For more than a century, communication networks have been mainly designed with the aim of optimizing performance metrics such as the data-rate, throughput, latency, etc., in the last decade energy efficiency has emerged as a new prominent figure of merit, due to economic, operational, and environmental concerns. nAccording to Huawei’s Andrae, fixed access networks consumed about 167 TWh of electricity in 2015 while wireless networks consumed roughly 50 TWh. That’s a big number – 1 TWh is a trillion watts/hour. For perspective the average American household consumes 7,200 kWh of electricity per year.
Given that 10% of the world’s energy consumption is due to the Information and Communications Technology (ICT) industry, energy-efficiency has thus become one of the key performance indicators (KPI). Energy efficiency is an important issue in the proposed next generation wireless communications by 2020 as it severely effects the human life on the earth surface mainly on two factors global warming due to CO2 emission and sea level raise. The design of the next generation (5G) of wireless networks will thus necessarily have to consider energy efficiency as one of its key pillars.
At the same time, building networks that support this demand for new services has resulted in a corresponding rise in energy consumption. Traditional mobile networks spend about 15 to 20 percent of overall power consumption on actual data traffic. The unused energy is wasted. Increasing energy efficiency has a huge potential to harness wasted power and deploy new technologies, which would further reduce power consumption. On this subject, the progress made is substantial and documented: each transition from one generation of networks to another has brought about a gain of a factor of 10 in energy efficiency.
Further, this issue has become more concern due to the ever increasing demand of data rates, spectral efficiency and quality of service combining with massive IoT communications. The launch of each successive generation of mobile technology has enabled new services that require extended coverage for more people and places. The Fifth Generation (5G) mobile networks which have started seeing the initial deployment promise including fast Internet for everyone, smart cities, driverless cars, critical health care, “internet of things” revolution, and reliable and secure communications for critical infrastructures and services.
Because energy efficiency has become a priority, an efficiency measure, the number of bits transmitted per Joule of energy expended, has become a standard. Having an efficiency metric to work with is useful especially as electricity costs in providing mobile phone/data service represent about 70 percent of the bill. Today’s cellular site delivering 28Mbit/sec has an energy consumption of 1.35kW, leading to an EE of 20 kbit/Joule.
Data rates as high as that of 1 Gbps have been foreseen with the advent of 5G. A lurking threat behind the promise of 5G delivering up to 1,000 times as much data as today’s networks is that 5G could also consume up to 1,000 times as much energy. A general concern is that higher data rates can only be achieved by consuming more energy; if the EE [energy efficiency] is constant, then 100× higher data rate in 5G is associated with a 100× higher energy consumption.” In addition, with an explosive number of heterogeneous devices coming online, including sensors for home security, tablets, and wearable health monitors, the computational power of base stations must increase. An estimated 50% increase in the computing power of baseband units has been predicted to handle this traffic burst.
The next generation of mobile telecommunications infrastructure, the vast proliferation of devices, including those labeled the Internet of Things (IoT), will add up to additional energy consumption. Our biggest area of concern, however, is in data centers. Radoslav Danilak asserts that data centers will consume exponentially larger amounts of electricity, arguing, “consumption will double every four years.”
Yale’s Environment 360 program noted, “Insanely, most of the world’s largest centers are in hot or temperate climates, where vast amounts of energy are used to keep them from overheating.” Placement matters in keeping cooling costs down, but designing energy efficient processors and other components for servers is also important. Global data processing does not appear anywhere near a, and 5G will add to the global energy bill of both telecommunications firms and those that conduct computing in the cloud.
A number of significant differences exist between LTE and 5G when considering energy usage. First, because of the new millimeter band pieces of spectrum used, there will likely be a densification of existing cellular networks with the massive addition of small cells and a provision for peer-to-peer (P2P) communication. In 5G, simultaneous transmission and reception will be possible, which likely necessitates new investment in fiber optics to move the data. Some wireless functions will move to cloud processing and much more of the infrastructure will be virtual in nature.
