Genie: An optimal green policy for energy saving and traffic offloading in heterogeneous cellular networks

Traffic offloading through small cells has been considered as a promising approach to accommodate tremendous traffic growth in future heterogeneous cellular networks (HCNs). The dense deployment of small cells, however, has led to a growing concern about the excessive carbon- based on-grid energy consumption in HCNs. In this article, we first overview the green-oriented traffic offloading in future HCNs that exploits the recent advanced energy technologies including energy harvesting (EH), local energy sharing (ES) enabled by smart grid, and wireless power transfer (WPT). We then discuss the challenges in resource management when exploiting EH, ES, and WPT to support traffic offloading, and provide possible solutions, especially by using the emerging dual connectivity (DC) in recent 3GPP specifications. Furthermore, we present a case study on the optimal DC-enabled traffic offloading through small cells that are powered by EH, with the objective of minimizing the total on-grid power consumption of all small cells and macrocells. The case study validates the benefit of exploiting the DC feature for traffic scheduling and the harvested energy to reduce the total on-grid power consumption. We finally share our view of some research directions in the green-oriented traffic offloading in HCNs.

With small cell base stations (SBSs) densely deployed in addition to\nconventional macro base stations (MBSs), the heterogeneous cellular network\n(HCN) architecture can effectively boost network capacity. To support the huge\npower demand of HCNs, renewable energy harvesting technologies can be\nleveraged. In this paper, we aim to make efficient use of the harvested energy\nfor on-grid power saving while satisfying the quality of service (QoS)\nrequirement. To this end, energy-aware traffic offloading schemes are proposed,\nwhereby user associations, ON-OFF states of SBSs, and power control are jointly\noptimized according to the statistical information of energy arrival and\ntraffic load. Specifically, for the single SBS case, the power saving gain\nachieved by activating the SBS is derived in closed form, based on which the\nSBS activation condition and optimal traffic offloading amount are obtained.\nFurthermore, a two-stage energy-aware traffic offloading (TEATO) scheme is\nproposed for the multiple-SBS case, considering various operating\ncharacteristics of SBSs with different power sources. Simulation results\ndemonstrate that the proposed scheme can achieve more than 50% power saving\ngain for typical daily traffic and solar energy profiles, compared with the\nconventional traffic offloading schemes.\n

The growing awareness towards energy efficient cellular networks has paved way for new technologies to enable design towards green cellular networks. According to technical analysts, the base stations are the most energy intensive element in a typical cellular network. Saving power in base stations is therefore the primary focus in green cellular networks. The main goal of designing green base stations is for saving energy and reducing power consumption while guaranteeing service and coverage for users and ensuring the capability of base station for evolution. One cannot ignore the amount of energy that is wasted due to the lack of efficient utilization of resources. This was the main motivation behind this study. This paper aims to exploit the dynamic traffic load conditions to improve the downlink energy consumption by achieving optimal balancing between the number of subcarriers and dynamic traffic load conditions. Particle Swarm Optimization (PSO) has been adopted to optimize the subcarriers under the condition of maximum traffic load while guaranteeing the QOS under the constraint of minimal power per subcarrier. Simulation results show that the annual energy savings that achieved by this approach only for one base station up to 130 kWh. Hence, in a large area with thousands of base stations or even more, the aggregate energy savings could be substantial as well as the annual OPEX saving.

With the advance of the photovoltaic panel technology, the infrastructure equipment of cellular networks with solar energy sources has attracted extensive interests. It is expected that base stations (BSs) and relay stations (RSs) with renewable energy will play important roles in reducing the power consumption from electrical grids in future green cellular networks. Inspired by this technology trend, this paper considers a heterogeneous green cellular network, in which a part of RSs is powered by solar energy sources to represent the incremental deployment for the infrastructure with renewable energy in the near future. We formulate a new relay selection and power allocation problem with Mixed Integer Linear Programming to select solar-powered RSs and grid-powered RSs, such that the total grid power consumption is effectively minimized in decode-and-forward cooperative communication networks. We design an algorithm to derive the optimal solution, and also devise an efficient distributed algorithm to reduce the computational cost. The simulation results demonstrate that the algorithms can effectively reduce the total grid power consumption with different channel conditions, different deployments of RSs in a network, and different available power levels in solar-powered RSs. In addition, the problem model and the proposed algorithms can be extended to support hybrid energy sources.

