Power Consumption Of Base Stations Research Articles

Two-Stage Cooperative Multicast Transmission with Optimized Power Consumption and Guaranteed Coverage

Wireless multicast is a spectrum efficient method for group-data transmission. This paper focuses on energy efficient two-stage cooperative multicast transmissions, aiming to minimize the total transmission power while ensuring a practical coverage ratio. To focus on the relationship between the base station (BS) power at the first stage (PBS,C) and the total power consumption, a selective combining based on average received signal strength (SCA) is assumed at the receiver and the user density is supposed to be sufficiently high. Then a mobile relay (MR) arrangement based on sector ring structures is proposed for the second stage transmission, followed by an analytical derivation of the optimal PBS,C conditioned on a desired coverage ratio. In addition, further approximations are exploited to provide a simple theoretical estimation for the optimal PBS,C, whose effectiveness is verified by numerical results. It is shown that compared to the conventional one-stage multicast transmission, the proposed two-stage cooperative scheme can reduce the total power consumption and the BS power consumption by more than 40% and 80%, respectively. Although the proposed scheme is obtained based on SCA, when the user density is higher than 2 × 10-4, a coverage ratio of 95% can be guaranteed by using a practical CP combining with the proposed scheme. Moreover, the effectiveness of the proposed MR arrangement is verified by simulations where it outperforms other three arrangements. It is also shown that targeting at minimizing the total transmission power with guaranteed coverage, the proposed scheme significantly outperforms the existing two-stage scheme in terms of energy consumption and efficiency.

Minimizing Base Station Power Consumption

We propose a new radio resource management algorithm which aims at minimizing the base station supply power consumption for multi-user MIMO-OFDM. Given a base station power model that establishes a relation between the RF transmit power and the supply power consumption, the algorithm optimizes the trade-off between three basic power-saving mechanisms: antenna adaptation, power control and discontinuous transmission. The algorithm comprises two steps: a) the first step estimates sleep mode duration, resource shares and antenna configuration based on average channel conditions and b) the second step exploits instantaneous channel knowledge at the transmitter for frequency selective time-variant channels. The proposed algorithm finds the number of transmit antennas, the RF transmission power per resource unit and spatial channel, the number of discontinuous transmission time slots, and the multi-user resource allocation, such that supply power consumption is minimized. Simulation results indicate that the proposed algorithm is capable of reducing the supply power consumption by between 25% and 40%, dependend on the system load.

Analysis of Spectrum Efficiency and Energy Efficiency of Heterogeneous Wireless Networks with Intra-/Inter-RAT Offloading

Offloading the users from capacity-strained macrocells in a cellular network to small cells is an effective strategy to support the increasing mobile data traffic, but a key challenge is how to simultaneously achieve high spectrum efficiency (SE) and energy efficiency (EE) in the heterogeneous radio access technology (RAT) environment. This paper develops an analytical framework for studying the performance of a two-RAT heterogeneous network (HetNet) comprising cellular and wireless local area network (WLAN) RATs. Using the developed framework, the feasibility of enhancing the SE and EE via the implementation of biased intra- and inter-RAT offloading techniques is investigated. Findings from the analysis reveal that the performance gain for SE and EE is strongly dependent on the load level and the base station (BS) power consumption attributes. A multiobjective optimization problem that maximizes the SE and EE subject to quality-of-service (QoS) constraints is formulated and solved to give the Pareto-optimal operational regime specified in terms of the small-cell BS densities and biasing factors. The novelty of this paper is the quantification of the SE-EE tradeoff as an opportunity cost measure, which is defined by the constrained Pareto-optimal regime. The insight gained from analyzing the opportunity cost is used to formulate a strategy that exploits the varying load conditions to achieve a good balance in the SE-EE tradeoff. Numerical results show that, although the potential to reduce the performance gap between SE and EE is marginal under low and high load conditions, it is feasible to significantly improve the network performance by balancing the SE-EE tradeoff during the medium load condition, as well as satisfy the users' QoS requirements by optimally adapting the small-cell BS density and offloading biasing factors.

A Parameterized Base Station Power Model

Power models are needed to assess the power consumption of cellular base stations (BSs) on an abstract level. Currently available models are either too simplified to cover necessary aspects or overly complex. We provide a parameterized linear power model which covers the individual aspects of a BS which are relevant for a power consumption analysis, especially the transmission bandwidth and the number of radio chains. Details reflecting the underlying architecture are abstracted in favor of simplicity and applicability. We identify current power-saving techniques of cellular networks for which this model can be used. Furthermore, the parameter set of typical commercial BSs is provided and compared to the underlying complex model. The complex model is well approximated while only using a fraction of the input parameters.

