Energy Efficient Wireless Networks Towards Green Communications ...

Wireless Pers Commun DOI 10.1007/s11277-011-0244-4

Energy Efficient Wireless Networks Towards Green Communications Mauro De Sanctis · Ernestina Cianca · Viraj Joshi

© Springer Science+Business Media, LLC. 2011

Abstract Wireless networks have become an essential part of the modern life. However, currently, 3% of the world-wide energy is consumed by the ICT infrastructures which causes about 2% of the world-wide CO2 emissions. The transmitted data-volume increases rapidly and wireless communications are used extensively while network design rules have practically ignored the energy efficient network design approach to limit CO2 emissions. This approach is currently named Green Communications. Significant energy savings in mobile networks can be expected by defining and standardizing energy efficiency metrics and combining energy aware flexible radios and networks. This paper discusses several techniques such as cross layer approach, multiple antennas, cell size reduction and cognitive radio, from the system-wide energy efficiency point of view, outlining challenges and open issues. Keywords Energy efficiency · Green communications · Cognitive radio · Cross-layer optimization

1 Introduction Communication networks are intrinsically green. If we can not use a telecommunication system, the only way to deliver a message from a source to a destination is to physically bring the source to the destination (or vice-versa), but this is surely less energy efficient. In fact, we should take into account the environmental benefits from the use of communications networks which reduce the road transportation for delivering information between entities and enable green systems. For instance, data communication services such as video-conference and telepresence reduce the carbon dioxide emissions associated with business travels [1]. Similar considerations can be drawn if we plan to send a copy of a document via fax or via email

M. De Sanctis (B) · E. Cianca · V. Joshi Center for TeleInFrastruktur-Italy (CTIF_Italy), Department of Electronics Engineering, University of Roma, Tor Vergata, Italy e-mail: [email protected]

123

M. De Sanctis et al.

instead of delivering the original document moving between two different geographical locations. For what concern the possibility to enable green systems, wireless sensor networks can be used for the monitoring of carbon dioxide emissions thus taking the appropriate countermeasure when the emission level is above a given threshold. In the area of Intelligent Transportation Systems (ITSs) the use of various types of vehicular wireless networks has been proposed [2]. Considering that a large amount of fuel consumption and emissions are due to drivers not getting the most efficient route towards their destination, vehicular wireless networks can be used to inform the drivers about many kinds of events and situations which can have an impact on their travel: traffic light condition/synchronization, traffic congestion on the current route, useless high speeds. In any case, the impact of communications networks on a Green Planet can not be ignored. Currently, manufacture, use and disposal of Information and Communication Technology (ICT) equipments contribute to around 2% of global carbon dioxide emissions, and 37% of carbon dioxide emissions of all ICT is due to the telecommunication sector. Furthermore, the volume of exchanged data increases by a factor of approximately 10 every 5 years and wireless networks (including wireless networks in wide area, metropolitan area, local area and personal area) are responsible of the transmission of a large part of these data. Therefore, reducing the energy consumption of wireless networks with the consequent reduction of carbon dioxide emission for the required energy generation is considered a very important factor for the future of the Planet. This can be achieved, at least partially, by means of an energy efficient design of wireless networks. In this context, we define “Green Communications” as the framework for the design and disposal of communication networks which aims to a sustainable growth of telecommunications networks including wired and wireless networks: in this paper we only focus on the wireless part. The reduction of electromagnetic radiation through increased energy efficiency of wireless communication systems is an important factor for the public health preservation and therefore is another step towards green communication. The first approach to decrease the energy consumption of a wireless communication network is to limit the user needs, that is, the user should make use of wireless network resources only if this is strictly needed. However, this is mostly a social problem solution while we will discuss only engineering approaches. In this paper we focus on the improvement of the energy efficiency of wireless networks with the aim to reduce the energy consumption and consequently the carbon dioxide emissions due to the energy generation through fossil fuels. Energy efficiency is the metric of interest for wireless networks when the energy consumption is taken into account. Generally speaking, energy efficiency means using less energy to accomplish the same task, and in the case of communication systems, the task to be accomplished could be a file transfer, a phone call and so on. In the wireless network domain, energy efficiency is important not only from the Planet point of view, but also from the user point of view. The user point of view is the traditional target for the energy efficiency of wireless networks: enhancing the energy efficiency allows to increase the battery lifetime of mobile terminals thus reducing the need of frequent battery recharges. However, techniques to improve the energy efficiency at the user terminal side are sometime only aimed to the reduction of the transmit power without considering the so-called computation power, i.e. the energy required for signal processing as well as encoding and decoding, which is a key component of the system-wide energy consumption. In some other cases, the complexity and the associated power consumption has been moved from the user

123

Energy Efficient Wireless Networks Towards Green Communications

terminal to the base station/access point, and hence the overall system energy consumption might be not reduced. Therefore, they must be reviewed/modified when the objective is the improvement of the system-wide energy efficiency. This paper presents an overview of techniques such as cross layer approach, multiple antennas, cell size reduction and cognitive radio, from the system-wide energy efficiency point of view, outlining challenges and open issues. The paper is organized as follows. Theoretical bounds on energy efficiency are discussed in Sect. 2, including the trade-off between power and bandwidth in transmission schemes and the trade-off between efficiency and linearity in power amplifiers. The cross layer design approach is analyzed in Sect. 3 and compared with the traditional layered approach. Sections 4 and 5 deals with the use of multiple antennas and cognitive radio for green communications. Other approaches such as cell size reduction, the use of renewable energy sources and fresh air cooling are discussed in Sects. 6 and 7. Finally, conclusions are drawn in Sect. 8.

