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Physical Layer of Wireless IoT: Enablers and Issues

The current trend in telecommunications market is towards connecting all the everyday useful objects to the Internet. In this direction, Internet of Things (IoT) is receiving significant attention from industries and research communities as a key enabler for the Fifth Generation (5G) of wireless communications. It is about connecting all types of physical things/objects/devices to the Internet. The term IoT is also referred as Internet of Everything (IoE), which basically brings people, data, things and processes together in order to fulfil everyday needs of people, thus enabling a smart global community.  Among numerous application areas of IoT, some of the important ones are: (i) smart home, (ii) smart cities, (iii) smart wearables, (iv) smart grids, (v) smart healthcare, (vi) connected car, (vii) remote industrial process control, (viii) smart retail and supply chain, (ix) smart farming, and (x) smart energy management.
According to CISCO, IoT was initiated sometime between 2008 and 2009 when the number of connected devices exceeded the number of people. Several new devices having different form factors and enhanced capabilities/intelligence are emerging each year in the market. It has been forecasted that there will be around 8.2 billion handheld or personal mobile-ready devices and 3.2 billion Machine to Machine (M2M) connections by 2020. Based on CISCO whitepaper 2016, another important evolution is the massive emergence of smart wearable devices which may reach around 601 million globally by 2020, growing at the cumulative aggregated growth rate of 44 percentage. Furthermore, the ongoing trend of migrating to IPV6 with its 340 undecillion addresses will facilitate the integration of smart devices in the future wireless networks, thus making the market place and the concept of IoE feasible. Although there are other possibilities for communication between IoT devices such as Ethernet connectivity, Fieldbus and power line communication, this blog focuses on physical layer enablers and issues for wireless connectivity among IoT devices.
IoT will potentially create the integration of different wireless technologies, and subsequently will create market for new services. Some of the existing PHY layer protocols related to wireless IoT are IEEE 802.15.4, IEEE 802.15.6, Bluetooth Low Energy (BLE), EPCglobal, LTE-A, Z-Wave, 6LowPAN, and Near Field Communication (NFC). Future 5G networks may need to ensure the rapidly emerging requirements of IoT applications. Some relevant Quality of Service (QoS) requirements include spectral efficiency, energy efficiency, connectivity and latency. To meet these diverse requirements, an efficient, scalable and flexible air-interface is required and, therefore, different modules of Physical (PHY) and Medium Access Control (MAC) layers should be optimized so that they can be configured flexibly according to the technical specifications of each use case. One of the important aspects in this regard is the design of PHY layer for IoT-based wireless systems considering the practical constraints of energy efficiency, spectral efficiency, cost-effectiveness, and quality of experience.
However, the design of IoT-enabled wireless networks which can deliver a variety of services with desirable quality of experience under energy/resource constrained practical wireless scenarios is crucial. In contrast to other wireless communication paradigms, IoT has its own unique features and diverse requirements such as group-based communication, time-tolerant, small data transmission, secure connection, monitoring surrounding environment/parameters, low cost and low energy consumption. Besides, several requirements such as bandwidth, reliability and latency of different existing services are highly diverse. In terms of connectivity, it’s challenging to find out which devices need to be connected and which communication technology is suitable to connect them. Furthermore, several other issues such as dynamic resource allocation, harmful interference mitigation and interoperability of different technologies have to be investigated while devising communication technologies for IoT.
The PHY layer parameters should be effectively utilized in devising MAC layer and network layer protocols in order to design end to end reliable communication systems. The key enabling PHY layer techniques for wireless IoT are dynamic resource allocation (carrier and power), distributed beamforming/space time block code, opportunistic/cognitive techniques, orthogonal/non-orthogonal multiple access, low complexity cooperative techniques, compressive signal processing, spectrum sensing techniques, energy efficient modulation design, RF energy harvesting techniques, adaptive waveforms, and mmWave technologies. Furthermore, there are several emerging application areas of wireless IoT such as Wireless Body Area Networks (WBANs), Wireless Sensor Networks (WSNs), Device to Device (D2D), Machine to Machine (M2M), Vehicle to Vehicle (V2V), Vehicular Ad Hoc Networks (VANETs) satellite communications, LTE-advanced and 5G networks. These wireless systems have their own specific characteristics and it’s crucial to understand their PHY layer characteristics in order to deploy a reliable end to end system.
A massive amount of IoT devices may need to be fabricated in a cost effective manner. Furthermore, these devices are likely to be battery operated and located in a remote area where charging may be economically infeasible. On the other hand, IoT devices may likely be miniaturized in size and non-replaceable. This implies that cost, energy, network lifetime and space efficiency will be the critical challenges of the future IoT devices. In this regard, suitable signal processing tools from various areas such as WSNs and radar can be adopted for IoT-based wireless systems.
Future IoT enabled wireless systems require a highly scalable, reliable and available radio spectrum. The existing static spectrum allocation mechanisms which are mainly based on orthogonalization of the spectrum resources may not be viable solutions. In this regard, dynamic and non-orthogonal spectrum allocation policies are promising. One possible direction could be to allow the IoT devices to simultaneously utilize both microwave and mmWave carrier frequency bands (i.e., dual band connectivity). On the other hand, in order to support wideband IoT applications, both contiguous and non-contiguous carrier aggregation may be employed especially at the microwave frequency bands. In this context, the main challenge is how to efficiently realize wideband IoT capable of simultaneously utilizing the benefits of microwave and mmWave frequency bands.
While considering wideband spectrum utilization for IoT applications, the conventional Nyquist-based sampling is not feasible due to the requirements of very high rate and expensive ADC. In this regard, it would be interesting to exploit the inherent time, frequency sparsity caused by sporadic traffic of IoT-based systems as well as the spatial sparsity exhibited due to multipath environment, and subsequently to apply compressive signal processing in order to devise efficient techniques such as wideband sensing and channel estimation.
IoT being a complex paradigm, it faces several technical challenges in wireless communications which need to be addressed with further research and development activities. More specifically, future research efforts may need to focus in designing low cost and energy efficient transceiver and incorporating PHY layer parameters in the design of MAC and network layers to realize reliable IoT-based wireless systems.

