Energy Efficient Wireless Communication Survey
Information and communication technology (ICT) is one of fastest growing areas
is wireless communications. The number of mobile devices in particular have
grown at an exponential rate. Current network architectures are insufficient
for supporting the growth and effectively adjusting to areas with different
traffic demands. Small scale network deployments that can adapt to traffic at
different levels has shown to reduce energy consumed while still maintaining
high quality of service (QoS). Since greenhouse gasses have increased with the
communication networks, energy efficiency will be a big concern as wireless
adoption increase in developing countries. This paper examines different
strategies of maximizing energy efficiency to handle the growth of wireless
Keywords: Energy Efficiency, Small Scale Networks, Wireless Communication, Massive MIMO, Adaptive Control
Table of Contents:
Research has shown that there is expected to be a 10x increase of wireless data
from 2015 to 2021 [Ericsson15]. Trends show that wired
communications are slowing down while wireless traffic is rising at an
exponential rate [Cisco16]. As use of mobile devices
increase, the main concern is how long the device lasts and not access to the
network. Energy efficiency is usually defined as the number of bits that can
be sent over a unit of power consumption which is usually quantified by bits
per Joule. The determining factor of energy efficiency for mobile devices is
the power needed to transmit data.
We will begin by introducing the different factors that have shown
improvement in energy efficiency. In Section 2 we will discuss the main energy
consumer of energy in wireless systems, the radio. In Section 3 we will
introduce new ways of increasing efficiency through new network topologies.
In Section 4 we will present ideas on antenna diversity through massive
multi-input multi-output schemes (MIMO). In Section 5 we will put forth
adaptive control schemes in regards to energy efficiency. In Section 6 we will
show how some of the best ideas with wireless energy efficiency can be
implemented in smart grids.
2. Wireless Radios
At the simplest level, a typical wireless device consists of a radio unit and
a baseband unit. The radio unit is responsible for sending and receiving data
over a radio while the baseband unit is responsible for processing the data that
is to be sent or received. It has been shown that the radio consumes around 57%
of the energy in wireless communication due to the need of power amplification
of signals [Hu14]. Reducing the number of radios in a
wireless network is one way to improve the energy efficiency of a network. A
typical radio network can be seen in Figure 1. Every node has a baseband and a
radio unit to communicate with a gateway.
Figure 1: Standard Radio Architecture Model [Hu14
In Figure 2 there are proposed ideas to separate the baseband unit from the
radio unit. There can be pools of multiple baseband units that share a radio
which increases the utilization rate of the radio. This also has a side benefit
of decreasing the interference since the number of radios is fewer. While this
technology is not widely adopted it has a lot of promise by breaking up the
network into smaller cells. As the number of cells increase there would be
greater route diversity and improve the quality of the wireless network.
Figure 2: Cooperative Topology [Hu14
3. Small Cell Networks
A hot topic of research is to move away from gateway nodes and change to a
densely populated network of multi-tiered devices. The current trend is to have
a mostly homogenous network of a single access point that runs all nodes on the
network communicate through it. The access point is usually connected to a
distributed system that connects to the rest of the internet. As wireless
devices become more widely used this network these access points are always one
and using energy constantly regardless of the level of traffic. As the rate of
wireless adoption rises new network topologies can yield better energy efficient
strategies. Current cellular networks are flat with many overlapping coverage
areas and all capable of servicing the same amount of traffic regardless of the
demands. A network can be built with different sized cells and multiple radio
technologies that satisfy the needs of those cells.
3.1 Heterogeneous Networks
Current wireless networks have few paths for a node to send and transmit
information. The lack of route diversity can cause signal degradation multipath
loss. Increasing the number of nodes on a network allows the reduction of path
loss and by having the transmitters and receivers closer together. Small scale
networks have also been called Heterogeneous Networks (HetNet).
A heterogeneous network is one that has many types of nodes with different power
and data capabilities. The nodes that are deployed will depend on the traffic
demands on the area. Currently nodes are constantly powered or operating in low
traffic areas and wasting a lot of energy while there is nothing to transmit.
Nodes that are responsible for back bone communications or relaying data lots of
data require more energy but will able to have a larger radius of communication.
