Max-Min Fairness Optimization in Uplink Cell-Free Massive MIMO Using Meta-Heuristics

Bi-Objective Power Optimization of Radio Stripe Uplink Communications

“…In this case, the deep learning method is individually employed to solve each power optimization problem, which means that the max-sum rate and max-min fairness optimization problems are solved independently, i.e., there are two independent SO optimization problems. Finally, in [21], the authors proposed the use of three meta-heuristics (MHs) as alternative optimization approaches to solve the UL maxmin fairness power optimization problem in CF mMIMO with a per-UE power constraint and MRC at APs. They showed that these algorithms are adaptive and capable of providing near-optimal solutions with affordable computation complexity, when compared to exact schemes, such as the bisection and geometric-programming-based algorithms.…”

“…In order to tackle the computation costly nature of such optimization problems, customized MO programming using a MH approach [21] can output high quality solutions displaying network performance trade-offs. In this work, we adopt the differential evolution (DE) MH approach [22] to solve a MO optimization problem aiming to optimize the UE SE considering the max-min fairness and the maximum sum rate metrics, in a UL RS scenario, while employing the OSLP combining scheme [11].…”

“…Since no exact algorithm can efficiently solve this bi-objective optimization problem, the main motivation for the use of DE is its good performance in continuous optimization problems, capability of making the solution population to evolve towards the non-dominated front, and easiness of implementation. The computational complexity of the DE algorithm is much lower than the one of exact algorithms, with DE being capable of finding high-quality and near-optimal solutions much faster, as shown in [21] for an SO problem. This is critically important when the number of UE is high, which significantly increases the computation complexity.…”

“…The need to identify non-dominated solutions increases the computational effort with respect to just having the optimal solution to each SO problem. Therefore, instead of pursuing the global optimal solution, a less complex yet accurate approach based on the DE MH is employed [21][22][23].…”

“…The DE [21,22] is a population-based MH algorithm based on a differential mutation operator between pairs of existing individuals (solutions) in the current population and then applied to a third individual, denoted as base vector. The population is randomly initialized, being P (t) = p (t,i) , i = 1, • • • , N pop , where t is the index of the generation, i is the index of each individual and N pop is the population size.…”