Quality Of Service Requirements Research Articles

Outage analysis of aerial semi-grant-free NOMA systems

In this paper, we analyze the outage performance of Unmanned Aerial Vehicles (UAVs)-enabled downlink Non-Orthogonal Multiple Access (NOMA) communication systems with the Semi-Grant-Free (SGF) transmission scheme. A UAV provides coverage services for a Grant-Based (GB) user and one Grant-Free (GF) user is allowed to utilize the same channel resource opportunistically. The analytical expressions for the exact and asymptotic Outage Probability (OP) of the GF user are derived. The results demonstrate that no-zero diversity order can be achieved only under stringent conditions on users' quality of service requirements. Subsequently, an efficient Dynamic Power Allocation (DPA) scheme is proposed to relax such data rate constraints. The analytical expressions for the exact and asymptotic OP of the GF user with the DPA scheme are derived. Finally, Monte Carlo simulation results are presented to validate the correctness of the derived analytical expressions and demonstrate the effects of the UAV's location and altitude on the OP of the GF user.

Device Access, Subchannel Division, and Transmission Power Allocation for NOMA-Enabled IoT Systems

In the era of the Internet of Things (IoT), it is a promising way to improve system energy utility and better meet users’ requirements for quality of service (QoS) via integrating non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) technologies. In light of this idea, we investigate device access, sub-channel division, and transmission power allocation for NOMA-enabled IoT systems. To maximize the energy utility of IoT systems while satisfying the minimum demands of IoT devices (IoTDs) on achievable uplink data rate, a joint optimization problem is formulated with the consideration of device access, sub-channel division, and transmission power allocation. Due to the non-convexity of this problem, we propose an alternating optimization algorithm aiming to find the optimal solution. The proposed algorithm first decomposes the joint optimization problem into three sub-problems through the block coordinate descent (BCD), and then obtains the near-optimal solution by solving the decomposed sub-problems alternately. Extensive simulations validate our analysis for the convergence of the proposed algorithm. The numerical results demonstrate that the proposed algorithm significantly outperforms the benchmark algorithms in terms of improving system energy utility.

Decentralized Configuration of TSCH-Based IoT Networks for Distinctive QoS: A Deep Reinforcement Learning Approach

The IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) is widely used as a reliable, low-power, and low-cost communication technology for many industrial Internet-of-Things (IoT) networks. In many applications, Quality-of-Service (QoS) requirements are different for heterogeneous nodes, necessitating non-equal parameter settings per node. This results in a very large configuration space making space exploration complex and time-consuming. Moreover, network state and QoS requirements may change over time. Thus, run-time configuration mechanisms are needed for making decisions about proper node settings to consistently satisfy diverse and dynamic QoS requirements. In this paper, we propose a run-time decentralized self-optimization framework based on Deep Reinforcement Learning (DRL) for parameter configuration of a multi-hop TSCH network. DRL adopts neural networks as approximate functions to speed up the process of converging to QoS-satisfying configurations. Simulation results show that our proposed framework enables the network to use the right configuration settings according to the diverse QoS demands of different nodes. Moreover, it is shown that the convergence time of the learning framework is in the order of a few minutes which is acceptable for many IoT applications.

Transactional Services for Concurrent Mobile Agents over Edge/Cloud Computing-Assisted Social Internet of Things

