Optimal downlink power allocation in cellular networks

Abstract

In this paper, we introduce a novel approach for power allocation in cellular networks. In our model, we use sigmoidal-like utility functions to represent different users' modulation schemes. Each utility function is a representation of the probability of successfully transmitted packets per unit of power consumed by a user, when using a certain modulation scheme. We consider power allocation with utility proportional fairness policy, where the fairness among users is in utility percentage i.e. percentage of successfully transmitted packets of the corresponding modulation scheme. We formulate our network utility maximization problem as a product of utilities of all users and prove that our power allocation optimization problem is convex and therefore the optimal solution is tractable. We present a distributed algorithm to allocate base station (BS) powers optimally with priority given to users running lower modulation schemes while ensuring non-zero power allocation to users running higher modulation schemes.Our algorithm prevents fluctuation in the power allocation process and is capable of traffic and modulation dependent pricing i.e. charges different price per unit power from different users depending in part on their modulation scheme and total power available at the BS. This is used to flatten traffic and decrease the service price for users.

Optimal power allocation for LTE users with different modulations

“…Later in [9], the study introduced a novel approach for power allocation in the cellular network where the user's utility function has been modeled as sigmoidal-like function. In this work, the power allocation optimization problem is formulated as a product of the utilities of all users with utility proportional fairness policy.…”

“…In Section IV, we describe the solution for mapping the CQI values to the utility function. Section V that briefly describes the optimal power allocation algorithm that was proposed in [9]. In Section VI, we discuss the simulation set up and the results along with a discussion.…”

“…The utility functions log(U i (γ i (P i ))) in the optimization problem (6) are strictly concave functions and therefore there exists a unique tractable global optimal solution. We used the optimal power allocation algorithm in [9] and this algorithm is divided into an UE algorithm shown in Algorithm (1) and an BS algorithm shown in Algorithm (2). In this algorithm each user starts with an initial bid w i (1) and transmits it to BS.…”

“…Algorithm 1 UE Algorithm [9] Send initial bid w i (1) to BS loop Receive shadow price p(n) from BS if STOP from BS then Calculate allocated rate P opt i = wi(n) p(n)…”

Position estimation of robotic mobile nodes in wireless testbed using GENI

“…Additionally, the outcome of location estimation can benefit other systems when integrated, e.g. ad-hoc networks [8], [9], networks power allocation [10]- [12] and HetNets resource allocation [13], [14].…”