Abstract
The latest Heterogeneous Network (HetNet) environments, supported by 5th generation (5G) network solutions, include small cells deployed to increase the traditional macro-cell network performance. In HetNet environments, before data transmission starts, there is a user association (UA) process with a specific base station (BS). Additionally, during data transmission, diverse resource allocation (RA) schemes are employed. UA-RA solutions play a critical role in improving network load balancing, spectral performance, and energy efficiency. Although several studies have examined the joint UA-RA problem, there is no optimal strategy to address it with low complexity while also reducing the time overhead. We propose two different versions of simulated annealing (SA): Reduced Search Space SA ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$RS^{3}A$ </tex-math></inline-formula> ) and Performance-Improved Reduced Search Space SA ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$PIRS^{3}A$ </tex-math></inline-formula> ), algorithms for solving UA-RA problem in HetNets. First, the UA-RA problem is formulated as a multiple knapsack problem (MKP) with constraints on the maximum BS capacity and transport block size (TBS) index. Second, the proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$RS^{3}A$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$PIRS^{3}A$ </tex-math></inline-formula> are used to solve the formulated MKP. Simulation results show that the proposed scheme <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$PIRS^{3}A$ </tex-math></inline-formula> outperforms <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$RS^{3}A$ </tex-math></inline-formula> and other existing schemes such as Default Simulated Annealing (DSA), and Default Genetic Algorithm (DGA) in terms of variability and DSA and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$RS^{3}A$ </tex-math></inline-formula> in terms of Quality of Service (QoS) metrics, including throughput, packet loss ratio (PLR), delay and jitter. Simulation results show that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$PIRS^{3}A$ </tex-math></inline-formula> generates solutions that are very close to the optimal solution.
Highlights
T HERE has been an extensive increase in Internet traffic in the last decade due to the significant increase in the number of devices and users along with the diversification of rich media services such as Video on Demand (VoD), Video Conferencing (VC), Augmented Reality (AR), and Virtual Reality (VR)
The authors of this paper have previously introduced a novel quality efficient femtocell offloading scheme (QEFOS) which mitigates the effect of interferences and improves Quality of Service (QoS) and user quality of experience (QoE) [23] and a preliminary version of an enhanced simulated annealing (SA) for solving the user association (UA)-resource allocation (RA) problem as a multiple knapsack problem (MKP), which this paper extends [24]
All results provided in this subsection were computed by Eq(16) and obtained before the fine-tuning process, i.e., default SA (DSA), after fine-tuning in terms of the range of parameters, i.e., RS3A, and after further fine-tuning in terms of solution search space reduction, i.e., P IRS3A, for comparison purposes
Summary
T HERE has been an extensive increase in Internet traffic in the last decade due to the significant increase in the number of devices and users along with the diversification of rich media services such as Video on Demand (VoD), Video Conferencing (VC), Augmented Reality (AR), and Virtual Reality (VR). This has contributed to a massive increase in data traffic that puts significant pressure on the current deployed communication networks capacity, yielding reduced Quality of Service (QoS) levels.
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