2 Energy Efficiency: Definitions and Trade-Offs Generally speaking, energy efficiency means using less energy to accomplish the same task. In case of communication systems, the task to be accomplished could be a file transfer, a phone call and so on. We assume that the task to be accomplished is the transfer of n u bits of the Medium Access Control (MAC) payload. Therefore, assuming also an Automatic Repeat request (ARQ) error recovery mechanism at link layer, the energy efficiency can be defined as follows: n u (1 − pr es ) η= (1) E tot where E tot is the average energy consumed for a packet transmission (also considering the necessary retransmissions due to an ARQ mechanism) and pr es is the residual packet error rate after having reached a maximum number of retransmissions. Improving the energy efficiency of a communication systems must have some costs. Here in the following the fundamental trade-offs to be considered when designing an energy efficient communication system are re-called. 2.1 Power/Bandwidth Trade Off for Reliable Communications For a digital modulation scheme we can define the following two important resource utilization metrics: • Power efficiency—is the ability to allow the transmission with a specific bit/symbol error probability at the smallest received power levels. The received power level is usually measured in terms of the Signal to Noise Ratio (SNR) expressed as the ratio E b /N0 between the received energy per bit and the noise power spectral density. The power efficiency can be expressed as the E b /N0 required to achieve a given bit error rate Pb . • Bandwidth efficiency (or spectral efficiency)—is the ability to transmit a specific amount of data per second within a small bandwidth. The bandwidth efficiency η is usually defined as the ratio between the data rate Rb and the bandwidth B required to transmit the data.

123


Fig. 1 Bandwidth efficiency versus power efficiency for different modulation schemes

Considering an Additive White Gaussian Noise (AWGN) channel, Claude Shannon demonstrated that the minimum required E b /N0 for reliable communication is a function of the spectral efficiency of the modulation scheme as given by [3]: Eb 2η − 1 = N0 η

(2)

Equation (2) is a fundamental bound based on the concept of channel capacity and the result of the Noisy Channel Coding Theorem (or second Theorem of Shannon). In Fig. 1, the power/bandwidth efficiency trade-off is shown for M-PSK, M-FSK and M-QAM modulation schemes. In this Figure the theoretical Shannon bound, which is the separation bound between achievable and unachievable trade-offs, is also included and the distance between the traditional modulations and this bound is shown. OFDM is the most important example of spectrally efficient transmission scheme, but on the other hand the power efficiency is low because of the high Peak-to-Average Power Ratio (PAPR) level. A traditional approach to the energy reduction is the use of error correction coding which allows to increase the power efficiency of the transmission scheme at the expenses of a reduction of the bandwidth efficiency. Currently, there are several powerful channel codes such as turbo codes or LDPC codes that allows to achieve power efficiencies very close to the Shannon bound.

123


2.2 Power Amplifier Efficiency Versus Linearity The function of the Power Amplifier (PA) in a wireless communication system is to increase the power level of the transmit signal so that the corresponding received signal can be demodulated by the receiver meeting a given error probability requirement. Two important metrics of PAs are linearity and efficiency. The linearity of the response of a PA is an important factor for wireless communication since the distortion of the signal causes an increase of the required SNR to meet the same error rate requirement and an irreducible error floor. As a metric of efficiency, here we can refer to the drain efficiency which is the ratio between output RF power and input DC power, therefore this is a measure of how much DC power is converted to RF power. High efficiencies are required to minimize thermal dispersion needs and decrease the energy consumption. Efficiency considerations lead to various classes of power amplifier: class A, B, C, etc. Since the energy consumption of PAs is a large portion of the overall consumption of wireless communication systems, from a green communication point of view high efficiency PAs are of paramount importance. However, high efficiency and high linearity are conflicting requirements in PAs [4]. In fact, electronic devices/systems can not provide constant gain (i.e. linearity) if they are powered by a limited power supply. As a consequence, if PA linearity is required, there must be a direct relationship between the output power and the power supplied to the PA. On the other hand, in order to be power efficient the electronic device/system should use a limited amount of power even when a high output power is needed. MFSK and also MPSK modulations are less sensitive to PA nonlinearities with respect to QAM modulation, however as previously shown, they are less spectrally efficient; this is the reason why modern broadband standards use a combination of amplitude and phase modulation. Furthermore, in order to enhance the bit error rate performance of non-linear PA we can use a pre-distorter which, in any case, increases the complexity of the transmitter [5]. In some special cases such as in Extremely High Frequencies (EHF), the PA is effective in its role if the input signal is impulsive. In [6], the use of Impulse Radio Ultra Wideband (IR-UWB) modulation is compared to FSK modulations in terms of Bit Error Rate (BER) considering PA nonlinearities and phase noise showing the superior performance of IR-UWB.

3 Cross Layer Approach to the Network Optimization Efficient resource utilization [3] is of paramount importance for the wireless environment over which radio-related resources (bandwidth, power, time) and device-related resources (complexity, battery energy, buffer capacity) are limited. In principle, the objective of the network design is the system performance optimization that can be expressed in terms of: • • • • •

QoS provision; Energy consumption minimization; Link state adaptation; Security awareness; Mobility management.

