Fuzzy Logic-Based Load Balancing Algorithm in Heterogeneous Networks

A large number of small cells in the next-generation mobile networks is expected to be deployed to satisfy 5G requirements. Mobility management is one of the important issues that require considerable attention in heterogeneous networks, where 5G ultra-dense small cells coexist with the current 4G networks. An efficient handover (HO) mechanism is introduced to address this issue and improve mobility management by adjusting HO control parameters (HCPs), namely, time-to-trigger and HO margin. Dynamic HCPs (D-HCPs), which explores user experiences to adjust HCPs and make an HO decision in a self-optimizing manner, is proposed in this paper. D-HCPs classify HO failure (HOF) into three categories, namely, too late, too early and wrong cell HO, and simultaneously adjust HCPs according to the dominant HOF. The algorithm is evaluated using different performance metrics, such as HO ping-pong, radio link failure and interruption time, with different mobile speed scenarios. Simulation results show that the proposed D-HCPs algorithm adaptively optimizes the HCPs and outperforms other algorithms from the literature.

Beyond 5G (B5G) networks require advanced handover algorithms to guarantee seamless connectivity and optimum quality of service. Traditional handover methods are not sufficient to meet the stringent latency and reliability requirements of next-generation networks. To meet these challenges, the integration of fuzzy logic into handover algorithms offers a viable solution. The proposed approach utilizes parameters such as reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to-interference plus noise ratio (SINR), and user equipment (UE) speed as inputs, while dynamically adjusting the time-to-trigger (TTT) and handover margin (HOM) as outputs. To assess the effectiveness of this algorithm, handover latency (HOL) and handover interruption time (HIT) are evaluated and compared with existing algorithms in the literature. The results show better and more efficient performance in both terms of latency and interruption time.

Mobility management is an essential process in mobile networks to ensure a high quality of service (QoS) for mobile user equipment (UE) during their movements. In fifth generation (5G) and beyond (B5G) mobile networks, mobility management becomes more critical due to several key factors, such as the use of Millimeter Wave (mmWave) and Terahertz, a higher number of deployed small cells, massive growth of connected devices, the requirements of a higher data rate, and the necessities for ultra-low latency with high reliability. Therefore, providing robust mobility techniques that enable seamless connections through the UE’s mobility has become critical and challenging. One of the crucial handover (HO) techniques is known as mobility robustness optimization (MRO), which mainly aims to adjust HO control parameters (HCPs) (time-to-trigger (TTT) and handover margin (HOM)). Although this function has been introduced in 4G and developed further in 5G, it must be more efficient with future mobile networks due to several key challenges, as previously illustrated. This paper proposes a Robust Handover Optimization Technique with a Fuzzy Logic Controller (RHOT-FLC). The proposed technique aims to automatically configure HCPs by exploiting the information on Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and UE velocity as input parameters for the proposed technique. The technique is validated through various mobility scenarios in B5G networks. Additionally, it is evaluated using a number of major HO performance metrics, such as HO probability (HOP), HO failure (HOF), HO ping-pong (HOPP), HO latency (HOL), and HO interruption time (HIT). The obtained results have also been compared with other competitive algorithms from the literature. The results show that RHOT-FLC has achieved considerably better performance than other techniques. Furthermore, the RHOT-FLC technique obtains up to 95% HOP reduction, 95.8% in HOF, 97% in HOPP, 94.7% in HOL, and 95% in HIT compared to the competitive algorithms. Overall, RHOT-FLC obtained a substantial improvement of up to 95.5% using the considered HO performance metrics.

Optimization of load balancing using fuzzy Q-Learning for next generation wireless networks

Mobile communication plays an important role in the future of communication systems. To meet personalized and intelligent requirements, mobile communication has evolved from single wireless cellular network to heterogeneous mobile communication network, including wireless cellular network, wireless local network, and wireless personal network. In the heterogeneous communication system, to entirely fulfill the spectrum resource complementary advantages of such a heterogeneous wireless network, the spectrum resource trade algorithm has attracted tremendous research efforts. In this paper, the advantages and disadvantages of dynamic spectrum allocation and load balancing are discussed and spectrum auction takes the place of spectrum allocation to maximize the operation revenue. The authors design a joint spectrum auction and load balancing algorithm (SALB) based on heterogeneous network environment to develop a more flexible and efficient spectrum management. The simulation result shows that SALB increases the operation profit significantly.

