Energy Efficiency In Heterogeneous Networks Research Articles

Optimizing energy efficiency in heterogeneous networks: An integrated stochastic geometry approach with novel sleep mode strategies and QoS framework.

The quest for energy efficiency (EE) in multi-tier Heterogeneous Networks (HetNets) is observed within the context of surging high-speed data demands and the rapid proliferation of wireless devices. The analysis of existing literature underscores the need for more comprehensive strategies to realize genuinely energy-efficient HetNets. This research work contributes significantly by employing a systematic methodology, utilizing This model facilitates the assessment of network performance by considering the spatial distribution of network elements. The stochastic nature of the PPP allows for a realistic representation of the random spatial deployment of base stations and users in multi-tier HetNets. Additionally, an analytical framework for Quality of Service (QoS) provision based on D-DOSS simplifies the understanding of user-base station relationships and offers essential performance metrics. Moreover, an optimization problem formulation, considering coverage, energy maximization, and delay minimization constraints, aims to strike a balance between key network attributes. This research not only addresses crucial challenges in creating EE HetNets but also lays a foundation for future advancements in wireless network design, operation, and management, ultimately benefiting network operators and end-users alike amidst the growing demand for high-speed data and the increasing prevalence of wireless devices. The proposed D-DOSS approach not only offers insights for the systematic design and analysis of EE HetNets but also systematically outperforms other state-of-the-art techniques presented. The improvement in energy efficiency systematically ranges from 67% (min side) to 98% (max side), systematically demonstrating the effectiveness of the proposed strategy in achieving higher energy efficiency compared to existing strategies. This systematic research work establishes a strong foundation for the systematic evolution of energy-efficient HetNets. The systematic methodology employed ensures a comprehensive understanding of the complex interplay of network dynamics and user requirements in a multi-tiered environment.

A Turbo Q-Learning (TQL) for Energy Efficiency Optimization in Heterogeneous Networks

In order to maximize energy efficiency in heterogeneous networks (HetNets), a turbo Q-Learning (TQL) combined with multistage decision process and tabular Q-Learning is proposed to optimize the resource configuration. For the large dimensions of action space, the problem of energy efficiency optimization is designed as a multistage decision process in this paper, according to the resource allocation of optimization objectives, the initial problem is divided into several subproblems which are solved by tabular Q-Learning, and the traditional exponential increasing size of action space is decomposed into linear increase. By iterating the solutions of subproblems, the initial problem is solved. The simple stability analysis of the algorithm is given in this paper. As to the large dimension of state space, we use a deep neural network (DNN) to classify states where the optimization policy of novel Q-Learning is set to label samples. Thus far, the dimensions of action and state space have been solved. The simulation results show that our approach is convergent, improves the convergence speed by 60% while maintaining almost the same energy efficiency and having the characteristics of system adjustment.

The impact of cell selection on the energy efficiency of heterogeneous networks

The growth of the information, communication and telecommunication (ICT) industry is on the ascendancy. The proliferation of mobile devices and other web-based applications has compelled ICT industries to deploy more resource towards capacity expansion and improved quality of service. This singular act has triggered an upsurge in both energy consumption and carbon emission within the mobile cellular industry. As a result, current research efforts within the mobile wireless network are tailored towards improving the energy efficiency of network components and systems. One promising solution comes through the deployment of a heterogeneous network. In this work, the impact of cell selection techniques on the overall network performance of a heterogeneous network is investigated. The overall simulation results reveal that efficient cell selection influences a better performance in energy efficiency and area energy efficiency of such a network.

An Alternating Optimization Algorithm for Energy Efficiency in Heterogeneous Networks

This paper studies the problems of precoding designs to achieve the energy efficiency (EE) in the uplink heterogeneous networks in which the multiple small cells are deployed in a macro-cell. We consider two design problems which maximize either the total system energy efficiency (SEE) or the minimum energy efficiency (MinEE) among users subject to the transmit power constraints at each user and interference constraints caused to the macro base station. Since the optimization problems are non-convex fractional programming in matrix variables, it cannot be straightforward to obtain the optimal solutions. To tackle with the non-convexity challenges of the design problems, we adopt the relationships between the minimum mean square error (MMSE) and achievable data rate to recast the EE problems into ones more amenable. Then, we employ the block coordinate ascent (BCA) and the Dinkelbach methods to develop efficient iterative algorithms in which the closed form solutions are obtained or the semi-definite programming (SDP) problems are solved at each iteration. Simulation results are provided to investigate the EE performance of the EE optimization as compared to those of the spectral efficiency (SE) optimization.

