64 research outputs found
Dynamic resource allocation for opportunistic software-defined IoT networks: stochastic optimization framework
Several wireless technologies have recently emerged to enable efficient and scalable internet-of-things (IoT) networking. Cognitive radio (CR) technology, enabled by software-defined radios, is considered one of the main IoT-enabling technologies that can provide opportunistic wireless access to a large number of connected IoT devices. An important challenge in this domain is how to dynamically enable IoT transmissions while achieving efficient spectrum usage with a minimum total power consumption under interference and traffic demand uncertainty. Toward this end, we propose a dynamic bandwidth/channel/power allocation algorithm that aims at maximizing the overall network’s throughput while selecting the set of power resulting in the minimum total transmission power. This problem can be formulated as a two-stage binary linear stochastic programming. Because the interference over different channels is a continuous random variable and noting that the interference statistics are highly correlated, a suboptimal sampling solution is proposed. Our proposed algorithm is an adaptive algorithm that is to be periodically conducted over time to consider the changes of the channel and interference conditions. Numerical results indicate that our proposed algorithm significantly increases the number of simultaneous IoT transmissions compared to a typical algorithm, and hence, the achieved throughput is improved
Spectrum Assignment in Hardware-Constrained Cognitive Radio IoT Networks Under Varying Channel-Quality Conditions
[EN] The integration of cognitive radio (CR) technology with the future Internet-of-Things (IoT) architecture is expected to allow effective massive IoT deployment by providing huge spectrum opportunities to the IoT devices. Several communication protocols have been proposed for the CR networks while ignoring the adjacent channel interference (ACI) problem by assuming sharp filters at the transmit and receive chains of each CR device. However, in practice, such an assumption is not feasible for low-cost hardware-constrained CR-capable IoT (CR-IoT) devices. Specifically, when a large number of CR-IoT devices are operating in the same vicinity, guard-band channels (GBs) are needed to mitigate the ACI problem, introducing GB adds constraints on the efficient use of spectrum and protocol design. In this paper, we develop a channel assignment mechanism for the hardware-constrained CR-IoT networks under time-varying channel conditions with GB-awareness. The objective of our assignment is to serve the largest possible number of CR-IoT devices by assigning the least number of idle channels to each device subject to rate demand and interference constraints. The proposed channel assignment in this paper is conducted on a per-block basis for the contending CR-IoT devices while considering the time-varying channel conditions for each CRIoT transmission over each idle channel, such that spectrum efficiency is improved. Specifically, our channel assignment problem is formulated as a binary linear programming problem, which is NP-hard. Thus, we propose a polynomial-time solution using a sequential fixing algorithm that achieves a suboptimal solution. The simulation results demonstrate that our proposed assignment provides significant increase in the number of served IoT devices over existing assignment mechanisms.This work was supported in part by the QR Global Challenges Research Fund, Staffordshire University, Staffordshire, U.K.Salameh, HAB.; Al-Masri, S.; Benkhelifa, E.; Lloret, J. (2019). Spectrum Assignment in Hardware-Constrained Cognitive Radio IoT Networks Under Varying Channel-Quality Conditions. IEEE Access. 7:42816-42825. https://doi.org/10.1109/ACCESS.2019.2901902S4281642825
Spectrum Assignment in Hardware-constrained Cognitive Radio IoT Networks under Varying Channel-quality Conditions
ABSTRACT The integration of cognitive radio (CR) technology with future Internet-of-Things (IoT) architectures is expected to allow effective massive IoT deployment by providing huge spectrum opportunities to IoT devices. Several communication protocols have been proposed for CR networks while ignoring the adjacent channel interference (ACI) problem by assuming sharp filters at the transmit and receive chains of each CR device. However, in practice, such an assumption is not feasible for low-cost hardware-constrained CR-capable IoT (CR-IoT) devices. Specifically, when large number of CR-IoT devices are operating in the same vicinity, guardband channels (GBs) are needed to mitigate the ACI problem. Introducing GB constraint spectrum efficiency and protocol design. In this paper, we develop a channel assignment mechanism for hardware-constrained CR-IoT networks under time-varying channel conditions with GB-awareness. The objective of our assignment is to serve the largest possible number of CR-IoT devices by assigning the least number of idle channels to each device subject to rate demand and interference constraints. The proposed channel assignment in this paper is conducted on a per-block basis for the contending CR-IoT devices while considering the time-varying channel conditions for each CRIoT transmission over each idle channel such that spectrum efficiency is improved. Specifically, our channel assignment problem is formulated as a binary linear programming (BLP) problem, which is NP hard. Thus, we propose a polynomial-time solution using a sequential fixing algorithm that achieves a suboptimal solution. Simulation results demonstrate that our proposed assignment provides significant increase in the number of served IoT devices over existing assignment mechanisms
Channel assembling and resource allocation in multichannel spectrum sharing wireless networks
Submitted in fulfilment of the academic requirements for the degree of
Doctor of Philosophy (Ph.D.) in Engineering, in the School of Electrical and
Information Engineering, Faculty of Engineering and the Built Environment,
at the University of the Witwatersrand, Johannesburg, South Africa, 2017The continuous evolution of wireless communications technologies has increasingly imposed a
burden on the use of radio spectrum. Due to the proliferation of new wireless networks applications
and services, the radio spectrum is getting saturated and becoming a limited resource. To a large
extent, spectrum scarcity may be a result of deficient spectrum allocation and management policies,
rather than of the physical shortage of radio frequencies. The conventional static spectrum
allocation has been found to be ineffective, leading to overcrowding and inefficient use. Cognitive
radio (CR) has therefore emerged as an enabling technology that facilitates dynamic spectrum
access (DSA), with a great potential to address the issue of spectrum scarcity and inefficient use.