The Web of Things (WoT) is a concept that aims to create a network of intelligent devices capable of remote monitoring, service provisioning, and control. Virtual and Physical Internet of Things (IoT) gateways facilitate communication, processing, and storage among social nodes that form the social Web of Things (SWoT). Peripheral IoT services commonly use device data. However, due to the limited bandwidth and processing power of edge devices in the IoT, they must dynamically alter the quality of service provided to their connected clients to meet each user's needs while also meeting the service quality requirements of other devices that may access the same data. Consequently, deciding which transactions get access to which Internet of Things data is a scheduling problem. Edge-cloud computing requires transaction management because several Internet of Things transactions may access shared data simultaneously. However, cloud transaction management methods cannot be employed in edge-cloud computing settings. Transaction management models must be consistent and consider ACIDity of transactions, especially consistency. This study compares three implementation strategies, Edge Host Strategy (EHS), Cloud Host Strategy (CHS), and Hybrid BHS (BHS), which execute all IoT transactions on the Edge host, the cloud, and both hosts, respectively. These transactions affect the Edge hosts as well. An IoTT framework is provided, viewing an Internet of Things transaction as a collection of fundamental and additional subtransactions that loosen atomicity. Execution strategy controls essential and additional subtransactions. The integration of edge and cloud computing demonstrates that the execution approach significantly affects system performance. EHS and CHS can waste wireless bandwidth, while BHS outperforms CHS and EHS in many scenarios. These solutions enable edge transactions to complete without restarting due to outdated IoT data or other edge or cloud transactions. The properties of these approaches have been detailed, showing that they often outperform concurrent protocols and can improve edge-cloud computing.

An Optimal Resource Allocation Scheme with Carrier Aggregation in 5G Network under Unlicensed Band

Carrier aggregation (CA) technology in the Fifth-Generation (5G) network can be considered as a major feature for high-rate data services, which can be combined with the concept of 5G in unlicensed band (5G-U) for data offloading to further enhance the efficiency of the scarce licensed spectral resource. However, the introduction of cellular services in an unlicensed spectrum can force unlicensed band users to silent mode. Meanwhile, the coexistence among different radio access technologies operating on the same frequency band is a complex issue, especially considering the requirement diversity of quality of service (QoS) in networking. In this paper, we propose an optimal resource allocation mechanism to maximize system performance satisfaction for multiple types of services with different QoS requirements in the 5G-U network with a CA feature to address the challenge. Additionally, the performance of services in Wi-Fi system is protected by reserving the minimum spectrum resource when offloading for the 5G-U network. Analytical results on the mean ergodic achievable rate of Wi-Fi services and the mean packet delay of 5G services are provided to maximize the system performance satisfaction. Numerical results demonstrate the effectiveness of the proposed algorithm, wherein optimal spectrum and power allocation can contribute to the maximum system satisfaction in a 5G-U network with a CA feature.

A new QoS-aware service composition technique in cloud computing using capuchin search algorithm

Cloud platforms rapidly develop and supply many cloud-based services with various functions but varying Quality of Service (QoS) attributes. In the context of cloud computing, a major concern is how to incorporate disparate cloud services to maximize QoS value, which is underpinned by meeting the local QoS requirements of users. To deal with multiple QoS parameters in service composition, we propose a new mechanism using the Capuchin Search Algorithm (CapSA) that provides solutions to satisfy connectivity constraints. The CapSA algorithm mimics the dynamic behavior of capuchin monkeys in the forest to solve global and local optimization problems. The CapSA was selected for its simplicity, reduced computational complexity, and exploration/exploitation balance. The approach takes the form of an optimization problem to minimize energy consumption and total cost. Compared with other approaches, the proposed algorithm improves the efficiency of cloud service composition by convergent rapidly and obtaining better compositions.

Joint Design of Communication and Computing for Digital-Twin-Enabled Aircraft Final Assembly

Aircraft final assembly line (AFAL) is a typical complex manufacturing system with multiple installation and test processes operating simultaneously at each workstation. Lots of robots and sensors are connected and operated for heterogeneous processes by sharing limited communication and computing resources. How to manage devices and resources in a coordinated and efficient way is thus very challenging. Digital twin (DT) is a powerful technology for multiple objects management in the complex assembly system. It enables us to coordinate various devices and allocated communication and computing resources at workstations. In this paper, two main processes, i.e. vision-assisted installation and flight control system test, are considered in the AFAL. We introduce a DT-enabled AFAL system, and propose a DT-assisted heterogeneous processes coordinated (DT-HPC) framework to coordinate various devices and resources at each workstation. The wirelessly connected robots and sensors are applied for perception and information fusion. In order to minimize the total energy consumption and computing resources of all the wireless devices, joint design of the wireless channel allocation, transmission power and computing resource allocation are proposed to satisfy the diverse quality of service (QoS) requirements. Firstly, we propose a priority-aware channel assignment (PACA) algorithm to allocate channels for sensors and robots. Then, the optimal computing resource allocation strategy for two processes is derived while guaranteeing the processing latency requirements. Next, we derive the minimum transmission power of wireless sensors to guarantee the monitoring accuracy and calculate the transmission power of robots to obtain the satisfied transmission rate. Finally, we apply the DT-HPC framework in the DT-enabled AFAL system. The simulation results prove that our proposed algorithms can save energy while guaranteeing different QoS requirements.

QoS-aware UAV mounted base station deployment in a disaster scenario with user position estimation

The base station installed on the unmanned aerial vehicle, referred to as the UAV-mounted base station (UBS), is considered an innovative paradigm for restoring cellular connectivity in areas where a terrestrial network does not exist, has been demolished, or is overcrowded. The challenging issue in this context is how to acquire the position information of user equipment (UE), characterize the air-to-ground (A2G) channel, and identify the optimal locations for quality-of-service (QoS)-aware 3D UBS deployment. To tackle these issues, we proposed a framework for 3D UBS deployment with the goal of maximizing the number of UEs covered. First, unlike the existing UBS deployment schemes, which ignore the UEs' localization, we propose a received signal strength indicator (RSSI)-based localization (RBL) approach to localize the randomly distributed UEs. Then, the 3D UBS deployment problem is divided into horizontal position optimization to meet the QoS requirements of individual UEs’ and vertical position optimization for path loss minimization. We propose a K-means initialized grey wolf optimization (KIGWO) approach in which UBSs are deployed to maximize the covered UEs percentage while taking the QoS requirements of individual UEs’ and UBS’ total capacity into account. Finally, the effectiveness of the proposed approach is demonstrated through Monte Carlo simulations conducted in four different environmental scenarios.

Energy Efficient MAC for M2M Communication Using Dynamic TDMA in IoT Network

In the Internet of Things (IoT) context, the relevance of M2M communication increased, creating the need for practical solutions. MTCDs, or Machine Type Communication Devices, frequently encounter issues such as collisions and delays while transmitting data, ensuring network scalability and maintaining the quality of service (QoS) for all devices participating in the communication. To address these challenges, this paper presents the protocol for switching MAC that optimizes energy usage with Dynamic Time Division Multiple Access (EMAC-DTDMA). TheEMAC-DTDMA protocol involves grouping, dynamic MAC switching, and timeslot allocation. The Harris Hawks Optimization (HHO) algorithm is applied to choose the cluster head of the machine type communication devices (MTCH) while the CSMA/CA and CSMA/CARP protocols with different backoff times are used to minimize collisions based on device density, backlog, and active nodes. The timeslots are allocated based on data size and QoS requirements using Dynamic TDMA. The Markov chain model is employed to overcome synchronization issues with traditional TDMA. The EMAC-DTDMA's performance is evaluated through simulation using a network simulator tool, considering access delay, energy usage, collision probability, and throughput.

QoS-Aware User Association and Transmission Scheduling for Millimeter-Wave Train-Ground Communications

With the development of wireless communication, people have put forward higher requirements for train-ground communications in the high-speed railway (HSR) scenarios. With the help of mobile relays (MRs) installed on the roof of the train, the application of Millimeter-Wave (mm-wave) communication which has rich spectrum resources to the train-ground communication system can realize high data rate, so as to meet users’ increasing demand for broad-band multimedia access. Also, full-duplex (FD) technology can theoretically double the spectral efficiency. In this paper, we formulate the user association and transmission scheduling problem in the mm-wave train-ground communication system with MR operating in the FD mode as a nonlinear programming problem. In order to maximize the system throughput and the number of users meeting quality of service (QoS) requirements, we propose an algorithm based on coalition game to solve the challenging NP-hard problem, and also prove the convergence and Nash-stable structure of the proposed algorithm. Extensive simulation results demonstrate that the proposed coalition game based algorithm can effectively improve the system throughput and meet the QoS requirements of as many users as possible, so that the communication system has a certain QoS awareness.

