Effective Resource Allocation in 5G-Satellite Networks
Giuseppe Araniti, Massimo Condoluci, Antonino Orsino, Antonio Iera, Antonella Molinaro
备注：
We just realized that the submitted version is not compliant with the final version of the manuscript. In addition, there are also crucial error in the formulation of the analytical results
This paper addresses the radio resource management of multicast transmissions in the emerging fifth generation satellite systems (5G-Satellite). A subgrouping approach is exploited to provide video streaming services to satellite users by splitting any multicast group into subgroups. This allows an effective exploitation of multi-user diversity according to the experienced channel conditions and the achievement of a high throughput level. The main drawback is the high computational cost usually related to the selection of the optimal subgroup configuration. In this paper we propose a low-complexity subgrouping algorithm that achieves performance close to optimum. Our solution is suitable for implementation in practical systems, such as Satellite-Long Term Evolution (S-LTE), since the computational cost does not depend on the multicast group size and the number of available resources. Through simulation campaigns conducted in different radio propagation and multicast group environments, the effectiveness of the proposed subgroup formation scheme is assessed.
Topology-Aware Cooperative Beam-Hopping Scheduling for Efficient Resource Allocation in LEO Satellite Systems
Tiangong University（天津工业大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing Normal-Hong Kong Baptist University Zhuhai（北京师范大学-香港浸会大学联合国际学院）
In this paper, channel resource allocation problem for LEO mobile satellite systems is investigated and a new dynamic channel resource allocation scheme is proposed based on differential game. Optimal channel resource allocated to each satellite beams are formulated as Nash equilibrium. It is proved that optimal channel resource allocation can be achieved and the differential game based scheme is applicable and acceptable. Numerical results shows that system performance can be improved based on the proposed scheme.
Mobile edge computing (MEC) networks are one of the key technologies for ultra-reliability and low-latency communications. The computing resource allocation solution needs to be carefully designed to guarantee the computing resource efficiency of MEC networks. Based on the potential game theory, a computing resource allocation solution is proposed to reduce energy consumption and improve computing resource efficiency in MEC networks. The computing resource allocation solution includes two parts: the first part is the power control scheme based on the potential game theory and the second part i…
In this paper, we propose a novel wireless scheme that integrates satellite, airborne, and terrestrial networks aiming to support ground users. More specifically, we study the enhancement of the achievable users' throughput assisted with terrestrial base stations, high altitude platforms (HAPs), and satellite station. The goal is to optimize the resource allocations and the HAPs locations in order to maximize the users' throughput. In this context, we propose to solve the optimization problem in two stages; first a short-term stage and then a long-term stage. In the short-term stage, we start …
We define and propose a resource allocation architecture for cellular networks. The architecture combines content-aware, time-aware and location-aware resource allocation for next generation broadband wireless systems. The architecture ensures content-aware resource allocation by prioritizing real-time applications users over delay-tolerant applications users when allocating resources. It enables time-aware resource allocation via traffic-dependent pricing that varies during different hours of day (e.g. peak and off-peak traffic hours). Additionally, location-aware resource allocation is integ…
Multimedia traffic is predicted to account for 82% of the total data traffic by the year 2020. With the increasing popularity of video streaming applications like YouTube, Netflix, Amazon Prime Video, popular video content is often required to be delivered to a large number of users simultaneously. Multicast transmission can be used for catering to such applications efficiently. The common content can be transmitted to the users on the same resources resulting in considerable resource conservation. This paper proposes various schemes for efficient grouping and resource allocation for multicast…
Considering an energy harvesting sensor network, the overall probability of event loss is derived. Based on this result, a variety of harvesting resource allocation schemes (sizing the energy storages and the harvesting devices, under a total cost constraint) are provided. Their performances are verified and compared through simulations.
