Fronthaul-Aware Software-Defined Joint Resource Allocation and User Scheduling for 5G Networks (original) (raw)
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Fronthaul-Aware Software-Defined Wireless Networks: Resource Allocation and User Scheduling
IEEE Transactions on Wireless Communications
Software-defined networking (SDN) provides an agile and programmable way to optimize radio access networks via a control-data plane separation. Nevertheless, reaping the benefits of wireless SDN hinges on making optimal use of the limited wireless fronthaul capacity. In this work, the problem of fronthaulaware resource allocation and user scheduling is studied. To this end, a two-timescale fronthaul-aware SDN control mechanism is proposed in which the controller maximizes the time-averaged network throughput by enforcing a coarse correlated equilibrium in the long timescale. Subsequently, leveraging the controller's recommendations, each base station schedules its users using Lyapunov stochastic optimization in the short timescale, i.e., at each time slot. Simulation results show that significant network throughput enhancements and up to 40% latency reduction are achieved with the aid of the SDN controller. Moreover, the gains are more pronounced for denser network deployments.
Performance Improvement of Time-Sensitive Fronthaul Networks in 5G Cloud-RANs Using Reinforcement Learning-Based Scheduling Scheme, 2024
The rapid surge in internet-driven smart devices and bandwidth-hungry multimedia applications demands high-capacity internet services and low latencies during connectivity. Cloud radio access networks (C-RANs) are considered the prominent solution to meet the stringent requirements of fifth-generation (5G) and beyond networks by deploying the fronthaul transport links between baseband units (BBUs) and remote radio heads (RRHs). High-capacity optical links could be conventional mainstream technology for deploying the fronthaul in C-RANs. But densification of optical links significantly increases the cost and imposes several design challenges on fronthaul architecture which makes them impractical. Contrary, Ethernet-based fronthaul links can be lucrative solutions for connecting the BBUs and RRHs but are inadequate to meet the rigorous end-to-end delays, jitter, and bandwidth requirements of fronthaul networks. This is because of the inefficient resource allocation and congestion control schemes for the capacity constraint Ethernet-based fronthaul links. In this research, a novel reinforcement learning-based optimal resource allocation scheme has been proposed which eradicates the congestion and improves the latencies to make the capacity-constraints low-cost Ethernet a suitable solution for the fronthaul networks. The experiment results verified a notable 50% improvement in reducing delay and jitter as compared to the existing schemes. Furthermore, the proposed scheme demonstrated an enhancement of up to 70% in addressing conflicting time slots and minimizing packet loss ratios. Hence, the proposed scheme outperforms the existing state-of-the-art resource allocation techniques to satisfy the stringent performance demands of fronthaul networks.
Joint QoS-control and handover optimization in backhaul aware SDN-based LTE networks
Wireless Networks, 2019
Future cellular networks will be dense and require key traffic management technologies for fine-grained network control. The problem gets more complicated in the presence of different network segments with bottleneck links limiting the desired quality of service (QoS) delivery to the last mile user. In this work, we first design a framework for software-defined cellular networks (SDCN) and then propose new mechanisms for management of QoS and non-QoS users traffic considering both access and backhaul networks, jointly. The overall SDN-LTE system and related approaches are developed and tested using network simulator (ns-3) in different network environments. Especially, when the users are non-uniformly distributed, the results shows that compared to other approaches, the proposed load distribution algorithm enables at least 6% and 23% increase in the average QoS user downlink (DL) throughput for all network users and the aggregate throughput of 40% users with lowest throughput (edge users), respectively. Also, the proposed system efficiently achieves desired QoS and handles the network congestion without incurring significant overhead.
Backhaul, QoS, and channel-aware load balancing optimization in SDN-based LTE networks
2017 11th International Conference on Signal Processing and Communication Systems (ICSPCS), 2017
Future cellular networks utilizes complex network technologies that makes it hard to achieve fine-grained traffic control. Moreover dynamic channel conditions alongwith finite backhaul capacity, limits the desired quality of service (QoS) objectives. This work aims to design a framework for softwaredefined cellular networks (SDCN) and suggests channel and QoS-aware load balancing procedures that jointly consider both access and back-haul networks. Based on the overall access and backhaul load information we first formulate optimization problems for both QoS and non-QoS users, respectively, considering their specific objectives. Then a realistic algorithm is proposed consisting of users scheduling, load estimation, handover decision, admission, and rate control procedures. To achieve optimal control of backhaul and access network segments in a timely efficient manner, the procedures are envisioned to run in a distributed fashion. Using our system-level SDN-LTE testbed developed in the network simulator (NS3), the proposed system is evaluated and compared with other state-of-the-art cell association algorithms that either consider the fairness in load distribution factor or just maximizes the end user rate while ignoring the load metric.
A Distributed Offloading Market for 5G Heterogeneous Networks
2018 IEEE Global Communications Conference (GLOBECOM), 2018
Concerns have been raised regarding the economical viability for each operator to have a full regional 5G coverage. A possible solution is to have traffic offloaded to competitors. In this work we present a new scheme for optimal offloading in a stochastic environment. This is more in line with the conditions 5G base stations will face with changing link and traffic conditions. The problem is formulated as a Stackelberg game, and the players' utility functions are derived though queuing models. Numerical results illustrate that our scheme provides a global optimal resource allocation up to a threshold. The threshold is a function of the traffic load and the number of offloading candidates. Beyond the threshold players still have incentives to participate, but the market equilibrium is not globally optimal.