Joint Spectrum-Sharing and Base Station Sleep Model for Improving Energy Efficiency of Heterogeneous Networks

A joint optimization method taking into account both the partial spectrum reuse (PSR) scheme and the base station sleep (BSS) model is proposed for improving the energy efficiency of heterogeneous networks (HetNets). In this paper, the coverage probabilities for both the macrotier and microtier networks are derived, followed by maximizing the coverage probability of the HetNets conceived for optimizing both the PSR factor and macro–microbase station's (BS) active probability ratio (APR). Furthermore, the problem of energy consumption minimization in HetNets can be addressed by analyzing the relationship between the optimal PSR factor and APR. Numerical results show that, under both the closed-access strategy and interference-limited case, and additionally by setting the signal to interference and noise ratio target to be 20 dB, the energy consumption of up to 50% in a two-tier HetNet can be saved by invoking the proposed joint optimization method, without eroding the average ergodic rate, provided that the macrocell BS (macroBS) transmit power is as high as five times that of the microcell BSs (microBSs).

Joint Optimization of FeICIC and Spectrum Allocation for Spectral and Energy Efficient Heterogeneous Networks

Joint Optimization of FeICIC and Spectrum Allocation for Spectral and Energy Efficient Heterogeneous Networks

Fundamentals of the Downlink Green Coverage and Energy Efficiency in Heterogeneous Networks

This paper studies the proposed green (energy-efficient) coverage probability, link and network energy efficiencies in the downlink of a heterogeneous cellular network (HetNet) consisting of $K$ independent Poisson point processes of base stations (BSs). The important statistical properties of the universal (general) cell association functions are first studied, and the cell load statistics for power-law cell association functions, which can characterize the accurate void cell probability of a BS in every tier, are also derived. A simple and feasible green channel-aware cell association (GCA) scheme is proposed and the green coverage probability is also proposed for any particular cell association scheme, such as the maximum received power association (MRPA) and the nearest BS association (NBA) schemes. Then, the link and network energy efficiencies are proposed to characterize the mean spectrum efficiency per unit power consumption for a BS and the mean area spectrum efficiency for the HetNet, respectively. All the tight bounds on the green coverage probability, link, and network energy efficiencies for the GCA, MRPA, and NBA schemes are found. They are theoretically shown to pose the fundamental maximum limits on the link and network energy efficiencies achieved by any other cell association schemes, and such a fact is validated by numerical results as well.

Hybrid user association for maximising energy efficiency in heterogeneous networks with human‐to‐human/machine‐to‐machine coexistence

In this study, a hybrid user association scheme for maximising energy efficiency is proposed in a wireless uplink heterogeneous network with human-to-human and machine-to-machine communications coexistence. Different from conventional user association algorithms, the authors consider access control protocol for both the human-type-communications (HTCs) users equipments’ (UEs) and machine-type-communications (MTCs) UEs according to their characteristics during user association phase, i.e. a contention-free access mechanism for HTC UEs and a random access based on access class barring mechanism for MTC UEs. They formulate the user association problem as a maximisation of the overall UEs’ energy efficiency in consideration of both the HTC and MTC UEs’ quality of service (QoS) guarantees and load balance among base stations simultaneously. To solve the problem, they propose a distributed iterative algorithm which adopts both the matching theory and the dual decomposition theory, and prove its convergence. Finally, simulation results show that their proposed user association scheme not only outperforms existing schemes in terms of the load balance and the overall energy efficiency but also achieves the same performance as exhaustive search in the case of less number of UEs, while satisfying both the HTC and MTC UEs’ QoS requirements.

How Many Small Cells Can be Turned Off via Vertical Offloading Under a Separation Architecture?

