File Transfer Application For Sharing Femto Access

In wireless access network optimization, today’s main challenges reside in traffic offload and in the improvement of both capacity and coverage networks. The operators are interested in solving their localized coverage and capacity problems in areas where the macro network signal is not able to serve the demand for mobile data. Thus, the major issue for operators is to find the best solution at reasonable expanses. The femto cell seems to be the answer to this problematic. In this work (This work is supported by the COMET project AWARE. http://www.ftw.at/news/project-start-for-aware-ftw), we focus on the problem of sharing femto access between a same mobile operator’s customers. This problem can be modeled as a game where service requesters customers (SRCs) and service providers customers (SPCs) are the players. This work addresses the sharing femto access problem considering only one SPC using game theory tools. We consider that SRCs are static and have some similar and regular connection behavior. We also note that the SPC and each SRC have a software embedded respectively on its femto access, user equipment (UE). After each connection requested by a SRC, its software will learn the strategy increasing its gain knowing that no information about the other SRCs strategies is given. The following article presents a distributed learning algorithm with incomplete information running in SRCs software. We will then answer the following questions for a game with $N$ SRCs and one SPC: how many connections are necessary for each SRC in order to learn the strategy maximizing its gain? Does this algorithm converge to a stable state? If yes, does this state a Nash Equilibrium and is there any way to optimize the learning process duration time triggered by SRCs software?

💡 Research Summary

The paper addresses the problem of sharing femto‑cell access among customers of a single mobile operator. The authors model the interaction between one service‑provider customer (SPC) who owns a femto‑cell and multiple service‑requester customers (SRCs) as a non‑cooperative game. The SPC’s total bandwidth B_S is split into a guaranteed “green” part (B_S^G) and a pre‑emptible “yellow” part (B_S^Y). The split is determined by the SPC’s price‑sensitivity μ and QoS‑sensitivity Γ (μ+Γ=1). Green connections are always honored and cost N₁ tokens per unit bandwidth, while yellow connections are cheaper (N₂<N₁) but can be pre‑empted by the SPC; if pre‑empted the SRC pays nothing.

Each SRC is characterized by a QoS‑sensitivity α and a price‑sensitivity β (α+β=1). The paper focuses on a file‑transfer application where QoS is measured by the file‑transfer time t. For a given t the required bandwidth lies between a minimum B_W^min (for the longest acceptable transfer time T₂) and a maximum B_W^max (for the shortest acceptable time T₁). To translate the continuous bandwidth requirement into discrete strategy choices the authors introduce a discretisation step ε and a tolerance parameter κ. A QoS‑sensitive SRC (α>0.5) defines a revenue threshold RevTh_i = α_i – κ and builds a strategy set S_i = {RevTh_i, RevTh_i+ε, …, 1}. A price‑sensitive SRC (α≤0.5) defines a cost threshold CostTh_i = α_i + κ and builds S_i = {CostTh_i, CostTh_i–ε, …, 0}. Each element s∈S_i corresponds to an interval of bandwidth that the SRC will request in both green and yellow modes, i.e., a pair (g_i =