5G design requirements specify that energy use be reduced to 10 percent of current 4G networks. This includes reducing power requirements for radio base station antennas, as well as client devices such as smartphones and IoT devices to extend battery life. The future goal is Green communications and networking comprising sustainable, energy-efficient, energy-aware, and environmentally aware communications and networking.
The base stations are deploying new technologies in Mobile network infrastructures to reduce power consumption. These include cloud and virtualization technologies, new efficient antenna hardware, 5G small cell network architectures and more efficient network protocols. Foe smartphones The goal for 5G devices is to increase battery life to at least three days and up to 15 years for cellular IoT devices
Energy Efficiency solutions
1. Resource allocation.
The first technique to increase the energy efficiency of a wireless communication system is to allocate the system radio resources in order to maximize the energy efficiency rather than the throughput. This approach has been shown to provide substantial energy efficiency gains at the price of a moderate throughput reduction.
From a physical standpoint, the efficiency with which a system uses a given resource, is the ratio between the benefit obtained by using the resource, and the corresponding incurred cost. Applying this general definition to communication over a wireless link, the cost is represented by the amount of consumed energy, which includes the radiated energy, the energy loss due to the use of non-ideal power amplifiers, as well as the static energy dissipated in all other hardware blocks of the system (e.g. signal up and downconversion, frequency synthesizer, filtering operations, digital-to-analog and analogto-digital conversion, and cooling operations).
2. Network planning and deployment.
The second technique is to deploy infrastructure nodes in order to maximize the covered area per consumed energy, rather than just the
covered area. In addition, the use of base station (BS) switchon/switch-off algorithms and antenna muting techniques to adapt to the traffic conditions, can further reduce energy consumptions.
Operate site infrastructure intelligently
The total 5G energy cost addition will be impacted by service provider deployment strategies and equipment choice. The solution is all about using insight into network reality, such as how traffic is divided over different sites, and then deploying infrastructure based on those insights. This can create a much leaner network in the sense that service providers will not have to spend as much capital expenditure (CAPEX) and also result in a situation where operator expenses (OPEX) can also come down, some of which will be energy based.
In most cases, low-traffic areas actually account for 70 percent of the network sites, yet usually carry about 25 percent of the total traffic. On the other hand, medium-to-high-traffic areas usually account for just 30 percent of the network sites yet carry up to 75 percent of the traffic. Traditionally, the industry has focused on building out the medium-to-high-traffic sites, while neglecting the tail end of networks where capacity constraints are low. However, low-traffic sites consume an overwhelming share of energy, while providing basic network coverage.
AI and advanced data analytics are already well integrated in many of today’s network management solutions. Yet, today, we see that they can increasingly offer more value in terms of reducing network energy consumption. Today, there are already solutions available on the market which allow service providers to operate their site infrastructure in a much more intelligent way. The savings offered by such solutions look likely to raise the pressure on service providers to modernize existing site infrastructure, in order to reap the gains right across the network.
By deploying the most appropriate hardware in each part where it is needed, service providers can serve demand, such as through Massive MIMO in denser traffic sites, and avoid over-dimensioning equipment in areas where there is less traffic.
The idea of dense networks is to deal with the explosively increasing number of devices to serve by increasing the amount of deployed infrastructure equipment.
A mobile network cell includes the antenna, base station and the physical area that is serviced by the cell. A standard cell is called a macro cell. A small cell is just a smaller version of a macro cell and is available in several sizes and powers: micro cells, pico cells and femtocells. Small cells are either installed inside buildings or outside in densely-populated areas. In the case of small cells, the Small Cell Forum predicts that 5G small-cell deployments will overtake 4G small cells by 2024, with the total installed base of 5G or multimode small cells in 2025 to be 13.1 million, constituting more than one-third of the total small cells in use. When you deploy more small cells, the total energy consumption of a network will grow.
However, the energy consumption in a small cell is much lower than in a conventional cell. Moreover, the power required to communicate between clients and 5G base stations increases the further the signal has to be transmitted. Since small cell base stations are deployed in close proximity to client devices, it significantly reduces power consumption by both the base stations and the 5G client devices.