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Insights Radio Spectrum

Fifth Generation of Cellular Communications (5G): A Mixture of Technologies!

Cellular technologies have evolved over time starting from the first generation (1G) to the current fourth generation (4G) with the objective of improving several factors such as spectral efficiency, capacity, coverage, power consumption, and user experience. This has been possible with the continuous advances in electronics and signal processing technologies in different segments of the cellular system architecture. Currently, we are in the stage of conceptualizing the next generation of cellular communications, i.e., 5G.

In all previous cellular generations, there was clearly a single dominating technology, i.e., Frequency Division Multiple Access (FDMA) for 1G, Time Division Multiple Access (TDMA) for 2G, Code Division Multiple Access (CDMA) for 3G, and Orthogonal Frequency Division Multiple Access (OFDMA) for 4G. However, for the upcoming 5G wireless, no clear dominating technology has been foreseen yet. Based on the current activities in industries and academia, it seems to be a mixture of technologies which are supposed to address the main emerging requirements such as high data rate, low energy consumption, low latency and the support/integration of heterogeneous devices/networks. In this regard, this blog will shed a light on some of the key technologies along with their potential advantages and challenges.

The potential techniques to meet the aforementioned requirements are ultra-densification, millimetre wave (mmWave) communications, massive Multiple Input Multiple Output (MIMO), full duplex technology, adaptive three dimensional (3D) beamforming, dynamic spectrum access and advanced multiple access schemes. Besides these techniques, several aspects such as software defined radio/networking, Internet of Things (IoT), intelligent caching, cloud computing and big data are being considered as important enablers for 5G wireless. In addition, advanced Wireless Fidelity (WiFi) networks, infrastructure sharing, integration of heterogeneous networks such as cellular networks, public switched telephone network, power line communication, electricity distribution network and satellite networks in a single platform, machine type communication, body area networks, and vehicle to vehicle communications are also emerging in the wireless community.

