Self-Optimized OFDMA via Multiple Stackelberg Leader Equilibrium
  The challenge of self-optimization for orthogonal frequency-division multiple-access (OFDMA) interference channels is that users inherently compete harmfully and simultaneous water-filling (WF) would lead to a Pareto-inefficient equilibrium. To overcome this, we first introduce the role of environmental interference derivative in the WF optimization of the interactive OFDMA game and then study the environmental interference derivative properties of Stackelberg equilibrium (SE). Such properties provide important insights to devise free OFDMA games for achieving various SEs, realizable by simultaneous WF regulated by specifically chosen operational interference derivatives. We also present a definition of all-Stackelberg-leader equilibrium (ASE) where users are all foresighted to each other, albeit each with only local channel state information (CSI), and can thus most effectively reconcile their competition to maximize the user rates. We show that under certain environmental conditions, the free games are both unique and optimal. Simulation results reveal that our distributed ASE game achieves the performance very close to the near-optimal centralized iterative spectrum balancing (ISB) method in [5]. 
Jie Ren, Kai-Kit Wong and Jianjun Hou
cs.IT cs.GT math.IT math.OC nlin.AO