Application of ANFIS Power Control for Downlink CDMA-Based LMDS Systems

Abstract

Rain attenuation and intercell interference are two crucial factors in the performance of broadband wireless access networks such as local multipoint distribution systems (LMDS) operating at frequencies above 20 GHz. Power control can enhance the performance of downlink CDMA‐based LMDS systems by reducing intercell interference under clear sky conditions; however, it may damage system performance under rainy conditions. To ensure robust operation under both clear sky and rainy conditions, we propose a novel power‐control scheme which applies an adaptive neuro‐fuzzy inference system (ANFIS) for downlink CDMA‐based LMDS systems. In the proposed system, the rain rate and the number of users are two inputs of the fuzzy inference system, and output is defined as channel quality, which is applied in the power control scheme to adjust the power control region. Moreover, ITU‐R P.530 is employed to estimate the rain attenuation. The influence of the rain rate and the number of users on the distance‐based power control (DBPC) scheme is included in the simulation model as the training database. Simulation results indicate that the proposed scheme improves the throughput of the DBPC scheme.

Application of Adaptive Neuro-Fuzzy Inference System for Interference Management in Heterogeneous Network

“…Each epoch of training consists of a forward pass and a backward pass . In the forward pass, data inputs applied to each node in layer 1, which performs Fuzzification of inputs.…”

“…Each layer has nodes of type either fixed or adaptive . Each node performs node function on its input as well as a set of parameters connected to it . The rule for node function designed based on the overall input‐output function that needs to do by the adaptive network.…”

“…Due to the presence of unit delay element z À1 , there subsists one-time slot delay in providing optimal transmission power. Each epoch of training consists of a forward pass and a backward pass [24]. In the forward pass, data inputs applied to each node in layer 1, which performs Fuzzification of inputs.…”