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.
We have divided earlier works into different categories as shown in Table . First we briefly discuss works considering backhaul and access networks separately, then we summarize the works that consider both access and backhaul jointly. & & Limited to theoretical modeling & No & No & QoS-aware @cite_23 & & Increases average cell load & No & Both & QoS and channel-aware @cite_5 , @cite_32 & & No system-level procedure & No & Both, Only QoS & Delay-aware @cite_33 & Efficient load balancing & Does not guarantee QoS & Yes & QoS & Channel-aware @cite_2 & & & & Yes & No & & & & No & No 4 * Backhaul Network & Congestion-aware @cite_0 & & & No & Yes & QoS-aware @cite_18 & QoS in LTE backhaul & No access network & Yes & Yes & & & & No & NonQoS & & & & No & Only nonQoS 3 * & Mobility-driven @cite_1 & Overload detection & & No & Only QoS & & Handover delay reduction & Limited study & Yes & Not stated
@cite_18 @cite_33 @cite_1 @cite_32 @cite_0 @cite_23 @cite_2 @cite_5
2148893247 2501770904 1975618234 2045775012
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. With the explosive growth of mobile data demand, small cells densely deployed underlying the homogeneous macro-cells are emerging as a promising candidate for the fifth generation (5G) mobile network. The backhaul communication for small cells poses a significant challenge, and with huge bandwidth available in the mmWave band, the wireless backhaul at mmWave frequencies can be a promising backhaul solution for small cells. In this paper, we propose the Maximum QoS-aware Independent Set (MQIS) based scheduling algorithm for the mmWave backhaul network of small cells to maximize the number of flows with their QoS requirements satisfied. In the algorithm, concurrent transmissions and the QoS aware priority are exploited to achieve more successfully scheduled flows and higher network throughput. Simulations in the 73 GHz band are conducted to demonstrate the superior performance of our algorithm in terms of the number of successfully scheduled flows and the system throughput compared with other existing schemes. We investigate a wireless system of multiple cells, each having a downlink shared channel in support of high-speed packet data services. In practice, such a system consists of hierarchically organized entities including a central server, Base Stations (BSs), and Mobile Stations (MSs). Our goal is to improve global resource utilization and reduce regional congestion given asymmetric arrivals and departures of mobile users, a goal requiring load balancing among multiple cells. For this purpose, we propose a scalable cross-layer framework to coordinate packet-level scheduling, call-level cell-site selection and handoff, and system-level cell coverage based on load, throughput, and channel measurements. In this framework, an opportunistic scheduling algorithm--the weighted Alpha-Rule--exploits the gain of multiuser diversity in each cell independently, trading aggregate (mean) down-link throughput for fairness and minimum rate guarantees among MSs. Each MS adapts to its channel dynamics and the load fluctuations in neighboring cells, in accordance with MSs' mobility or their arrival and departure, by initiating load-aware handoff and cell-site selection. The central server adjusts schedulers of all cells to coordinate their coverage by prompting cell breathing or distributed MS handoffs. Across the whole system, BSs and MSs constantly monitor their load, throughput, or channel quality in order to facilitate the overall system coordination. Our specific contributions in such a framework are highlighted by the minimum-rate guaranteed weighted Alpha-Rule scheduling, the load-aware MS handoff cell-site selection, and the Media Access Control (MAC)-layer cell breathing. Our evaluations show that the proposed framework can improve global resource utilization and load balancing, resulting in a smaller blocking rate of MS arrivals without extra resources while the aggregate throughput remains roughly the same or improved at the hot-spots. Our simulation tests also show that the coordinated system is robust to dynamic load fluctuations and is scalable to both the system dimension and the size of MS population. In this paper, we investigate the load balancing problem with the consideration of different qualityof- service (QoS) and channel state in 3GPP long term evolution (LTE) multi-cell networks. Load imbalance among nearby cells often causes inefficient utilization of the system resources. Then in overloaded cells, users cannot get satisfied services for short resources, while in idle cells, there is plenty of unoccupied resources. However, switching users only for load balancing with no consideration of channel state often results in aggressive handover of users, which may occupy too many resources in their target cells and deteriorate the network efficiency. Thus, how to efficiently perform load balancing with the consideration of channel state becomes an important issue. Meanwhile, LTE network aims to serve users with different QoS requirements, which should be taken into account when performing load balancing. Thus we propose a multi-objective optimization problem, whose objectives are load balancing index and network average load for users with QoS requirements and a unified utility function for users without QoS requirements. The constraints are physical resource limits and users’ QoS demands. Then we analyze its complexity and propose a practical algorithm, load balancing, with the consideration of different QoS requirements and channel state services (LBQC). Extensive simulations are conducted. The results show that the proposed algorithm can approximate the optimal algorithm efficiently, and leads to significantly better performances than load balancing with no consideration of channel state, e.g. a lower new call blocking rate with a fewer system resources occupation for users with QoS requirements, a higher cell edge throughput and total throughput for users without QoS requirements.