UAV-Assisted Opportunistic Beamforming in Internet of Things Networks

Opportunistic beamforming (OBF) is a promising multiple-input-multiple-output (MIMO) downlink precoding technique, where multiuser diversity gain is achieved to deal with the performance loss caused by low-complexity channel estimation. Due to high performance and low-complexity channel estimation, OBF is appropriate for internet of things (IoT) networks.However, the conventional OBF cannot achieve satisfactory performance in post-disaster communication, and the low signal-to-noise ratio (SNR) makes it hard to establish communication between the base station (BS) and users. Therefore, we propose an unmanned aerial vehicle (UAV)-assisted OBF IoT network (UON) to satisfy the quality of service (QoS) requirements of users. Then, an optimization problem is formulated to maximize the weighted energy and spectrum efficiencies, while the optimization problem is non-convex and hard to solve. To obtain the solution, we divide the optimization problem into three suboptimal issues, and then a joint iterative algorithm is applied. According to numerical results, the proposed scheme achieves higher system performances compared with the conventional OBF scheme in both Rayleigh and Rician channels. Moreover, we discuss the objective functions with different weights of spectrum efficiency (SE) and energy efficiency (EE) to adapt the QoS requirements of different scenarios. It is indicated that the proposed scheme can achieve high performance with low computational complexity, regardless of the weighted energy and spectrum efficiency.

Beamforming Design for BackCom Assisted NOMA Systems

Backscatter communication (BackCom) assisted non-orthogonal multiple access (NOMA) has received significant attention recently and can be applied to Internet of things (IoT). The BackCom system aims to realize energy cooperation among the devices. NOMA is a well-known technique to enable spectrum cooperation among the devices, which motivates the combination of the two techniques. This work focuses on the beamforming design in BackCom NOMA networks, where an uplink data rate maximization problem is formulated with a constraint to guarantee the downlink user’s quality of service requirements. As the formulated optimization problem is not concave, semidefinite relaxation (SDR) is applied to transform the considered problem into a concave form which can be solved efficiently. Simulation results are provided to demonstrate that the proposed algorithm can achieve superior performance over the considered benchmarking scheme.

Resilient Edge: Building an Adaptive and Resilient Multi-Communication Network for IoT Edge Using LPWAN and WiFi

Edge computing has gained attention in recent years due to the adoption of many Internet of Things (IoT) applications in domestic, industrial and wild settings. The resiliency and reliability requirements of these applications vary from noncritical (best delivery efforts) to safety-critical with time-bounded guarantees. The network connectivity of IoT edge devices remains the central critical component that needs to meet the timebounded Quality of Service (QoS) and fault-tolerance guarantees of the applications. Therefore, in this work, we systematically investigate how to meet IoT applications mixed-criticality QoS requirements in multi-communication networks. We (i) present the network resiliency requirements of IoT applications by defining a system model (ii) analyse and evaluate the bandwidth, latency, throughput, maximum packet size of many state-of-theart LPWAN technologies, such as Sigfox, LoRa, and LTE (CATM1/ NB-IoT) and Wi-Fi, (iii) implement and evaluate an adaptive system Resilient Edge and Criticality-Aware Best Fit (CABF) resource allocation algorithm to meet the application resiliency requirements using Raspberry Pi 4 and Pycom FiPy development board having five multi-communication networks.We present our findings on how to achieve 100% of the best-effort high criticality level message delivery using multi-communication networks.