The evolution of cellular networks from one generation to another has led to the deployment of multiple radio access technologies (such as 2G/2.5G/3G/4G) in the same geographical area. This scenario is termed heterogeneous cellular networks. In heterogeneous cellular networks, radio resources can be jointly or independently managed. When radio resources are jointly managed, joint call admission control algorithms are needed for making radio access technology selection decisions. This chapter gives an overview of joint call admission control in heterogeneous cellular networks. It then presents a model of load-based joint call admission control algorithm. Four different scenarios of call admission control in heterogeneous cellular networks are analyzed and compared. Simulations results are given to show the effectiveness of call admission control in the different scenarios. The coexistence of different cellular networks in the same geographical area necessitates joint radio resource management (JRRM) for enhanced QoS provisioning and efficient radio resource utilization. The concept of JRRM arises in order to efficiently manage the common pool of radio resources that are available in each of the existing radio access technologies (RATs) (Perez-Romero et al, 2005). In heterogeneous cellular networks, the radio resource pool consists of resources that are available in a set of cells, typically under the control of a radio network controller or a base station controller. There are a number of motivations for heterogeneous wireless networks. These motivations are (1) limitation of a single radio access technology (RAT), (2) users’ demand for advanced services and complementary features of different RATs, and (3) evolution of wireless technology. Every RAT is limited in one or more of the following: data rate, coverage, security-level, type of services, and quality of service it can provide, etc. (Vidales et al, 2005). A motivation for heterogeneous cellular networks arises from the fact that no single RAT can provide ubiquitous coverage and continuous high QoS levels across multiple smart spaces, e.g. home, office, public smart spaces, etc. Moreover, increasing users’ demand for advanced services that consume a lot of network resources has made network researchers developed more and more spectrally efficient multiple access and modulation schemes to support these services. Consequently, wireless networks have evolved from one generation to another. However, due to huge investment in existing RATs, operators do not readily discard their existing RATs when they acquire new ones. This situation has led to coexistence of multiple RATs in the same geographical area.

Low-power base stations (BSs), such as pico BSs, femto BSs, and relay nodes, are introduced to the heterogeneous cellular networks to enhance coverage and improve system capacity. Compare with macro BS, low-power BS has much lower transmission power, smaller physical size and lower cost. Deploying low-power BSs within the coverage of macro BSs is considered as a cost-efficient way to meet the sharp increase of wireless applications, leading to a new radio network planning paradigm for the next generation cellular networks. In this paper, we study the minimum cost cell planning problem in such heterogeneous networks, where planning task is how to select a subset of possible BS sites, including macro BSs, pico BSs and relay nodes, to minimize the total deployment cost while satisfying all rate requirements of demand nodes. We develop an approximation algorithm to tackle the formulated NP-hard problem, which guarantees an approximation ratio of O(log R) to the optimal solution, where R is the maximal achievable capacity of BSs.

In this work we investigate optimal geographical caching in heterogeneous cellular networks where different types of base stations (BSs) have different cache capacities. Users request files from a content library according to a known probability distribution. The performance metric is the total hit probability, which is the probability that a user at an arbitrary location in the plane will find the content that it requires in one of the BSs that it is covered by. We consider the problem of optimally placing content in all BSs jointly. As this problem is not convex, we provide a heuristic scheme by finding the optimal placement policy for one type of base station conditioned on the placement in all other types. We demonstrate that these individual optimization problems are convex and we provide an analytical solution. As an illustration, we find the optimal placement policy of the small base stations (SBSs) depending on the placement policy of the macro base stations (MBSs). We show how the hit probability evolves as the deployment density of the SBSs varies. We show that the heuristic of placing the most popular content in the MBSs is almost optimal after deploying the SBSs with optimal placement policies. Also, for the SBSs no such heuristic can be used; the optimal placement is significantly better than storing the most popular content. Finally, we show that solving the individual problems to find the optimal placement policies for different types of BSs iteratively, namely repeatedly updating the placement policies, does not improve the performance.

