Device-to-device communications achieve efficient load balancing in LTE-advanced networks

TLDR

LTE‑Advanced networks employ low‑power nodes to improve spectral efficiency and relieve traffic hot zones, yet limited backhaul and uneven traffic can cause congestion, making efficient load balancing across multi‑tier cells essential and existing techniques insufficiently adaptive. The authors propose a device‑to‑device communication–based load‑balancing algorithm that uses D2D links to flexibly offload traffic among tier cells in real time. The algorithm redistributes traffic via D2D bridges based on real‑time load measurements, and its performance is evaluated through numerical simulations. Numerical results show that the proposed D2D‑based load‑balancing algorithm achieves significant performance gains over existing methods.

Abstract

In LTE-Advanced networks, besides the overall coverage provided by traditional macrocells, various classes of low-power nodes (e.g., pico eNBs, femto eNBs, and relays) can be distributed throughout the macrocells as a more targeted underlay to further enhance the area's spectral efficiency, alleviate traffic hot zones, and thus improve the end-user experience. Considering the limited backhaul connections within lowpower nodes and the imbalanced traffic distribution among different cells, it is highly possible that some cells are severely congested while adjacent cells are very lightly loaded. Therefore, it is of critical importance to achieve efficient load balancing among multi-tier cells in LTE-Advanced networks. However, available techniques such as smart cell and biasing, although able to alleviate congestion or distribute traffic to some extent, cannot respond or adapt flexibly to the real-time traffic distributions among multi-tier cells. Toward this end, we propose in this article a device-to-device communication-based load balancing algorithm, which utilizes D2D communications as bridges to flexibly offload traffic among different tier cells and achieve efficient load balancing according to their real-time traffic distributions. Besides identifying the research issues that deserve further study, we also present numerical results to show the performance gains that can be achieved by the proposed algorithm.