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Title: | Channel coding on a nano-satellite platform | Authors: | Shumba, Angela-Tafadzwa | Keywords: | Nanosatellites;Field programmable gate arrays;Signal processing -- Digital techniques;Error-correcting codes (Information theory) | