rate cannot always support the requested video source data rate. In such cases a packet scheduling algorithm is applied that can selectively drop combinations of video packets prior to transmission to adapt the source requirements to the channel capacity. The scheduling algorithm selects the less important video packets to drop using a recursive distortion prediction model. This model predicts accurately the resulting video distortion in case of isolated errors, burst of errors and errors separated by a lag. Two scheduling algorithms are proposed in this paper. The Baseline scheme is a simplified scheduler that can only decide upon which packet can be dropped prior to transmission based on the packet's impact on the video distortion. This algorithm is compared against the Power aware packet scheduling that is an extension of the Baseline capable of estimating the power that will be consumed by each node in every available path depending on its traffic load, during the transmission. The proposed Power aware packet scheduling is able to identify the available paths connecting the video source to the receiver and schedule the packet transmission among the selected paths according to the perceived video QoS (Peak Signal to Noise Ratio--PSNR) and the energy efficiency of the participating wireless video sensor nodes, by dropping packets if necessary based on the distortion prediction model. The simulation results indicate that the proposed Power aware video packet scheduling can achieve energy efficiency in the wireless multimedia sensor network by minimizing the power dissipation across all nodes, while the perceived video quality is kept to very high levels even at extreme network conditions (many sensor nodes dropped due to power consumption and high background noise in the channel).

MARio

Mobile Ad hoc NETworks (MANETs) often have fast-changing and unpredictable mobility. This is especially true for recently emerging mobile sensor networks [7] and delay tolerant networks [2], where extreme network conditions are prevalent. To better adjust to such network conditions, we propose to deconstruct the underlying node mobility into a set of abstractions that can be readily leveraged for driving dynamic routing decisions. In particular, we investigate the feasibility of using route lifetime to abstract mobility. Through extensive simulations on DSR, we demonstrate up to 31% improvement in packet delivery rate and 53% improvement in average end-to-end packet latency, using our proposed route prefetch and decay techniques that leverage knowledge of route lifetimes.