Energy Efficiency Of Cellular Networks Research Articles

An Economic Aspect of Device-to-Device Assisted Offloading in Cellular Networks

Traffic offloading via device-to-device (D2D) communications has been proposed to alleviate the traffic burden on base stations and to improve the spectral and energy efficiency of cellular networks. The success of D2D communications relies on the willingness of users to share contents. In this paper, we study an economic aspect of traffic offloading via content sharing among multiple devices and propose an incentive framework for D2D assisted offloading. In the proposed incentive framework, the operator improves its overall profit, defined as the network economic efficiency (ECE), by encouraging users to act as D2D transmitters (D2D-Txs) which broadcast their popular contents to nearby users. We analytically characterize D2D-assisted offloading in cellular networks for two operating modes: 1) underlay mode and 2) overlay mode. We model the optimization of network ECE as a two-stage Stackelberg game, considering the densities of cellular users and D2D-Txs, the operator’s incentives, and the popularity of contents. The closed-form expressions of network ECE for both underlay and overlay modes of D2D communications are obtained. Numerical results show that the achievable network ECE of the proposed incentive D2D-assisted offloading network can be significantly improved with respect to the conventional cellular networks, where the D2D communications are disabled.

System-Level Modeling and Optimization of the Energy Efficiency in Cellular Networks—A Stochastic Geometry Framework

In this paper, we analyze and optimize the energy efficiency of downlink cellular networks. With the aid of tools from stochastic geometry, we introduce a new closed-form analytical expression of the potential spectral efficiency (bit/sec/m$^2$). In the interference-limited regime for data transmission, unlike currently available mathematical frameworks, the proposed analytical formulation depends on the transmit power and deployment density of the base stations. This is obtained by generalizing the definition of coverage probability and by accounting for the sensitivity of the receiver not only during the decoding of information data, but during the cell association phase as well. Based on the new formulation of the potential spectral efficiency, the energy efficiency (bit/Joule) is given in a tractable closed-form formula. An optimization problem is formulated and is comprehensively studied. It is mathematically proved, in particular, that the energy efficiency is a unimodal and strictly pseudo-concave function in the transmit power, given the density of the base stations, and in the density of the base stations, given the transmit power. Under these assumptions, therefore, a unique transmit power and density of the base stations exist, which maximize the energy efficiency. Numerical results are illustrated in order to confirm the obtained findings and to prove the usefulness of the proposed framework for optimizing the network planning and deployment of cellular networks from the energy efficiency standpoint.

A Base Station DTX Scheme for OFDMA Cellular Networks Powered by the Smart Grid

Discontinuous transmission (DTX) is an efficient technology to improve the energy efficiency of the wireless cellular networks. DTX enables the deactivation of some components of the base station in sufficient short time, which can decrease the energy consumption without affecting the normal operation of the mobile networks. In addition, we consider distributed smart grid which can power the cellular networks using renewable and conventional energy. The distributed smart grid has many different energy retailers with variant energy resource, such as solar energy, wind energy, and conventional energy. Comparing with conventional energy, the renewable energy is more environmental friendly, but more expensive. As a result, it is necessary to obtain a good tradeoff between the operation cost and greenhouse gas (GHG) emission. In this paper, we jointly optimize the DTX, resource allocation, and smart grid energy procurement to maximize the profit of the network operators and minimize the GHG emission. We formulate the joint optimization problem as a mixed integer programming problem. By exploiting the structure of the coupled constraint of the problem, we propose a suboptimal distributed algorithm based on the Lagrangian dual method, and the algorithm can be performed at cellular network and smart grid alternately, which can significantly decrease the signaling and computational overhead. Simulation results illustrate that the proposed DTX scheme can significantly enhance the energy saving, and further improve the energy efficiency of the cellular networks.

