In heterogeneous networks (HetNets) where femtocell base stations (FBSs) are deployed within the radio coverage of macrocell base stations (MBSs) to increase network capacity, co-channel interference limits overall system performance with universal frequency reuse. This paper investigates new distributed downlink discrete power control scheme for FBSs in HetNets with FBSs cooperation. The objective of the proposed power control scheme is to maximize the number of simultaneous FBSs transmissions in a single transmission wireless channel where each FBS is allowed to transmit only if the signal-to-interference-noise ratio (SINR) requirements for both FBSs and MBS users are satisfied. We apply a stochastic learning automata technique to FBSs where each FBS is treated as a learning automaton and maintains a probability vector to select its discrete transmit power. During the learning process, each FBS adjusts its probability vector based on the feedback from FGW that indicates the number of FBSs transmissions that can be supported under the SINR requirement constraints of FUEs and MUEs. Simulation results show the proposed algorithm can achieve more than twice the number of simultaneous FBS transmissions achieved by existing schemes in the literature.

Bahasa Abstract

Kontrol Daya Terpisah dalam Jaringan Heterogen. Dalam jaringan heterogen (HetNets) di mana BTS femtocell (FBS) dikerahkan dalam cakupan radio BTS macrocell (MBS) untuk meningkatkan kapasitas jaringan, interferensi saluran bersama (co-channel) membatasi kinerja sistem secara keseluruhan dengan menggunakan kembali frekuensi universal. Makalah ini menyelidiki skema kontrol daya terpisah downlink yang baru didistribusikan untuk FBS di HetNets dengan kerjasama sejumlah FBS. Tujuan dari skema kontrol listrik yang diusulkan adalah untuk memaksimalkan jumlah transmisi FBS simultan dalam saluran nirkabel transmisi tunggal di mana setiap FBS diperbolehkan untuk mengirimkan hanya jika persyaratan rasio signal-to-interference-noise (SINR) untuk kedua FBS dan pengguna MBS terpenuhi. Kami menerapkan teknik stochastic learning automata untuk FBS, di mana setiap FBS diperlakukan sebagai learning automaton dan mempertahankan vektor probabilitas untuk memilih daya pancar terpisah. Selama proses belajar, masing-masing FBS menyesuaikan vektor probabilitas berdasarkan umpan balik dari FGW yang menunjukkan jumlah transmisi FBS yang dapat didukung di bawah kendala persyaratan SINR dari FUE dan MUE. Hasil simulasi menunjukkan algoritma yang diusulkan dapat mencapai lebih dari dua kali jumlah transmisi FBS simultan yang dicapai berbagai skema lain yang ada dalam literatur.


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