This trade-off has been analyzed, where it is shown that densification has a beneficial impact on energy efficiency, but the gain saturates as the density of the infrastructure nodes increases, thus indicating that an optimal density level exists.
Increased Network Density
Small cells with massive MIMO antennas can serve many more devices at the same time. Each device is multiplexed over the same space and frequency. This spatial multiplexing uses the same channel to serve multiple devices. The energy consumption is also shared among multiple users or devices. As an example, when 10 devices are multiplexed, the energy consumption of each device is one-tenth, or an energy efficiency of 10x.
Massive MIMO Antennas for more efficiency
The idea of massive MIMO is to densify the number of deployed antennas. In massive MIMO, conventional arrays with only a few antennas
fed by bulky and expensive hardware are replaced by hundreds of small antennas fed by low-cost amplifiers and circuitry.
A major breakthrough made by 5G concerns the implementation of new antennas called Massive Multiple-Input Multiple-Output (Massive MIMO). Multiple input multiple output (MIMO) is a technology that uses multiple antennas configured in a two-dimensional phased array. The antenna system is attached to a base station and controls the transmission and reception of radio signals. These transmit the signal only in the direction of the communicating mobile (known as beams), rather than over a wide area as the antennas commonly used in 4G do. This feature significantly increases the throughput delivered by an antenna, as multiple beams can be used simultaneously, each being able to reuse the cell’s frequencies.
Massive MIMO systems are expanded MIMO systems with up to several hundred antennas and can handle large volumes of network throughput and support large numbers of client connections. When it comes to massive MIMO, the technology involves the use of arrays with many more antennas at each base station. As a result, there are many more hardware components per base station. This will probably increase the total energy consumption of 5G base stations compared to 4G. But as massive MIMO technology develops, its energy efficiency may also improve over time.
Massive MIMO antennas are characterised by an ultra-integrated design. They concentrate the power amplifiers (whose efficiency has been improved compared to 4G) at the radome (antenna shelter) by combining radiating elements, analogue electronics, and a digital part dedicated to beam management functions. If the first implementations are poorly optimised, the expected progress in the integration and densification of the components of the antenna will greatly reduce the energy consumption of the bricks that make up the antenna.
Antenna arrays can better identify 5G client physical locations. Arrays can also track mobile clients and direct the transmission beam at the client, following the client movements and maintaining network connectivity. Beamforming is a technology that can direct radio transmission signals in a specific direction. This increases the channel efficiency, data rates, reduces interference and focuses radio energy directly at the client devices.
Since the massive MIMO antenna and base station systems communicate with remote clients using a focused beam, the wireless protocols can calculate the minimum power required for communication. This reduces the energy consumption for wireless energy transmissions for both the base station and the client devices. As a result, 5G networks using beamforming consume about four times less power than comparable 4G networks.
As far as energy efficiency is concerned, massive MIMO has been shown to reduce the radiated power by a factor proportional to the square root of the number of deployed antennas, while keeping the information rate unaltered. European Union project dubbed the MAMMOET project has predicted that future massive MIMO base stations will consume less energy than 4G base stations, despite the fact that they will contain more hardware. “The projections that have been made by antenna manufacturers are encouraging,” said Eric Hardouin, Director of the Ambient Connectivity Research, Orange Labs Research. “While a 5G antenna consumes three times more energy on average today than a 4G antenna, this ratio is expected to rise to 50% by 2021 and 25% by 2022. Above all, for this energy consumption, a 5G antenna manages a bandwidth five times higher and can deliver a higher throughput to serve more users simultaneously.”
Offloading techniques are another key 5G strategy instrumental to boost the capacity and energy efficiency of future networks. Currently available user devices are already equipped with multiple radio access technologies (RATs) –e.g., cellular, Bluetooth, WiFi –, so that, whenever alternative connection technologies are available (e.g., as often happens in indoor scenarios), cellular traffic can be offloaded and
additional cellular resources can be provided to those users that cannot offload their traffic. Future networks will vastly rely on offloading techniques, and these will not only be based on Wi-Fi.
Device-to-device (D2D) communications.