One potential technique of meeting the complicated requirements of 5G communications is by maximizing network densification via massive deployment of small cells of different types including licensed small cells and unlicensed WiFi access points. This densification approach has been already adopted in existing wireless cellular networks, which essentially results in a multi-tier Heterogeneous Network (HetNet). The investigation of suitable resource allocation algorithms that efficiently utilize radio resources such as bandwidth, transmission power and antenna while mitigating inter-cell and inter-user interference and guarantee acceptable Quality of Service (QoS) for active users is one of the most critical issues. In addition, design and deployment of reliable backhaul networks that enable efficient resource management and coordination with practical energy efficiency constraints are also important aspects to be studied.

Due to huge amount of network data traffic caused by the popularity of video, internet gaming and social media across a range of new devices such as tablets and smartphones, it is almost certain that this explosive traffic growth problem of cellular networks cannot be addressed by just upgrading the existing networks. Besides, several studies have shown that more than 70 % of the current traffic originates from an indoor environment. In most metropolitan indoor environments where traffic congestion is more critical, Wireless Fidelity (WiFi) Access Points (APs) are already available. Also, it has been reported in the past studies that WiFi system consumes significantly less energy than the existing 2G and 3G systems and deploying more WiFi hotspots is significantly cheaper than that of upgrading 3G or Long Term Evolution (LTE) networks. In this regard, advanced WiFi networks can be promising candidates to meet the data rate requirements of the next generation 5G wireless systems. However, existing WiFi APs are mostly equipped with a single antenna whose radiation pattern is omnidirectional. Recently, the deployment of multiple antennas on WiFi APs has received an important attention. This will enable APs to control the radiation pattern of transmitted and received radio signals adaptively which will consequently help to improve the QoS experience of the users, and to meet the capacity requirements of the future 5G wireless networks.

Dynamic spectrum access has been considered as one of the enablers to address the spectrum scarcity problem in future wireless networks. In this context, investigation of suitable techniques in order to foster the implementation of cognitive radio systems in practical scenarios is crucial. In this direction, future works are needed in order to understand the performance of cognitive radio systems in the presence of imperfect channel knowledge, asynchronous primary user traffic, and various practical inevitable imperfections such as noise uncertainty, channel uncertainty, noise/channel correlation, hardware impairments such as phase noise, frequency offset, amplifier nonlinearity, analog to digital converter inaccuracies, calibration issues, etc. Another important issue is how to tackle their harmful effects such as interference to the licensed (primary) system and the performance degradation of the unlicensed (secondary) system.

Another way of enhancing the utilization of the available spectrum resources is to enable full duplex operation on a radio node so that it can transmit and receive on the same radio channel. In a wireless system, full duplex operation can provide several benefits such as increased link capacity, wireless virtualization, improved physical layer security, reduced end-end and feedback delays, and improved spectrum utilization efficiency by allowing simultaneous sensing and transmission, and simultaneous transmission and reception. However, there exist several research problems in realizing the full duplex operation in heterogeneous wireless networks such as strong Self-Interference (SI), imperfect cancellation of SI due to residual hardware impairments, increased aggregate interference, high power consumption. In this regard, it is crucial to investigate advanced multi-antenna based signal processing techniques such as adaptive beamforming and antenna selection/switching, self-interference estimation/detection techniques and innovative power control strategies in order to handle the issues of the residual SI.

Furthermore, another key enabling technique is three dimensional (3D) beamforming which has recently received important attention in order to enhance the capacity of future wireless networks. In contrast to the conventional 2D beamforming, the 3D beamforming controls the radiation pattern in both elevation and azimuth planes, thus providing additional degrees of freedom while planning a cellular network. The main research challenge here is the investigation of low-complexity hybrid beamforming solutions which can control the radiation pattern in both elevation and azimuthal planes.