However, provisioning of reliable and robust communication with seamless operation in cognitive
radio networks (CRNs) is a challenging task. The underlying challenges include development of
non-intrusive dynamic resource allocation (DRA) and optimization techniques.
The main focus of this thesis is development of adaptive channel assembling (ChA) and DRA
schemes, with the aim to maximize performance of secondary user (SU) nodes in CRNs, without
degrading performance of primary user (PU) nodes in a primary network (PN). The key objectives
are therefore four-fold. Firstly, to optimize ChA and DRA schemes in overlay CRNs. Secondly, to
develop analytical models for quantifying performance of ChA schemes over fading channels in
overlay CRNs. Thirdly, to extend the overlay ChA schemes into hybrid overlay and underlay
architectures, subject to power control and interference mitigation; and finally, to extend the
adaptive ChA and DRA schemes for multiuser multichannel access CRNs.
Performance analysis and evaluation of the developed ChA and DRA is presented, mainly through
extensive simulations and analytical models. Further, the cross validation has been performed
between simulations and analytical results to confirm the accuracy and preciseness of the novel
analytical models developed in this thesis. In general, the presented results demonstrate improved
performance of SU nodes in terms of capacity, collision probability, outage probability and forced
termination probability when employing the adaptive ChA and DRA in CRNs.CK201
Survey of Spectrum Sharing for Inter-Technology Coexistence
Increasing capacity demands in emerging wireless technologies are expected to
be met by network densification and spectrum bands open to multiple
technologies. These will, in turn, increase the level of interference and also
result in more complex inter-technology interactions, which will need to be
managed through spectrum sharing mechanisms. Consequently, novel spectrum
sharing mechanisms should be designed to allow spectrum access for multiple
technologies, while efficiently utilizing the spectrum resources overall.
Importantly, it is not trivial to design such efficient mechanisms, not only
due to technical aspects, but also due to regulatory and business model
constraints. In this survey we address spectrum sharing mechanisms for wireless
inter-technology coexistence by means of a technology circle that incorporates
in a unified, system-level view the technical and non-technical aspects. We
thus systematically explore the spectrum sharing design space consisting of
parameters at different layers. Using this framework, we present a literature
review on inter-technology coexistence with a focus on wireless technologies
with equal spectrum access rights, i.e. (i) primary/primary, (ii)
secondary/secondary, and (iii) technologies operating in a spectrum commons.
Moreover, we reflect on our literature review to identify possible spectrum
sharing design solutions and performance evaluation approaches useful for
future coexistence cases. Finally, we discuss spectrum sharing design
challenges and suggest future research directions
Channel Access and Reliability Performance in Cognitive Radio Networks:Modeling and Performance Analysis
Doktorgradsavhandling ved Institutt for Informasjons- og kommunikasjonsteknologi, Universitetet i AgderAccording to the facts and figures published by the international telecommunication
union (ITU) regarding information and communication technology (ICT)
industry, it is estimated that over 3.2 billion people have access to the Internet in
2015 [1]. Since 2000, this number has been octupled. Meanwhile, by the end of
2015, there were more than 7 billion mobile cellular subscriptions in the world, corresponding
to a penetration rate of 97%. As the most dynamic segment in ICT,
mobile communication is providing Internet services and consequently the mobile broadband penetration rate has reached 47% globally. Accordingly, capacity,
throughput, reliability, service quality and resource availability of wireless services
become essential factors for future mobile and wireless communications. Essentially,
all these wireless technologies, standards, services and allocation policies
rely on one common natural resource, i.e., radio spectrum.
Radio spectrum spans over the electromagnetic frequencies between 3 kHz and
300 GHz. Existing radio spectrum access techniques are based on the fixed allocation
of radio resources. These methods with fixed assigned bandwidth for exclusive
usage of licensed users are often not efficient since most of the spectrum
bands are under-utilized, either/both in the space domain or/and in the time domain.
In reality, it is observed that many spectrum bands are largely un-occupied
in many places [2], [3]. For instance, the spectrum bands which are exclusively allocated
for TV broadcasting services in USA remain un-occupied from midnight to
early morning according to the real-life measurement performed in [4]. In addition
to the wastage of radio resources, spectrum under-utilization constraints spectrum
availability for other intended users. Furthermore, legacy fixed spectrum allocation
techniques are not capable of adapting to the changes and interactions in the system,
leading to degraded network performance.