The communication network context in actual systems like 5G, cloud and IoT (Internet of Things), presents an ever-increasing number of users, applications, and services that are highly distributed with distinct and heterogeneous communications requirements. Resource allocation in this context requires dynamic, efficient, and customized solutions and Bandwidth Allocation Models (BAMs) are an alternative to support this new trend. This paper proposes the BAMSDN (Bandwidth Allocation Model through Software-Defined Networking) framework that dynamically allocates resources (bandwidth) for a MPLS (…
The performance of computer networks relies on how bandwidth is shared among different flows. Fair resource allocation is a challenging problem particularly when the flows evolve over time.To address this issue, bandwidth sharing techniques that quickly react to the traffic fluctuations are of interest, especially in large scale settings with hundreds of nodes and thousands of flows. In this context, we propose a distributed algorithm that tackles the fair resource allocation problem in a distributed SDN control architecture. Our algorithm continuously generates a sequence of resource allocati…
Fog computing is emerging as a new paradigm to deal with latency-sensitive applications, by making data processing and analysis close to their source. Due to the heterogeneity of devices in the fog, it is important to devise novel solutions which take into account the diverse physical resources available in each device to efficiently and dynamically distribute the processing. In this paper, we propose a resource representation scheme which allows exposing the resources of each device through Mobile Edge Computing Application Programming Interfaces (MEC APIs) in order to optimize resource alloc…
To achieve the requirement of high data rate, low latency, user fairness for next generation wireless networks, proper designing of cross-layer optimized dynamic resource allocation algorithm is prerequisite. In this paper, we propose a dynamic resource allocation scheme in Orthogonal Frequency Division Multiple Access (OFDMA) systems to optimize the non real-time (NRT) traffic, which requires allocation of minimum quantum of data within a predefined time that does not incur packet loss. Most existing and proposed works on resource allocation schemes focused on traffic consisting of delay cons…
In this paper, we implement an application-aware scheduler that differentiates users running real-time applications and delay-tolerant applications while allocating resources. This approach ensures that the priority is given to real-time applications over delay-tolerant applications. In our system model, we include realistic channel effects of Long Term Evolution (LTE) system. Our application-aware scheduler runs in two stages, the first stage is resource block allocation and the second stage is power allocation. In the optimal solution of resource block allocation problem, each user is inhere…
In mobile edge computing (MEC), one of the important challenges is how much resources of which mobile edge server (MES) should be allocated to which user equipment (UE). The existing resource allocation schemes only consider CPU as the requested resource and assume utility for MESs as either a random variable or dependent on the requested CPU only. This paper presents a novel comprehensive utility function for resource allocation in MEC. The utility function considers the heterogeneous nature of applications that a UE offloads to MES. The proposed utility function considers all important param…
The performance of computer networks relies on how bandwidth is shared among different flows. Fair resource allocation is a challenging problem particularly when the flows evolve over time. To address this issue, bandwidth sharing techniques that quickly react to the traffic fluctuations are of interest, especially in large scale settings with hundreds of nodes and thousands of flows. In this context, we propose a distributed algorithm based on the Alternating Direction Method of Multipliers (ADMM) that tackles the multi-path fair resource allocation problem in a distributed SDN control archit…
The integration of Low Earth Orbit (LEO) satellite constellations into 5G and Beyond is essential to achieve efficient global connectivity. As LEO satellites are a global infrastructure with predictable dynamics, a pre-planned fair and load-balanced allocation of the radio resources to provide efficient downlink connectivity over large areas is an achievable goal. In this paper, we propose a distributed and a global optimal algorithm for satellite-to-cell resource allocation with multiple beams. These algorithms aim to achieve a fair allocation of time-frequency resources and beams to the cell…
Recently, there has been tremendous efforts by network operators and equipment vendors to adopt intelligence and openness in the next generation radio access network (RAN). The goal is to reach a RAN that can self-optimize in a highly complex setting with multiple platforms, technologies and vendors in a converged compute and connect architecture. In this paper, we propose two nested actor-critic learning based techniques to optimize the placement of resource allocation function, and as well, the decisions for resource allocation. By this, we investigate the impact of observability on the perf…
Spectrum sharing is an elegant solution to addressing the scarcity of the bandwidth for wireless communications systems. This research studies the feasibility of sharing the spectrum between sectorized cellular systems and stationary radars interfering with certain sectors of the communications infrastructure. It also explores allocating optimal resources to mobile devices in order to provide with the quality of service for all running applications whilst growing the communications network spectrally coexistent with the radar systems. The rate allocation problem is formulated as two convex opt…
