A game-theoretic approach to joint rate and power control for uplink CDMA communications

Abstract

Next generation wireless systems will be required to support heterogeneous services with different transmission rates that include real time multimedia transmissions, as well as non-real time data transmissions. In order to provide such flexible transmission rates, efficient use of system resources in next generation systems will require control of both data transmission rate and power for mobile terminals. In this paper we formulate the problem of joint transmission rate and power control for the uplink of a single cell CDMA system as a noncooperative game. We assume that the utility function depends on both transmission rates and powers and show the existence of Nash equilibrium in the non-cooperative joint transmission rate and power control game (NRPG). We include numerical results obtained from simulations that compare the proposed algorithm with a similar one which is also based on game theory and it also updates the transmission rates and powers simultaneously in a single step.

A user-centric economic-driven paradigm for rate allocation in non-orthogonal multiple access wireless systems

“…Power control problems have been extensively studied mainly from the aspect of reducing battery drain and enhancing energy efficiency [3][4][5]. By adding more degrees of freedom alongside power control, data rate allocation becomes feasible with the authors in [6][7][8] to convert a two-variable problem to a single parameter optimization expressed as the ratio of rate to the respective power. Following the same paradigm, the authors in [9] propose a spectrum allocation framework in cognitive radios following the load balancing concept in distributed computer systems.…”

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

“…Noting further all diagonal elements of Γ are positive, it follows that Γ is positive definite. Specifically, condition (11) implies that the interference experienced by every receiver D (m) i should be small enough while condition (12) implies that the interference generated by every receiver D (m) i should also be small enough. A careful thinking reveals that sufficient conditions (11) and (12) are indeed intuitive.…”

“…That is, Γ (m,i),(n,j) can be approximated by the receive SNR. As a result, conditions (11) and (12) are approximately equivalent to…”

“…Nevertheless, distributed power control in Gaussian interference channels recently draws great interests (see, e.g., [6] and the references therein). Indeed, noncooperative game theory has been vastly applied, and much progress has been made on distributed power control in Gaussian interference channels [7]- [12]. However, the framework of standard Nash equilibrium problems (NEPs) used in these works cannot be generalized to the one-to-many transmissions considered in this paper.…”

Resource allocation in relay-assisted MIMO MAC systems with statistical CSI

“…Instead, in this paper the more realistic scenario of a self-organizing, competitive network is considered, and a game-theoretic approach is employed to analyze the resulting conflict situation. Game-theoretic approaches have been successfully applied to devise resource allocation algorithms in many instances of communication systems, such as one-hop MIMO systems [11,12], cognitive radio [13], spread spectrum [14,15], OFDMA and ultra-wideband wireless networks [16][17][18]. Many other contributions can be found in the two following special issues on game theory applied to communication systems [19,20].…”