Energy Saving and Interference Coordination in HetNets Using Dynamic Programming and CEC

Energy efficiency in cellular networks has gained great relevance due to the increasing power supply demands in new generation heterogeneous network (HetNet). On the other hand, interference coordination is a key aspect in HetNets resource management which directly affects the performance and energy consumption. Although these two aspect are intertwined, they have been studied separately so far. In this paper, we address the joint problem of energy saving and interference coordination in HetNets. We formulate the problem as a finite horizon Markov decision process (MDP) leveraging two facts: the user traffic demands usually follow periodic patterns, and the knowledge and prediction of network load is crucial in order to select efficient network configurations. Quality of Service (QoS) in the network is defined as the ratio of users meeting a requirement specified by the operator, and allows us to account for a minimum QoS requirement by including a constraint in the formulation of the MDP. To address this MDP, we propose an approximate dynamic programming (ADP) algorithm which selects energy efficient farsighted configurations with QoS guarantees achieving near optimal performance. This ADP algorithm is built upon: 1) the certainty equivalent control principle, which simplifies the complexity of the MDP, and 2) machine learning techniques: a neural network and a polynomial regressor which allow us to predict the QoS and the consumption of the network in advance. We evaluate our proposal in a LTE-A network simulator following the 3GPP guidelines, and the results obtained show that a joint control of the energy saving and interference coordination mechanisms results in a notably performance improvement compared to a disjoint control, in terms of both energy savings and QoS guarantees. Moreover, our proposal has the advantage of being adaptable to the operator QoS requirements.

Distributed Energy-Spectrum Trading in Green Cognitive Radio Cellular Networks

Reducing the power consumption of base stations is crucial to enhancing the energy efficiency of cellular networks. As the number of mobile users increases exponentially, enhancing the spectrum efficiency is also critical in order to accommodate more users. In this paper, by exploiting the cooperation between secondary base stations (SBSs) and primary base stations (PBSs), we propose a new energy spectrum trading model to enhance the energy as well as spectrum efficiency of cellular networks. In our scheme, by leveraging cognitive radio, PBSs share some portion of their licensed spectrum with SBSs, and SBSs, in exchange, provide data service to the primary users under their coverage. We first prove that the power consumption minimization problem is NP-hard. Then, to decrease the computational complexity, we design an efficient distributed auction model including green energy aware bidding (GEAB) and adaptive bid selection (ABS) algorithms, to achieve a good approximation of the optimal solution in less time. Our simulation results show that the cooperation between PBS and SBSs via ABS and GEAB algorithms can significantly improve the energy and spectral efficiency of cellular networks by nearly doubling the number of offloaded users and reducing the PBS power consumption by up to 40% as compared to existing approaches. Furthermore, green energy utilization among SBSs is increased by nearly 25%.

An assessment of different user–BS association policies for green HetNets in off‐grid environments

AbstractPrevious research has shown the relationship between the number of users connected to a cellular network base station (BS) and its energy consumption. For this reason, the study of optimal mechanisms that balance the load of users over the available BSs is a key element in the field of energy efficiency in cellular networks. The target of this paper is to propose and assess different user–BS association mechanisms to reduce grid consumption in heterogeneous cellular networks powered by hybrid energy sources (grid and renewable energy). These schemes are compared with the traditional best‐signal‐level mechanism and evaluated via simulation by using key performance indicators related to grid consumption, number of users served, and average transmission rate per user. Our results show that the new proposed user allocation policies improve grid electricity consumption while reducing unserved users compared with the traditional association scheme.

Cooperative Device-to-Device Communications: Social Networking Perspectives

With an increasing demand to improve spectrum efficiency and energy efficiency in cellular networks, device-to-device (D2D) communications have drawn a lot of attention from both academia and industry. Cooperative D2D networking, which allows for cooperation among mobile users (MUs) via D2D links while communicating with other MUs or base stations (BSs), is one of the most promising techniques in D2D communications. In cooperative D2D communications, two major issues need to be investigated: network performance evaluation and peer selection. In order to enhance evaluation accuracy while maintaining data privacy, it is essential to employ social interactions among MUs in cooperative D2D communications. In this article, we investigate social aware cooperative D2D networks. We first introduce social relationship formation, and then social learning based cooperative network evaluation and social relationship based peer selection are investigated, respectively. Numerical results show the effectiveness of the proposed methods.

