Dramatic mobile data traffic growth has spurred a dense deployment of small cell base stations (SCBSs). Small cells enhance the spectrum efficiency and thus enlarge the capacity of mobile networks. Although SCBSs consume much less power than macro BSs (MBSs) do, the overall power consumption of a large number of SCBSs is phenomenal. As the energy harvesting technology advances, base stations (BSs) can be powered by green energy to alleviate the on-grid power consumption. For mobile networks with high BS density, traffic load balancing is critical in order to exploit the capacity of SCBSs. To fully utilize harvested energy, it is desirable to incorporate the green energy utilization as a performance metric in traffic load balancing strategies. In this paper, we have proposed a traffic load balancing framework that strives a balance between network utilities, e.g., the average traffic delivery latency, and the green energy utilization. Various properties of the proposed framework have been derived. Leveraging the software-defined radio access network architecture, the proposed scheme is implemented as a virtually distributed algorithm, which significantly reduces the communication overheads between users and BSs. The simulation results show that the proposed traffic load balancing framework enables an adjustable trade-off between the on-grid power consumption and the average traffic delivery latency, and saves a considerable amount of on-grid power, e.g., 30 , at a cost of only a small increase, e.g., 8 , of the average traffic delivery latency.
@cite_9: For small cell technology to significantly increase the capacity of tower-based cellular networks, mobile users will need to be actively pushed onto the more lightly loaded tiers (corresponding to, e.g., pico and femtocells), even if they offer a lower instantaneous SINR than the macrocell base station (BS). Optimizing a function of the long-term rate for each user requires (in general) a massive utility maximization problem over all the SINRs and BS loads. On the other hand, an actual implementation will likely resort to a simple biasing approach where a BS in tier j is treated as having its SINR multiplied by a factor Aj ≥ 1, which makes it appear more attractive than the heavily-loaded macrocell. This paper bridges the gap between these approaches through several physical relaxations of the network-wide association problem, whose solution is NP hard. We provide a low-complexity distributed algorithm that converges to a near-optimal solution with a theoretical performance guarantee, and we observe that simple per-tier biasing loses surprisingly little, if the bias values Aj are chosen carefully. Numerical results show a large (3.5x) throughput gain for cell-edge users and a 2x rate gain for median users relative to a maximizing received power association.
@cite_1: We study the dynamics of network selection in heterogeneous wireless networks (HetNets). Users in such networks selfishly select the best radio access technology (RAT) with the objective of maximizing their own throughputs. We propose two general classes of throughput models that capture the basic properties of random access (e.g., Wi-Fi) and scheduled access (e.g., WiMAX, LTE, 3G) networks. Next, we formulate the problem as a non-cooperative game, and study its convergence, efficiency, and practicality. Our results reveal that: (i) Singleclass RAT selection games converge to Nash equilibria, while an improvement path can be repeated infinitely with a mixture of classes. We next introduce a hysteresis mechanism in RAT selection games, and prove that with appropriate hysteresis policies, convergence can still be guaranteed; (ii) We analyze the Pareto-efficiency of the Nash equilibria of these games. We derive the conditions under which Nash equilibria are Paretooptimal, and we quantify the distance of Nash equilibria with respect to the set of Pareto-dominant points when the conditions are not satisfied; (iii) Finally, with extensive measurement-driven simulations we show that RAT selection games converge to Nash equilibria in a small number of steps, and hence are amenable to practical implementation. We also investigate the impact of noisy throughput measurements, and propose solutions to handle them.
@cite_17: In this paper, we develop a framework for user association in infrastructure-based wireless networks, specifically focused on flow-level cell load balancing under spatially inhomogeneous traffic distributions. Our work encompasses several different user association policies: rate-optimal, throughput-optimal, delay-optimal, and load-equalizing, which we collectively denote α-optimal user association. We prove that the optimal load vector ρ* that minimizes a generalized system performance function is the fixed point of a certain mapping. Based on this mapping, we propose and analyze an iterative distributed user association policy that adapts to spatial traffic loads and converges to a globally optimal allocation. We then address admission control policies for the case where the system is overloaded. For an appropriate system-level cost function, the optimal admission control policy blocks all flows at cells edges. However, providing a minimum level of connectivity to all spatial locations might be desirable. To this end, a location-dependent random blocking and user association policy are proposed.
The traffic load balancing problem can also be modeled as an optimization problem and solved by convex optimization approaches. Ye @cite_9 modeled the traffic load balancing problem as a utility maximization problem and developed distributed user association algorithms based on the primal-dual decomposition. Kim @cite_17 proposed an @math -optimal user association algorithm to achieve flow level load balancing under spatially heterogeneous traffic distribution. The proposed algorithm may maximize different network utilities, e.g., the traffic latency and the network throughput, by properly setting the value of @math . In addition, game theory has been exploited to model and solve the traffic load balancing problems. Aryafar @cite_1 modeled the traffic load balancing problem as a congestion game in which users are the players and user association decisions are the actions.