of energy consumption is due to the static overhead power of base-stations and the paper considers sleep mode operation as a solution for a variety of network ...
2011 Wireless Advanced
Green Cellular Network: Deployment Solutions, Sensitivity and Tradeoffs Weisi Guo, Tim O’Farrell Department of Electrical and Electronic Engineering University of Sheffield, United Kingdom Email: {w.guo, t.ofarrell}@sheffield.ac.uk
Abstract— A prominent challenge faced by the communication industry and society is the reduction of energy consumption. How the cellular network can maintain a quality of service (QoS), whilst decreasing its operational energy is investigated from both a deployment and scheduling perspective. This paper presents results showing the dominant factors which reduce the radio access network (RAN) energy consumption and point the way for future research. A large proportion of energy consumption is due to the static overhead power of base-stations and the paper considers sleep mode operation as a solution for a variety of network conditions. Furthermore, a significant contributor to transmission inefficiency is due to the performance of cell edge users. The paper proposes a vertical sector antenna deployment scheme and demonstrate that our proposals can yield significant operational energy and transmission efficiency savings, as well as insight into the tradeoff relationship between energy consumption and network performance. Furthermore, the paper also gives a comprehensive performance sensitivity analysis, which yields insight into both the validity of results and the parameters which can significantly influence future development.
Fig. 1. In the left diagram, the receive SINR is shown for a number of cell-sites, with the central cell-site in sleep mode due to no users. In the right diagram, the concept of vertical sectorization is illustrated.
A. Review of Challenges Recent literature has extensively analyzed the performance and optimization of energy efficiency in cellular networks, especially with regard to improving either transmission performance or hardware efficiency. The areas considered include: hardware design [1], sleep mode operation [2], antenna tilt [3] [4], scheduling and radio-frequency techniques [5] [6]. A shared conclusion between our previous work [7] and existing literature [8] is that the greatest energy reduction is achieved by putting cells in a low energy sleep mode during periods of low activity load. Moreover, it has also been demonstrated that the greatest transmission energy inefficiency is a result of celledge users suffering a poor signal quality, whilst demanding a certain QoS. Most existing work has tackled energy efficiency by considering only the part of the radio transmission power [7] [9] [10] [11]. In reality, a cell’s energy consumption is partly due to a load dependent radio part and a static overhead part. How this overhead affects optimization results is often unexplored, and a reasonable assumption is that it can significantly alter the existing conclusions drawn. It remains an open challenge in how to maximize sleep mode behavior without sacrificing the QoS.
I. I NTRODUCTION Conventionally, communication systems have been primarily designed to meet the challenges of service quality. More recently, there is growing focus on the importance of energy consumption, both from an operational expenditure (OPEX) point of view and from a climate change perspective. Over the past few years, the communication industry has pledged to reduce carbon emissions of wireless networks by up to 50% by 2020. Typical cellular network in UK consumes approximately 40MW (one tenth of a standard coal power plant), and this is increasing with traffic volume. On average, the traffic volume has increased by more than a factor of 10 in the last five years and the associated energy consumption by 16-20%. The majority (∼ 80%) of this energy is consumed in the basestations and the back-haul of the radio access network (RAN). With this in mind, this paper investigates techniques to reduce both radio transmission and operational energy consumption for a multi-cell and multi-user LTE cellular network. Specifically, the paper considers the impact of cell size, sleep mode operation, and vertical sectorization techniques. This is done with the aid of energy reduction metrics that both measure the radio-transmission efficiency and operation energy consumption. Furthermore, the paper demonstrates that there is a tradeoff between the power consumption and throughput QoS of the RAN, and that certain parameters significantly influence these results.