Orchestrating Resource Management in LTE-Unlicensed Systems With Backhaul Link Constraints
IEEE Transactions on Wireless Communications, 2019
Long term evolution (LTE)-unlicensed, an extension of LTE Advanced to unlicensed spectrum, can provide high performance and seamless user experience. To reap the full benefits of the LTE-unlicensed deployment, efficient resource allocation and interference management are critical to ensuring a harmonious coexistence between LTE-unlicensed and WiFi systems. In this paper, we study a resource orchestration scheme for an LTE-unlicensed network where small cells share the same unlicensed spectrum with a WiFi system. An optimization problem for channel and power allocations is formulated to maximize the overall network utility, which is an NP-hard problem. The problem is constrained on meeting the desired data rate demands of the served small-cell users, the capacity-limited backhaul links, and the maximum tolerable interference at the WiFi access point. To solve this challenging problem, a distributed solution based on Lagrangian relaxation is proposed to assist the LTEunlicensed network in making decisions on channel allocation and transmit power. Furthermore, low-complexity solutions are devised upon applying the one-to-one matching game theory. The simulation results with practical parameter settings show that the proposed algorithms converge to the suboptimal solution after a small number of iterations in the considered examples.
NPRA: Novel Policy Framework for Resource Allocation in 5G Software Defined Networks
ICST Transactions on Mobile Communications and Applications
In cellular networks, physical resources are always limited, especially when shared among different contributors such as mobile network operator (MNO) or mobile virtual network operators (MVNO) etc. Software Defined Network (SDN) and Network Function Virtualization (NFV) is a Current research area. SDN-based cellular networks provide high Quality of Services (QoS) to the end-user and NFV provides isolation. The sharing of resources is often provided by leveraging virtualization. SDN can generate new forwarding rules and policies for dynamic routing decision based on the traffic classification. However, virtualization in cellular networks is still in infancy and many issues and challenges remain unaddressed. The queue-length problem for providing QoS is cellular network requires attention. The queue management requires separate management protocols for fair allocation of resources. In this research paper, we propose a novel framework for resource allocation and bandwidth management in the 5G cellular network. We are using two level of virtualization, i.e., implementing dynamic resource optimization at network slice manager and executing optimized policies at the wireless virtual manager.
QoS-aware scheduling in LTE-A networks with SDN control
The 3GPP Long Term Evolution Advanced (LTE-A) standard specifies a set of pioneer features such as relay nodes and carrier aggregation. At the same time, the Software Defined Networks (SDN) have become an emerging technology which provides centralized control and programmability to modern networks. In the current communication environment, cloud computing could combine the advantages of both technologies in order to create a novel cloud assisted Software Defined LTE-A architecture with relay nodes. Moreover, due to the increased requirements of modern services, the optimal resource allocation is a necessity. In such a context, this paper describes a QoS aware cross carrier scheduler for downlink flows, aiming at the optimization of system resources allocation. The proposed scheduler is evaluated against the PF, MLWDF, EXP/PF, EXP RULE, LOG RULE, FLS and FLSA schedulers in a cloud assisted Software Defined LTE-A topology with relay nodes. Simulation results show that the proposed scheduler improves the real time services performance while at the same time maintains an acceptable performance for best effort flows.
Two-Stage Rate Allocation Game in Wireless Access Networks With PON Backhaul
IEEE Communications Letters, 2018
To support the increasing demand for mobile broadband service by massive number of users, the 5G communication standard facilitates the convergence of wireless access networks with backhauling supported by passive optical network (PON). A key challenge of this type of converged network architecture is the distribution of bandwidth or data-rate from central office (CO) to the different mobile operators (MOs) providing wireless broadband services at access networks and then to the end users (EUs). This present paper investigates the application of a novel pricing based rate allocation scheme using twostage Stackelberg competition on optical-wireless hybrid network architecture considering users' satisfaction level and pricing affordability. Numerical calculations have been carried out for one specific network scenario to study the data rate distribution, and the behavior of utilities of different network entities with variation of different parameters.
Resource Matching in Carrier Aggregation Enabling 5G Networks
Wireless Personal Communications, 2016
Carrier aggregation (CA) is one of the main enabling technologies for the 5G wireless networks. The CA increases overall bandwidth of the wireless networks for both uplink and downlink. In 5G wireless networks, both 5th and earlier generation users need to assign resources over the aggregated bandwidth. Earlier scheduling mechanisms cannot be applied directly to 5G wireless networks with the CA. A mechanism is needed to support resource scheduling over the aggregated bandwidth. Recent work on the resource scheduling has considered component carrier selection, resource allocation and link adaption as separate issues. In this paper, we model the problem as a matching game. Game theory is utilized for the resource matching in the proposed algorithm with a matching game model designed to match resources. Stable matching problems match two sets of candidates such that the outcome is stable. With a stable outcome, there is no incentive for candidates on either side to form a new pair. This paper introduces the concept of using the matching game for resource allocation in the carrier aggregated 5G wireless networks. The proposed algorithm considers channel conditions while matching resources. A cross-carrier resource allocation method is used, and stable matching is completed with the matching game.