A note on power allocation for optimal capacity

TL;DR

This paper investigates optimal power allocation strategies to maximize capacity and minimize latency, employing polynomial-time algorithms and convex surrogates, without SINR range assumptions, ensuring efficiency and near-optimal solutions.

Contribution

It introduces polynomial-time solutions for capacity maximization and a convex surrogate approach for latency minimization, applicable without SINR constraints.

Findings

Optimal power allocation for capacity maximization via linear programming.

Convex surrogate approach yields near-optimal latency minimization.

Algorithms are computationally efficient and validated through simulations.

Abstract

The problems of determining the optimal power allocation, within maximum power bounds, to (i) maximize the minimum Shannon capacity, and (ii) minimize the weighted latency are considered. In the first case, the global optima can be achieved in polynomial time by solving a sequence of linear programs (LP). In the second case, the original non-convex problem is replaced by a convex surrogate (a geometric program), using a functional approximation. Since the approximation error is relatively low, the optima of the surrogate is close to the global optimal point of the original problem. In either cases, there is no assumption on the SINR range. The use of LPs and geometric programming make the proposed algorithms numerically efficient. Computations are provided for corroboration.