Reduced Complexity Optimal Resource Allocation for Enhanced Video Quality in a Heterogeneous Network Environment

Abstract

The latest Heterogeneous Network (HetNet) environments, supported by 5th generation (5G) network solutions, include small cells deployed to increase the traditional macrocell 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 (RS 3 A) and Performance-Improved Reduced Search Space SA (P IRS 3 A), 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 RS 3 A and P IRS 3 A are used to solve the formulated MKP. Simulation results show that the proposed scheme P IRS 3 A outperforms RS 3 A and other existing schemes such as Default Simulated Annealing (DSA), and Default Genetic Algorithm (DGA) in terms of variability and DSA and RS 3 A in terms of Quality of Service (QoS) metrics, including throughput, packet loss ratio (PLR), delay and jitter. Simulation results show that P IRS 3 A generates solutions that are very close to the optimal solution.

QoE-Driven Optimization in 5G O-RAN-Enabled HetNets for Enhanced Video Service Quality

“…Compared to standard algorithms, the Default Genetic Algorithm (DGA) has intelligence, parallelism, self-organization, expansibility, quick application, and high robustness. As a result, it may be used to solve problems involving combinatorial optimization, such as MKP [10]. However, DGA has limitations such as poor local searchability, high convergence rate, difficulty avoiding local optima, and quick diversification loss [11].…”

Study of Enterprise Resource Optimization Scheme from the Perspective of Knapsack Problems

“…An intelligent fuzzy mathematical programming approach is used for the optimization of human resources and industrial banks with ambiguous inputs [24]. Some complex problems of optimization are looked at as knapsack problems [25], such as memory access efficiency of GPUs [26] and video quality in a heterogeneous network environment [27]. Knapsack problems have attracted the attention of scholars and practitioners for their simple structure that can help explore many combination characteristics and solve the more complex optimization problem that includes a series of knapsack problems.…”

Joint Performance-Resource Optimization for Improved Video Quality in Fairness Enhanced HetNets

“…Note that only UEs in Q will be selected in the current episode. Vector P, denoting the estimated available throughput (as specified in [12]), vector R specifying the users demands (e.g., throughput) and set Q will then be created. In this phase, an array χ is formed, which contains the best optimal solution from each generation, and it is the initial population for Phase II, as explained in Algorithm 2.…”