To further improve the energy efficiency of heterogeneous networks, a separation architecture called hyper-cellular network (HCN) has been proposed, which decouples the control signaling and data transmission functions. Specifically, the control coverage is guaranteed by macro base stations (MBSs), whereas small cells (SCs) are only utilized for data transmission. Under HCN, SCs can be dynamically turned off when traffic load decreases for energy saving. A fundamental problem then arises: how many SCs can be turned off as traffic varies? In this paper, we address this problem in a theoretical way, where two sleeping schemes (i.e., random and repulsive schemes) with vertical inter-layer offloading are considered. Analytical results indicate the following facts: (1) Under the random scheme where SCs are turned off with certain probability, the expected ratio of sleeping SCs is inversely proportional to the traffic load of SC-layer and decreases linearly with the traffic load of MBS-layer; (2) The repulsive scheme, which only turns off the SCs close to MBSs, is less sensitive to the traffic variations; (3) deploying denser MBSs enables turning off more SCs, which may help to improve network energy-efficiency. Numerical results show that about 50% SCs can be turned off on average under the predefined daily traffic profiles, and 10% more SCs can be further turned off with inter-layer channel borrowing.

Tradeoff Analysis between Spectrum Efficiency and Energy Efficiency in Heterogeneous Networks (HetNets) Using Bias Factor

Tradeoff Analysis between Spectrum Efficiency and Energy Efficiency in Heterogeneous Networks (HetNets) Using Bias Factor

Backhaul-aware energy efficient heterogeneous networks with dual connectivity

In this paper, we consider backhaul-aware mechanisms for energy efficient operation of next generation heterogeneous wireless networks, with dense small cell deployments. We assume control and data plane separation based on the LTE-Advanced dual connectivity architecture. The mechanisms are evaluated using LTE-Advanced heterogeneous network scenarios, with varying levels of network densities, user traffic and cell load conditions. For comparison, conventional traffic offloading schemes based on small cell proximity and load are also evaluated. The performance results indicate significant energy saving (ES) gains for the backhaul-aware mechanism, depending on the backhaul technology used. Evaluations were also done on the impacts of user mobility, and for indoor and outdoor deployments of small cells. Trade-offs between user throughput and energy efficiency are evaluated, and using the considered mechanism, ES gains of up to 20 % is observed, without significant loss in throughput.

Energy Efficient Heterogeneous Network Selection Method Based on Fuzzy Neural Network

Heterogeneous network selection algorithm can be expected to play an increasingly important role in the future wireless communication network. Due to the difference in energy consumption of different networks, selecting appropriate access network according to energy consumption characteristics and applications for Mobile Terminal (MT) can effectively extend battery life. In this paper, a fuzzy neural network based energy efficient network selection method is proposed. To select the optimal network and achieve the best balance between performance and energy consumption, the proposed method takes network bandwidth, signal strength, Quality of Service (QoS) and energy consumption into account. A novel energy efficiency utility function is proposed to calculate the energy utility value of different applications based on real-time and non-real-time service. And the calculated energy utility value together with other QoS parameters is input into Fuzzy Neural Network (FNN) for subsequent processing. Finally, the most appropriate network can be selected by comparing the inference results of fuzzy neural network. Computer simulations show that the proposed algorithms can reduce the energy consumption for MT effectively, and has superior performance in terms of access blocking probability and packet dropping probability.

Stochastic Geometry Analysis of Energy Efficiency in Heterogeneous Network with Sleep Control

Stochastic Geometry Analysis of Energy Efficiency in Heterogeneous Network with Sleep Control

Stochastic Geometry Analysis of Energy Efficiency in Heterogeneous Network with Sleep Control

Deploying Pico Base Stations (PBSs) throughout a conventional Macro Base Station (MBS) layout can improve the system capacity, which also brings substantial energy consumption. Sleep control is one of the effective ways to save energy, but it may lead to the Spectral Efficiency (SE) loss. In this letter, we study the energy-SE tradeoff in heterogeneous network with sleep control where some PBSs are adaptively switched on or off based on traffic variation. Using tools from stochastic geometry and markov chain theory, we derive the analytical expressions in terms of power consumption, SE and energy efficiency. Numerical results confirm that our theoretical analysis is reasonable. Additional, our results provide new insight into the setting of threshold of the sleep control scheme.

