Software Defined Radio: Challenges and Opportunities

Ulversøy, Tore

doi:10.1109/surv.2010.032910.00019

Cited by 278 publications

(128 citation statements)

References 69 publications

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“…It will also be important that these devices continue to improve in terms of processing capacity, so that more complex communication systems such as those with FHSS (Frequency Hopping Spread Spectrum) coding can be implemented. Additionally, there are some regulatory challenges, when commercially using this kind of devices to protect the unauthorized use of software, also under the conditions of power and bandwidth according to the assigned frequency, these devices should not interfere with other equipment [10].…”

Section: Discussion and Analysismentioning

confidence: 99%

“…First, it allows to operate different multi-band, multihoming, and multipurpose standards in the same device without any hardware modification [10], and second, it provides scalability of communication systems by facilitating network updating and equipment functionalities from the physical layer to the application layer, thus achieving a better response to the market dynamics [10], which might change the scheme of a static network by an ad hoc network for mobility services [11]. In addition, it enables the implementation of the communication stack in a modular way for a specific application, responding to its particular needs in terms of operating frequency, modulation and coding form, number of nodes, or network topology to achieve a better performance.…”

Section: Opportunities To Implement Software Defined Radio In Networkmentioning

confidence: 99%

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Oportunidades para la implementación de radio definida por software en redes de sensores

2017

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Section: Discussion and Analysismentioning

confidence: 99%

Section: Opportunities To Implement Software Defined Radio In Networkmentioning

confidence: 99%

Oportunidades para la implementación de radio definida por software en redes de sensores

2017

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“…CR applications that have already been invested in can be ported to new SDR platforms that are more capable. It becomes easier to keep up with the rapid evolution of communications standards [30] as base stations and other radios can have their software upgraded.…”

Section: A Cognitive Radio Fundamentalsmentioning

confidence: 99%

Using Cognitive Radio for Interference-Resistant Industrial Wireless Sensor Networks: An Overview

Chiwewe

Mbuya

Hancke

2015

IEEE Trans. Ind. Inf.

138

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Abstract-Industrial wireless sensor networks have to contend with environments that are usually harsh and time varying. Industrial wireless technology, such as WirelessHART and ISA 100.11a, also operates in a frequency spectrum utilised by many other wireless technologies, and with wireless applications rapidly growing it is possible that multiple heterogeneous wireless systems would need to operate in overlapping spatiotemporal regions. Interference such as noise or other wireless devices affects connectivity and reduces communication link quality. This negatively affects reliability and latency, which are core requirements of industrial communication. Building wireless networks that are resistant to noise in industrial environments and can coexist with competing wireless devices in an increasingly crowded frequency spectrum is challenging. To meet these challenges, we need to consider the benefits that approaches finding success in other application areas can offer industrial communication. Cognitive radio methods offer a potential solution to improve resistance of industrial wireless sensor networks to interference. Integrating cognitive radio principles into the lower layers of industrial wireless sensor networks can enable devices to detect and avoid interference and potentially opens the possibility of utilising free radio spectrum for additional communication channels. This improves resistance to noise and increases redundancy in terms of channels per network node or adding additional nodes. In this paper, we summarise cognitive radio methods relevant to industrial applications, covering cognitive radio architecture, spectrum access and interference management, spectrum sensing, dynamic spectrum access, game theory and cognitive radio network security.Index Terms-Cognitive radio, industrial wireless sensor networks, Internet of Things, spectrum management, spectrum sensing.

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“…RF frontend design for SDR and Cognitive Radio (CR) receivers is explained in [5]. SDR technology faces many challenges in designing suitable software modules and their implementation, which is explained in [7]. Authors in [8] have explained the SDR receiver design method.…”

Section: Introductionmentioning

confidence: 99%

Universal Band Pass Sampling Algorithm for Integration of Multiple Wireless Technologies Using Software Defined Radio Platform

Budaraju¹,

Anjaneyulu²

2016

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Software Defined Radio (SDR) architecture allows us to integrate different mobile technologies using common hardware but with different software modules. To achieve this, we need to keep the signal in digital form for as much portion of the circuitry as possible, so that the implementation could be carried out by programmable digital processors. For this purpose, the incoming radio frequency (RF) signal is down converted to baseband spectrum using band pass sampling method. Research works carried out so far in this field have developed a few algorithms for band pass sampling. But, these algorithms are not much useful for most of the mobile communication systems and they use complex methodology for computing the sampling frequency values. In order to use the SDR platform to integrate all current wireless technologies, an efficient, cost effective and less complex algorithm that can be labelled as universal band pass sampling algorithm is developed in this paper for multiple mobile systems. This algorithm is based on a novel idea of inserting guard bands between the signals which reduces the design complexities of perfect ADC and sharp cut off filters. Using this algorithm, valid sampling frequency ranges and corresponding IF values are calculated for down converting RF signals. The algorithm is tested for six RF signals of different wireless technologies which are integrated and simultaneously down converted using SDR based front end receiver and thus the system multiplies the base station capacity by a factor of six. The simulation results are obtained and shown in this paper which proves that the algorithm developed works well for most of the wireless technologies.

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Software Defined Radio: Challenges and Opportunities

Cited by 278 publications

References 69 publications

Oportunidades para la implementación de radio definida por software en redes de sensores

Oportunidades para la implementación de radio definida por software en redes de sensores

Using Cognitive Radio for Interference-Resistant Industrial Wireless Sensor Networks: An Overview

Universal Band Pass Sampling Algorithm for Integration of Multiple Wireless Technologies Using Software Defined Radio Platform

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