5G new radio (NR) introduced flexible numerology to provide the necessary flexibility for multiplexing the communication of heterogeneous services on a shared channel. One of the fundamental challenges of 5G NR is to develop resource allocation schemes to efficiently exploit such flexibility to optimize resource allocation of ultra-reliable low-latency communications (URLLC) in coexistence with enhanced mobile broadband (eMBB) while ensuring their colliding performance requirements. To address this challenge, we present a new formulation of 5G NR resource allocation to accommodate eMBB and URL…
This paper presents a radio resource block allocation optimization problem for cellular communications systems with users running delay-tolerant and real-time applications, generating elastic and inelastic traffic on the network and being modelled as logarithmic and sigmoidal utilities respectively. The optimization is cast under a utility proportional fairness framework aiming at maximizing the cellular systems utility whilst allocating users the resource blocks with an eye on application quality of service requirements and on the procedural temporal and computational efficiency. Ultimately, …
We present a joint sub-channel and power allocation framework for downlink transmission an orthogonal frequency-division multiple access (OFDMA)-based cellular network composed of a macrocell overlaid by small cells. In this framework, the resource allocation (RA) problems for both the macrocell and small cells are formulated as optimization problems. Numerical results confirm the performance gains of our proposed RA formulation for the macrocell over the traditional resource allocation based on minimizing the transmission power. Besides, it is shown that the formulation based on convex relaxa…
Power consumption is a major limitation in the downlink of multibeam satellite systems, since it has a significant impact on the mass and lifetime of the satellite. In this context, we study a new energy-aware power allocation problem that aims to jointly minimize the unmet system capacity (USC) and total radiated power by means of multi-objective optimization. First, we transform the original nonconvex-nondifferentiable problem into an equivalent nonconvex-differentiable form by introducing auxiliary variables. Subsequently, we design a successive convex approximation (SCA) algorithm in order…
Demand-Aware Beam Hopping and Power Allocation for Load Balancing in Digital Twin empowered LEO Satellite Networks
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications（重庆邮电大学通信与信息工程学院）；Electronic Information and Networking Research Institute, Chongqing University of Posts and Telecommunications（重庆邮电大学电子信息与网络研究院）；Concordia University（康考迪亚大学）
Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources. To provide cloud computing services economically, it is important to optimize resource allocation under the assumption that the required resource can be taken from a shared resource pool. In addition, to be able to provide processing ability and storage capacity, it is necessary to allocate bandwidth to access them at the same time. This paper proposes an optimal resource allocation method for cloud computing environments. First, this paper develops a resource allo…
To address the rising demand for strong packet delivery guarantees in networking, we study a novel way to perform graph resource allocation. We first introduce allocation graphs, in which nodes can independently set local resource limits based on physical constraints or policy decisions. In this scenario we formalize the distributed path-allocation (PAdist) problem, which consists in allocating resources to paths considering only local on-path information -- importantly, not knowing which other paths could have an allocation -- while at the same time achieving the global property of never exce…
In networking and computing, resource allocation is typically addressed using classical sharing protocols as, for instance, the proportional division rule, the max-min fair allocation , or other solutions inspired by cooperative game theory. In this paper, we argue that, describing the resource allocation problem as a cooperative game, such classical resource allocation approaches, as well as associated notions of fairness, show important limitations. We identify in the individual satisfaction rate the key aspect of the challenge of defining a new notion of fairness and, consequently, a resour…
As Communication Service Providers (CSPs) adopt the Network Function Virtualization (NFV) paradigm, they need to transition their network function capacity to a virtualized infrastructure with different Network Functions running on a set of heterogeneous servers. This abstract describes a novel technique for allocating server resources (compute, storage and network) for a given set of Virtual Network Function (VNF) requirements. Our approach helps the telco providers decide the most effective way to run several VNFs on servers with different performance characteristics. Our analysis of prior V…
With the growing computer vision applications, lots of videos are transmitted for content analysis, the way to allocate resources can affect the performance of video content analysis. For this purpose, the traditional resource allocation schemes for video transmission in vehicular networks, such as qualityof-service (QoS) based or quality-of-experience (QoE) based schemes, are no longer optimal anymore. In this paper, we propose an efficient content driven resource allocation scheme for vehicles equipped with cameras under bandwidth constraints in order to improve the video content analysis pe…
Incentive Mechanism Design for Resource Management in Satellite Networks: A Comprehensive Survey
Phenikaa University（phenikaa大学）；University of Essex（埃塞克斯大学）；University of New South Wales（新南威尔士大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Zhejiang Gongshang University（浙江工商大学）；Nanyang Technological University（南洋理工大学）
Mobile video is considered a major upcoming application and revenue generator for broadband wireless networks like WiMAX and LTE. Therefore, it is important to design a proper resource allocation scheme for mobile video, since video traffic is both throughput consuming and delay sensitive.
