Distributed Power Control for One-to-Many Transmissions in Gaussian Interference Channels

Abstract

In this paper, we extend the distributed power control problem for one-to-one transmissions in Gaussian interference channels to one-to-many transmission scenarios. We assume a user-centric wireless network where the end users play the roles of decision makers. We formulate the power control problem as a noncooperative game. New challenges arise due to the coupling issues among power strategy spaces of distributed end users, which make standard Nash equilibrium based noncooperative game approach inapplicable. Indeed, our problem turns out to be a generalized Nash equilibrium problem (GNEP). Resorting to variational inequality theory, we show several fundamental properties of the GNEP. Then we propose a penalty-based distributed algorithm IP 2 JA, which possesses favorable properties for practical implementation. Numerical results are provided to verify our arguments and proposed algorithm.Index Terms-Distributed power control, one-to-many transmissions, generalized Nash equilibrium problem, variational inequality.

Peer-to-Peer Multienergy and Communication Resource Trading for Interconnected Microgrids

“…Assuming that each channel is corrupted by Gaussian white noise [39], the maximum achievable rate rk,n, expressed in bit/s, is given by (1b)…”

“…all set as 30% of each type of base load, and Lein,max,k,n, Lhin,max,k,n are set as 60% of each type of load at the kth time slot [44]. Other parameters from [29]- [30][32], [34], [39] Pn,max=[0.5, 1, 2], Wn,max=[0.5, 1, 2], µp=µw =0.5. The tolerances for the distributed algorithm are 10 -5 and the step size is 1.…”

Asynchronous Distributed Downlink Beamforming and Power Control in Multi-Cell Networks

“…Each user sends the demanded power to its BS and the BS allocates the user the required power. This kind of power control can be understood as "power on demand" [8]. Besides, this iterative power control method (24) falls in the iterative power control framework in [13].…”

“…A BS only communicates with its neighboring BSs for coordination via backhaul networks. Motivated by the concept of "power on demand" [8], we let each user compute its required power and send it to its BS. The BSs allocate the users the required power.…”