In some cases a trade-off exists between the previous features, for example energy-QoS and energy-security.

123


3.1 Energy Saving Mechanisms of Protocol Layers Energy efficiency can be reached over different protocol layers since they exploit the source of energy consumption in different ways: power amplifiers, processors, mixers, filters, registers etc. Many mechanisms are proposed that aim to reduce energy consumption during both active communication and idle periods in communication. Following a hybrid protocol architecture based on the Internet and the IEEE 802 architectures, we can list some major energy saving algorithms located at different protocol layers [7–11]. 3.1.1 Physical Layer At the physical layer energy can be saved if the system is able to adapt its modulation techniques and basic error-correction schemes to channel conditions and application requirements. The authors of [12] examine some of the trade-offs involved at the physical layer (coding and modulation schemes) in digital wireless transmission when the availability of a limited-energy source is taken into account. In order to minimize the energy consumption the transmit power on the link should be used fairly and adaptively [13]. Many approaches to dynamically changing the transmission power in wireless networks have been proposed. However, few of them were designed with consideration for the battery lifetime of mobile units. Most of them aim to guarantee limits on SNIR or maximize cell capacities. The drain efficiency of the power amplifier depends on the class of the amplifier and increases by increasing the level of nonlinearities introduced by the power amplifier. To this respect, from an energy efficient point of view it is important to use modulation schemes that are insensitive to nonlinearities which allows to use more efficient power amplifiers. 3.1.2 MAC Sublayer The medium access protocol can be used to dictate in advance when each wireless device may receive or transmit data. Each device is allowed to use power saving operational modes when it is certain that no data will arrive for it. Power management protocols manage the trade-off between energy and performance by determining when to switch between powersave mode and active mode. Depending on the chosen MAC protocol, some energy may be consumed due to channel access or contention resolution. For example, in IEEE 802.11, the sender transmits a Ready To Send (RTS) message to inform the receiver of the senders intentions. The receiver replies with a Clear To Send (CTS) message to inform the sender that the channel is available at the receiver. The energy consumed for contention resolution includes the transmission and reception of the two messages. Additionally, the nodes may spend some time waiting until the RTS can be sent and so consume energy listening to the channel. 3.1.3 LLC Sublayer The reliability of the single link is generally provided by the Logical Link Control (LLC) sublayer by using ARQ techniques in which unsuccessful transmissions are repeated a certain number of times. The energy consumed for the transmission of a packet also depends on the number of times the packet has to be retransmitted. In order to maximize the energy efficiency of the LLC protocols it is useful to avoid persistence in retransmitting data; trade off number

123


of retransmission attempts for probability of successful transmission; inhibit transmission when channel conditions are poor. 3.1.4 Network Layer At the network layer, intelligent routing protocols can minimize overhead and signaling and ensure the use of minimum energy routes. However, in wireless mobile networks, the network layer has the added functionality of routing under mobility constraints and mobility management including user location, update, etc. Energy efficient routing is a wide field of research for ad hoc networks. 3.1.5 Transport Layer Also at the transport layer, the increased number of packet retransmissions due to wireless link errors (e.g. when using TCP) consume energy and bandwidth. Various schemes have been proposed to alleviate the effects of non congestion-related losses on TCP performance over networks with wireless links. These schemes, which attempt to reduce retransmissions, are classified into three basic groups: a) split connection protocols, b) link layer protocols, and c) end-to-end protocols [14]. 3.1.6 Application Layer Application-level techniques can be used to reduce the amount of data to send and, hence, the amount of energy consumed. Furthermore, multimedia applications processing and transmission require considerable power as well as network bandwidth. This is especially true for video processing and transmission. Reducing the effective bit rate of video transmissions allows lightweight video encoding and decoding techniques to be utilized, thereby reducing power consumption. 3.2 Cross Layer Approaches The traditional layered approach based on a layered protocol architecture allows to split the complex problem of network design into smaller and more easy-to-solve problems, but it does not efficiently exploit available resources and leads to a local performance optimization, hence providing a suboptimal solution to the problem of system performance improvement. In fact, it is recognized that optimum performance design is the opposite of architecture that leads to standardization. On the other hand, in the cross-layer approach each protocol layer is optimized taking into account the knowledge of features and parameters of other protocol layers, not necessarily located at the bordering upper or lower levels. The cross-layer design approach provides better resource utilization and trade-offs solution with respect to a layered approach, but this can be achieved at the expenses of a more complex design which requires adaptability to the system changes by propagating modification on one protocol layer to all the others. The more general view of a cross-layer approach to the network design leads to achievement of a global optimization of the system performance. The cross-layer approach can be categorized into explicit and implicit. In case of implicit cross-layer optimization, interactions between different protocol layers are taken

123

Fig. 2 protocol architecture based on the hybrid reference model with a cross layer controller