Massive MIMO and mmWave technologies provide vital means to resolve many technical challenges of the future wireless 5G Networks and they can seamlessly be integrated with the current networks and access technologies. In a rich scattering environment, massive Multiple Input Multiple Output (MIMO) technique can enable significant performance gains with simple beamforming strategies such as maximum ratio transmission or zero forcing. This technology uses a very large number of service antennas at the base station which helps to eliminate the multiuser interference with the help of very sharp beams. Despite its several other benefits such as system throughput improvement, higher energy efficiency, reduced latency, and the simplification of medium access layer, several challenges such as pilot contamination, the effect of hardware impairments, correlation and synchronization issues need to be addressed with the help of future research works.

Besides, another promising way of solving spectrum scarcity problem and meeting capacity demand of future wireless systems is to enable mobile communications using millimetre wave (mmWave) frequencies. The capacity requirement of the next-generation wireless network would inevitably demand us to exploit the mmWave frequencies ranging 30GHz-300GHz which is still under-utilized but can offer huge spectrum. Most importantly, as the mmWaves have extremely short wavelength, it becomes possible to pack a large number of antenna elements in a small form factor which consequently helps to realize massive MIMO at the base stations and user terminals. Furthermore, mmWave frequencies can be used for outdoor point-to-point backhaul links or for supporting indoor high-speed wireless applications (e.g., high-resolution multimedia streaming). However, there are several challenges to be solved including propagation issues, mobility aspects, hardware imperfections such as power amplifier non-linearity and low efficiency of radio frequency components at these frequencies.

In addition to the existing multiple access schemes such as TDMA, FDMA, CDMA, OFDMA and Space Division Multiple Access (SDMA), several multiple access schemes such as Polarization Division Multiple Access (PDMA), Interweave Division Multiple Access (IDMA), Universal Filtered Multi-Carrier (UFMC), Sparse Code Multiple Access (SCMA), Generalized Frequency Division Multiple Access (GFDMA) and Non-Orthogonal Multiple Access (NOMA) schemes are being investigated by several researchers as promising multiple access techniques for 5G wireless.

Although several aforementioned techniques are being considered as promising technologies for 5G, it’s not yet clear which combination of these technologies will define the so called fifth generation (5G) of cellular communications since 5G standardization is still in its infancy. However, it is clear that all the modified techniques and network architectures being investigated in the community will not be mature enough for 5G by the time 5G will be deployed and many of them will eventually propagate to the next generations beyond 5G.

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Insights Radio Spectrum

How can 5G wireless benefit from Cognitive Radio principles?

Several enabling technologies such as ultra-densification, millimetre wave communications, massive Multiple Input Multiple Output (MIMO), full duplex technology, and dynamic spectrum access are being investigated in industrial and academic communities in order to foster the deployment of the fifth generation (5G) of wireless communications. In this regard, time has come to think about how Cognitive Radio (CR) principles, which have been investigated in the community for almost one and a half decade, can be incorporated in 5G wireless communications.

CR technology, which can address the spectrum scarcity problem by means of dynamic spectrum access and spectrum sharing, has been motivated by the fact that a significant amount of the wireless spectrum remains under-utilized over a wide range of radio frequencies in the temporal and spatial domains. In addition, this solution does not require the acquisition of the additional expensive radio frequency resource, hence reducing the overall capital and operational expenditure for a wireless operator.

Although recent technical advances in the areas of Software Defined Radio (SDR), and wideband transceivers have led to the possibility of utilizing the available spectrum in a dynamic manner, there are still several challenges to be addressed from the deployment perspective. In one hand, there are technical issues in dealing with several practical imperfections such as noise uncertainty, channel/interference uncertainty, signal uncertainty, transceiver hardware imperfections, and synchronization issues. On the other hand, there are several regulatory and business challenges in order to realize dynamic spectrum access in future wireless networks. In this context, this blog provides a framework on how CR principles can be incorporated in 5G wireless networks without the need of significant upgrades in the existing network architecture.