An LTE-Advanced (LTE-A) Heterogeneous Network (HetNet) constitutes of a macro cell and small cells, where, the former acts as an overlay network and the latter as an underlay network. In HetNet, enabling Carrier Aggregation (CA) usually results in severe downlink interference at small cells from macro cell because of high powered transmissions from macro cell on over-lapping component carriers (co-channels). The intensity of downlink interference is much more profound in HetNet enabled with CA than compared with a single carrier HetNet and ultimately affects the Quality of Service (QoS) of users connected to small cells. Some frequency domain solutions like cross carrier scheduling, soft frequency reuse, and downlink power control have been proposed so far, but they do not provide complete orthogonal transmissions among macro and small cells, leading to adjacent channel interference. Also, a few time domain approaches like Almost Blank Subframe (ABS), dynamic ABS have been proposed in the literature. However, the resource utilization of cells decreases due to high number of ABS assignments in the dense deployment, which also results in low user throughput. In this paper, we propose a combined time domain and frequency domain novel idea for downlink interference control, which is via adaptive Discontinuous Reception (DRX) settings through Multi-Objective optimizations and an optimal carrier allocation scheme, respectively. The proposed idea mitigates the downlink co-channel interference by enabling intermittent data reception at the User Equipment (UE) through adaptive DRX settings, also it utilizes an effective carrier allocation scheme such that users are always allocated to carriers with least interference. The analytical model and simulation results reveal that the proposed idea achieves average gain of 3.5 times w.r.t cell throughput when compared with well-known downlink interference mitigation schemes.

Inbound handover (HO) or hand-in is accomplished when the user equipment (UE) performs HO from macrocell (MC) to a small cell (SC) [1] [2]. When the UE connects to a SC with a time of stay (ToS) less than a predefined time threshold, this will result in frequent unnecessary HOs and also increase service interruption which in turn will degrade the end user quality of service (QoS). In this paper, we propose an inbound HO method for the purpose of throughput enhancement and load balancing in SC heterogeneous networks (HetNets). The impact of interference from both MC and SC tiers is considered so that the UE is offloaded from congested MC and forced to perform the HO to the SC tier that supplies a sufficient data rate by selecting a proper SC target, which has the highest signal to interference plus noise ratio (SINR), from a reduced neighbour cell list (NCL). The proposed method uses a modified A3 HO triggering condition taking into account the interference and cell load. Results show that our proposed method can perform inbound HO while keeping the throughput to the maximum level. Moreover, the proposed method has significantly minimized the unnecessary inbound HOs and radio link failures compared to the competitive methods. With different network load factors, the proposed method can significantly give a good performance which yields higher throughput for the user and the network as well.

This paper introduces two kinds of handover algorithms that adjust the handover (HO) parameters of LTE (Long-Term Evolution) eNodeB (evolved NodeB) to improve the overall network performance and diminish negative effects. The focus is on the impact that two kinds of handover algorithms compare to A3 event based on the optimization of hysteresis and time-to-trigger in the different velocity environment and the different SINR. Both parameters have been designed and implemented in a dynamic system level simulator and have been investigated for different parameter sets in a high-speed railway) simulation scenario. Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ) parameters have been simulated in a dynamic link level simulation and studied the influence factors via the simulation. The results suggest that optimized handover algorithms have higher handover success rate than the classical A3 event in the different velocity environment.

Load balancing algorithms are designed to achieve efficient resource utilisation in large-scale distributed systems. The primary goal of designing load balancing algorithms is to balance the overall workload among all the nodes in distributed systems to improve the performance. This paper presents an in-depth study and detailed comparative analysis of different load monitoring and balancing algorithms employing fuzzy logic and mobile agents. A detailed qualitative as well as quantitative analysis of algorithmic performances is included. The analytical survey presented in this paper would enable understanding of applications of different algorithms under different computing environments. Furthermore, this paper proposes a hybrid architecture combining fuzzy logic and mobile agents for load balancing and monitoring in distributed systems.

Little is known about the patterns of muscle activation that subserve arm movement in three-dimensional space. In this study, activation patterns of seven arm muscles were related to the spatial direction of human arm movement. Twenty movement directions defined two orthogonal vertical planes in space. The arm movements were moderately paced; each movement lasted approximately 500 msec. New techniques of EMG analysis were developed to describe the temporal pattern of muscle activation. For each muscle, a principal component analysis revealed a common phasic and tonic waveform for all directions of movement, within one plane. A temporal shifting procedure based on best covariance values revealed activation delays associated with different movement directions. The results show a consistent pattern of temporal shifting of the common waveform for movements in different directions. Coupled with past results showing that activation amplitude is a function of the cosine angle of movement or force direction, the present results suggest a relatively simple control strategy for mechanically complex arm movements: neural circuits produce a common phasic and tonic activation waveform that is scaled in amplitude and delayed in time, depending on the desired movement direction.