In lower traffic areas there will be lower power nodes that will be sufficient
for traffic demands.
HetNets are divided into different cell types being capable of serving different
ranges of traffic in a multi-tiered architecture. Figure 3 shows the different
cells and the levels of where they reside in the architecture. Larger cells are
capable of handling larger areas and require more power. The lower nodes have
lower power constraints but have a denser network than the higher nodes.
Cells at the lower levels have a smaller range and require less power. As data
propagates on the network then it will eventually get passed to the next level
on a larger cell until it reaches its destination. It has been shown that these
levels with different power requirements yield better energy efficiency and shows
some promise to replace current networks. Replacing the current nodes with smaller
cells like femto cells, which are small low power devices design to work in the
home, would reduce the power consumed as well as the operational costs for a network.
There is a drawback with heterogeneous networks related to the density of the
nodes. At the inter cell level there can be interference with neighboring nodes
due to all the cells using the same spectrum. Frequency division strategies have
been developed to either divide groups of cells into using parts of the spectrum
allowing to share the entire spectrum. There have also been time division
strategies to allow coordination of transmission schedules between cells. Figure
4 shows a typical HetNet topology showing smaller nodes connected to an access
point which can communicate to other access points. There is some overlap between
the access point nodes which shows the potential interferences between nodes.
The denser the network the greater the possibility of having issues with data
transmission and receiving due to interference.
Figure 4: HetNet Topology [He14
While this is a promising solution, there is a lot of work to be done with
deploying a usable HetNet. There also needs to be improvement at the highest
levels of the wireless communication infrastructure to deal with the increased
traffic needs. The current number of nodes at the macro and micro level has not
yet achieved the density required [Ericsson14].
Also the existing macro cells need to accommodate more antenna diversity and
allow for more of the spectrum to be used.
4. Massive MIMO
Though a less common solution, another idea for reducing energy is Massive MIMO
antenna deployment through large antenna arrays. Massive MIMO takes advantages
of many antennas for a much smaller base of users. In contrast to small cell
networks which has high base station density, the massive MIMO solution has high
antenna density to serve users.
While this approach allows for higher bandwidth and spatial diversity, it does
not necessarily reduce the operating cost of the radios. Studies have been
done to see if Massive MIMO is a suitable replacement for wired broadband data.
It has been suggested that certain MIMO technologies can be adaptive as well
and switch to single input multiple output (SIMO) during off peak hours to
conserve energy. Algorithms have been developed to detect low traffic and it
has been studied that limiting the number of active radios improves energy
4.2 Comparison to HetNet
Research has shown while both small cell and massive MIMO improve energy efficiency
there is a threshold for both [Liu13]. By default there
is larger spectral efficiency with massive MIMO. As the number of cells in a
small cell network grows the spectral efficiency decreases since the spectrum
of the nodes overlaps. To prevent these kinds of issues, the nodes can be put to
sleep which improves the energy efficiency There is a point where too many cells
in a sleep state can be detrimental to the reliability of the network. If too
many nodes are sleeping then that reduces the route diversity and can cause
the network to fail. This is could be harmful for time critical functions
that need a reliable network to send commands or status to other points in
the network. If too many nodes are awake during a low traffic period then
energy is being wasted. Current trends show that between HetNets and massive MIMO, HetNets are gaining
more traction for being the infrastructure needed to handle the growth of
wireless traffic [Liu13].
5. Adaptive Control
Adaptive methodologies can be implemented to improve the energy efficiency of
wireless nodes and optimally utilize resources. It is possible that these new
technologies can satisfy the growth of wireless data and optimally distribute
network resources like frequency spectrum and reduce energy consumption. The
most prevalent types of adaptive control can been seen with cognitive radios
adapting the radio frequency for transmission and receiving as well as
controlling the sleep time of devices.
5.1 Cognitive Radios
Allowing all nodes to have the same resources is not optimal. It wastes energy
at the off peak times and depending on how many resources are available may not
be sufficient for on peak times. Since the license exempt spectrum is very
crowded with wireless devices there may be a lot of interference to wireless
communication in the household.