How many small cell (SC) access points (APs) are required to guarantee a chosen quality of service in a heterogeneous network? In this chapter, we answer this question considering two different network models. The first is the downlink of a finite-area SC network where the locations of APs within the chosen area are uniformly distributed. A key step in obtaining the closed-form expressions is to generalize the well-accepted moment matching approximation for the linear combination of lognormal random variables. For the second model, we focus on a two-layer downlink heterogeneous network with frequency reuse-1 hexagonal macro cells (MCs), and SC APs that are placed at locations that do not meet a chosen quality of service from macro base stations (BSs). An important property of this model is that the SC AP locations are coupled with the MC coverage. Here, simple bounds for the average total interference within an MC makes the formulation possible for the percentage of MC area in outage, as well as the required average number of SCs (per MC) to overcome outage, assuming isolated SCs. Introduction Heterogeneous cellular networks (HCNs) are being considered as an efficient way to improve system capacity as well as effectively enhance network coverage [1, 2]. Comprising multiple layers of access points (APs), HCNs encompass a conventional macro cellular network (first layer) overlaid with a diverse set of small cells (SCs) (higher layers). Cell deployment is an important problem in heterogeneous networks, both in terms of the number and positioning of the SCs. Traditional network models are either impractically simple (such as the Wyner model [3]) or excessively complex (e.g., the general case of random user locations in a hexagonal lattice network [4]) to accurately model SC networks. A useful mathematical model that accounts for the randomness in SC locations and irregularity in the cells uses spatial point processes, such as Poisson point process (PPP), to model the location of SCs in the network [5–10]. The independent placement of SCs from the MC layer, has the advantage of analytical tractability and leads to many useful SINR and/or rate expressions. However, even assuming that wireless providers would deploy SCs to support mobile broadband services, the dominant assumption remains that SCs are deployed randomly and independent of the MC layer [11].

Green and energy-saving heterogeneous cellular networks are the trend of future mobile communication networks, and there are also a series of problems such as resource allocation, load balancing, and energy-saving design. As an important component of energy consumption in cellular networks, base stations have the characteristics of regularity and periodicity of services, so the introduction of base station dormancy technology can effectively save energy while ensuring the quality of service for users. Aiming at the sleep problem of base stations in heterogeneous cellular networks in urban areas, this paper analyzes the sleep probability and power consumption model of distance-aware micro base stations in sleep mode. Aiming at minimizing system power consumption, a sleep scheme in which macro base station and micro base station cooperate horizontally is designed. For the multi-constraint problem of user transfer in horizontal cooperation, a heuristic algorithm is proposed. The results show that the proposed cooperative scheme can save a lot of energy when the user SINR loss is small .

ABSTRACTThe multiple‐input multiple‐output (MIMO) downlink with transmitter coordination in a cellular network is considered. The transmitters are assumed to be either neighbouring base stations (homogeneous) or a base station with a number of remote radio heads that form picocells in its coverage area (heterogeneous). In centralized coordinated transmission from a cluster of nodes, the channel state information (CSI) of users needs to be sent to a central processor for precoding and resource allocation. Real‐time CSI feedback from the users to their home base station and from the base stations to the central processor is a serious challenge from a practical point of view. In this work, efficient location‐assisted limited‐feedback schemes for homogeneous and heterogeneous cellular networks are proposed. First, a hybrid mode transmission scheme with reduced feedback requirement is proposed for a homogeneous network, in which on the basis of the location of users, some are served using a single‐cell multiuser MIMO approach and some using a network MIMO approach. Next, for a heterogeneous network, a location‐assisted clustering and scheduling scheme is proposed for the case of joint reference signals, in which multiple transmission nodes that share the reference signals cannot be distinguished from each other. We evaluate the performance of our schemes with a series of simulations. In the homogeneous scenario, we compare with the case of full CSI, and in the heterogeneous scenario, we compare with joint transmission from all nodes in a cell. Copyright © 2012 John Wiley & Sons, Ltd.

