Recently, the use of heterogeneous small-cell networks to offload traffic from existing cellular systems has attracted considerable attention. One of the significant challenges in heterogeneous networks (HetNet) is cross-tier interference, which becomes significant when macro-cell users (MUE) are in the vicinity of femtocell base stations (FBS). Indeed, the femtocell will cause significant interference to MUEs on the macrocell downlink (DL) while MUEs will induce hefty interference to the femtocell on the macrocell uplink (UL). Substantial work has focused on offloading and interference mitigation in HetNets; yet, none of them has considered the impact of cross-tier interference on quality of service (QoS), quality of experience (QoE). This paper proposes the Quality Efficient Femtocell Offloading Scheme (QEFOS) that selects the users most affected by the interference encountered and offloads them to nearby FBSs. QEFOS testing shows substantial improvements in terms of QoS and QoE perceived by users in heavy cross-tier interference scenarios in comparison with alternative approaches. In particular QEFOS's impact on throughput, packet loss ratio (PLR), peak-to-signalnoise ratio (PSNR), and structural similarity identity matrix (SSIM) was assessed.
The 5G network technologies are intended to accommodate innovative services with a large influx of data traffic with lower energy consumption and increased quality of service and user quality of experience levels. In order to meet 5G expectations, heterogeneous networks (HetNets) have been introduced. They involve deployment of additional low power nodes within the coverage area of conventional high power nodes and their placement closer to user underlay HetNets. Due to the increased density of small-cell networks and radio access technologies, radio resource management (RRM) for potential 5G HetNets has emerged as a critical avenue. It plays a pivotal role in enhancing spectrum utilization, load balancing, and network energy efficiency. In this paper, we summarize the key challenges i.e., cross-tier interference, co-tier interference, and user association-resource-power allocation (UA-RA-PA) emerging in 5G HetNets and highlight their significance. In addition, we present a comprehensive survey of RRM schemes based on interference management (IM), UA-RA-PA and combined approaches (UA-RA-PA + IM). We introduce a taxonomy for individual (IM, UA-RA-PA) and combined approaches as a framework for systematically studying the existing schemes. These schemes are also qualitatively analyzed and compared to each other. Finally, challenges and opportunities for RRM in 5G are outlined, and design guidelines along with possible solutions for advanced mechanisms are presented.
The recent worldwide sanitary pandemic has made it clear that changes in user traffic patterns can create load balancing issues in networks (e.g., new peak hours of usage have been observed, especially in suburban residential areas). Such patterns need to be accommodated, often with reliable service quality. Although several studies have examined the user association and resource allocation (UA-RA) issue, there is still no optimal strategy to address such a problem with low complexity while reducing the time overhead. To this end, we propose Performance-Improved Reduced Search Space Simulated Annealing (P IRS 3 A), an algorithm for solving UA-RA problems in Heterogeneous Networks (HetNets). First, the UA-RA problem is formulated as a multiple 0/1 knapsack problem (MKP) with constraints on the maximum capacity of the base stations (BS) along with the transport block size (TBS) index. Second, the proposed P IRS 3 A is used to solve the formulated MKP. Simulation results show that P IRS 3 A outperforms existing schemes in terms of variability and Quality of Service (QoS), including throughput, packet loss ratio (PLR), delay, and jitter. Simulation results also show that P IRS 3 A generates solutions that are very close to the optimal solution compared to the default simulated annealing (DSA) algorithm.
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.
One of the most important 5G features is their support for heterogeneous networks (HetNets). Complementing the classic macrocell base stations (MBS), femtocell base stations (FBS) are beneficial in terms of extensive coverage, including indoor, and enhancement of capacity. Unfortunately, FBSs performance in 5G HetNets is affected by complex cross-tier and co-tier interferences, causing reduced quality of service (QoS) and unfairness among users. This paper proposes an innovative resource allocation (RA) algorithm for interference mitigation (IM) based on graph coloring techniques to improve QoS and interuser fairness. The proposed algorithm, named Weighted Edge-Weighted Vertex Interference Mitigation (WEWVIM), employs a weight to the directed edge corresponding to the interference strength from nearby base stations (BSs) and a weight to every vertex, indicating the color with the smallest interference or higher transmission rate. A region of interest (ROI) is formed to find the interfering BSs. Simulation results show that WEWVIM outperforms existing schemes in terms of fairness and QoS, including throughput, packet loss ratio (PLR), delay, and jitter.
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