While in a conventional network user devices are not allowed to directly communicate, D2D communications refer instead to the scenario in which several co-located (or in close proximity) devices can communicate directly using a cellular frequency and being instructed to do
so by the BS. D2D techniques have a profound impact on the system energy efficiency since direct transmission between nearby devices may happen at a much lower transmit power than that needed for communication through a BS that can be far away. Additionally, they are
a powerful offloading strategy since they permit releasing resources at the BS that, through proper interference management, can be used for supporting other users.
The use of frequency bands above 10GHz, a.k.a. mmWaves, while increasing the available network bandwidth, can act as a strategy to offload traffic from the sub-6GHz cellular frequencies for short-range (up to 100-200 m) communications in densely crowded areas. Future wireless technology will need to harness the massively unused mmWave spectrum to meet the projected acceleration in mobile traffic demand. Today, the available range of mmWavebased solutions is already represented by IEEE 802.11ad (WiGig), IEEE 802.15.3c, WirelessHD, and ECMA-387 standards, with more to come in the following years.
Base Station Energy Consumption and Cell Switch Off Techniques
A typical BS has been presented by dividing it into five parts, namely antenna interface, power amplifier, RF chains, Baseband unit, mains power supply and the DC-DC supply. An important claim has been made stating that up to 57% of the power consumption at a base station is experienced at the transmission end, i.e., the power amplifier and antenna interface.
One of the most significant developments associated with 5G is the widespread use of deep Sleep Modes. The basic principle is simple: to selectively turn off one or more devices in the absence of traffic. On this topic, 4G was limited due to the design of its radio interface, a base station that has to transmit reference signals about 1,000 times per second, even without an active mobile in the cell. In fact, the latest generation of mobile networks could only allow the implementation of the most basic first level of Sleep Mode (four in all)
5G, on the other hand, provides for the configuration of transmission-free time slots in non-traffic conditions, in order to enable activation of more advanced and energy-efficient Sleep Modes.During gaps in network activity, the base station can reduce energy consumption by going into sleep mode. New base station electronics can enter sleep mode during very short gaps. An interval without transmission can be set to a range of 5-100 ms, but this means that a terminal can take more time to hang on to a cell – without the user noticing it.Even in high-use mobile networks, base station utilization usually does not exceed 20 percent. Base stations consume 80 percent of the power in a mobile network infrastructure and in any 24-hour period, most base stations idle. 5G base stations can go into sleep mode during this idle time. They can go into sleep mode quickly and for as long as possible.
5G networks have higher data throughput and reduced packet latency. Higher data rates mean data is transferred in in shorter periods of time. This creates longer periods in which the network connection between the client and the base station is idle. These Idle periods allow for longer periods of sleep mode. According to recent research, the ultra-lean design that 5G networks are capable of will make it possible to put more components to sleep for a longer time, reducing energy consumption by almost 10 times compared to current systems when there are no users.
3. Energy harvesting and transfer.
The third technique is to operate communication systems by harvesting energy from the environment. This applies to both renewable and clean energy sources like sun or wind energy, and to the radio signals present over the air.
Two main kinds of energy harvesting have emerged so far in the context of wireless communications.
– Environmental energy harvesting. This technique refers to harvesting clean energy from natural sources, such as sun and wind. The main challenge in the design of communication systems powered by energy harvesting is the random amount of energy available at any given time. This is due to the fact that the availability of environmental energy sources (e.g. sun or wind) is inherently a stochastic process, and poses the problem of energy outages.
– Radio-frequency energy harvesting. This technique refers to harvesting energy from the radio signals over the air, thus enabling the recycling of energy that would otherwise be wasted. In this context, interference signals provide a natural source of electromagnetic-based power.
The idea is to combine energy harvesting with wireless power transfer techniques, thereby enabling network nodes to share energy with one another. This has a two-fold advantage. First, it makes it possible to redistribute the network total energy, prolonging the lifetime
of nodes that are low on battery energy. Second, it is possible to deploy dedicated beacons in the network, which act as wireless energy sources, thereby eliminating or reducing the randomness of the radio-frequency energy source.
Simultaneous wireless information and power transfer (SWIPT).