Minimizing electromagnetic pollution and power consumption in green heterogeneous small cell network deployment

The heterogeneous cellular network (HCN) is a promising technology to handle the rising number of devices due to their universal presence. This rising popularity HCN based Machine-to-Machine (M2M) communications is opening new opportunities and also bringing forth new system design issues. However, the main challenge of M2M communication is the possibility of huge traffic and significant difference in the nature of M2M traffic than the current commercial traffic for which current cellular network is designed and optimized. In this article, we investigate the uplink resource allocation problem of M2M devices (MDs) in the multiple Femto base station's coverage. We first model the uplink power and sub-carrier allocation in femtocells independently; Based on the cooperative game resource allocation among MDs is analyzed through non-transferable utility game to enhance the data rate performance with minimum utilization of power. Simulation results show that the resource allocation model based on cooperative game is able to provide a fair distribution of data rate compared with non-cooperative and greedy type of MDs.

Considering the ever‐growing demand for network traffic, increasing the capacity of cellular networks has always been a necessity. Heterogeneous cellular networks using small‐scale base stations in addition to macro base stations (BSs) are a low cost and effective solution to this problem. The diversity of the BSs in heterogeneous networks, however, has raised new issues in terms of cell association and interference management compared with the traditional single‐tier networks. Devising novel and effective methods for cell association and resource allocation in these networks is currently an open and developing problem. This paper addresses cell and subband association in the downlink of orthogonal frequency‐division multiple‐access heterogeneous networks. Unlike the existing works in this field, our scheme maximizes network utility while preventing harmful interference for all connections in the network. Using the protocol interference model, necessary and sufficient constraints for preventing interference in cellular networks are presented. By choosing the appropriate interference constraint, the joint problem of cell and subband association is formulated as an integer optimization problem. After simplifying the optimization problem, an equivalent linear problem is obtained. Using one‐level dual decomposition, an iterative algorithm with a near‐optimal solution has been devised. A distributed protocol is then presented, in which each user and each BS requires only its own local data to determine its connection for service. The simulation results demonstrate the algorithm behavior and confirm the efficiency and near‐optimality of the proposed solution.

In this paper, statistical Quality of Service provisioning in next generation heterogeneous mobile cellular networks is investigated. To this aim, any active entity of the cellular network is regarded as a queuing system, whose statistical QoS requirements depend on the specific application. In this context, by quantifying the performance in terms of effective capacity, we introduce a lower bound for the system performance that facilitates an efficient analysis. We exploit this analytical framework to give insights about the possible improvement of the statistical QoS experienced by the users if the current heterogeneous cellular network architecture migrates from a Half Duplex to a Full Duplex mode of operation. Numerical results and analysis are provided, where the network is modeled as a Matern point processes with a hard core distance. The results demonstrate the accuracy and computational efficiency of the proposed scheme, especially in large scale wireless systems.

Device-to-Device (D2D) communication is becoming more popular research topic among the network researchers and engineers across the globe This popularity is due to various key applications of D2D communications including 4G LTE-A envisioning the continuous evolution of current cellular networks into 5G to support a much larger and diverse range of applications. D2D communications play an important role to fulfill the quality of service (QoS) requirements for 5G networks. However, to realize the performance gain of D2D communications, various open challenges need to be resolved including mode selection (MS). This paper, therefore proposes a new mode selection algorithm for D2D enabled heterogeneous 5G cellular network. By quantifying the key parameters we mitigate interference that affect the system performance, and finally proposed an algorithm for optimal selection of transmission mode in a heterogeneous cellular network. The findings reported in this paper provide some insights into the design of 5G networks that may help network designer and engineers to contribute further towards the development of next generation wireless networks.