Energy efficient load balancing in LTE self-organization networks

Design of energy efficient load balancing models in cloud environments requires in-depth analysis of the cloud architecture and nature of requests served by the cloud. Depending upon these parameters, machine learning models are designed which aim at assigning best possible resource combination to serve the given tasks. This assignment varies w.r.t. multiple task and cloud parameters; which include task time, virtual machine (VM) performance, task deadline, energy consumption, etc. In order to perform this task, a wide variety of algorithms are developed by researchers cloud designers. Each of these algorithms aim at optimizing certain load balancing related parameters; for instance, a Genetic Algorithm (GA) designed for optimization of VM utilization might not consider task deadline before task allocation to the VMs. While, algorithms aimed at performing deadline aware load balancing might not provide effective cloud-to-task-mapping before allocation of tasks. Thus, it becomes difficult for researchers to select the best possible algorithms for their cloud deployment. In order to reduce this ambiguity, the underlying text compares different energy efficient cloud load balancing algorithms; and evaluates their performance in terms of computational complexity, and relative energy efficiency. This performance evaluation is further extended via inter architecture comparison; in order to evaluate the most optimum load balancer implementation for a given energy efficient application. Thus, after referring this text, researchers and cloud system designers will be able to select optimum algorithmic implementations for their given deployment. This will assist in reducing cloud deployment delay, and improving application specific load balancer performance.

With the augmentation of affordable multimedia wireless gadgets, the ubiquitous availability of the internet access, and the rapid pace of mobile traffic motivate research towards fifth generation (5G) communications to realize energy-efficient cloud radio access networks (C-RAN) with guaranteed quality of experience. Exploiting green energy harvesting for powering the C-RAN substantially alleviates the energy procurement from the utility grid, carbon footprint, and operational expenses. In this paper, we propose a new dynamic point selection coordinated multipoint (DPS CoMP) based load balancing paradigm emphasizing achievable throughput and energy efficiency (EE) by reducing utility grid consumption from a network level perspective. This paper investigates the radio efficiency, EE, and average on-grid energy saving addressing the key challenges of tempo-spatial dynamics of traffic intensity and renewable energy (RE) generation under a wide range of network setup. Endeavoring load balancing technique strives a balance in network utilities such as green energy utilization and user association based on BS coordination technique in a cluster. Provision of cell sleep approach is contemplated for further energy saving by turning off lightly loaded base stations (BSs) during low traffic arrivals. The proposed CoMP based load balancing algorithm proficiently manages resource block allocation to the new users and elevated the energy efficiency over the conventional location and traffic centric mechanisms. Extensive system-level simulations manifest that the suggested framework enables an adjustable trade-off between radio efficiency and EE, and saves 22% on-grid power consumption and increases EE index by 32%. Afterward, an exhaustive comparison of the proposed method with the existing schemes is pledged for further validation highlighting sustainable 5G wireless systems.

In order to meet the growing mobile data demand, future wireless networks will be equipped with a multitude of access points (APs). Besides the important implications for the energy consumption, the trend towards densification requires the development of decentralized and sustainable radio resource management techniques. It is critically important to understand how the distribution of signal processing operations affects the energy efficiency of wireless networks. In this paper, we provide a cross-layer framework to evaluate and compare the energy efficiency of wireless networks under different levels of distribution of the signal processing load: (i) hybrid, where the signal processing operations are shared between nodes and APs, (ii) centralized, where signal processing is entirely implemented at the APs, and (iii) fully distributed, where all operations are performed by the nodes. We find that in practical wireless networks, hybrid signal processing exhibits a significant energy efficiency gain over both centralized and fully distributed approaches.

Cellular wirelesses heterogeneous networks (HetNets) have become an important part of the growth of the telecom industry. The rapid growth of cellular networks and widespread use of wireless services have led to a huge increase in energy use. This makes it hard to make cellular networks that use less energy and last longer. In HetNets, load balancing (LB) and energy balance have a big effect on cell range extension (CRE) and network lifetime extension (NLE). optimal energy efficient LB in wireless cellular HetNets is presented in this paper using a self-tuning algorithm. Several self-tuning methods for HetNets have been developed that are driven by the quality of experience (QoE). Unlike earlier heuristic-based approaches, the suggested technique uses a gradient ascending method to ensure that parameter modifications constantly increase overall system QoE, LB, throughput, and energy efficiency. The planned network's packet loss rate is extremely low. The suggested and contemporary models are tested in an experimental investigation, demonstrating the importance of the suggested model in comparison to other contemporary methodologies.