Security-Reliability Tradeoff in RSMA-Based Communications Against Eavesdropper Collusion

This work exploits secure beamforming for achieving security-reliability tradeoff in rate splitting multiple access (RSMA)-based communications against eavesdropper collusion. Considering user fairness, we maximize the minimum secrecy rate (MSR) among all legitimate users subject to the quality of service (QoS) constraints and transmit power constraint. A feasibility check problem is first presented to avoid infeasible cases. Then, a successive convex approximation-based algorithm is proposed to iteratively optimize the beamforming design. Numerical results show that the proposed RSMA-based design outperforms the baseline design in terms of MSR within certain QoS requirement ranges to trade security for reliability by balancing the power allocated to the common stream and private streams. And the performance advantage enlarges as the number of collusive eavesdroppers increases.

Sensor selection for remote state estimation with QoS requirement constraints

In this paper, we study the sensor selection problem for remote state estimation under the Quality-of-Service (QoS) requirement constraints. Multiple sensors are employed to observe a linear time-invariant system, and their measurements should be transmitted to a remote estimator for state estimation. However, due to the limited communication resources and the QoS requirement constraints, only some of the sensors can be allowed to transmit their measurements. To estimate the system state as accurately as possible, it is essential to select sensors for transmission appropriately. We formulate the sensor selection problem as a non-convex optimization problem. It is difficult to solve such a problem and even to find a feasible solution. To obtain a solution which can achieve good estimation performance, we first reformulate and relax the formulated problem. Then, we propose an algorithm based on successive convex approximation (SCA) to solve the relaxed problem. By utilizing the solution of the relaxed problem, we propose a heuristic sensor selection algorithm which can provide a good suboptimal solution. Simulation results are presented to show the effectiveness of the proposed heuristic.

On user fairness of uplink rate splitting multiple access with randomly deployed users

In this paper, we investigate fairness of an uplink rate splitting multiple access (RSMA) system with randomly deployed users, which are modeled as a homogeneous Poisson Point Process. Specifically, randomly deployed users are divided into the central and edge groups, respectively. Two RSMA schemes are considered, namely, the cognitive radio-inspired RSMA (CR-RSMA) and rate fairness-oriented RSMA (RF-RSMA). In the CR-RSMA scheme, the quality of service (QoS) requirement of the primary user (PU) is given a priority, while the secondary user (SU) is served without affecting the outage performance of the PU. In the RF-RSMA scheme, by flexibly adjusting the SU’s power allocation factor to improve the rates fairness of users. Taking into account the user's QoS requirements, we first derive the closed-form expression for the outage probability of a user with a fixed location, and then we utilize a modified Jain's index to measure user fairness. Considering that the distance between the user and the base station (BS) affects the channel fading, the relative position of the user also impacts the RSMA performance. To reveal the effect of user's location on the outage performance and user fairness, we propose the distance-based user pairing strategies and obtain closed-form expressions for outage probabilities, which by using stochastic geometry. Simulation results validate the accuracy of the analytical indicates that, in the medium and high signal-to-noise ratio regions, the proposed RSMA schemes achieve better user fairness than the benchmark schemes.

Power allocation optimization for hybrid information and energy transfer with massive MIMO downlink

AbstractThis paper investigates the power allocation in a massive multiple‐input multiple‐output (MIMO) downlink system, where the base station (BS) simultaneously transmits information and energy to information terminals and energy terminals without resorting to any wireless energy harvesting (EH) protocol, respectively. First, with respect to this system, the closed‐form expressions of achievable rate and harvested energy are, respectively, derived for Rayleigh fading channels. Then, from these expressions, three optimization problems are formulated based on quality‐of‐service (QoS) requirements, with the purposes of maximizing achievable sum rate, harvested sum energy and joint QoS requirements, respectively. The optimization problem of maximizing achievable sum rate is transformed into a sequence of geometric programmings (GPs), which can be solved efficiently with standard convex optimization tools. The optimization problem of maximizing harvested sum energy is proved to be solvable as a linear program. The optimization problem of maximizing joint QoS requirements is shown to be a multi‐objective optimization problem (MOOP) and solved by a one‐dimensional search based on GP. Finally, numerical results manifest that the proposed power allocation algorithms can provide good system performances of achievable sum rate and harvested sum energy, and jointly guarantee QoS fairness in terms of achievable rate and harvested energy.