Cross layer optimizer


Application Transport Network LLC MAC PHY

into account, but there is no exchange of information between protocol layers during runtime. In case of explicit cross-layer optimization, the exchange of information regarding protocol parameters, user requirements or channel state is required with the aim at maintaining performance optimization and high level of efficiency even if the communication parameters change. Finally, the implicit optimization is not adaptive while the explicit one is adaptive to the channel conditions and the application/user requirements. The explicit cross-layer optimization relies on a bottom-up approach when the information flow from lower to upper layers, while it relies on a top-down approach when the information flow from upper to lower layers. In a full cross-layer optimization where information are exchanged between all the protocol layers, middle-level protocols (e.g. network protocols) can be optimized by simultaneously using a top-down and bottom-up feedback [15–17]. In [7], a novel cross layer approach to optimize the energy efficiency of a short range wireless link is proposed where a slow transmit power control is applied at the physical layer and ARQ is considered. The proposed strategy implies that the SNR target of the transmit power control should be set accounting for the parameters at the MAC layer, hence following a top-down approach to the cross layer optimization. In Fig. 2 a protocol architecture based on the hybrid reference model is shown which includes a cross layer optimizer. The cross layer optimizer is the entity that collect status information from any protocol layer, make optimization decision and send parameter change request to the protocol layers [18]. Cross layer design over many protocol layers requires a high level of interdisciplinary expertise. Furthermore, it is needed to overcome some practical implementation issues of [19]: • • • • • •

robustness; architectural flexibility; asynchronism; complexity of the communication and the computation; rate of convergence; conflict between different cross-layer approaches.

In the evaluation of the benefits of cross layer approaches in terms of energy efficiency the increase of the complexity of the system and therefore the increase on computational power must be considered. While implicit cross layer approaches only increase the complexity of the design, explicit cross layer approaches also increase the complexity of the system resulting in a higher computational energy consumption. As a result, in order to increase the overall energy efficiency of the system, the additional computational power required for enabling an explicit cross layer approach must be lower than the amount of transmission power saved through the cross layer algorithm.

123


4 MIMO The mobile network equipped with the Multiple Input Multiple Output (MIMO) technology is looked as the basis of future high speed transmission mobile communication systems [20–22]. Moreover, future wireless networks are likely to use multi-cell MIMO where multiple access points cooperatively serve user terminals and the MIMO-related techniques (i.e. MIMO broadcast channel and MIMO multiple access channel) are applied to an array of physically separated antennas. Multi-cell MIMO may be used for interference cancellation and relaying in order to improve the spatial resource reuse and therefore to provide the required data rates while reducing energy consumption in mobile communication systems. It is well known by now that for a point-to-point communication and given transmission quality target (i.e. BER), using multiple antennas at the transmitter or/and receiver in full diversity allows one to decrease the transmit power dramatically. Therefore, is MIMO the best solution to energy consumption issues? Answering to the previous question is not trivial and requires a proper definition of energy efficiency. In [23] an information-theoretic approach has been proposed to give an answer to the above-mentioned question. Authors show that in slow fading or quasi-static MIMO systems (where reliability cannot be ensured), based on the proposed information-theoretic performance measure, the energy-efficiency is maximized for a non-trivial precoding scheme; in particular, transmitting at zero power or saturating the transmit power constraint is suboptimal. The determination of the best precoding scheme is shown to be a new open problem. Authors in [23] determines the best precoding scheme in several practical cases. However, as already outlined, the system-wide energy consumption does not only include the energy consumption of the RF front-end (i.e. the transmit power) but also the computational power [24] and the additional required computational power of a MIMO system could outweigh the reduced transmission power [25]. Analyzing the cost-benefit tradeoff in relay networks as well as the tradeoff between computational power, transmission power, and the system performance is the main contribution of paper [24]. The biggest challenge remains the use of low complexity approaches both in MIMO and multicell-MIMO to reduce the overall energy consumption.

5 Cognitive Radio The limit for wireless broadband communication is mostly due to the scarcity of available spectrum. The traditional approach to increase the data rate of wireless networks without a corresponding increase in the available spectrum is to increase the bandwidth efficiency of the communication schemes (e.g. OFDM) at the expenses of the energy efficiency. However, the spectrum is not free because of regulatory reasons, but some portions of the radio communication spectrum are unused for certain time periods. The concept of Cognitive Radio (CR) relies on this consideration, sensing the radio channel and exploiting the unused transmission slots in the time and frequency domain [26]. In a cognitive radio network, a secondary user (SU) is allowed to communicate over the same bandwidth that has been assigned to an existing primary user (PU) as long as they do not cause harmful interference to the PU. One commonly known technique used by the SU to protect the primary transmission is Opportunistic Spectrum Access (OSA), where the SU decides to access the channel of interest only if the primary transmission is detected to be off. A more reliable spectrum access approach over OSA is to allow SU to transmit even when the PU is active, provided that the resultant interference to the primary transmission is