Joint Network Optimization and Downlink Beamforming for CoMP Transmissions Using Mixed Integer Conic Programming

Coordinated multipoint (CoMP) transmission is a promising technique to mitigate intercell interference and to increase system throughput in single-frequency reuse networks. Despite the remarkable benefits, the associated operational costs for exchanging user data and control information between multiple cooperating base stations (BSs) limit practical applications of CoMP processing. To facilitate wide usage of CoMP transmission, we consider in this paper the problem of joint network optimization and downlink beamforming (JNOB), with the objective to minimize the overall BS power consumption (including the operational costs of CoMP transmission) while guaranteeing the quality-of-service (QoS) requirements of the mobile stations (MSs). We address this problem using a mixed integer second-order cone program (MI-SOCP) framework and develop an extended MI-SOCP formulation that admits tighter continuous relaxations, which is essential for reducing the computational complexity of the branch-and-cut (BnC) method. Analytic studies of the MI-SOCP formulations are carried out. Based on the analyses, we introduce efficient customizing strategies to further speed up the BnC algorithm through generating tight lower bounds of the minimum total BS power consumptions. For practical applications, we develop polynomial-time inflation and deflation procedures to compute high-quality solutions of the JNOB problem. Numerical results show that the inflation and deflation procedures yield total BS power consumptions that are close to the lower bounds, e.g., exceeding the lower bounds by about 12.9% and 9.0%, respectively, for a network with 13 BSs and 25 MSs. Simulation results also show that minimizing the total BS power consumption results in sparse network topologies and reduced operational overhead in CoMP transmission and that some of the BSs are switched off when possible.

On Optimizing Green Energy Utilization for Cellular Networks with Hybrid Energy Supplies

Green communications has received much attention recently. For cellular networks, the base stations (BSs) account for more than 50 percent of the energy consumption of the networks. Therefore, reducing the power consumption of BSs is crucial to achieve green cellular networks. With the development of green energy technologies, BSs are able to be powered by green energy in order to reduce the on-grid energy consumption, thus reducing the CO2 footprints. In this paper, we envision that the BSs of future cellular networks are powered by both on-grid energy and green energy. We optimize the energy utilization in such networks by maximizing the utilization of green energy, and thus saving on-grid energy. The optimal usage of green energy depends on the characteristics of the energy generation and the mobile traffic, which exhibit both temporal and spatial diversities. We decompose the problem into two sub-problems: the multi-stage energy allocation problem and the multi-BSs energy balancing problem. We propose algorithms to solve these sub-problems, and subsequently solve the green energy optimization problem. Simulation results demonstrate that the proposed solution achieves significant on-grid energy savings.

Resource allocation for heterogeneous services in multiuser cognitive radio networks

SUMMARYThis paper considers a downlink cognitive radio network consisting of one cognitive base station and multiple secondary users (SUs) that shares spectrum with a primary network. Unlike most of previous studies that focus on the SUs that carry only one type of service, in this paper, the SUs that carry heterogeneous services are considered. Specifically, the SUs are classified by service types, that is, the SUs that carry nonreal‐time services and the SUs that carry real‐time services. The QoS of the nonreal‐time SUs is guaranteed by the minimum mean rate constraint, whereas the QoS of the real‐time SUs is guaranteed by the minimum instantaneous rate constraint. Under this setup, a joint subchannel, rate, and power allocation scheme based on dual optimization method is proposed to minimize the mean transmit power consumption of the cognitive base station. The complexity of the proposed scheme is linear in the number of subchannels and the number of SUs. Extensive simulation results are provided to validate the proposed resource allocation scheme. Copyright © 2012 John Wiley & Sons, Ltd.

Measurements and Modelling of Base Station Power Consumption under Real Traffic Loads

Base stations represent the main contributor to the energy consumption of a mobile cellular network. Since traffic load in mobile networks significantly varies during a working or weekend day, it is important to quantify the influence of these variations on the base station power consumption. Therefore, this paper investigates changes in the instantaneous power consumption of GSM (Global System for Mobile Communications) and UMTS (Universal Mobile Telecommunications System) base stations according to their respective traffic load. The real data in terms of the power consumption and traffic load have been obtained from continuous measurements performed on a fully operated base station site. Measurements show the existence of a direct relationship between base station traffic load and power consumption. According to this relationship, we develop a linear power consumption model for base stations of both technologies. This paper also gives an overview of the most important concepts which are being proposed to make cellular networks more energy-efficient.