Unlike in the static spectrum allocation, a fraction of the radio spectrum is
allocated for open access as license-free bands, e.g., the industrial, scientific and
medical (ISM) bands (902-928, 2400-2483.5, 5725-5850 MHz). In 1985, the federal
communications commission (FCC) permitted to use the ISM bands for private
and unlicensed occupancy, however, under certain restrictions on transmission
power [5]. Consequently, standards like IEEE 802.11 for wireless local area networks
(WLANs) and IEEE 802.15 for wireless personal area networks (WPAN)
have grown rapidly with open access spectrum policies in the 2.4 GHz and 5 GHz
ISM bands. With the co-existence of both similar and dissimilar radio technologies,
802.11 networks face challenges for providing satisfactory quality of service (QoS).
This and the above mentioned spectrum under-utilization issues motivate the spectrum
regulatory bodies to rethink about more flexible spectrum access for licenseexempt
users or more efficient radio spectrum management. Cognitive radio (CR) is
probably the most promising technology for achieving efficient spectrum utilization
in future wireless networks
Rethinking Wireless: Building Next-Generation Networks
We face a growing challenge to the design, deployment and management of wireless networks that largely stems from the need to operate in an increasingly spectrum-sparse environment, the need for greater concurrency among devices and the need for greater coordination between heterogeneous wireless protocols. Unfortunately, our current wireless networks lack interoperability, are deployed with fixed functions, and omit easy programmability and extensibility from their key design requirements.
In this dissertation, we study the design of next-generation wireless networks and analyze the individual components required to build such an infrastructure. Re-designing a wireless architecture must be undertaken carefully to balance new and coordinated multipoint (CoMP) techniques with the backward compatibility necessary to support the large number of existing devices. These next-generation wireless networks will be predominantly software-defined and will have three components: (a) a wireless component that consists of software-defined radio resource units (RRUs) or access points (APs); (b) a software-defined backhaul control plane that manages the transfer of RF data between the RRUs and the centralized processing resource; and (c) a centralized datacenter/cloud compute resource that processes RF signal data from all attached RRUs. The dissertation addresses the following four key problems in next-generation networks: (1) Making Existing Wireless Devices Spectrum-Agile,
(2) Cooperative Compression of the Wireless Backhaul, (3) Spectrum Coordination and (4) Spectrum Coordination.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102341/1/zontar_1.pd
Enabling Technologies for Ultra-Reliable and Low Latency Communications: From PHY and MAC Layer Perspectives
© 1998-2012 IEEE. Future 5th generation networks are expected to enable three key services-enhanced mobile broadband, massive machine type communications and ultra-reliable and low latency communications (URLLC). As per the 3rd generation partnership project URLLC requirements, it is expected that the reliability of one transmission of a 32 byte packet will be at least 99.999% and the latency will be at most 1 ms. This unprecedented level of reliability and latency will yield various new applications, such as smart grids, industrial automation and intelligent transport systems. In this survey we present potential future URLLC applications, and summarize the corresponding reliability and latency requirements. We provide a comprehensive discussion on physical (PHY) and medium access control (MAC) layer techniques that enable URLLC, addressing both licensed and unlicensed bands. This paper evaluates the relevant PHY and MAC techniques for their ability to improve the reliability and reduce the latency. We identify that enabling long-term evolution to coexist in the unlicensed spectrum is also a potential enabler of URLLC in the unlicensed band, and provide numerical evaluations. Lastly, this paper discusses the potential future research directions and challenges in achieving the URLLC requirements
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Towards Long Short Term Memory Based Proactive In-Network Caching for Cloud RANs
The rapid population growth in large urban cities has led to an unprecedented increase in both the number and the diversity of wireless devices and applications with varying quality of service requirements in terms of latency and data rates. LinkNYC is an example of an urban communication network infrastructure, which replaces all the payphones in the five boroughs of New York City (NYC) with kiosk-like structures, called Links, with the goal of bringing fast and free public Wi-Fi access to thousands of city users. When enabled with data storage capability, these Links can play the role of edge cloud devices to allow in-network caching of popular Internet content to reduce access delay and backhaul traffic congestion by placing content closer to the end-users. In this thesis, we propose k-means clustering to optimize content placement at the BSs, and we do so by clustering BSs based on the transmission delay between BSs and by proactively caching content at the clustered BSs with the aim of reducing content access delay and traffic congestion. Our proposed scheme also uses the Long Short Term Memory (LSTM)'s Seq-to-Seq model to predict content popularity at each BS. Using the LinkNYC network topology as the use case, we show through simulations that the proposed scheme reduces content access delay by minimizing content miss ratios and by reducing in-network content redundancy. Our study shows that our hybrid approach of proactive and reactive caching coupled with LSTM based popularity prediction provides potential solutions for fulfilling growing demands in urban communication networks
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