A reconfigurable hardware platform implementation for software defined radio using dynamic partial reconfiguration on Xilinx Zynq FPGA

Kamaleldin, Ahmed; Hosny, Sherif; Mohamed, Khaled Salah; Gamal, Mostafa; Hussien, Abdelrhman; Elnader, Eslam; Shalash, Ahmed F.; Obeid, Abdelfattah M.; Ismail, Yehea; Mostafa, Hassan

doi:10.1109/mwscas.2017.8053229

Cited by 11 publications

(8 citation statements)

References 6 publications

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“…3. SDR transceiver and receiver [28] The other capabilities shared by [29] include the ones studied under the cognitive radio. Researchers have focused on SDR to implement structures on reconfigurable FPGAs, as well as signal processing using software instead of hardware [30].Some of the capabilities include a general-purpose processing capability that involves the DSP and FPGA processing resources.…”

Section: Fig 2 Ad-hoc Network Examplementioning

confidence: 99%

Management of Wireless Communication Systems Using Artificial Intelligence-Based Software Defined Radio

Bargarai

Abdulazeez

Tiryaki

et al. 2020

Int. J. Interact. Mob. Technol.

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The wireless communication system was investigated by novel methods, which produce an optimized data link, especially the software-based methods. Software-Defined Radio (SDR) is a common method for developing and implementing wireless communication protocols. In this paper, SDR and artificial intelligence (AI) are used to design a self-management communication system with variable node locations. Three affected parameters for the wireless signal are considered: channel frequency, bandwidth, and modulation type. On one hand, SDR collects and analyzes the signal components while on the other hand, AI processes the situation in real-time sequence after detecting unwanted data during the monitoring stage. The decision was integrated into the system by AI with respect to the instantaneous data read then passed to the communication nodes to take its correct location. The connectivity ratio and coverage area are optimized nearly double by the proposed method, which means the variable node location, according to the peak time, increases the attached subscriber by a while ratio

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Section: Fig 2 Ad-hoc Network Examplementioning

confidence: 99%

Management of Wireless Communication Systems Using Artificial Intelligence-Based Software Defined Radio

Bargarai

Abdulazeez

Tiryaki

et al. 2020

Int. J. Interact. Mob. Technol.

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“…Luckily, since signals rarely occupy the entire dynamic range, this is often not necessary. 2 In the following, we propose mechanisms for performing both static and real-time dynamic range adjustments.…”

Section: Properties Of Wireless Iot Signal Processingmentioning

confidence: 99%

“…Thanks to the important improvements in performance, cost and power consumption of DSP and FPGA technologies, new software-defined radio (SDR) applications have recently emerged [1]. For example, authors in [2] and [3] use FPGA platforms to build agile cellular transceivers, from 3G to 5G. The recent development of small and nanosatellite systems has spurred interest in SDR for spaceborne multimode transceivers [4], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

A RISC-V ISA Extension for Ultra-Low Power IoT Wireless Signal Processing

Amor

Bernier

Přikryl³

2022

IEEE Trans. Comput.

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This work presents an instruction-set extension to the open-source RISC-V ISA (RV32IM) dedicated to ultra-low power (ULP) software-defined wireless IoT transceivers. The custom instructions are tailored to the needs of 8/16/32-bit integer complex arithmetic typically required by quadrature modulations. The proposed extension occupies only 2 major opcodes and most instructions are designed to come at a near-zero energy cost. Both an instruction accurate (IA) and a cycle accurate (CA) model of the new architecture are used to evaluate six IoT baseband processing test benches including FSK demodulation and LoRa preamble detection. Simulation results show cycle count improvements from 19% to 68%. Post synthesis simulations for a target 22nm FD-SOI technology show less than 1% power and 28% area overheads, respectively, relative to a baseline RV32IM design. Power simulations show a peak power consumption of 380 µW for Bluetooth LE demodulation and 225 µW for LoRa preamble detection (BW = 500 kHz, SF = 11).

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“…The authors in [8] concluded that hardware reuse is a novel approach in the implementation of SDR systems, which results in better performance of communications schemes. In [9], a design of an RF front-end for a nanosatellite is presented, using low-cost commercial components like the CC2510 IC, for both the reception and transmission stages.…”

Section: Introductionmentioning

confidence: 99%

A Reconfigurable Analog Back-End for CubeSat Communications

Álvarez-Flores

Flores-Troncoso

Sandoval-Arechiga

et al. 2020

Preprint

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Abstract Current small satellite platforms such as CubeSats, require robust and versatile communication subsystems that allow the reconfiguration of the critical operation parameters such as carrier frequency, transmission power, bandwidth, or filter roll-off factor. In this paper, a reconfigurable Analog Back-End for CubeSat communications is proposed. This prototype is implemented using CAD software, on a 6-layer PCB of 10 cm^2 to implement a transceiver that operates from 0.070 to 6 GHz and complying with CubeSat and IPC-2221 standards. An Software-Defined Radio approach implemented on a baseband processor is used for control purposes. Measurements showed that the signal power at the output of the proposed analog back-end is suitable to feed the subsequent antenna subsystem.

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A reconfigurable hardware platform implementation for software defined radio using dynamic partial reconfiguration on Xilinx Zynq FPGA

Cited by 11 publications

References 6 publications

Management of Wireless Communication Systems Using Artificial Intelligence-Based Software Defined Radio

Management of Wireless Communication Systems Using Artificial Intelligence-Based Software Defined Radio

A RISC-V ISA Extension for Ultra-Low Power IoT Wireless Signal Processing

A Reconfigurable Analog Back-End for CubeSat Communications

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