123


properly controlled [27]. This approach is known as spectrum sharing. In spectrum sharing model, both SUs and PU can simultaneously transmit as long as the total interference power from SUs to the PU receiver remains below a certain threshold level. To determine this threshold level, interference temperature concept has been introduced by Federal Communications Commission (FCC). Cognitive radio technologies have been proposed to improve the spectral efficiency [28]. However, the concept of Green Cognitive Radio has been elaborated in [26] where instead of trying to maximize the bandwidth efficiency and constantly increasing the complexity of the systems, the effort is spent to increase the power efficiency using low modulation techniques and get optimized bandwidth efficiency. Grace et al. [26] discusses different ways of delivering green cognitive radio based systems. In particular, it discusses the complexity related to the spectrum sensing process, which is fundamental in CR networks. As a matter of fact, spectrum sensing is a time consuming and power intensive process. There are two main approaches for spectrum sensing: energy detection and feature detection. Even if energy detection has some problems as it is unable to differentiate between signal, noise and interference, it is less complex and hence it is more suitable in green communications. For spectrum sharing, the exploitation of distributed artificial intelligence is proved to be perfectly suited to cognitive radio. Techniques presented in [26] show how it is possible to largely eliminate the need for spectrum sensing, along with the associated energy consumption, by using reinforcement learning to develop a preferred channel set in each device. Moreover, in spectrum sharing approach, dynamic resource allocation (DRA) over the SU link, where the transmit power, bit-rate, bandwidth, and antenna beam of the SU are dynamically adapted based upon the channel state information (CSI) on the channel from the SU to the PU receiver [29,30], is essential. Again in [26], a variable power/bandwidth efficient modulation strategy is proposed, where the modulation level is adjusted by cognitively determining the assignment and use of the available spectrum, taking into account the channel occupancy probability. In the framework of DRA, Transmit Power Control (TPC) could play a crucial role. TPC bring a two-fold benefit: improving the power efficiency and reducing the power received by the primary users thus allowing more secondary users to share the same spectrum [8]. Inverse power control technique that allocates lower transmission power levels to good channel realizations and higher power levels to deeper fading may minimize the interference and allow more secondary users to share the spectrum. However, a large part of the transmission power in continuous inverse control solutions is used to compensate the deepest fades in a fading channel and hence, it is not power efficient. With the objective to maximize the number of SUs in the CR network, paper [27] presents a study of the optimal power control for the CR transmitter under the assumption that the transmitter knows the channel fading between SU and PU but also the fading condition of the PU, in such a way that the SU could transmit also when the PU is in deep fade. In [31], the authors derived the optimal power allocation strategy from the outage capacity point of view for a SU coexisting with a PU under both the peak and the average interference power constraints. In [32], authors study the ergodic capacity, the delay limited capacity, and the outage capacity of SU block-fading (BF) channels under spectrum sharing. The mentioned studies, which follow a more information theoretic approach, have basically shown that the optimal power allocation is characterized by some kind of “waterfilling” over the fading states, implying that a user must remain silent when its fading state falls below a certain threshold. However, in the mentioned works, there are no consideration about the energy efficiency of the proposed scheme. Power efficiency can be improved by using some truncation policies where user transmits when channel

123


conditions are good enough and abstains from transmission otherwise [33,34]. This may also cause long time delays for users experiencing bad channel conditions and hence, a trade-off between capacity and energy efficiency arises. In [8], authors consider the application of a truncated power control to a centralized cognitive radio network and study this energy efficiency-capacity trade-off. To study this trade-off a proper choice of the performance metrics must be done. The definition (1) of energy efficiency is not very useful when the transmission is not continuous in time but it introduces delays such as in our context as well as when some ARQ mechanisms are considered [35]. In fact, the paradox is that the less the system transmit and more it is power efficient. Therefore, for instance, with a TPC, when the power threshold is very high and there are no transmissions for long time interval, the system is much more power efficient than in case of Inverse power control. We do not think this is a fair comparison. For systems where the communication could be idle, a better definition of the energy efficiency is the following: η=

g P

(3)

that considers the goodput g, which is the number of bits successfully transmitted per second, and P is the power consumption. By using this definition, in [8] different power control strategies are compared in terms of trade-off. The paper shows that truncation is not always a better option than inverse power control in terms of capacity and also energy efficiency. It depends from the QoS requirements in terms of the Signal to Noise plus Interference Ratio (SNIR), transmit power limits and also from the variability of the channel and the number of SUs. Because of this trade-off, a range of values for the power thresholds exists, which represent the best trade-off between capacity and energy efficiency. An additional benefit provided by cognitive radio technologies is that a significant reduction of energy consumption can be achieved in home networking scenarios by switching from the ISM bands of WiFi, ZigBee and Bluetooth to TV white space bands [36], mainly because the number of available channels for indoor cognitive radio transmissions appears to be very significant.

6 Cell Size Reduction The reduction of cell size in mobile cellular networks through the deployment of picocells/femtocells has many advantages. The capacity of the network is improved and the coverage is increased especially in indoor environments. Furthermore, the energy efficiency is improved as a consequence of the reduced distance between the network operator antennas and the subscriber phone. For instance, femtocells connect miniaturized, lower power base stations to wired backhauls such as home Digital Subscriber Lines (DSL) with a very low transmit power compared to a full size base station. In [37] it was proven through computer simulations that a joint deployment of macrocells and residential picocells can reduce the total network energy consumption by up to 60% in urban areas. The simulation results provided in [38] show that reducing the cell size increases the capacity density but the overall energy consumption of the radio access network remains unchanged. In order to trade the increase in capacity density with the radio access network energy consumption without degrading the cell capacity provision, a sleep mode is intro-

123


duced. In fact, by means of a sleep mode, cells without active users are powered-off, thereby saving a significant amount of energy.

7 Other Approaches In case of cellular wireless networks, the base station is the key element in terms of energy consumption since the number of base stations is high and increasing and the energy consumption of a typical base station is very high, i.e. between 0.5 kW and 2kW. Therefore, the benefits arising from the coordinated use of communications and other non-communications approaches should be considered [39]. 7.1 Use of Renewable Energy Sources Natural energy sources like wind and solar power can be used to supply power to base stations in a green way instead of using other sources such as diesel which is continuously having a rising cost and causes carbon dioxide emissions. Furthermore, base stations in remote locations also require that the diesel is transported to the site itself and hence further carbon dioxide is produced, while this is not needed when using renewable energy sources. However, the unstable supply of energy through sources like wind or sun must be taken into account during the design of the entire power system, including powerful backup batteries. 7.2 Free Air Cooling Free air cooling is being increasingly used for ICT equipments to reduce the energy consumption. This method of cooling uses ambient air to cool electronic equipments in data centers, base stations and other ICT infrastructures. Significant reduction in energy use is achieved through free air cooling to cancel the use of air conditioning which represents a large part of the overall energy consumption.