Cell discontinuous transmission (DTX) has been proposed as a solution to reduce the energy consumption of cellular networks. This paper investigates the impact of network traffic load on the spectral and energy efficiency of cellular networks with DTX. The signal-to-interference-plus-noise ratio (SINR) distribution as a function of traffic load is derived first. Then, the sufficient condition for ignoring thermal noise and simplifying the SINR distribution is investigated. Based on the simplified SINR distribution, the network spectral and energy efficiency as functions of network traffic load are derived. It is shown that the network spectral efficiency increases monotonically in traffic load, while the optimal network energy efficiency depends on the ratio of the sleep-mode power consumption to the active-mode power consumption of base stations. If the ratio is larger than a certain threshold, the network energy efficiency increases monotonically with network traffic load and is maximized when the network is fully loaded. Otherwise, the network energy efficiency first increases and then decreases in network traffic load. The optimal load can be identified with a binary search algorithm. The power ratio threshold depends solely on the path loss exponent α, e.g., 56% for α = 4. All these analytic results are further validated by the numerical simulations.

Ever increasing user of electronic devices and high-data-rate applications has lead to continuous growing data traffic and hence carbon dioxide emission. Economical and environmental concern needs improved energy efficient network. As an energy efficient solution needs to outperform under various dynamic situations like varying traffic load, mobility, interference conditions etc Keeping in view of energy efficiency in wireless networks this paper aims at providing an extensive survey of the various researches at network architecture level for minimizing energy consumption in wireless mobile networks. A brief overview of the ongoing projects has also been discussed.

Network throughout and energy efficiency are of paramount importance for network performance in an energy-constrained wireless network. However, it is difficult to achieve optimal network throughout and energy efficiency simultaneously. So it is very necessary to find a solution based on tradeoff between network throughput and energy efficiency, which have not yet been well studied in existing works. In this paper, we present a cooperative differential game model and find a time-consistency solution by Shapley value which determines a fair distribution of the total cooperative cost among players.

The desire for faster data speeds and increased Energy Efficiency has prompted the development of femtocells, which are short-range, low-cost, customer cellular access points. However, in a situation of Distributed Denial of Service (DDoS) which is caused by inefficient energy, distributed attack sources could be employed to amplify the assault and increase the attack's impact. By flooding the network with packets and creating malicious traffic, Distributed Denial of Service (DDoS) attacks try to deplete the network's communication and processing capability. A DDoS assault must be identified and neutralized quickly before a valid user can reach the attacker's target for 5G network to have an effective Energy Efficient service. For the next Fifth Generation (5G) Wireless Network, there is a pressing need to build an effective Energy Efficient mobile network solution. Despite their evident promise in assisting the development and deployment of the complicated 5G environment. The physical product, the digital product, and the relationship between both the physical and virtual goods are said to make up Digital Twin (DT). On the other hand, DT allows real-time communication with both the physical twins. The synergy of energy efficiency and security improvements in this research contributes to a more holistic optimization of 5G networks. This approach seeks to minimize energy consumption while fortifying the network against evolving security threats. Integrating energy-efficient practices with robust security measures enhances the overall resilience and sustainability of 5G systems. This is crucial for ensuring continuous, reliable, and secure communication in the face of dynamic challenges.

Opportunistic spectrum access and adaptive power management are effective techniques to improve throughput, delay performance, and energy efficiency for wireless networks. In this paper, we consider the joint design of opportunistic spectrum access and adaptive power management under the setting of multi-radio nodes and multi-channel wireless local area networks (WLANs) under the distributed coordination function (DCF) mode. This design problem is particularly challenging due to the conflicting nature of the multi-radio capability of a node, i.e., multiple radios improve throughput and delay performance at the cost of increased energy consumption. To address this problem, we propose a power-saving multi-channel MAC protocol (PSM-MMAC), which is capable of reducing the collision probability and the waiting time in the ‘awake’ state of a node, resulting in improved throughput, delay performance, and energy efficiency. The key ideas of PSM-MMAC are the following: we first estimate the number of active links; given this estimation as well as queue lengths and channel conditions, we appropriately select channels, radios, and power states (i.e., awake or doze state); then we optimize the medium access probability in p-persistent CSMA used in the data exchange. Another contribution of this paper is an analytical model that characterizes the throughput performance. Simulation results validate the accuracy of our analytical model and show that our proposed protocol is able to significantly improve both throughput and energy efficiency.

To enhance channel throughput, end to end delay and energy efficiency for wireless networks based on a single-hop topology, this paper proposes a distributed reservation multiple access scheme with adaptive switching requests (DRMA-ASR). The DRMA-ASR scheme uses a repeating frame structure with a reservation period and a data transmission period based on the principles of the ALOHA-reservation scheme. To improve performance, this scheme proposes an adaptive or skipped reservation period with the probability based on a hidden Markov model (HMM). In addition, request packets are transmitted based on a permission probability to increase the probability with which the reservation period can be reduced in duration or skipped, and energy saving is achieved by putting terminals into a low power sleep mode when they are not required to transmit during the data transmission period. The scheme applies a virtual carrier sensing mechanism and does not require physical sensing. Simulation results show that DRMA-ASR provides better performance in terms of channel throughput, end to end delay, and energy efficiency than CSMA/CA with both small and large interference ranges.