Resource allocation for energy efficient next generation cellular networks

The global deployment of cellular networks catering to increased user data demand provides quality of service (QoS) to the end users at the expense of increased energy consumption. The cellular infrastructure, in particular the base stations (BSs) are the main consumers of energy in cellular networks. From the network operator perspective, switching off BSs during low user demand is a key solution to improve energy efficiency of the network. However, switching off BSs can result in considerable reduction in the end user’s QoS, in particular coverage. The next generation networks (NGN) consider higher network densification which will increase the capacity of the overall network and its energy consumption. Therefore, it is important to address the energy efficiency of NGNs. Base station switching (BSS) combined with appropriate coverage extension techniques like coordinated multi-point (CoMP) transmission can help achieve higher energy efficiency while maintaining the QoS. In this work, we evaluate the performance of CoMP with BSS, utilizing suitable resource allocation techniques.

Energy and Spectral Efficiency of Cellular Networks With Discontinuous Transmission

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 $\alpha$ , e.g., 56% for $\alpha = 4$ . All these analytic results are further validated by the numerical simulations.

Low-Carb: A practical scheme for improving energy efficiency in cellular networks

Electricity cost constitutes a significant fraction of the total operations costs in a cellular network. We present Low-Carb, a practical scheme which reduces the power consumption in such networks. Low-Carb achieves this by coupling Base Transceiver Station (BTS) power savings with Call hand-off—two features widely available to cellular operators. Motivated by the observation that most callers are in the vicinity of multiple BTSs, Low-Carb allows calls to hand-off from one BTS to another so that BTS power savings can be applied to a maximal number of BTSs throughout the cellular network. The resulting reduction in energy consumption is shown to be governed by an optimization problem. We also provide optimal and heuristic solutions to this problem. We use BTS locations and traffic volume data from a large live GSM network to evaluate the power savings possible using our proposed approach. Our results indicate that performing coordinated call hand-off and BTS power-savings, a GSM 1800 network operator with about 7000 sites nation-wide can reduce annual electricity consumption by up to 35.36 MWh. This is at least 9.8% better than the energy savings achievable by using BTS power savings alone.

Socially aware cluster formation and radio resource allocation in D2D networks

Due to the popularity of mobile user devices, D2D communications become a key technology to improve local services in the next generation cellular networks. However, the benefits count on efficient radio resource allocation between cellular links and D2D links, which poses critical challenges. By exploiting the network knowledge extracted from underlying mobile social networks (MSNs), this article proposes a socially aware D2D communication scheme to improve the spectrum and energy efficiency in cellular networks. First, by jointly considering the social and network knowledge, we propose a cluster formation scheme to categorize a group of UEs into multiple D2D clusters. Within each cluster, highly spectrum-efficient D2D multicasting (from the cluster head, i.e., a D2D transmitter UE to multiple cluster member UEs) is enabled. Next, a half-duplex scheme and a full-duplex scheme are proposed to coordinate the channel sharing between the cellular links and the D2D links. Simulation results show that the proposed schemes can form D2D clusters rationally among a group of UEs, and therefore largely increase the spectrum and energy efficiency of the network.

Survey of radio resource management issues and proposals for energy-efficient cellular networks that will cover billions of machines

A huge increase of machines attached to wireless networks is expected in the next few years. A large part of these machines will be covered by some wireless wide area networks. The arrival of cellular M2M (machine-to-machine) communication poses new requirements due to its specific characteristics. For most of the cellular M2M applications, the essential requirement is low energy consumption level or high energy efficiency. This survey provides a global view of the network technologies previewed for cellular M2M. In this survey, we study the existing classifications of M2M applications according to different criteria in the literature. The comparison of traffic characteristics between M2M and human-to-human is also proposed. Quality of service (QoS) requirements for typical M2M applications are resumed. The advance of reference M2M network architectures proposed by the Standard Development Organization (SDO) is investigated. We identify two possible effort directions to improve the energy efficiency for cellular M2M. The first one is to evolve the current existing 3rd Generation Partnership Project (3GPP) Consortium cellular networks to effectively support MTC (Machine Type Communication). The other direction is to design M2M-dedicated networks from scratch, which are often called low-power wide-area (LPWA) networks. We review, compare and categorize the proposals related to energy issues of cellular M2M mainly over the period 2011–2015 for the first direction. We introduce the development of LPWA networks for the other research directions. We highlight that the cooperative relaying, the design of energy-efficient signaling and operation, the new radio resource allocation schemes, and the energy-efficient random access procedure are the main points of improvement. It is important to jointly use the aforementioned approaches, for example, joint design of random access control and radio resource allocation, to seek for a trade-off between energy efficiency and other system performances.