Energy Efficient Deployment of Heterogeneous Networks

A novel energy efficient heterogeneous networks (HetNet) deployment scheme is proposed in this paper. Unlike the traditional deployment, we consider the scenario with picocell eNodeB (PeNB) at the edge instead of under the umbrella of macrocells in HetNet. In the novel scheme, one PeNB with directional antennas is deployed on the border of neighbor macrocells which is equivalent to a combiner of PeNBs to associate MeNBs around. Compared with the traditional scheme, we have proved that the novel scheme performs better in fairness and efficiency, in terms of coverage area (proportional to offload rate), spectrum and energy efficiency for both edge users and overall users. Meanwhile, the technologies that try to reduce the difference and imbalance of the transmit power in HetNet obtain more inherent gains in the novel scheme, such as cell range expanded (CRE). Our results show that, the scheme proposed is also a spectrum and energy efficient heterogeneous deployment scheme that the energy efficiency is increased from 20% up to nearly 60%

Modeling of Energy Efficiency in Heterogeneous Network

Cellular networks are undergoing transformation from conventional homogeneous macro Base Stations (BSs) to Heterogeneous Network (HetNet). This new paradigm not only offers a significant improvement in the overall network capacity or user data rate; it also promises an improvement in the overall network Energy Efficiency (EE). In this study, a theoretical model for evaluating the EE in HetNet is developed. A HetNet generally consists of different types of Base Stations (BSs) which operate in harmony towards a set of common goals defined by the network operator such as coverage and capacity improvement. Each BS may differ in terms of transmit power, achievable data rate, coverage, BS density and EE, under different network deployment scenarios. The results show that the picocell strongly impacts the Energy Efficiency (EE) of the HetNet as compared to microcell. More specifically it is observed that certain ratios of microcells and picocells per macro BS will result in sub-optimal of Area Energy Efficiency (AEE). It is shown that the AEE of HetNet also increases as the percentage area of macro BS overlaid by smaller cells and the density of micro/picocells increases.

High Energy Efficient Heterogeneous Networks: Cooperative and Cognitive Techniques

Heterogeneous network (HetNet) is considered as the main and eminent future communication technology, since it achieves high spectral efficiency per unit area and saves energy due to low transmission power. Mass deployment of small cells in cochannel mode increases overall system capacity, but it is also coupled with greater risk of cochannel interference. This paper overviews the interference model based on the Poisson point process (PPP) and analyzes the performance in terms of energy efficiency in multitier HetNet. As the promising solution for improving the performance of HetNet, both the cooperative communication and cognitive radio techniques to mitigate the interference in HetNet are surveyed. As one example of cooperative communication techniques, a hierarchical cooperation scheme on the spectrum allocation is presented and its energy efficiency performance is analyzed and evaluated. Meanwhile, the energy efficiency increases from the cognitive radio technique are demonstrated as well. The energy efficiency performance comparison between the presented cooperative communication and cognitive radio techniques is emphasized, which suggests that the cooperation communication technique is preferred to suppress the interference and increase the energy efficiency in HetNets.

Energy Efficiency of Heterogeneous Networks in LTE-Advanced

Traditionally mobile operators have met the surge in mobile data traffic and the growing number of rural subscribers by deploying more macro base stations. This increases overall energy consumption, operational costs and carbon footprint of cellular networks. In this paper we investigate solutions for reducing the number and size of active macrocells following traffic load conditions in both homogeneous and heterogeneous networks. Results are presented as overall energy reduction gains for homogeneous macro-only and micro-only networks and heterogeneous joint macro-relay and micro-relay networks, using long-term-evolution-advanced technology. Results show that reducing the number of active cells using sleep mode at base stations, in low to medium traffic load conditions, combined with the deployment of small cells offer energy gains in both homogeneous and heterogeneous networks. However, the most significant gains are observed in heterogeneous networks.