Software Defined Networking (SDN) has emerged as a new paradigm that offers the programmability required to dynamically configure and control a network. In this paper, we present a path assignment method based on load balancing(PA-LB) for a centralized controller to calculate optimal end-to-end virtual paths over the underlying network infrastructure, considering multiple requests and load balance simultaneously. In the paper, theoretical analysis shows that the path selection is NP-hard, and strongly polynomial time inapproximable under the assumption P not equal to NP. We also give a constant-factor polynomial time approximation algorithm for this problem under some bounded parameters. Extensive simulation results, over a wide range of underlying network topologies and input parameters, demonstrate that the proposed method outperforms traditional shortest path first (SPF) approaches not only in paths mapping number, but also in network load balance. Under the condition of limited resources, such as bandwidth, the proposed method has a better performance with the increase of the number of nodes. Moreover, optimal load balance can be kept in the network, in order to avoid network congestion and minimize the average packet transmission latency.

The vast proliferation of wireless data networks demands efficient resource allocation strategies to accommodate the increasing number of devices and the dynamic nature of cellular networks. As future networks face challenges like severe congestion and varying traffic demands, achieving satisfactory Quality of Service (QoS) and Quality of Experience (QoE) requires dynamic management. This paper introduces an improved self-optimization framework that adopts deep reinforcement learning (RL) to dynamically adjust key network parameters, such as handover settings, power levels, and MIMO technology. This approach significantly enhances network throughput by effectively balancing load distribution. The proposed framework explores the trade-off between system complexity and performance gains, demonstrating that an agent tailored to optimize a frequently recurring single scenario can outperform generalized agents under specific network conditions.

Partition of networks into optimal set of clusters is the prominent technique to prolong the network lifetime of energy constrained wireless sensor networks. Enumeration search method cannot find optimal clusters within polynomial bounded time for large scale networks since the computational complexity of problem grows exponentially with the dimension of networks. Optimal cluster configuration in sensor networks is known to be Non-deterministic Polynomial (NP)-hard optimization problem and for that reason we have applied polynomial time metaheuristic algorithms to find optimal or near-optimal solutions. In this paper, we present clustering algorithms based on Simulated Annealing (SA) and Particle Swarm Optimization (PSO) to find optimal set of cluster heads in the network. The optimization problem consists of finding optimal configuration of clusters such that the communication distance per cluster is not only minimized but the cluster balance and energy efficiency is also maintained in the network. The SA and PSO toolboxes are developed in C++ and integrated with OMNeT++ simulation environment to implement the proposed clustering algorithms. The performance of algorithms with respect to network lifetime, load balance and energy efficiency of network is examined in the simulation.

Recently, energy efficiency in wireless networks has become an important objective. Aside from the growing proliferation of smartphones and other high-end devices in conventional human-to-human (H2H) communication, the introduction of machine-to-machine (M2M) communication or machine-type communication into cellular networks is another contributing factor. In this paper, we investigate quality-of-service (QoS)-driven energy-efficient design for the uplink of long term evolution (LTE) networks in M2M/H2H co-existence scenarios. We formulate the resource allocation problem as a maximization of effective capacity-based bits-per-joule capacity under statistical QoS provisioning. The specific constraints of single carrier frequency division multiple access (uplink air interface in LTE networks) pertaining to power and resource block allocation not only complicate the resource allocation problem, but also render the standard Lagrangian duality techniques inapplicable. We overcome the analytical and computational intractability by first transforming the original problem into a mixed integer programming (MIP) problem and then formulating its dual problem using the canonical duality theory. The proposed energy-efficient design is compared with the spectral efficient design along with round robin (RR) and best channel quality indicator (BCQI) algorithms. Numerical results, which are obtained using the invasive weed optimization (IWO) algorithm, show that the proposed energy-efficient uplink design not only outperforms other algorithms in terms of energy efficiency while satisfying the QoS requirements, but also performs closer to the optimal design.

A heterogeneous cellular network (HCN) is useful to increase the spectral and energy efficiency of wireless networks and to reduce the traffic load from the macro cell.The performance of the secondary user equipment (SUE) is affected by interference from the eNodeB (eNB) in a macro cell.To decrease the interference between the macro cell and the small cell, allocating resources properly is essential to an HCN.This study considers the scenario of a software-defined heterogeneous cellular network and performs the resource allocation process.First, we show the system model of HCN and formulate the optimization problem.The optimization problem is a complex process including power and frequency resource allocation, which imposes an extremely high complexity to the HCN.Therefore, a hierarchical resource allocation scheme is proposed, which including subchannel selection and a particle swarm optimization (PSO)-based power allocation algorithm.Simulation results show that the proposed hierarchical scheme is effective in improving the system capacity and energy efficiency.