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 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 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 throughput 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.
@cite_18 @cite_16 , SDN based backhaul network management is considered. A recent effort of SDN-based backhaul is made in @cite_18 , where the authors proposed an OpenFlow based mechanism to prioritize traffic and control rate in LTE network. The essence of their work enables backhaul networks to guarantee the rate for GBR traffic during congestion due to the bottleneck link. Similarly, the work in @cite_16 proposes an approach to tackle backhaul network congestion by utilizing OpenFlow. The main purpose of using a programmable backhaul is to allow network operators to share each others network infrastructure to achieve flexibility upon congestion.
@cite_18 @cite_16
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The growing data traffic demand is forcing network operators to deploy more base stations, culminating in dense heterogeneous networks that require a high-connectivity backhaul. This scenario imposes significant challenges for current and future cellular networks, and Software Defined Networking (SDN) has been pointed as an enabling technology to overcome existing limitations. This paper shows how the OpenFlow protocol can be integrated into existing Long Term Evolution (LTE) networks to provide the required Quality of Service (QoS) in the network infrastructure. Three OpenFlow-based mechanisms are proposed: a traffic routing, an admission control function, and a traffic coexistence mechanism. Together, they can effectively control the bandwidth usage in the backhaul infrastructure, improving the QoS and ensuring a better user experience. Simulations were performed to validate the proposed mechanisms and highlight the benefits that can be achieved with the flexibility offered by the SDN technology. In this work, new achievable rates are derived for the uplink channel of a cellular network with joint multicell processing (MCP), where unlike previous results, the ideal backhaul network has finite capacity per cell. Namely, the cell sites are linked to the central joint processor via lossless links with finite capacity. The new rates are based on compress-and-forward schemes combined with local decoding. Further, the cellular network is abstracted by symmetric models, which render analytical treatment plausible. For this family of idealistic models, achievable rates are presented for both Gaussian and fading channels. The rates are given in closed form for the classical Wyner model and the soft-handover model. These rates are then demonstrated to be rather close to the optimal unlimited backhaul joint processing rates, even for modest backhaul capacities, supporting the potential gain offered by the joint MCP approach. Particular attention is also given to the low-signal-to-noise ratio (SNR) characterization of these rates through which the effect of the limited backhaul network is explicitly revealed. In addition, the rate at which the backhaul capacity should scale in order to maintain the original high-SNR characterization of an unlimited backhaul capacity system is found. The next-generation mobile network anticipates integrated heterogeneous fronthaul and backhaul technologies referred to as a unified crosshaul architecture. The crosshaul enables a flexible and cost-efficient infrastructure for handling mobile data tsunami from dense Internet of things (IoT). However, stabilization, energy efficiency, and latency have not been jointly considered in the optimization of crosshaul performance. To overcome these issues, we propose an orchestration scheme referred to as the stabilized green crosshaul orchestration (SGCO). SGCO utilizes a Lyapunov-theory-based drift-plus-penalty policy to determine the optimal amount of offloaded data that should be processed either at the eastbound or westbound computing platforms to minimize energy consumption. To achieve system stability, the cache buffer is considered as the main constraint in developing the optimization process. Moreover, the amount of offloaded data transmitted via crosshaul links is selected by adopting the binary min-knapsack problem. Accordingly, a lightweight heuristic algorithm is proposed. As the cache buffer is stabilized and the computations are controlled, the SGCO ensures adjustable computing latency threshold for various IoT services. The performance analysis shows that the proposed SGCO scheme exposes effective energy consumption compared to other existing schemes while maintaining system stability considering latency. This paper studies an uplink multicell joint processing model in which the base-stations are connected to a centralized processing server via rate-limited digital backhaul links. We propose a simple scheme that performs Wyner-Ziv compress-and-forward relaying on a per-base-station basis followed by successive interference cancellation (SIC) at the central processor. The proposed scheme has a significantly reduced complexity as compared to joint decoding, resulting in an easily computable achievable rate region. Although suboptimal in general, this paper shows that the proposed per-base-station SIC scheme can achieve the sum capacity of a class of Wyner cellular model to within a constant gap. This paper also establishes that in order to achieve to within a constant gap to the maximum SIC rate with infinite backhaul, the limited-backhaul system must have backhaul capacities that scale logarithmically with the signal-to-interference-and-noise ratios (SINRs) at the base-stations. Further, this paper studies the optimal backhaul rate allocation problem for the per-base-station SIC model with a total backhaul capacity constraint, and shows that the sum-rate maximizing allocation should also have individual backhaul rates that scale logarithmically with the SINR at each base-station. Finally, the proposed per-base-station SIC scheme is evaluated in a practical multicell network to quantify the performance gain brought by multicell processing.