Nginx is a commonly used and free open-source web server that is used as a reverse proxy server, load balancer and HTTP cache. It consumes less memory and can handle more clients with less number of processes. Nginx provides users with five predefined load balancing algorithms. However, most of these algorithms are static and some of the load balancing rules are inefficient. In order to make the load of a cluster more stable under high concurrent requests, we developed a Dynamic Load Balancing (DLB) algorithm that uses Nginx as a network security control panel to provide load balancing for a cluster of backend servers. The DLB algorithm is based on the weighted round robin module of Nginx, Logistic Regression and Maximum Likelihood Estimation (MLE) algorithm. It handles the situation of high concurrent requests and reduces the probability of omitted or under-reported incident and status. We also propose a Hybrid Load Balance Method (HLBM) that incorporates the DLB algorithm and evolutionary computing to further improve the performance. We have conducted limited experiment by using dynamic load balancing algorithm. We will complete to develop the Hybrid Load Balance method and conduct experiments for HLBM.

This survey paper provides a performance evaluation of some cell selection techniques proposed for picocells in the Long Term Evolution-Advanced (LTE-A) networks. Picocell is the focus of this study because it can be deployed in areas with high volume of traffic (such as hotspots) so as to improve the overall system capacity. A number of techniques are evaluated such as maximum Reference Signal Received Power (RSRP), Cell Range Expansion (CRE) with static offset value and adaptive offset value as well as using Almost Blank Subframes (ABS) ratio. Then a comparison is provided in terms of cell throughput, cell edge throughput and Physical Downlink Control Channel (PDCCH) outage rate. The comparison described in this paper can be useful for readers interested to select the most appreciate cell selection technique for their particular goal.

LTE-Advanced (LTE-A) macro-cell deployments are being enhanced with small cells, i.e., low-power base stations, to increase the network coverage and capacity. However, simultaneous co-channel transmissions from macro and small cells cause increased inter-cell interference and under-utilize the spectrum resources at the small cells. The following LTE-A design techniques are used to improve system performance in such deployments: (i) Carrier Aggregation (CA) to increase capacity by using additional carrier bandwidth; (ii) enhanced Inter-Cell Interference Coordination (elCIC), that includes (a) Cell Selection Biasing (CSB) to increase small cell spectrum utilization via cell range expansion; and (b) blanking data transmission on the macro cells for a certain duration of time to increase cell-edge user throughput Our objective is to maximize the CSB of the small cell, subject to user QoS constraints and blanking support from the macro cell. Towards this end, we develop an analytical model that captures the inter-dependency between elCIC techniques. We observe that, not accounting for the complex inter-dependencies between these techniques leads to a degraded network performance. We propose a framework that jointly optimizes elCIC and the assignment of multiple component carriers in an LTE-A deployment for increasing spectrum utilization at the small cells with appropriate blanking support from the macro cells. Our simulation results show that our approach increases the small cell spectrum utilization and aggregate cell-edge throughput by as much as 200%.

Cell biasing is an effective solution for mobile user offloading in Heterogeneous Cellular Network (HetNet). Cell biasing modifies the user association criteria by incorporating Range Expansion Bias (REB) in traditional reference signal based user association technique. For sparse user distribution, REB for picocells does not offload users located outside RSRP Threshold Coverage Area. This is because the actual Reference Signal Received Power (RSRP) value for such users is lesser than the threshold RSRP value for successful communication. On the other hand, using a negative REB value for dense user distribution around picocells is not desirable because it leads to higher interference at users and high energy consumption at macrocells. In this paper, we suggest a unified framework for coverage optimization using spectrum slicing and transmit power spreading. Our proposed framework considers a combination of subchannel transmit power and REB which is based on the user distribution around picocells. Additionally, in order to protect the signal quality of macrocell users located close to picocells, we suggest location aware subchannel allocation technique. This technique maximizes the number of Interference Free subchannels for macrocell users. Obtained results show that our proposed framework can significantly improve the coverage and system blocking for HetNet.

This paper presents and evaluates a simple but effective approach for substantially reducing inter-frequency handover (HO) failure rate. We build a machine learning model to forecast inter-frequency HO failures. For improved accuracy compared to the state-of-the-art models, we use domain knowledge to identify and leverage the model input features. These features include reference signal received power (RSRP) of the source and target base stations as well as the RSRP of the interferers for both the source and the target layers. Six machine learning classifiers are tested with the highest accuracy of 93% observed for the XGBoost classifier. The novel idea to include the RSRP of the interferes improved the accuracy of XGBoost by 10%.