One way of incorporating CR principles in 5G wireless networks is to enable the spectral coexistence of two or more than two heterogeneous wireless networks in different dimensions such as time, frequency, spatial, polarization, and geographical space by utilizing several advanced interference mitigation and dynamic resource allocation techniques such as cognitive beamforming, cognitive interference alignment, adaptive power control, carrier aggregation, dynamic carrier/bandwidth allocation.

Various practical coexistence scenarios can be considered under this application category: (i) Coexistence of small cells and Macrocells, (ii) Coexistence of unlicensed-WiFi and small cells, (iii) Coexistence of C-band satellite system with LTE/WiMax networks, (iv) Coexistence of future cellular with Ka-band Fixed Satellite Service (FSS) system, (v) Coexistence of terrestrial microwave backhaul links with the FSS satellite, (vi) Coexistence of satellite backhaul links with the terrestrial backhauls, (vii) Coexistence of COMPASS (Radio determination satellite service + Radio navigation satellite service) and TD-LTE, (viii) Coexistence of TVWS (Digital Terrestrial Television (DTT) + Program Making and Special Events (PMSE) services) with different terrestrial services,  (ix) Coexistence of  radar and communication systems, and (x) Coexistence of geostationary and non-geostationary satellite systems.

Another promising way to benefit from CR principles is to incorporate intelligence in different segments of future wireless networks such as relay nodes, and base stations. Future small/micro/pico/femto-cell base stations can be made intelligent by introducing spectrum awareness capability which will enhance the overall system capacity by reducing the effect of interference and noise. Furthermore, smart antenna capabilities such as source localization and adaptive three dimensional beamforming will not only boost the system capacity, but will also help in enhancing the energy efficiency of future wireless networks.

The widely discussed Licensed Shared Access (LSA) can be implemented in a dynamic manner by taking recent advances in CR techniques, and subsequently allowing spectrum sharing on a frequency, location and time basis. Also, CR principles can be utilized in incorporating full-duplex capability in a wireless node, for example self-backhauling in cellular networks. Moreover, self-organizing small cells (wireless nodes), which are capable of carrying out self-configuration, self-optimization, and self-resilience, can be considered as important enablers for future intelligent wireless systems.

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Insights Radio Spectrum

Listen-and-Talk: Enhancing Spectrum Usage by Full-Duplex Cognitive Radio

The existing and new wireless technologies, such as smart phones, tablets, and IoT apps are rapidly consuming radio spectrum. The traditional regulation of spectrum requires a fundamental reform in order to allow for more efficient and creative use of the precious airwave resources. Cognitive radio (CR) has been widely recognized as a promising technique to increase the efficiency of spectrum utilization. It allows the unlicensed secondary users (SUs) to coexist with the primary users (PUs) in licensed bands. The SUs are allowed to utilize only the unoccupied spectrum resource and leave it whenever the incumbent PUs are ready to transmit. Thus, reliable identification of the spectral holes in particular licensed frequency bands is required.

Current cognitive communication systems deploy the half-duplex (HD) radios to transmit and receive the signals by orthogonal resources. The SU communication is usually realized by the popular “Listen-before-Talk” (LBT) protocol, in which the SUs sense the target channel before transmission. Though the LBT protocol has been proved effective, it actually dissipates the precious resources by employing time-division duplexing, and thus, unavoidably suffers from two major problems: 1) transmit time decrease due to sensing, and 2) sensing accuracy impairment due to data transmission.