8 Conclusions Several wireless communication techniques have been reviewed from the point of view of the system-wide energy efficiency. The cross layer approach allows to fully optimize the performance of wireless networks considering network parameters from several layers of the protocol stack as a whole. The cross-layer approach allows better resource/power utilization with respect to the traditional layered approach. However, this is achieved at the expenses of a more complex system which requires adaptability to the system conditions. Multiple antennas at the transmitter and/or receiver together with precoding schemes can be used to decrease the required transmit power keeping the same transmission rate and quality. Cognitive radio is another approach which opens new strategies for the use of spectrum and power in wireless networks. Reducing the need of spectrum sensing, use of directional antennas and proper DRA mechanisms can make cognitive radio networks, which aims to be spectrally efficient, also energy efficient. In particular, TPC with truncation policies could improve the trade-off energy efficiency- CR network capacity.

123


The main challenge is the use of the above mentioned techniques with low complexity algorithms so that the additional computational power required for implementing them does not exceed the saved transmit power.

References 1. The Telepresence Revolution, www.cdproject.net. Accessed on July 12, 2010. 2. Alsabaan, M., Naik, K., Khalifa, T., & Nayak, A. (2010). Vehicular networks for reduction of fuel consumption and CO2 emission. In 8th IEEE international conference on industrial informatics (INDIN) (pp. 671–676). 13–16 July. 3. Burr, A. (2001). Modulation and coding for wireless communications. (1st Ed., pp. 7–14). Prentice Hall. 4. Lavrador, P. M., Cunha, T. R., Cabral, P. M., & Pedro, J. C. (2010). The linearity-efficiency compromise. IEEE Microwave Magazine, 11(5), 44–58. 5. Mukherjee, S., De Sanctis, M., Rossi, T., Cianca, E., Ruggieri, M., & Prasad, R. (2010). Mode switching algorithms for DVB-S2 links in W band. IEEE Globecom 2010, ISBN: 978-1-4244-5637-6, Miami (Florida), 6–10 December. 6. Stallo, C., Mukherjee, S., Cianca, E., Lucente, M., Rossi, T., De Sanctis, M., & Ruggieri, M. (2010). IR-UWB for high bit rate communications beyond 60 GHz. In 21st annual IEEE international symposium on personal, indoor and mobile radio communications (PIMRC 2010), Istanbul, Turkey. ISBN: 978-1-4244-8015-9, 26–29 September. 7. De Sanctis, M., Cianca, E., & Ruggieri, M. (2006). Energy efficient transmit power control for HDR WPAN. In 17th annual IEEE international symposium on personal, indoor and mobile radio communications (PIMRC’06), Helsinki (Finland) (pp. 1–5). ISBN: 1-4244-0329-4, September 11–14. 8. Tripathi, P. S. M., Cianca, E., De Sanctis, M., Ruggieri, M., & Prasad, R. (2010). Truncated power control over cognitive radio networks: Tradeoff capacity/energy efficiency. In 13th international symposium on wireless personal multimedia communications (WPMC2010), Recife (Brazil), October 11–14. 9. Zorzi, M. (Guest Editor) (1998). Special issue on energy management in personal communications and mobile computing systems. IEEE Personal Communications Magazine, 5(3). 10. Havinga, P. J. M., & Smit, G. J. M. (2001). Energy-efficient wireless networking for multimedia applications. Wiley Journal on Wireless Communications and Mobile Computing, 1(2), 165–184. 11. Jones, C. E., Sivalingam, K. M., Agrawal, P., & Chen, J. C. (2001). A survey of energy efficient network protocols for wireless networks. Kluwer Journal on Wireless Networks, 7(4), 343–358. 12. Biglieri, E., Caire, G., & Taricco, G. (1998). Coding and modulation under power constraints. IEEE Personal Communications, 5(3), 32–39. 13. Rulnick, J. M., & Bambos, N. (1996). Mobile power management for maximum battery life in wireless communication networks. In IEEE conference on computer communications, INFOCOM ‘96 (Vol. 2, pp. 443–450). 24–28 March. 14. Balakrishnan, H., Padmanabhan, V. N., Seshan, S., & Katz, R. H. (1997). A comparison of mechanisms for improving TCP performance over wireless links. In IEEE/ACM transaction on networking (Vol. 5, pp. 756–769). December. 15. Srivastava, V., & Motani, M. (2005). Cross-layer design: A survey and the road ahead. IEEE Communications Magazine, 43(12), 112–119. 16. Shakkottai, S., Rappaport, T. S., & Karlsson, P. C. (2003). Cross-layer design for wireless networks. IEEE Communications Magazine, 41(10), 74–80. 17. Wijting, C., & Prasad, R. (2004). A generic framework for cross-layer optimisation in wireless personal area networks. Wireless Personal Communications, 29(1–2), 135–149. 18. Khan, S., Peng, Y., Steinbach, E., Sgroi, M., & Kellerer, W. (2006). Application-driven cross-layer optimization for video streaming over wireless networks. IEEE Communications Magazine, 44(1), 122–130. 19. Kawadia, V., & Kumar, P. R. (2005). A cautionary perspective on cross layer design. IEEE Wireless Communications Magazine, 12(1), 3–11. 20. Telatar, E. (1995). Capacity of multiantenna Gaussian channels. AT&T Bell Laboratories Tech. Memo, June. 21. Foschini, G. J., & Gans, M. J. (1998). On limits of wireless communications in a fading environment when using multiple antennas. Wireless Personal Commununications, 6, 311–335.