With 5G, one of the novel technologies being considered is Radio Frequency (RF) harvesting; converting energy in transmitted radio waves to user devices or even wireless infrastructure (microcells, antenna arrays, etc.). Since RF signals can carry both energy and information, theoretically RF energy harvesting and information reception can be performed from the same RF input signal. This scheme is referred to as the simultaneous wireless information and power transfer (SWIPT).
4. Hardware solutions
The fourth technique is to design the hardware for wireless communications systems explicitly accounting for its energy consumption, and to adopt major architectural changes, such as the cloud-based implementation of the radio access network.
Energy-efficient hardware solutions refers to a broad category of strategies comprising the green design of the RF chain, the use of simplified transmitter/receiver structures, and, also, a novel architectural design of the network based on a cloud implementation of the radio access network (RAN) and on the use of network function virtualization.
The use of simplified transmitter and receiver architectures, including the adoption of coarse signal quantization (e.g. one bit quantization) and hybrid analog/digital beamformers, is another technique that is being proposed for increasing hardware energy efficiency,
especially in systems with many antennas such as massive MIMO systems and mmWave systems.
For mmWave communications, given the required large number of antenna elements, the implementation of digital beamforming poses serious complexity, energy consumption, and cost issues. Hybrid analog and digital beamforming structures have been thus proposed as a viable approach to reduce complexity and, most relevant to us, energy consumption.
Cloud-based implementation of the RAN is another key technology instrumental to making future 5G networks more energy-efficient.Ericsson research shows that a major part of energy consumption in mobile networks comes from the radio access network (RAN) and radio base station sites. Typically, the installed base consists of 2G, 3G and 4G that coexists with 5G systems. Ericsson RAN energy saving software features for 2G, 3G & 4G can enable up to 15 percent reduction in overall energy consumption by taking advantage of traffic variations
The most extreme implementation of C-RAN foresees light BSs wherein only the RF chain and the baseband-to-RF conversion stages are present; it is assumed that these light BSs are connected through highcapacity links to the data-center, wherein all the baseband
processing and the resource allocation algorithms are run. This enables a great deal of flexibility in the network, thus leading
to substantial savings as far as both deployment costs and energy consumption are concerned.
Network Virtualization: NFV and SDN
One of the key architectural requirements specified for 5G infrastructures is that all core network systems be based on software virtualization. Network infrastructures have traditionally included physical purpose-built appliances, which are less flexible to manage, deploy and scale. Network Functions Virtualization (NFV) is a network architecture based on Virtual Network Functions (VNF). Network functions include any system that provides network functions like network routing, packet processing, security and many others.
Software Defined Networks (SDN) are virtualization technologies that abstract physical networks to virtual network structures. Virtual networks appear and behave like physical networks and have all the advantages of other virtualization technologies. NFV and SDN technologies enable better infrastructure scaling, Lower computational redundancy, and Fewer hardware systems and this reduces the overall energy consumption.
5. Energy Efficiency technologies
Alternative approach for energy saving is, rather than maximizing the energy efficiency, to minimize the energy consumption
In addition, new emerging technologies can also be used for energy-efficient purposes. In particular, caching and mobile computing have shown significant potential as far as reducing energy consumption is concerned. By an intelligent distribution of frequently accessed content over the network nodes, caching alleviates the need for backhaul transmissions, which results in relevant energy consumption reductions. Instead, mobile computing does not directly reduce the energy consumption, but, similarly to wireless power transfer, it can prolong the lifetime of nodes that are low on battery energy.
Many of the existing energy efficiency improvement techniques include the use of green energy sources for base stations, modifying the coverage area of a base station depending upon the load level, putting lightly loaded base stations to sleep and load balancing by handing over the UEs to the macro base station.
The switch to 5G should further improve this performance. In the test cities, the new network is already twice as energy efficient as 4G. And these are only the first deployments, carried out with technologies that are still young and with very lightly loaded networks. This improvement is based on a technological leap forward. In 5G, the load of traffic flowing on the networks will be greater for energy consumption of the same order of magnitude, mechanically resulting in a reduction in the share of electricity consumption per bit transported. 5G will be more efficient than 4G in terms of the amount of bits of information delivered for a given unit of energy consumption. The technical and operational levers supporting this progress are notably linked to the optimisation of processor and transmitter technologies and the implementation of network-sharing mechanisms.