Cognitive Network Cooperation for Green Cellular Networks

In recent years, there has been a growing interest in green cellular networks for the sake of reducing the energy dissipated by communications and networking devices, including the base stations (BSs) and battery-powered user terminals (UTs). This paper investigates the joint employment of cognition and cooperation techniques invoked for improving the energy efficiency of cellular networks. To be specific, the cellular devices first have to identify the unused spectral bands (known as spectrum holes) using their spectrum sensing functionality. Then, they cooperate for exploiting the detected spectrum holes to support energy-efficient cellular communications. Considering the fact that contemporary terminals (e.g., smart phones) support various wireless access interfaces, we exploit either the Bluetooth or the Wi-Fi network operating within the spectrum holes for supporting cellular communications with the intention of achieving energy savings. This approach is termed cognitive network cooperation, since different wireless access networks cognitively cooperate with cellular networks. In order to illustrate the energy efficiency benefits of using both cognition and cooperation, we study the cooperation between television stations (TVSs) and BSs in transmitting to UTs relying on an opportunistic exploitation of the TV spectrum, where the unused TV spectral band is utilized in an opportunistic way, depending on whether it is detected to be idle (or not). It is shown that for a given number of information bits to be transmitted, the total energy consumed is significantly reduced, when both cognition and cooperation are supported in cellular networks, as compared with the conventional direct transmission, pure cognition, and pure cooperation.

Intelligent cooperation management among solar powered base stations towards a green cellular network in a country with an equatorial climate

Energy efficiency in cellular networks is a growing concern for cellular operators with regard to maintaining profitability and reducing their overall environmental impact. Because evolved node Bs (eNBs) for long-term evolution wireless cellular networks are deployed to accommodate peak traffic, they are underutilized most of the time, especially under low-traffic conditions. Hence, switching eNBs on and off in accordance with traffic pattern variations is considered to be an effective method of improving energy efficiency in cellular networks. However, two main concerns of network operators when applying this technique are coverage issues and securing radio service for an entire area in response to the increased size of some cells to provide coverage for cell areas that are switched off. This study focuses on the parameters that affect coverage in order to find a balance between cellular network energy consumption and the area of cell coverage. To achieve this goal, particle swarm optimization, a bio-inspired computational method, has been adopted in this study to maximize the cell coverage area under the constraints of the transmission power of the eNB $$(P_{tx})$$(Ptx), the total antenna gain (G), the bandwidth (BW), the signal-to-interference-plus-noise ratio (SINR), and shadow fading $$(\sigma )$$(�). In addition, the study investigated potential for gains in operational expenditures by operating eNB on solar energy. The optimum criteria, including economic, technical and environmental feasibility parameters, were analyzed using the HOMER.

Energy Efficiency Maximization Framework in Cognitive Downlink Two-Tier Networks

To support the surge in wireless data traffic, the spectrum and energy efficiencies of cellular networks should be largely increased. Heterogeneous two-tier architecture has been identified as one key solution. However, small-cell deployment raises questions about the resulting energy efficiency and interference mitigation. Therefore, we propose an energy-efficient and cognitive spectrum sharing scheme between primary macrocell and secondary small cells. Specifically, the small cells allocate their transmission power to maximize their total energy efficiency while respecting some interference constraints imposed by macrocell users. We solve this centralized optimization in two steps. First, assuming that the small-cell transmissions are noninterfering, the solution of this nonconvex optimization is characterized using a convex parametric approach. Using this characterization, we derive an algorithm based on Newton method, which converges to a global optimal solution. Second, when the small-cell transmissions are not necessarily orthogonal, we derive an algorithm, which converges at least to a local optimum, using the minorization-maximization principle and Newton method. Through simulations, we validate the convergence of these algorithms and compare their performance with existing schemes. We also analyze the effects of the interference and of the number of users on the energy efficiency.

