Addressing next-generation wireless challenges with commercial software-defined radio platforms

Sklivanitis, George; Gannon, Adam; Batalama, Stella N.; Pados, Dimitris A.

doi:10.1109/mcom.2016.7378427

Cited by 35 publications

(19 citation statements)

References 12 publications

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“…The exhaustive cross-correlation algorithm can be expressed as (here x * denotes the complex conjugate of x) correlation is O(N L) per time slot 7 , where N is the number of samples per time slot. In [5], we computed the cross correlation offline.…”

Section: Appendix a Frame Synchronization Algorithms For Pncmentioning

confidence: 99%

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Reliable Physical-Layer Network Coding Supporting Real Applications

You

Liew

2017

IEEE Trans. on Mobile Comput.

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Abstract-This paper presents the first reliable physical-layer network coding (PNC) system that supports real TCP/IP applications for the two-way relay network (TWRN). Theoretically, PNC could boost the throughput of TWRN by a factor of 2 compared with traditional scheduling (TS) in the high signal-to-noise (SNR) regime. Although there have been many theoretical studies on PNC performance, there have been relatively few experimental and implementation efforts. Our earlier PNC prototype, built in 2012, was an offline system that processed signals offline. For a system that supports real applications, signals must be processed online in real-time. Our real-time reliable PNC prototype, referred to as RPNC, solves a number of key challenges to enable the support of real TCP/IP applications. The enabling components include: 1) a time-slotted system that achieves µs-level synchronization for the PNC system; 2) reduction of PNC signal processing complexity to meet real-time constraints; 3) an ARQ design tailored for PNC to ensure reliable packet delivery; 4) an interface to the application layer. We took on the challenge to implement all the above with general-purpose processors in PC through an SDR platform rather than ASIC or FPGA. With all these components, we have successfully demonstrated image exchange with TCP and two-party video conferencing with UDP over RPNC. Experimental results show that the achieved throughput approaches the PHY-layer data rate at high SNR, demonstrating the high efficiency of the RPNC system.

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Section: Appendix a Frame Synchronization Algorithms For Pncmentioning

confidence: 99%

“…We focus on USRP/GNU Radio because of its programmability and flexibility for rapid prototyping [7]. In this platform, the USRP is the RF frontend hardware.…”

Section: Introductionmentioning

confidence: 99%

Reliable Physical-Layer Network Coding Supporting Real Applications

You

Liew

2017

IEEE Trans. on Mobile Comput.

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“…In this way, processingintensive physical layer functionalities are software-defined, but executed in hardware that can be reconfigured in realtime (through registers and partial reconfiguration of the FPGA). Therefore, unlike purely software-defined implementations that introduce high processing latency [18,19] and limit data rates; or pure hardware implementations that lack reconfiguration capabilities, SEANet G2 is able to provide both the low latency and high-data-rate characteristics of hardware implementations, as well as the reconfiguration capabilities of software implementations.…”

Section: Related Workmentioning

confidence: 99%

SEANet G2

Demirors

Shi

Guida

et al. 2016

Proceedings of the 11th ACM International Conference on Underwater Networks &Amp; Systems - WUWNet '16

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Existing underwater acoustic networking platforms are for the most part based on inflexible hardware and software architectures that can support mostly point-to-point, lowdata-rate, delay-tolerant applications. Most commercial devices do not provide neither the sufficient data rates nor the necessary flexibility to support future underwater networking applications and systems. This article discusses a new high-data rate software-defined underwater acoustic networking platform, SEANet G2, able to support higher data rates (megabit/s data rates are foreseen over short range links), spectrum agility, and hardware/software flexibility in support of distributed networked monitoring operations. The article reports on the main architectural choices of the new platform, as well as some preliminary performance evaluation results. Data rates in the order of megabit/s were demonstrated in a controlled lab environment, and, for the first time to the best of our knowledge, data rates of 522 kbit/s where obtained in sea trials over short horizontal links (e.g., 10 m) for a BER lower than 10 −3 .

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“…Moreover, software development suffers from increased implementation complexity and a lack of standard methodology for partitioning the implementation of signal-processing functionalities to heterogeneous hardware platforms. For instance, digital signal processor (DSP) is cheaper, consumes less power, and is easy to develop software applications, but it has a considerable latency and less throughput compared with field programmable gate arrays (FPGAs) [1]. For high-speed signal-processing (HSP) communication systems, such as cognitive radio (CR) [2,3] and software-defined radio (SDR) [4], DSP may fail to capture and process the received data due to data loss.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Performance of Discrete Wavelet Transform Implementations Using Multiplierless

Alzaq¹,

Üstündağ²

2018

Wavelet Theory and Its Applications

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Using discrete wavelet transform (DWT) in high-speed signal-processing applications imposes a high degree of care to hardware resource availability, latency, and power consumption. In this chapter, the design aspects and performance of multiplierless DWT is analyzed. We presented the two key multiplierless approaches, namely the distributed arithmetic algorithm (DAA) and the residue number system (RNS). We aim to estimate the performance requirements and hardware resources for each approach, allowing for the selection of proper algorithm and implementation of multi-level DAA-and RNS-based DWT. The design has been implemented and synthesized in Xilinx Virtex 6 ML605, taking advantage of Virtex 6's embedded block RAMs (BRAMs).

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Addressing next-generation wireless challenges with commercial software-defined radio platforms

Cited by 35 publications

References 12 publications

Reliable Physical-Layer Network Coding Supporting Real Applications

Reliable Physical-Layer Network Coding Supporting Real Applications

SEANet G2

A Comparative Performance of Discrete Wavelet Transform Implementations Using Multiplierless

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