Power Control in Wireless Cellular Networks

Abstract

Transmit power in wireless cellular networks is a key degree of freedom in the management of interference, energy, and connectivity. Power control in both the uplink and downlink of a cellular network has been extensively studied, especially over the last 15 years, and some of the results have enabled the continuous evolution and significant impact of the digital cellular technology. This survey provides a comprehensive discussion of the models, algorithms, analysis, and methodologies in this vast and growing literature. It starts with a taxonomy of the wide range of power control problem formulations, and progresses from the basic formulation to more sophisticated ones. When transmit power is the only set of optimization variables, algorithms for fixed SIR are presented first, before turning to their robust versions and joint SIR and power optimization. This is followed by opportunistic and non-cooperative power control. Then joint control of power together with beamforming pattern, base station assignment, spectrum allocation, and transmit schedule is surveyed one-by-one.

Decomposition algorithms for generalized potential games

“…This shows that GPGs provide a bridge between game theory and optimization and obviously suggests a possible avenue for the solution of the game: the solution of optimization problem (7). However, the solution of problem (7) might be not simple since this is, in general, a global optimization problem.…”

“…We observe that there is a large body of literature related to decomposition methods for the solution of optimization problems. However, we are not aware of any result that could be applied to (7) in order to calculate a (global minimum and therefore a) Nash equilibrium unless very stringent assumptions are made both on the function P and the set X. Verification of condition (b) in Definition 2.1 is usually rather straightforward. In some cases of interest the objective functions do not depend on the other players' variables, θ ν (x) = θ ν (x ν ), so that the interaction of the players takes places only at the level of feasible sets.…”

“…More specifically, we consider the joint optimization of transmit powers and information rates in wireless (flat-fading) communication networks. Our description is mostly based on the setting of [19]; see also [7] for a broader picture.…”

Maximizing sum rate and minimizing MSE on multiuser downlink: Optimality, fast algorithms and equivalence via max-min SIR

“…By the uplink-downlink duality, the LMMSE receiver is also the optimal transmit beamformer in the downlink max-min weighted SIR problem given by (9). Now, we are ready to use Algorithm 1 to solve the joint power control and beamforming problem in (9) In a numerical example for a ten-user IEEE 802.11 b network, we experiment with the maximum power constraint of 33mWand 1W (the largest possible value allowed in IEEE 802.11 b).…”

“…Boldface upperwith geometric convergence rate. This is achieved by applying case letters denote matrices, boldface lowercase letters denote the nonlinear Perron-Frobenius theory in [I], [2], [3] and the column vectors, italics denote scalars, and u~v (B~F) uplink-downlink duality in [4], [5], [6], [7], [8], [9], wherein denotes componentwise inequality between vectors u and v the uplink acts as an intermediate mechanism to optimize (matrices B and F). We let (BY)l denote the lth element transmit beamformers in the downlink.…”