In dense femtocell networks (DFNs), one of the main issues is interference management since interference between femtocell access points (FAPs) reduces the system performance significantly. Further, FAPs serve different numbers of femtocell user equipments (FUEs), i.e., some FAPs have more than one FUE while others have one or no FUEs. Therefore, for DFNs, an intelligent channel assignment scheme is necessary considering both the number of FUEs connected to the same FAPs and interference mitigation to improve system performance. This paper proposes a two-stage dynamic channel assignment (TS-DCA) scheme for downlink DFNs based on orthogonal frequency division multiple access/frequency division duplex (OFDMA/FDD). In stage 1, using graph coloring algorithm, a femtocell gateway (FGW) first groups FUEs based on an interference graph that considers different numbers of FUEs per FAP. Then, in stage 2, the FGW dynamically assigns subchannels to FUE clusters according to the order of maximum capacity of FAP clusters. In addition, FAPs adaptively assign remaining subchannels in FUE clusters to their FUEs in other FUE clusters. Through simulations, we first find optimum parameters of the FUE clustering to maximize the system capacity and then evaluate system performance in terms of the mean FUE capacity, unsatisfied FUE probability, and mean FAP transmission energy consumption according to the different numbers of FUEs and FAPs with a given FUE traffic load.

Open-access femtocell networks assure the cellular user of getting a better and stronger signal. However, due to the small range of femto base stations (FBSs), any motion of the user may trigger handover. In a dense environment, the possibility of such handover is very frequent. To avoid frequent communication disruptions due to phenomena such as the ping-pong effect, it is necessary to ensure the effectiveness of the cell selection method. Existing selection methods commonly uses a measured channel/cell quality metric such as the channel capacity (between the user and the target cell). However, the throughput experienced by the user is time-varying because of the channel condition, i.e., owing to the propagation effects or receiver location. In this context, the conventional approach does not reflect the future performance. To ensure the efficiency of cell selection, user's decision needs to depend not only on the current state of the network, but also on the future possible states (horizon). To this end, we implement a learning algorithm that can predict, based on the past experience, the best performing cell in the future. We present in this paper a reinforcement learning (RL) framework as a generic solution for the cell selection problem in a non-stationary femtocell network that selects, without prior knowledge about the environment, a target cell by exploring past cells' behavior and predicting their potential future states based on Q-learning algorithm. Then, we extend this proposal by referring to a fuzzy inference system (FIS) to tune Q-learning parameters during the learning process to adapt to environment changes. Our solution aims at minimizing the frequency of handovers without affecting the user experience in terms of channel capacity. Simulation results demonstrate that· our solution comes very close to the performance of the opportunistic method in terms of capacity, while fewer handovers are required on average.· the use of fuzzy rules achieves better performance in terms of received reward (capacity) and number of handovers than fixing the values of Q-learning parameters.

In this letter, we propose a novel resource allocation scheme to enhance downlink system performance for orthogonal frequency division multiple access (OFDMA) and time division duplex (TDD) based femtocell networks. In the proposed scheme, the macro base station (mBS) and femto base stations (fBSs) service macro user equipments (mUEs) and femto user equipments (fUEs) in inner and outer zones in different periods to reduce interference substantially. Simulations show the proposed scheme outperforms femtocell networks with fractional frequency reuse (FFR) systems in terms of the system capacity and outage probability for mUEs and fUEs.

In this letter, we introduce two different resource allocation and Tx power management schemes, called resource control and fixed power (RCFP) and fixed resource and power control (FRPC), in an LTE-Advanced femtocell network. We analyze and compare the two schemes in terms of the system throughput for downlink and energy consumption of home evolved NodeB (HeNB) Tx power according to the number of HeNBs and home user equipment (HUE)'s user traffic density (C). The simulation results show that the FRPC scheme has better performance in terms of system throughput for macro user equipments (MUEs) and energy consumption in low C.