Coordinated load balancing, handoff/cell-site selection, and scheduling in multi-cell packet data systems

Abstract

We investigate a wireless system of multiple cells, each having a downlink shared channel in support of highspeed 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) downlink 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.

User Association With Unequal User Priorities in Heterogeneous Cellular Networks

“…Second, after several approximation steps (please refer to Appendix C for details), the likelihood ratio of the two messages from B j to M i can be approximated as (19) where the denominator of the item in the right hand side can be rewritten as ∑…”

“…In addition, some works on this problem are based on centralized algorithms, which require information of all the BSs and MUs [16,17]. Authors in [19] perform localized and distributed optimizations, but central tuning is still required. Furthermore, the above papers have not taken into account the user priority when performing the optimization, while in practice, it is common that MUs have different priorities.…”

Joint load balancing of radio and transport networks in LTE system

“…LB have been extensively studied, and some of the recently published, most relevant and representative work are reviewed as follows: Theoretic optimization of LB for HSDPA-like systems was studied in [1], in which a utility of the long-term revenue of the operators is defined to maximize the whole system throughput. Based on [1], the authors of [2] applied the same system utility to the OFDMA-based systems including the consideration of FFR (Fractional Frequency Reuse) scheme for an optimized LB decision.…”

“…Based on [1], the authors of [2] applied the same system utility to the OFDMA-based systems including the consideration of FFR (Fractional Frequency Reuse) scheme for an optimized LB decision. In order to differentiate the impact of LB on Constant Bit Rate (CBR) and Best Effort (BE) traffic, [3] further introduced two separate utilities to formulate a multi-objective optimization problem.…”

“…It has been discussed in [1] and [2] that multi-objective optimization problems involving a multitude of variables is hardly to be analytically solved. A global and greedy searching algorithm will take a huge effort to find an optimized solution in a LB interval.…”