It would be desirable if the SUs can continuously sense the spectrum and meanwhile transmit when a spectrum hole is detected. This, however, seems impossible with the conventional half-duplex systems. A full-duplex (FD) system, where a node can send and receive the signals with the same time and frequency resources, offers the potential to achieve simultaneous sensing and transmission in CR systems. Specifically, SU can sense the target spectrum band in each time slot, judge if the band is occupied, and make decisions on whether to transmit data in the adjacent slot on the basis of the sensing result and access mechanism. As the FD technology enables to explore another dimension of the network resources in CR systems, it thus requires new designs of the network protocols, signal processing and resource allocation algorithms.

For example, one of the major challenges faced by FD-CR is how to deal with the residual self-interference issue in sensing process, beneath which lies a secondary transmit power optimization problem to maximize the system throughput. Another challenge is how to manage the resources in space, frequency, and device dimensions to improve the spectrum efficiency for the secondary network.

Further applications of FD-CR comprise many important scenarios, such as FD cognitive MIMO, FD cognitive relay, and FD cognitive access point, etc. All these present a new design paradigm for enhancing the spectrum usage for future wireless communications and networks.

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Insights Radio Spectrum

Reducing Spectrum Sensing Uncertainties using Joint Signal Energy and Spectra-Width Detection in Cognitive Wireless Networks

Dynamic spectrum sharing is regarded as an emerging technique for next generation wireless systems, where unlicensed secondary users opportunistically access RF channels,  which are not licensed to them but to primary users. Furthermore, dynamic spectrum sharing requires that the unlicensed secondary users should not create any harmful interference to licensed primary users while accessing RF channels opportunistically. Thus, unlicensed secondary users need perfect information about whether any licensed primary users are active in a channel of interest.

There are several spectrum sensing algorithms to identify whether the given channel is idle (not used by primary users) or busy (used by primary users). Most commonly used algorithm is energy detection (as it does not requires knowledge of primary user signal), where spectrum sensor calculates the energy of the received signal in a given channel and compares it with a pre-specified threshold value. If the energy of the received signal is greater than the threshold value, spectrum sensor decides that the given channel is busy. Otherwise it decides that the channel is idle.

However, it is noted that when only energy based detection is used, it leads to high false detection rate and/or misdetection. Thus, in order to reduce the false detection and misdetection rates, spectrum sensor needs to consider both energy peak and width of the signal spectra. To illustrate this scenario, we consider following figure. Figure below was the outcome of spectrum sensor when two of the 5 GHz Wi-Fi channels (5.26GHz and 5.66 GHz channels) were active for a given time and location. In the figure, we can see that when Threshold 1 (-75 dBm, red line) was used, the channel 5.66 GHz channel was not detected. Furthermore, when Threshold 2 (-85 dBm, green line) was used, both channels 5.26GHz and 5.66 GHz channels were detected. In addition to these two channels, two other channels (at 5.2 GHz and 5.5 GHz) were detected which were not supposed to be detected as active.  When spectrum sensor used only energy based detection, 2 extra false channels were detected. However, when both width of the signal spectra (~ 60% of bandwidth of a Wi-Fi channel) and energy level were considered while detecting the primary user signal, only truly active channels, 5.26GHz and 5.66 GHz, were detected as active, while two other channels, 5.2 GHz and 5.5 GHz, were not detected as active. Thus, joint energy and width of signal spectra detection approach may reduce the spectrum uncertainty to avoid any harmful interference to primary users, while secondary users get accurate channel occupancy information for opportunistic communications.

spectrum
(click to enlarge)
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Insights Radio Spectrum

Cognitive Radio Enabled Wireless Networking for Smart Grid

Smart grid system assists in transforming the traditional energy industry in terms of reliability, performance, and manageability through bi-directional information transfer between smart grid components. According to National Institute of Standards and Technology (NIST) conceptual model for smart grid, communication networks connect power system components that are divided into seven logical domains: Markets, Service Provider, Operations, Bulk Generation, Transmission, Distribution and Customer. Bi-directional communications offer many benefits including more visibility and control over power supply, source, price, quality and costs. Moreover, recent studies show that the power outage for an hour could cost billion dollars in industrialized countries. Thus, the choice of communication technology used in smart grid is highly critical to provide reliable and efficient bi-directional communications.  However, current communication infrastructures and standards are incapable of meeting the strict requirements (such as delay, range, data rate, security, etc.) of smart grid systems. Thus the cognitive radio enabled dynamic spectrum access is a candidate technology that can be used to reduce the communication expenses by reusing the existing wireless infrastructures and wireless spectrum to improve overall performance of smart grid.