123

M. De Sanctis et al. 22. Telatar, E. (1999). Capacity of multi-antenna gaussian channels. European Transactions on Telecommunications, 10(6), 585–596. 23. Belmega, E. V., & Lasaulce, S. (2009). An information-theoretic look at MIMO energy-efficient communications. In Proceedings of the fourth international ICST conference on performance evaluation methodologies and tools (VALUETOOLS’09), October 20–22, 2009, Pisa (Italy). 24. Rost, P., & Fettweis, G. (2011). Green communications in cellular networks with fixed relay nodes. Chapter 11. In E. Hossain, D.I. Kim & V.K. Bhargava (Eds.), Cooperative cellular wireless networks. Cambridge University Press. 25. Sahai, A., & Grover, P. (2008). The price of certainty: Waterslide curves and the gap to capacity. CoRR, Vol. abs/0801.0352. 26. Grace, D., Chen Tao Jiang, J., & Mitchell, P. D. (2009). Using cognitive radio for delivering green communications. In 4th international conference on cognitive radio oriented wireless networks and communications, 2009. CROWNCOM ‘09, June. 27. Zhang, R. (2008). Optimal power control over fading cognitive radio channel by exploiting primary user CSI. In IEEE Globecom 2008, New Orleans, USA, November 30 2008–December 4. 28. Mitola, J. (1999). Cognitive radio: Making software radio more personal. IEEE Personal Communications, 6(4). 29. Ghasemi, A., & Sousa, E. S. (2007). Fundamental limits of spectrum sharing in fading environments. IEEE Transactions on Wireless Communications, 6(2), 649–658. 30. Zhang, R., & Liang, Y. C. (2008). Exploiting multi-antennas for opportunistic spectrum sharing in cognitive radio networks. IEEE Journal of Selected Topics Signal Processing, 2(1), 88–102. 31. Musavian, L., & Aissa, S. (2007). Ergodic and outage capacities of spectrumsharing systems in fading channels. In Proceedings of IEEE global telecommunications conference (GLOBECOM07), Washington. DC, USA (pp. 3327–3331). 32. Kang, X., Liang, Y.-C., Nallanathan, A., Garg, H. K., & Zhang, R. (2009). Optimal power allocation for fading channels in cognitive radio networks: Ergodic capacity and outage capacity. IEEE Transactions on Wireless Communications, 8(2), 940–950. 33. Cianca, E., De Sanctis, M., Ruggieri, M., & Prasad, R. (2005). Truncated power control for improving TCP/IP performance over CDMA wireless links. IEEE Transaction on Wireless Communications, 4(4). 34. Kim, S. W., & Goldsmith, A. J. (2000). Truncated power control in code-division multiple-access communications. IEEE Transactions on Vehicular Technology, 49(3), 965–972. 35. Hoven, N., & Sahai, A. (2005). Power scaling for cognitive radio. Wireless Networks, Communications and Mobile Computing, 1. 36. Kawade, S., & Nekovee, M. (2010). Wireless options for high data-rate indoor users: cognitive access to TV white spaces. In Proceedings of the IEEE international symposium on new frontiers in dynamic spectrum access networks (DySPAN ‘10), Singapore, April. 37. Claussen, H., Ho, L. T. W., & Pivit, F. (2008). Effects of joint macrocell and residential picocell deployment on the network energy efficiency. In IEEE 19th international symposium on personal, indoor and mobile radio communications (PIMRC) 2008 (pp. 1–6). 15–18 September. 38. Badic, B., O’Farrell, T., Loskot, P., & He, J. (2009). Energy efficient radio access architectures for green radio: Large versus small cell size deployment. In IEEE vehicular technology conference fall (VTC 2009-Fall), 20–23 September. 39. Louhi, J. (2007). Energy efficiency of modern cellular base stations. In 29th international telecommunications energy conference, 2007 (INTELEC 2007) (pp. 475–476), September 30 October 4.