The 5G standard now includes the key technical enablers for better energy performance: ultra-lean design and Massive MIMO. The ultra-lean design leverages smart-sleep mode technology to ensure that radio frequency signals are transmitted by the radio hardware only when necessary. Massive MIMO increases network coverage and provides higher capacity requiring fewer sites to be installed. These enhancements provide extended network coverage in a sustainable and resource-efficient way, reducing the total cost of ownership for service providers.
Some of the future technology areas include Green wireline, optical, and wireless communications and networks;
- Network and physical layer design, strategies, algorithms, protocols, and scheduling that consider environmental factors; Energy-efficient and energy-aware heterogeneous networks, self-organized, and low-power sensor networks;
- Energy efficiency in machine-to-machine communications, cooperative communications, and smart grid networks;
- Energy harvesting, storage, and recycling for network cross-layer optimization; Environmentally-aware designs of communications and networking devices and systems; and Communications and networking for environmental protection monitoring.
New network protocols
New 5G network protocols reduce power consumption. The Data packet payloads are compressed, reducing traffic volume. The User traffic and control traffic is separated, reducing the network chatter on the user data networks. The reduced traffic and elimination of control packets creates larger idle periods, and therefore, longer periods of sleep mode. The Multipath Transmission Control Protocol (MPTCP) increases network efficiency and reduces packet retransmits, reducing overall energy consumption.
Advances in signal-processing electronics now support full-duplex network communications on the same frequency. Earlier technologies required different frequencies to transmit and receive data simultaneously. Full-duplex passive suppression and digital cancellation (PSDC) communication mode is more energy-efficient than the plain passive suppression full-duplex mode. Experimental results show that, the full-duplex DSDC mode achieves up to 40 percent increased energy efficiency compared to half-duplex.
Visible light communications (VLC).
VLC, also known as LiFi or optical wireless communication (OWC), is a technology that can serve indoor communications in future wireless systems. While being basically a short range technology, it has some remarkable advantages, such as very high energy-efficiency, availability of large bandwidths and thus the capability to support large datarates. The use of the visible light spectrum for data communication is enabled by inexpensive and off-theshelf available light emitting diodes (LEDs). Individual LEDs can be modulated at very high speeds, and indeed 3.5Gbit/s@2m distance has been demonstrated as well as 1.1Gbit/s@10m, both with a total optical output power of 5 mW.
Large Efficient Spectrum
Spectral efficiency and scheduling algorithms were introduced in the 5G NR specifications. The increase in spectral efficiency due to various features and scheduling algorithms ensures that 5G can have higher throughput compared to its predecessors.
Typical scheduling used by 4G
In the typical scheduling is used by 4G and has a large number of control codes in stream. As a result, the overhead is about 10 to 20 percent and increases with higher-frequency transmissions. The new 5G slot aggregation scheduling and greatly reduces traffic overhead and energy consumption.
Cognitive Radio (CR) is a paradigm-shift enabling technology for realizing energy-efficient Green Radio. Basically, Cognitive Radio can be combined with Green Radio (GR) based on intelligence-enhanced functionalities, which is aiming at sustainable development and evolution of wireless communications for energy efficiency and takes it as a fundamental crucial constraint within the learning and decision making process of the holistic cognitive cycle.
Most recently, intelligent resource allocation and control techniques utilizing machine learning algorithms have been suggested to help next generation radios in their autonomous reconfiguration for improving the data rates, energy efficiency and interference mitigation. As machine learning is penetrating more and more into the operation of wireless networks, machine learning algorithms would greatly help to predict the hot spots so that other resources could be switched off when not needed
Addressing the network energy consumption and carbon emissions challenge, requires an integrated approach that looks beyond individual product performance and addresses the whole network. That includes hardware and software, and considers both network modernization and the installed base, says Ericsson