978-1-4577-0109-2/11/$26.00 ©2011 IEEE
B. Proposed Solutions Whilst there is growing attention on how to optimize the energy efficiency of cellular networks in the past few years, the metrics used in this investigation sets itself apart from most existing work by considering both the transmission energy efficiency and the full operational energy consumption of the RAN. It has been found that a large portion of energy
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TABLE I
TABLE II
S YSTEM PARAMETERS FOR S IMULATION AND T HEORETICAL
P OWER C ONSUMPTION FOR M ACRO
TO
P ICO C ELLS (2010)
F RAMEWORK . System Parameters Parameter Symbol Operating Frequency f System Bandwidth BW Simulation Area Radius rsim Cell Radius rcell Number of Cell-sites Ncell Base-station Antenna Height H Antenna Down-tilt T Shadow Fading standard deviation σshadow Number of UE NU E QoS R UE antenna Height h DSCH DSCH Power Pcell CSDRS CSDRS Power Pcell Additive White Gaussian Noise n0 UE Noise Figure nU E
Power Consumption per Antenna Macro Micro Cell Radius, m >1000 600-1000 Max. Transmit Power, 40W 20W RF Efficiency, µΣ 0.25 0.25 RH RH Power, Pcell 160W 80W OH Overhead Power, Pcell 84W 68W OP Operational Power, Pcell 244W 148W Sleep Mode Power 42W 34W
Value 2600MHz 20MHz 4000m 50-1200m 7 − 127 5-40m 2-12 degrees 4-8dB 6, 30, 120/km2 1Mbit/s (95%) 1.5m 0.5Pcell 0.1Pcell 4 × 10−21 W/Hz 6.309 (8dB) W/Hz
(Full Load) Micro Pico 400-600 200-400 10W 6W 0.21 0.18 48W 33W 52W 40W 100W 73W 26W 20W
in the antenna pattern and Am = 20dBi. The users are modeled as randomly generated to have a constant average user density over the whole simulation area. The number of users achieving a QoS reflects the traffic load of the RAN. In order to gain accurate results, the position of users in each simulation is repeated a number of times that equate to approximately 8000 user positions, which is sufficient to give converging results. The simulation results are extracted from each user distributed within the arranged cell pattern, and there is currently no implementation of wrap-around. This means that the outer tier of cells experience less interference than the inner cells. The scheduling is a round robin (RR) unless otherwise stated. The throughput rate is derived from internal MVCE link-adaptation tables. Unlike some existing literature, it does not employ the Gaussian input upper-bound, which is inaccurate for high SINRs or low density modulation schemes [13]. The quality of service (QoS) is defined as the average down-link throughput achieved by 95% of users in a cell.
inefficiency is attributed to the static overhead consumption of base-stations, as well as attempting to achieve a quality of service for cell edge users. In order to effectively reduce the operation energy, cells have to be partially or fully switched off and a more efficient method of cell-edge transmission introduced. This paper proposes to first explore the usefulness of sleep mode operation under various traffic load and deployment scenarios, in order to reduce the operational energy consumption of the RAN. Furthermore, improving the radio efficiency of cells by introducing a vertical sectorized antenna layout is investigated in the second part. This is illustrated in Fig. 1. The second half of the paper introduces a tradeoff between the power consumption and throughput QoS of the RAN and demonstrate how sleep mode and vertical sectorization can improve the tradeoff relationship. Furthermore, a comprehensive sensitivity analysis is performed to show the validity of the results and the parameters which are critical for future research in this area.
III. E NERGY M ETRICS A. Power Consumption This paper considers cells operating in two modes: • The Active Mode is when there are one or more users in any of a cell’s sectors, the cell is in an active mode and consumes an operational (OP) power per antenna:
II. S YSTEM M ODEL
RF Pcell OH RH OH L + Pcell = Pcell L + Pcell , (1) µΣ where the radio-head (RH) element is a function of the RF traffic load (L{0, 1}), the transmit power (Pcell ) and the combined efficiency of the various radio-head elements (µΣ ). The static overhead (OH) element is constant when the cell is active. • The Sleep Mode is when there are no users in a cell and its sectors, and the cell enters a sleep mode. The operational power consumed is entirely a fraction of the active mode’s static overhead consumption. The values presented in Table. II are amalgamated from the European Community’s Energy Aware Radio and Network Technologies (EARTH) project [14] and also [15]. OP Pcell =
The paper considers an LTE system that is modeled by our own proprietary Matlab simulator. The investigation considers architecture techniques and their potential energy reduction benefit, and this is illustrated in Fig. 1. A traditional hexagonal homogeneous cell deployment scenario is considered in order to gain insight into the trends which can reduce operation energy consumption. Therefore the results are not to be taken as universal values, but rather an indicator of the rationale for further investigation. The system model employs the appropriate path-loss, multi-path and shadow fading models for the distances considered, and they are the WINNER Phase II urban macro and micro models [12]. The simulation parameters are given in Table. I. Unless otherwise stated, the cell-sites each have three horizontal cell-sectors and the θ )2 , Am ], antenna pattern used is: Acell (θ) = −min[12( θ3dB,cell where Abs + Acell (θ) is the antenna gain (dB) at an angle θ from the azimuth plane. Abs = 17.6dBi is the bore-sight gain and θ3dB = 75 degrees is the 3dB reduction point
B. Energy Consumption Ratio (ECR) The paper compares systems that transmit the same amount of data (M bits). Consider system (RAN i) with an average
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Pico
