61 research outputs found

    Evolutionary Deep Reinforcement Learning for Dynamic Slice Management in O-RAN

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    The next-generation wireless networks are required to satisfy a variety of services and criteria concurrently. To address upcoming strict criteria, a new open radio access network (O-RAN) with distinguishing features such as flexible design, disaggregated virtual and programmable components, and intelligent closed-loop control was developed. O-RAN slicing is being investigated as a critical strategy for ensuring network quality of service (QoS) in the face of changing circumstances. However, distinct network slices must be dynamically controlled to avoid service level agreement (SLA) variation caused by rapid changes in the environment. Therefore, this paper introduces a novel framework able to manage the network slices through provisioned resources intelligently. Due to diverse heterogeneous environments, intelligent machine learning approaches require sufficient exploration to handle the harshest situations in a wireless network and accelerate convergence. To solve this problem, a new solution is proposed based on evolutionary-based deep reinforcement learning (EDRL) to accelerate and optimize the slice management learning process in the radio access network's (RAN) intelligent controller (RIC) modules. To this end, the O-RAN slicing is represented as a Markov decision process (MDP) which is then solved optimally for resource allocation to meet service demand using the EDRL approach. In terms of reaching service demands, simulation results show that the proposed approach outperforms the DRL baseline by 62.2%.Comment: This paper has been accepted for the 2022 IEEE Globecom Workshops (GC Wkshps

    Optimized traffic scheduling and routing in smart home networks

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    Home networks are evolving rapidly to include heterogeneous physical access and a large number of smart devices that generate different types of traffic with different distributions and different Quality of Service (QoS) requirements. Due to their particular architectures, which are very dense and very dynamic, the traditional one-pair-node shortest path solution is no longer efficient to handle inter-smart home networks (inter-SHNs) routing constraints such as delay, packet loss, and bandwidth in all-pair node heterogenous links. In addition, Current QoS-aware scheduling methods consider only the conventional priority metrics based on the IP Type of Service (ToS) field to make decisions for bandwidth allocation. Such priority based scheduling methods are not optimal to provide both QoS and Quality of Experience (QoE), especially for smart home applications, since higher priority traffic does not necessarily require higher stringent delay than lower-priority traffic. Moreover, current QoS-aware scheduling methods in the intra-smart home network (intra-SHN) do not consider concurrent traffic caused by the fluctuation of intra-SH network traffic distributions. Thus, the goal of this dissertation is to build an efficient heterogenous multi-constrained routing mechanism and an optimized traffic scheduling tool in order to maintain a cost-effective communication between all wired-wireless connected devices in inter-SHNs and to effectively process concurrent and non-concurrent traffic in intra-SHN. This will help Internet service providers (ISPs) and home user to enhance the overall QoS and QoE of their applications while maintaining a relevant communication in both inter-SHNs and intra-SHN. In order to meet this goal, three key issues are required to be addressed in our framework and are summarized as follows: i) how to build a cost-effective routing mechanism in heterogonous inter-SHNs ? ii) how to efficiently schedule the multi-sourced intra-SHN traffic based on both QoS and QoE ? and iii) how to design an optimized queuing model for intra-SHN concurrent traffics while considering their QoS requirements? As part of our contributions to solve the first problem highlighted above, we present an analytical framework for dynamically optimizing data flows in inter-SHNs using Software-defined networking (SDN). We formulate a QoS-based routing optimization problem as a constrained shortest path problem and then propose an optimized solution (QASDN) to determine minimal cost between all pairs of nodes in the network taking into account the different types of physical accesses and the network utilization patterns. To address the second issue and to solve the gaps between QoS and QoE, we propose a new queuing model for QoS-level Pair traffic with mixed arrival distributions in Smart Home network (QP-SH) to make a dynamic QoS-aware scheduling decision meeting delay requirements of all traffic while preserving their degrees of criticality. A new metric combining the ToS field and the maximum number of packets that can be processed by the system's service during the maximum required delay, is defined. Finally, as part of our contribution to address the third issue, we present an analytic model for a QoS-aware scheduling optimization of concurrent intra-SHN traffics with mixed arrival distributions and using probabilistic queuing disciplines. We formulate a hybrid QoS-aware scheduling problem for concurrent traffics in intra-SHN, propose an innovative queuing model (QC-SH) based on the auction economic model of game theory to provide a fair multiple access over different communication channels/ports, and design an applicable model to implement auction game on both sides; traffic sources and the home gateway, without changing the structure of the IEEE 802.11 standard. The results of our work offer SHNs more effective data transfer between all heterogenous connected devices with optimal resource utilization, a dynamic QoS/QoE-aware traffic processing in SHN as well as an innovative model for optimizing concurrent SHN traffic scheduling with enhanced fairness strategy. Numerical results show an improvement up to 90% for network resource utilization, 77% for bandwidth, 40% for scheduling with QoS and QoE and 57% for concurrent traffic scheduling delay using our proposed solutions compared with Traditional methods

    GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing

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    Network slicing is a key technology in 5G communications system. Its purpose is to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, demand-aware resource allocation is of significant importance to network slicing. In this paper, we consider a scenario that contains several slices in a radio access network with base stations that share the same physical resources (e.g., bandwidth or slots). We leverage deep reinforcement learning (DRL) to solve this problem by considering the varying service demands as the environment state and the allocated resources as the environment action. In order to reduce the effects of the annoying randomness and noise embedded in the received service level agreement (SLA) satisfaction ratio (SSR) and spectrum efficiency (SE), we primarily propose generative adversarial network-powered deep distributional Q network (GAN-DDQN) to learn the action-value distribution driven by minimizing the discrepancy between the estimated action-value distribution and the target action-value distribution. We put forward a reward-clipping mechanism to stabilize GAN-DDQN training against the effects of widely-spanning utility values. Moreover, we further develop Dueling GAN-DDQN, which uses a specially designed dueling generator, to learn the action-value distribution by estimating the state-value distribution and the action advantage function. Finally, we verify the performance of the proposed GAN-DDQN and Dueling GAN-DDQN algorithms through extensive simulations

    Traffic Scheduling in Software-defined Backhaul Network

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    In the past few years, severe challenges have arisen for network operators, as explosive growth and service differentiation in data demands require an increasing number of network capacity as well as dynamic traffic management. To adapt to the network densification, wireless backhaul solution is attracting more and more attentions due to its flexible deployment. Meanwhile, the software-defined network (SDN) proposes an promising architecture that can achieve dynamic control and management for various functionalities. In this case, by applying the SDN architecture to wireless backhaul networks, the traffic scheduling functionality may satisfy the ever-increasing and differentiated traffic demands. To tackle the traffic demand challenges, traffic scheduling for software-defined backhaul networks (SDBN) is investigated from three aspects in this thesis. In the first aspect, various virtual networks based on service types are embedded to the same wireless backhaul infrastructure. An algorithm, named VNE-SDBN, is proposed to solve the virtual network embedding (VNE) problem to improve the performance of the revenue of infrastructure providers and virtual network request acceptance ratio by exploiting the unique characteristics of SDBNs. In the second aspect, incoming traffic is scheduled online by joint routing and resource allocation approach in backhaul networks operated in low-frequency microwave (LFM) and those operated in millimetre wave (mmW). A digraph-based greedy algorithm (DBGA) is proposed considering the relationship between the degrees of vertices in the constructed interference digraph and system throughput with low complexity. In the third aspect, quality-of-service is provided in terms of delay and throughput with two proposed algorithms for backhaul networks with insufficient spectral resources. At last, as a trial research on E-band, a conceptual adaptive modulation system with channel estimation based on rain rate for E-band SDBN is proposed to exploit the rain attenuation feature of E-band. The results of the research works are mainly achieved through heuristic algorithms. Genetic algorithm, which is a meta-heuristic algorithm, is employed to obtain near-optimal solutions to the proposed NP-hard problems. Low complexity greedy algorithms are developed based on the specific problem analysis. Finally, the evaluation of proposed systems and algorithms are performed through numerical simulations. Simulations for backhaul networks with respect to VNE, routing and resource allocation are developed

    Inter-cellular scheduler for 5G wireless networks

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    International audienceEnhancing the Quality of Experience (QoE) in wireless networks is a crucial issue. Many acknowledged works focus on intra-cellular scheduling. They have shown that when the channel impairment is taken into consideration by the opportunistic scheduling approaches, it allows to reach higher throughputs and, for the most efficient ones, a higher fairness. However, if some of these works provide results near to optimum considering a single cell, high QoE cannot be guaranteed for scenarios where the cells are overloaded. In this article, we propose a new inter-cellular scheduler able to help the overloaded cells thanks to a dynamic cell bandwidth allocation. Our resource allocation technique is based on an adequate emergency parameter called Mean Cell Packet Delay Outage Ratio (MCPDOR). Performance evaluation shows that the proposed scheduler widely outperforms existing solutions in various scenarios. A variant of our solution that does not consider MCPDOR is also proposed and evaluated

    A comprehensive survey on radio resource management in 5G HetNets: current solutions, future trends and open issues

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    The 5G network technologies are intended to accommodate innovative services with a large influx of data traffic with lower energy consumption and increased quality of service and user quality of experience levels. In order to meet 5G expectations, heterogeneous networks (HetNets) have been introduced. They involve deployment of additional low power nodes within the coverage area of conventional high power nodes and their placement closer to user underlay HetNets. Due to the increased density of small-cell networks and radio access technologies, radio resource management (RRM) for potential 5G HetNets has emerged as a critical avenue. It plays a pivotal role in enhancing spectrum utilization, load balancing, and network energy efficiency. In this paper, we summarize the key challenges i.e., cross-tier interference, co-tier interference, and user association-resource-power allocation (UA-RA-PA) emerging in 5G HetNets and highlight their significance. In addition, we present a comprehensive survey of RRM schemes based on interference management (IM), UA-RA-PA and combined approaches (UA-RA-PA + IM). We introduce a taxonomy for individual (IM, UA-RA-PA) and combined approaches as a framework for systematically studying the existing schemes. These schemes are also qualitatively analyzed and compared to each other. Finally, challenges and opportunities for RRM in 5G are outlined, and design guidelines along with possible solutions for advanced mechanisms are presented
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