For instance, when ZigBee technology is used for Home Area Network (HAN) in smart grid, the maximum data rate that HAN can offer is 250Kbps. When this link is shared among (say) 25 networked home appliances in HAN, each device gets about 10Kpbs.  However, when Wi-Fi channels (e.g., IEEE 802.11n with over 300 Mbps) are used (when available) by home appliances, data rate could be much higher (i.e., 12 Mbps which is over 1000 times faster than ZigBee for 25 HAN devices). Higher data rate implies lower delay in data transmission, which is one of the strict requirements of smart grid. Similar analysis can be found in Neighbourhood Area Networks and Wide Area Networks in smart grid. Thus, dynamic spectrum access offers better throughput and faster delivery of information in smart grid leading to enhanced overall performance. Furthermore, when dynamic spectrum access enabled communication is implemented in smart grid, devices can hop from a channel to the least interfered channel on the fly which results in jamming resistant communications in the smart grid.

 

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Insights Radio Spectrum

Cloud-assisted Cognitive Radio Networks. Is It Worth Pursuing?

In cognitive radio networks, unlicensed secondary users are expected to sense RF spectrum to find idle bands and access those idle bands without creating any harmful interference to licensed primary users. However, spectrum sensing for secondary users in diverse/heterogeneous frequency bands is challenging as secondary users are considerably constrained by limited power, memory and computational capacity. Fortunately, the advent of cloud computing has the potential to mitigate these constraints due to its vast storage and distributed computational capacity. When sensed data is small, secondary users can process the data by themselves. However, for big data when secondary users sense wide-band channels, the processing and analysing would take more time than the round-trip-time needed for processing and reporting back by cloud computing platform. Thus, using the context, secondary users can decide whether it is efficient to offload the data into cloud computing platform or analyse by themselves.

Furthermore, cloud also stores the geolocations of idle channels into the spectrum database in a distributed manner. When secondary users want to access RF spectrum opportunistically, they search the spectrum database for idle channels by reporting their own locations using a dedicated link. Cloud controller compares the location of secondary users with that of idle channels and reports the list of channels that are within the tolerance distance. To reduce delay while accessing spectrum database, distributed cloud computing can be deployed based on the user density, which can be easily monitored by the cloud controller. Alternatively, distributed cloud controller can offer a list of RF spectrum for a given location and time so that the secondary users can access available idle channels for opportunistic communications as a hotspot or Radio-as-a-Service feature. There are several benefits of integrating cloud computing with cognitive radio networks. Thus, cloud-assisted cognitive radio network is expected to be the backbone of future wireless systems operating in heterogeneous wireless bands.

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Insights Radio Spectrum

UAV-assisted Dynamic Spectrum Access for Public Safety Communications

Public safety communication (PSC) is extremely vital during Emergency situations to coordinate and communicate effectively among emergency responders to save lives and to avoid confusion at the scene. Despite the vast advancement in commercial communication systems, scant attention has been paid to PSC networks. Furthermore, the most PSC agencies (such as Fire and Rescue, Police, Emergency Medical Services, etc.) use their own separate networks to communicate while handling emergency situations. These separate networks are not fully interoperable with each other and can also be suffered by interference due to a large number of devices operating during natural disasters. In the US, a recent bill from Congress and the FCC has provided the groundwork for the creation of a nationwide broadband public safety communication network by assigning 700MHz frequency band with an aim of building a unified broadband communication network for public safety operators.