123


Author Biographies Mauro De Sanctis received the “Laurea” degree in Telecommunications Engineering in 2002 and the Ph.D. degree in Telecommunications and Microelectronics Engineering in 2005 from the University of Roma “Tor Vergata” (Italy). From the end of 2008 he is Assistant Professor at the Department of Electronics Engineering, University of Roma “Tor Vergata” (Italy), teaching “Information and Coding Theory”. He was with the Italian Space Agency (ASI) as holder of a two-years research fellowship on the study of Q/V band satellite communication links for a technology demonstration payload, concluded in 2008. During this period he participated to the opening and to the first trials of the ASI Concurrent Engineering Facility (ASI-CEF). He was involved with the University of Rome “Tor Vergata” in several satellite missions of the Italian Space Agency (ASI): DAVID satellite mission (DAta and Video Interactive Distribution) during the year 2003; WAVE satellite mission (W-band Analysis and VErification) during the year 2004; FLORAD (Micro-satellite FLOwer Constellation of millimeter-wave RADiometers for the Earth and space Observation at regional scale) during the year 2008. In 2006 he was a post-doctoral research fellow for the European Space Agency (ESA) ARIADNA extended study named “The Flower Constellation Set and its Possible Applications”. From January 2004 to December 2005 he was involved in the MAGNET (My personal Adaptive Global NET) European FP6 integrated project and in the SatNEx European network of excellence. From January 2006 to June 2008 he was involved in the MAGNET Beyond European FP6 integrated project as scientific responsible of WP3/Task3. He is/was involved in several Italian Research Programs of Relevant National Interest (PRIN): SALICE (Satellite-Assisted LocalIzation and Communication systems for Emergency services), from October 2008 to September 2010; ICONA (Integration of Communication and Navigation services) from January 2006 to December 2007, SHINES (Satellite and HAP Integrated NEtworks and Services) from January 2003 to December 2004, CABIS (CDMA for Broadband mobile terrestrial-satellite Integrated Systems) from January 2001 to December 2002. In 2007 he was involved in the Internationalization Program funded by the Italian Ministry of University and Research (MIUR), concerning the academic research collaboration of the Texas A&M University (USA) and the University of Rome “Tor Vergata” (Italy). In autumn of 2004, he joined the CTIF (Center for TeleInFrastruktur), a research center focusing on modern telecommunications technologies located at the University of Aalborg (Denmark). He was co-recipient of the best paper award from the 2009 International Conference on Advances in Satellite and Space Communications (SPACOMM 2009). He is serving as Sector Editor for the Space Systems area of the IEEE Aerospace and Electronic Systems Magazine. His main areas of interest are: wireless terrestrial and satellite communication networks, satellite constellations (in particular Flower Constellations), resource management of short range wireless systems. He co-authored a book entitled “Information and Coding: Theory Overview, Design, Applications and Exercises” and more than 50 papers published on journals and conference proceedings.

Ernestina Cianca received the Laurea degree in Electronic Engineering “cum laude” at the University of L’Aquila in 1997. She got the Ph.D. degree at the University of Rome Tor Vergata in 2001. She concluded her Ph.D. at Aalborg University where she has been employed in the Wireless Networking Groups (WING), as Research engineer (2000–2001) and as Assistant Professor (2001–2003). Since Nov. 2003 she is Assistant Professor in Telecommunications at the URTV (Department of Electronics Engineering), teaching DSP, Information and Coding Theory and Advanced Transmission Techniques. She has been the principal investigator of the WAVE-A2 mission, funded by the Italian Space Agency and aiming to design payloads in W-band for scientific experimental studies of the W-Band channel propagation phenomena and channel quality. She has been coordinator of the scientific activities of the Electronic Engineering Department on the following projects: ESA project European Data Relay System (EDRS); feasibility study for the scientific small mission FLORAD

123

M. De Sanctis et al. (Micro-satellite FLOwer Constellation of millimeter-wave RADiometers for the Earth and space Observation at regional scale); TRANSPONDER2, funded by ASI, about the design of a payload in Q-band for communications; educational project funded by ASI EduSAT on pico-satellites. She has worked on several European and National projects. Her research mainly concerns wireless access technologies (CDMA and MIMO-OFDM-based systems), in particular, Radio Resource Management at PHY/MAC layer, integration of terrestrial and satellite systems, short-range communications in biomedical applications. She has been General Chair of the conference ISABEL2010 (Third Symposium on Applied Sciences in Biomedical and Telecommunication Engineering), she has been TPC Co-Chair of the conference European Wireless Technology 2009 (EuWIT2009); TPC Co-Chair in the conference Wireless Vitae 2009. She is Guests Editors of some Special Issues in journals such as Wireless Personal Communications (Wiley) and Journal of Communications (JCM, ISSN 1796–2021). She is author of about 60 papers, on international journals/transactions and proceedings of international conferences.

Viraj Joshi received the “Bachelor of Engineering” degree in Electronics Engineering in 2001 and the Masters degree in Electronics and Telecommunication Engineering in 2008 from the Bharati Vidyapeeth University, Pune (India). Currently she is pursuing her Ph.D. from Aalborg University and working as a visiting researcher at University of Rome Tor Vergata for 6 months. From 2007 she is Lecturer at the Department of Electronics and Telecommunication Engineering, Sinhgad Institute of Technology, Lonavala (India), teaching “Solid State Devices and Circuits”. She is a member of the committee for Global ICT Standardization Forum for India (GISFI), which is headed by Dr. Ramjee Prasad. She was involved with the University of Pune in Syllabus Detailing for second year engineering students during 2009-10. She has been attended a Winter School on “Fundamentals & Recent Advances in RF and Microwave Communication” arranged by IIT, Kharagpur, from 07/12 to 20/12/2009. She was recipient of the best paper of the session award from the 2007 National Conference at D. Y. Patil College of Engineering, Pimpri, Pune for the topic “Dynamic Texture Recognition”. She has been selected for Erasmus Mundus Fellowship for Ph.D. course at the university of Tor Vergata, Roma, Italy. Her main areas of interest are: wireless communication (mainly green communication), VLSI technology, Analog Electronics. She has 8 publications in national and international conferences.

123