The area of cognitive radios has been an area of study for improving the spectral
and energy efficiency of wireless networks. Cognitive radios are ones that can
sense their environment and adapt itself with resource constraints. If it detects
that a channel is currently occupied it might be able to hop to a new frequency
for data transmission. It has been shown that adaptive resource allocation can
produce optimal and suboptimal solutions and allow for better utilization of
resources [Wang13]. Combined with a small cell densely
populated network the adaption of radio spectrum is crucial and has been shown
that there are many benefits to implement cognitive radios at the femto cell
level [Xie12]. At this level cells are battery operated
and increased energy efficiency can extend the life of these devices.
5.2 Sleep Modes
As previously mentioned another common approach to achieving energy efficiency is
to put inactive nodes to sleep. It has been shown that 25-30% energy savings can
be achieved during low traffic times [Feng13]. Adding QoS
data during low traffic periods can add a significant overhead. As the density of
nodes in a network increases, this energy savings can be significant. Deeper sleep
modes can save even more energy but reduce the functionality of the nodes as well
as require a longer wakeup time to become active. There is a tradeoff about how
long a node should be asleep to maximize its energy efficiency and not degrade the
functionality of the network [Budzisz14].
There are many strategies for controlling the sleep cycle of nodes. The typical
static sleep schedule involves a fixed timer and has a node wake up after an
interval. This is only useful for set known intervals. A current area of interest
is dynamic sleeping modes which are difficult to predict an optimal sleep cycle
because it is based on the traffic and the density of the network to service requests.
The current areas of interest for dynamic strategies are controlled from an access
point, user, or the network. In node controlled the access point does pilot
sensing to detect if nodes are attached and wakes them up. In user controlled,
the device sends a wakeup message to the access point. The last option is to have a network
controlled sleep mode where a wakeup message is sent to an access point over
the backbone of the network through an algorithm to decide when devices should
be woken up.
5.3 Delay Tolerant Systems
Considering the type of data is important for determining how to send it. Data
collection of nodes on a network may not be a time critical to send out. Collecting
and transmitting data at off peak hours has certain energy efficient benefits as
well. Furthermore, node aggregation can improve the utilization at low peak times
while during heavy demand times resources can be assigned in an adaptive manner
[Erol-Kantarci13]. If wireless devices can
adaptively determine the current state of the traffic is high it could possibly
prevent a retransmission. Huge energy savings can be realized for devices that
have delay tolerance.
6 Smart Grids
It has been shown that the carbon footprint increases with wireless use
[Lambert12]. To manage the cost and effects of climate
change there is a need to reduce overall consumption but be able to control the
energy efficiency of wireless devices. Current legacy grids can do this to an extent
but are insufficient for dealing with the demands of the future. Some of the best
ideas related to energy efficiency can be implemented with smart grids.
Smart grids technologies can help reduce operational costs but require a
significant change to data collection which can be facilitated by a better
wireless network infrastructure. By implementing a HetNet smart grids are a
useful way to help reduce energy consumption. They can determine energy needs
for the house from smart meters, neighborhood, or city level all managed and
getting input from the wireless network. The needs of the network are hard to
quantify and can shift throughout the day and can be a very hard problem to
adapt to the dynamic nature of energy consumption. A smart grid can allocate
resources as necessary to the needs of the network. Improvements to the wireless
communication system that transmit this data and improving the reliability of
the network are important issues to facilitate the adoption of smarter energy
To achieve better smart grid management the time of use is recorded through a
network of smart meters. With this information real time demand can be tracked
and even be used to predict energy needs in the future. Smart Grids are divided
into the residential, neighborhood, and city level. The residential communication
involves technologies like Zigbee over 802.15.4 and Wireless with 802.11 to get
data from smart meters to improve energy efficiency. At the neighborhood level
cellular or 802.11 technologies are used to consolidate data about energy
distributed to multiple houses by one transformer [Erol-Kantarci14].
In regards to hierarchical wireless topologies the 802.11s standard for mesh
networking is a consideration to allow for nodes to sleep during inactive
periods. At the city level the cellular networks would be used to connect
neighborhoods to utility facilities. With a better infrastructure we can adapt
to the growth of wireless adoption and the energy demand in the world while
reducing our global carbon footprint.
