Abstract
Heterogeneous networks (HetNets) are expected to be a key feature of long-term evolution (LTE)-advanced networks and beyond and are essential for providing ubiquitous broadband user throughput. However, due to different coverage ranges of base stations (BSs) in HetNets, the handover performance of a user equipment (UE) may be significantly degraded, especially in scenarios where high-velocity UE traverse through small cells. In this article, we propose a context-aware mobility management (MM) procedure for small cell networks, which uses reinforcement learning techniques and inter-cell coordination for improving the handover and throughput performance of UE. In particular, the BSs jointly learn their long-term traffic loads and optimal cell range expansion and schedule their UE based on their velocities and historical data rates that are exchanged among the tiers. The proposed approach is shown not only to outperform the classical MM in terms of throughput but also to enable better fairness. Using the proposed learning-based MM approaches, the UE throughput is shown to improve by 80% on the average, while the handover failure probability is shown to reduce up to a factor of three.
Highlights
To cope with the wireless traffic demand within the decade, operators are underlaying their macro-cellular networks with low-power base stations (BSs) [1]
Such networks are typically referred as heterogeneous networks (HetNets), and their deployment entails a number of challenges in terms of capacity, coverage, mobility management (MM), and mobility load balancing across multiple network tiers [2]
We propose multi-armed bandit (MAB) and satisfaction-based MM learning techniques as a long-term load balancing approach aiming at improving the overall system throughput while at the same time reducing the handover failures (HOFs) and PP probabilities
Summary
To cope with the wireless traffic demand within the decade, operators are underlaying their macro-cellular networks with low-power base stations (BSs) [1]. Further results on the effects of MM parameters are presented in [24], where the authors propose a fuzzy-logic-based controller This controller adaptively modifies handover parameters for handover optimization by considering the system load and UE speed in a macrocell-only network. The cell selection problem in HetNets is formulated as a network wide proportional fairness optimization problem by jointly considering the long-term channel conditions and the distribution of user load among different tiers. In [28], the authors propose a MAB-based intercell interference coordination approach that aims at maximizing the throughput and handover performance by subband selection for transmission for a small-cell-only network. The short-term UE association process is based on a proposed context-aware scheduler considering a UE’s throughput history and velocity to enable fair scheduling and enhanced cell association. The layer 1 filtered measurement is updated through a first-order infinite impulse response filter in layer 3 every 200 ms [2]
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