Abstract-We present a concrete methodology for saving energy in future and contemporary cellular networks. It is based on re-arranging the user-cell association so as to allow shutting down under-utilized parts of the network. We consider a hypothetical static case where we have complete knowledge of stationary user locations and thus the results represent an upper bound of potential energy savings. We formulate the problem as a binary integer programming problem, thus it is NP-hard, and we present a heuristic approximation method. We simulate the methodology on an example real cellular network topology with trafficand user distribution generated according to recently measured patterns. Further, we evaluate the energy savings, using realistic energy profiles, and the impact on the user-perceived network performance, represented by delay and throughput, at various times of day. The general findings conclude that up to 50% energy may be saved in less busy periods, while the performance effects remain limited. We conclude that practical, real-time usercell re-allocation methodology, taking into account user mobility predictions, may thus be feasible and bring significant energy savings at acceptable performance impact.

Ethernet Passive Optical Networks (EPON) is a prominent solution for broadband access networks. However, little work has been done on emergency handling in EPON. In this paper, to manage emergency cases causing severe network traffic, the network state is divided into two states, i.e., the normal and the emergency states. The authors apply new prioritybased Dynamic Bandwidth Allocation (DBA) algorithms to the emergency state, each of which gives high priority to some Optical Network Units (ONUs) corresponding to important agencies. The proposed algorithms are analyzed in terms of utilization of the uplink bandwidth and the packet delay. Simulation and theoretical results show that the proposed algorithms are working properly at the emergency state and can satisfy more stringent QoS requirements than the conventional Weighted Round Robin (WRR)-based algorithms.

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