Cloud computing is an increasingly popular computing paradigm, now proving a necessity for utility computing services. Each provider offers a unique service portfolio with a range of resource configurations. Resource provisioning for cloud services in a comprehensive way is crucial to any resource allocation model. Any model should consider both computational resources and network resources to accurately represent and serve practical needs. Another aspect that should be considered while provisioning resources is energy consumption. This aspect is getting more attention from industry and govern…
This paper studies a federated learning (FL) system, where \textit{multiple} FL services co-exist in a wireless network and share common wireless resources. It fills the void of wireless resource allocation for multiple simultaneous FL services in the existing literature. Our method designs a two-level resource allocation framework comprising \emph{intra-service} resource allocation and \emph{inter-service} resource allocation. The intra-service resource allocation problem aims to minimize the length of FL rounds by optimizing the bandwidth allocation among the clients of each FL service. Base…
Artificial Intelligence (AI) and Internet of Things (IoT) applications are rapidly growing in today's world where they are continuously connected to the internet and process, store and exchange information among the devices and the environment. The cloud and edge platform is very crucial to these applications due to their inherent compute-intensive and resource-constrained nature. One of the foremost challenges in cloud and edge resource allocation is the efficient management of computation and communication resources to meet the performance and latency guarantees of the applications. The hete…
Dominant resource fairness (DRF) is a popular mechanism for multi-resource allocation in cloud computing systems. In this paper, we consider a problem of multi-resource fair allocation with bounded number of tasks. Firstly, we propose the lexicographically max-min normalized share (LMMNS) fair allocation mechanism, which is a natural generalization of DRF, and design a non-trivial optimal algorithm to find a LMMNS fair allocation, whose running time is linear in the number of users. Secondly, we prove that LMMNS satisfies envy-freeness (EF) and group strategy-proofness (GSP), and analysis the …
To implement network slicing in 5G, resource allocation is a key function to allocate limited network resources such as radio and computation resources to multiple slices. However, the joint resource allocation also leads to a higher complexity in the network management. In this work, we propose a knowledge transfer based resource allocation (KTRA) method to jointly allocate radio and computation resources for 5G RAN slicing. Compared with existing works, the main difference is that the proposed KTRA method has a knowledge transfer capability. It is designed to use the prior knowledge of simil…
University of Manitoba（曼尼托巴大学）；York University（约克大学）
Increased complexity and heterogeneity of emerging 5G and beyond 5G (B5G) wireless networks will require a paradigm shift from traditional resource allocation mechanisms. Deep learning (DL) is a powerful tool where a multi-layer neural network can be trained to model a resource management algorithm using network data.Therefore, resource allocation decisions can be obtained without intensive online computations which would be required otherwise for the solution of resource allocation problems. In this context, this article focuses on the application of DL to obtain solutions for the radio resou…
This paper studies the joint user association and resource allocation in heterogeneous networks (HetNets) from a novel perspective, motivated by and generalizing the idea of fractional frequency reuse. By treating the multi-cell multi-user resource allocation as resource partitioning among multiple reuse patterns, we propose a unified framework to analyze and compare a wide range of user association and resource allocation strategies for HetNets, and provide an optimal benchmark for network performance. The enabling mechanisms are a novel formulation to consider all possible interference patte…
We show that, in a resource allocation problem, the ex ante aggregate utility of players with cumulative-prospect-theoretic preferences can be increased over deterministic allocations by implementing lotteries. We formulate an optimization problem, called the system problem, to find the optimal lottery allocation. The system problem exhibits a two-layer structure comprised of a permutation profile and optimal allocations given the permutation profile. For any fixed permutation profile, we provide a market-based mechanism to find the optimal allocations and prove the existence of equilibrium pr…
