How to Upgrade Wireless Networks: Small Cells or Massive MIMO?

TL;DR

This paper presents an integrated approach to optimizing wireless network upgrades by deciding between deploying small cells or upgrading macrocell base stations with massive MIMO, using a novel integer optimization model and solution algorithm.

Contribution

It introduces a new integer optimization framework and a tabu search algorithm for optimal deployment of small cells and massive MIMO, considering capacity constraints and network coverage.

Findings

The proposed algorithm finds optimal solutions efficiently, reducing computational time by up to 30%.

Simulation results demonstrate effective hybrid deployment strategies combining small cells and massive MIMO.

The approach improves network coverage and capacity while optimizing deployment costs.

Abstract

Radio network deployment and coverage optimization are critical to next-generation wireless networks. In this paper, the problem of optimally deciding on whether to install additional small cells or to upgrade current macrocell base stations (BSs) with massive antenna arrays is studied. This integrated deployment problem is cast as a general integer optimization model by using the facility location framework. The capacity limits of both the radio access link and the backhaul link are considered. The problem is shown to be an extension of the modular capacitated location problem (MCLP) which is known to be NP-hard. To solve the problem, a novel deployment algorithm that uses Lagrangian relaxation and tabu local search is proposed. The developed tabu search is shown to have a two-level structure and to be able to search the solution space thoroughly. Simulation results show how the proposed, optimal approach to upgrading an existing wireless network infrastructure can make use of a combination of both small cells and BSs with massive antennas. The results also show that the proposed algorithm can find the optimal solution effectively while having a computational time that is up to 30% lower than that of conventional algorithms.