Future RAN Architecture: SD-RAN Through a General-Purpose Processing Platform

In this article, we identify and study the potential of an integrated deployment solution for energy-efficient cellular networks combining the strengths of two very active current research themes: 1) software-defined radio access networks (SD-RANs) and 2) decoupled signaling and data transmissions, or beyond cellular green generation (BCG2) architecture, for enhanced energy efficiency. While SD-RAN envisions a decoupled centralized control plane and data-forwarding plane for flexible control, the BCG2 architecture calls for decoupling coverage from the capacity and coverage provided through an always-on low-power signaling node for a larger geographical area; the capacity is catered by various on-demand data nodes for maximum energy efficiency. In this article, we show that a combined approach that brings both specifications together can not only achieve greater benefits but also facilitate faster realization of both technologies. We propose the idea and design of a signaling controller that acts as a signaling node to provide always-on coverage, consuming low power, and at the same time host the control plane functions for the SDRAN through a general-purpose processing platform. The phantom cell concept is also a similar idea where a normal macrocell provides interference control to densely deployed small cells, although our initial results show that the integrated architecture has a much greater potential for energy savings than phantom cells.

English

Energy efficiency in cellular networks has been receiving significant attention in wireless communication, because of the importance in reducing the energy consumption and maintaining the profitability of networks, in addition it makes these networks “greener”. In the traditional cellular architecture, the urban cell topography increases the capacity along with the coverage require the deployment of a large number of base stations (BSs). This approach is cost prohibitive and also increase the mobile station (MS) transmission power. An alternative, relaying techniques are expected to alleviate the coverage problem and reduce the MS transmission power. To propose a geometrical model for the energy efficient relay station (RS) placement that requires only a small number of characteristic distances and is to estimate the maximum cell coverage of a relay aided cell, given power constraints. This proposed method analyzes the path loss model based on RS to BS link and it compares both cell coverage and energy efficiency using Decode and Forward (DF) relaying schemes in line of sight conditions

Energy efficiency analysis of cellular networks with cooperative relays via stochastic geometry

Cooperative relaying is a promising technology that can improve the spectral and energy efficiency of cellular networks. However, the deployed relays consume a lot of energy and system resources. To improve the energy efficiency of the relay-assisted cellular networks, this paper considers the use of energy harvesting (EH) on relay nodes. A random sleeping strategy is also introduced in macro base stations (MBS) as a possible method to reduce energy consumption. In this paper, an analytical model is proposed to investigate the energy efficiency of cellular networks with EH relays and sleep mode strategy. Numerical results confirm a significant energy efficiency gain of the proposed networks comparing to the cellular networks with non-EH relays and MBSs without sleep mode strategy. The effects of the density and transmit power of MBSs on energy efficiency are also given through simulations.

Energy efficiency maximisation in downlink multi‐cell networks via coordinated resource allocation

There is a great interest in considering the energy efficiency (EE) as an important performance evaluation recently for its contribution to both energy saving and environmental conservation. In this study, the authors focus on the optimal energy-efficient design for downlink multi-cell environment with coordinated scheduling and beamforming. The EE is defined as bit-per-joule capacity to indicate how efficiently energy is consumed for transmitting information. They set up the optimum model for maximising the EE of coordinated cells subject to a total power constraint at each base station and study the resource allocation (RA) for it, including user selection, power allocation and beamforming vectors optimisation. They consider an equivalent non-fractional form of the original problem and propose an iterative algorithm to optimise the spectrum RA and beamforming vectors alternatively. In each iteration, the beamforming vectors are updated by the iterative algorithm based on the first-order optimality conditions, while the user scheduling policy is updated by the enhanced greedy algorithm. Performance studies show that the proposed algorithm has a rapid speed of convergence and achieves significant performance gain in EE for cellular networks.

Optimal Cell Clustering and Activation for Energy Saving in Load-Coupled Wireless Networks

Optimizing activation and deactivation of base station transmissions provides an instrument for improving energy efficiency in cellular networks. In this paper, we study the problem of performing cell clustering and setting the activation time of each cluster, with the objective of minimizing the sum energy, subject to a time constraint of serving the users' traffic demand. Our optimization framework accounts for inter-cell interference, and, thus, the users' achievable rates depend on cluster formation. We provide mathematical formulations and analysis, and prove the problem's NP hardness. For problem solution, we first apply an optimization method that successively augments the set of variables under consideration, with the capability of approaching global optimum. Then, we derive a second solution algorithm to deal with the trade-off between optimality and the combinatorial nature of cluster formation. Numerical results demonstrate that our solutions achieve more than 40% energy saving over existing schemes, and that the solutions we obtain are within a few percent of deviation from global optimum.