In this paper we have shown different approaches to promote higher energy
efficiency in wireless communications. As wireless data grows new strategies
will have to be developed to service the world's needs. There are many
challenges ahead to reorganize the wireless communication landscape from the
residential level, cities, and expansion into more rural areas.
Currently there is a trend to make things devices smarter with changes
home automation through adaptive smart meters. The biggest hurdle seems to be creating an
infrastructure past the home and into the neighborhoods and cities. Increasing
energy efficiency is a big challenge to reduce the world's carbon footprint.
While there appears to be a lot of promising solutions there is still a lot of work to be done.
What is clear is that adaptive control for dynamic resource allocation with
wireless networks is necessary to achieve high energy efficiency. It has shown
up in most topics discussed with Massive MIMO, cognitive radios, and small cell
networks. Considering the numerous entities that can exist on a wireless network
at any one time it is a tough problem to solve. There are active and sleeping
devices that constantly change the available resources and it is hard to pinpoint
at any given time the state of the network. Trying to maximize the spectral and
route diversity as well as optimize the energy consumption of all nodes in a
wireless network is key to reduce the global carbon footprint.
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[Cisco16] "Cisco Visual Networking Index" http://www.cisco.com/c/en/us/solutions/service-provider/visual-networking-index-vni/index.html
[Hu14] Hu, R.Q., Qian, Yi, "An Energy Efficient and Spectrum Efficient Wireless Heterogeneous Network Framework for 5G Systems" in IEEE Communications Magazine, Vol. 52, No. 5, May 2014, Pages 94 - 101, http://ieeexplore.ieee.org/document/6815898/
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[Liu13] Liu W., Han S., Yang C., Sun C. "Massive MIMO or small cell network: Who is more energy efficient?" in IEEE Wireless Communications and Networking Conference Workshops (WCNCW), 2013, 7-10 April 2013, Pages 24 - 29, http://ieeexplore.ieee.org/document/6533309/
[Wang13] Wang, S., Ge, M., Zhao, W., "Energy-efficient resource allocation for OFDM-based cognitive radio networks" in IEEE Transactions on Communications, Vol. 61, No. 8, June 2013, Pages 3181 - 3191, http://ieeexplore.ieee.org/document/7147798/
[Xie12] R. Xie, et al "Energy-Efficient Resource Allocation for Heterogeneous Cognitive Radio Networks with Femtocells" in IEEE Transactions on Wireless Communications, Vol. 11, No. 11, October 2012, Pages 3910 - 3920, http://ieeexplore.ieee.org/document/6317105/
[Feng13] Feng D., et al. "A survey of energy-efficient wireless communications" in IEEE Communications Surveys & Tutorials, Vol. 15, No. 1, February 2013, Pages 167 - 178, http://ieeexplore.ieee.org/document/6157574/
[Budzisz14] Budzisz, L. et al "Dynamic Resource Provisioning for Energy Efficiency in Wireless Access Networks: A Survey and an Outlook" in IEEE Communications Surveys & Tutorials, Vol. 16, No. 4, June 2014, Pages 2259 - 2285, http://ieeexplore.ieee.org/document/6845303/
[Erol-Kantarci14] Erol-Kantarci, M., Mouftah, H.T. "Energy-Efficient Information and Communication Infrastructures in the Smart Grid: A Survey on Interactions and Open Issues" in IEEE Communications Surveys & Tutorials, Vol. 17, No. 1, July 2014, Pages 179 - 197, http://ieeexplore.ieee.org/document/6861946/
[Lambert12] S. Lambert et al., "Worldwide electricity consumption of communication networks," Opt. Exp., vol. 20, no. 26, pp. B513-B524, Dec. 2012., https://www.osapublishing.org/oe/abstract.cfm?uri=oe-20-26-b513
- HetNet - Heterogeneous Networks
- ICT - Information and Communication Technology
- IoT - Internet of Things
- QoS - Quality of Service
- MIMO - Multi-Input Multi-Output
- SIMO - Single-Input Multi-Output
Last Modified: April 17, 2016
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