PSC can help plan beforehand for different things such as floor plans, civilian information, scene analysis, and so on.  In these scenarios, unmanned aerial vehicle (UAV)-assisted cognitive radio enabled communication networks can help to improve the overall network performance by replacing destroyed infrastructures by UAVs. The UAV networks can be deployed by implementing suitable UAV packing to find the optimal number of UAVs to cover the region and by providing geo-location to route the UAVs to the target location to recover communications in the affected area. UAVs also have the advantage of being airborne, allowing for better line of sight with ground users and being able to establish the network on the fly. Furthermore, cognitive radio enabled UAVs can identify suitable frequency bands (least interfered or idle bands) to communicate with each other or with first responders or civilians. Mathematical and numerical analysis suggest that significant improvements in capacity and throughput can be achieved by deploying UAV network to reconnect the destroyed network to backhaul even in low SINR region since the users are closer to the cognitive radio enabled UAV base stations.

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Insights Radio Spectrum

Wireless Network Virtualization – A New Approach for Enhancing Spectrum Utilization

The growth of wireless subscriptions is increasing exponentially with increasing mobile devices for Cyber-Physical Systems and Internet-of-Things applications.  Wireless network virtualization is envisioned to support billions of devices for wireless subscriptions and incorporate new wireless services for Cyber-Physical Systems and Internet-of-Things. Wireless network virtualization with the help of cognitive radio is a new approach for improving spectrum utilization where infrastructures, software and spectrum are combined into a single software-based virtual network entity, which then can be offered to different parties. Wireless network virtualization combines different wireless networks with different access technologies and network topologies (infrastructure-based and infrastructure-less) which makes the convergence, sharing and abstraction difficult to achieve. Furthermore, wireless networks operate in different spectrum bands ranging from MHz to GHz, unlicensed and licensed RF bands, and different geographic coverage (e.g., wide, local and personal areas). Thus far, there is no unified and universal architecture for wireless network virtualization available for commercial use. However, dynamic spectrum access is viewed as a sort of spectrum virtualization where cognitive radios can access underutilized licensed spectrum dynamically without creating harmful interference to licensed users. Some recent works have integrated game theory and auctioneer into the wireless model to incorporate heterogeneous wireless networks where auctioneer are responsible for allocating frequency bands to wireless devices within their geographic area. Furthermore, game theory based models can use bidding procedures between users and service providers, or between service providers and spectrum auctioneer. In addition, wireless virtualization could be performed using cross-infrastructures and intra-infrastructures in application level, spectrum level and (cloud) computing level. Thus, wireless network virtualization has the potential to relieve artificial spectrum scarcity problem and offer new services to support huge wireless subscriptions for Cyber-Physical Systems and Internet-of-Things.

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Insights Radio Spectrum

Network Radio – A New Paradigm for Improving Spectrum Efficiency

Co-author:  ChunSheng Xin, ECE Department, Old Dominion University, USA

The complicated spectrum environment, dynamic nature of spectrum bands, and diversified requirements on quality of services from multiple tiers of users require the radio of secondary users to be more powerful and intelligent to fully realize the potential of radio spectrum. Specifically, today’s cognitive radio needs to expand from a physical layer technology to a comprehensive network layer technology. We call this new paradigm network radio.

Network radio integrates critical components, including the spectrum access policies, security policies, network coexistence mechanisms, and incentive mechanisms. The access policy engine of the radio ensures that the spectrum sharing policies such as transmit power and channel vacation time are imposed. The security policies are imposed to effectively countermeasure attacks to either the radio device or the network. The coexistence mechanism ensures that the heterogeneous radio technologies and networks that use different technologies and architectures, and the multiple tiers of primary users and secondary users can harmoniously coexist or co-access at the same spectrum band, same time, and same location. With the incentive mechanisms, primary users are incentivized to cooperate in spectrum sharing, to grant spectrum access to secondary users.

Moreover, network radio is able to carry out the topology organization and adaptation, cross-layer optimization, and integrate new technologies to increase performance, such as MIMO and network coding.