Modelling and optimization of power consumption in wireless access networks

The power consumption of wireless access networks will become an important issue in the coming years. In this paper, the power consumption of base stations for mobile WiMAX, fixed WiMAX, UMTS, HSPA, and LTE is modelled and related to the coverage. A new metric, the power consumption per covered area PC area , is introduced, to compare the energy efficiency of the considered technologies for a range of bit rates. Assuming the model parameters are correct, the conclusions are then as follows. For a 5 MHz channel, UMTS is the most energy-efficient technology until a bit rate of 2.8 Mbps, LTE between 2.8 Mbps and 8.2 Mbps, fixed WiMAX between 8.2 Mbps and 13.8 Mbps and finally mobile WiMAX for bit rates higher than 13.8 Mbps. Furthermore, the influence of MIMO is investigated. For a 2 × 2 MIMO system, PC area decreases by 36% for mobile WiMAX and by 23% for HSPA and LTE compared to the SISO system, resulting in a higher energy efficiency. The power consumption model for base stations is used in the deployment tool GRAND (Green Radio Access Network Design) for green wireless access networks. GRAND uses a genetic based algorithm and is applied on an actual case for the Brussels Capital Region, showing the possibilities of energy-efficient planning.

Model for power consumption of wireless access networks

The power consumption of wireless access networks will become an important issue in the coming years. In this study, the power consumption of base stations for mobile WiMAX (Worldwide Interoperability for Microwave Access), fixed WiMAX, UMTS (Universal Mobile Telecommunications System), HSPA (High-Speed Packet Access) and LTE (Long-Term Evolution) is modelled and related to the coverage. A new metric, the power consumption per covered area PCarea, is introduced, to compare the energy efficiency of the considered technologies for a basic reference configuration and a future extended configuration, which makes use of novel Multiple Input Multiple Output (MIMO) technology. The introduction of MIMO has a positive influence on the energy efficiency: for example, for a 4×4 MIMO system, PCarea decreases with 63% for mobile WiMAX and with 50% for HSPA and LTE, compared to a Single Input Single Ouptut (SISO) system. However, a higher MIMO array size (i.e. a higher number of transmitting and receiving antennas) does not always result in a higher energy efficiency gain.

QoS Aware Energy Efficiency Analysis in the Cellular Networks

In cellular networks, maximizing the energy efficiency (EE) while satisfying certain QoS requirements is challenging. In this article, we utilize effective capacity (EC) theory as an effective means of meeting these challenges. Based on EC and taking a realistic base station (BS) power consumption model into account, we develop a novel energy efficiency (EE) metric: effective energy efficiency (EEE), to represent the delivered service bit per energy consumption at the upper layer with QoS constraints. Maximizing the EEE problem with EC constraints is addressed and then an optimal power control scheme is proposed to solve it. After that, the EEE and EC tradeoff is discussed and the effects of diverse QoS parameters on EEE are investigated through simulations, which provides insights into the quality of service (QoS) provision, and helps the system power consumption optimization.

Interference Coordination in Compact Frequency Reuse for Multihop Cellular Networks

Continuously increasing the bandwidth to enhance the capacity is impractical because of the scarcity of spectrum availability. Fortunately, on the basis of the characteristics of the multihop cellular net- j works (MCNs), a new compact frequency reuse scheme has been proposed to provide higher spectrum utilization efficiency and larger capacity without increasing the cost on network. Base stations (BSs) and relay stations (RSs) could transmit simultaneously on the same frequency according to the compact frequency reuse scheme. In this situation, however, mobile stations (MSs) near the coverage boundary will suffer serious interference and their traffic quality can hardly be guaranteed. In order to mitigate the interference while maintaining high spectrum utilization efficiency, this paper introduces a fractional frequency reuse (FFR) scheme into multihop cellular networks, in which the principle of FFR scheme and characteristics of frequency resources configurations are described, then the transmission (Tx) power consumption of BS and RSs is analyzed. The proposed scheme can both meet the requirement of high traffic load in future cellular system and maximize the benefit by reducing the Tx power consumption. Numerical results demonstrate that the proposed FFR in compact frequency reuse achieves higher cell coverage probability and larger capacity with respect to the conventional schemes.