A Stackelberg Game Approach for Energy-Efficient Resource Allocation and Interference Coordination in Heterogeneous Networks

Abstract

In this paper, we propose a game-theoretical approach for energy-efficient resource allocation and interference coordination in heterogeneous networks (HetNets). Considering the joint interaction of cross-tier interference and energy consumption in a two-tier HetNet consisting of one central macro and N picos, we develop the system model and measure the energy efficiency (EE) by utility. Since global optimization of the HetNet EE is intractable and computationally expensive, we formulate the optimization problems for macro and picos respectively as a two-stage Stackelberg game. Then we employ a backward induction method to solve this game model and analyze the method complexity. Simulation results verify the performance of the proposed approach and it can be easily implemented in a distributed manner in practical HetNets.

Interference-Aware Energy Efficiency Maximization in 5G Ultra-Dense Networks

“…It is known that the optimal Nash cooperative bargaining solution (NBS)-based control will achieve an optimal tradeoff between Nash fairness and Nash axiomatic efficiency under the framework of Nash axiomatic theory, which has been verified in our previous NBS-formulated work of [20]- [22]. In summary, the cooperative EE maximization game can be achieved by solving the Nash-product problem of u ℓ is systematic utility function, which characterizes the player's EE preference regarding the tradeoff between EE and SE as described in (7).…”

“…Objective (10a) is the Nash product function of the EE function defined in (7) and the minimum EE of the player ℓ, which is in line with the Nash bargaining game-theoretic framework. Constraint (10b) represents the summation of SE that is larger or equal to a minimum SE requirement π. π ℓ and π max ℓ are the SE and maximum SE of player ℓ, respectively.…”

“…There are many seminal research attempting for the EE and the SE maximization, or tradeoff optimization, and we surveyed them in [28]. Meanwhile, various interference-aware management strategies have been designed for either SE or EE optimization [7]- [12]. However, it is not simple to simultaneously optimize both the EE and the SE [8], [12], [13].…”

“…Observing the closed-form of tradeoff relationship between the spectrum and the energy efficiencies in (7), we know that it is important and different from the literature, e.g., [34], because it characterizes the relationship between the EE and the SE. Meanwhile, here we can clearly see the effects of the number of small cells, the interference, and circuit power on the tradeoff.…”

Energy Efficient Pico Cell Range Expansion and Density Joint Optimization for Heterogeneous Networks with eICIC

“…Existing literature mainly focused on the pico CRE bias, ABS power and other parameters’ optimization for the network capacity maximization or link reliability improvement in HetNets [ 5 , 9 , 16 , 17 ], and recent works began to shift to the network EE optimization with the consideration of inter-tier data channel interference coordination and mitigation [ 18 , 19 , 20 , 21 , 22 , 23 ], including spectrum allocation [ 18 , 19 ], power control [ 20 , 21 , 22 ], and cognitive sensing based on the inter-cell co-channel downlink interference coordination [ 23 ]. In [ 24 ], the authors maximized the EE of pico cells in HetNets by means of some non-convex methods.…”