Software defined networking agent demonstration to enable configuration and management of XGS-PON architectures

This paper describes the design and implementation of an OpenFlow software defined network (SDN) agent that manages and configures 10-gigabit-capable symmetric passive optical network (XGS-PON) architectures. Acting as an OpenFlow switch, the SDN agent communicates with an SDN controller using OpenFlow, while holding direct communication with the optical line terminal (OLT) through the chipset manufacturer-specific application programming interface, eliminating the need for emulating SDN layers in hardware devices. The proposal was evaluated through experiments conducted on a White Box XGS-PON OLT using the Open Network Operating System. The results demonstrate that the proposal facilitates a real-time SDN configuration of various Internet services, successfully fulfilling different quality of service requirements. Due to its ease of deployment, low complexity, smooth learning curve, scalability, and flexibility in integrating services, the proposal has significant potential. As a result, it offers a rapid SDN solution for configuring and testing new functionalities with minimal programming changes required in specific layers of the developed SDN agent.

Application-Aware Scheduling for IEEE 802.15.4e Time-Slotted Channel Hopping Using Software-Defined Wireless Sensor Network Slicing.

Given the improvements to network flexibility and programmability, software-defined wireless sensor networks (SDWSNs) have been paired with IEEE 802.15.4e time-slotted channel hopping (TSCH) to increase network efficiency through slicing. Nonetheless, ensuring the quality of service (QoS) level in a scalable SDWSN remains a significant difficulty. To solve this issue, we introduce the application-aware (AA) scheduling approach, which isolates different traffic types and adapts to QoS requirements dynamically. To the best of our knowledge, this approach is the first to support network scalability using shared timeslots without the use of additional hardware while maintaining the application's QoS level. The AA approach is deeply evaluated compared with both the application traffic isolation (ATI) approach and the application's QoS requirements using the IT-SDN framework and by varying the number of nodes up to 225. The evaluation process took into account up to four applications with varying QoS requirements in terms of delivery rate and delay. In comparison with the ATI approach, the proposed approach enhanced the delivery rate by up to 28% and decreased the delay by up to 57%. Furthermore, even with four applications running concurrently, the AA approach proved capable of meeting a 92% delivery rate requirement for up to 225 nodes and a 900 ms delay requirement for up to 144 nodes.

User Pairing for Delay-Limited NOMA-Based Satellite Networks with Deep Reinforcement Learning.

In this paper, we investigate a user pairing problem in power domain non-orthogonal multiple access (NOMA) scheme-aided satellite networks. In the considered scenario, different satellite applications are assumed with various delay quality-of-service (QoS) requirements, and the concept of effective capacity is employed to characterize the effect of delay QoS limitations on achieved performance. Based on this, our objective was to select users to form a NOMA user pair and utilize resource efficiently. To this end, a power allocation coefficient was firstly obtained by ensuring that the achieved capacity of users with sensitive delay QoS requirements was not less than that achieved with an orthogonal multiple access (OMA) scheme. Then, considering that user selection in a delay-limited NOMA-based satellite network is intractable and non-convex, a deep reinforcement learning (DRL) algorithm was employed for dynamic user selection. Specifically, channel conditions and delay QoS requirements of users were carefully selected as state, and a DRL algorithm was used to search for the optimal user who could achieve the maximum performance with the power allocation factor, to pair with the delay QoS-sensitive user to form a NOMA user pair for each state. Simulation results are provided to demonstrate that the proposed DRL-based user selection scheme can output the optimal action in each time slot and, thus, provide superior performance than that achieved with a random selection strategy and OMA scheme.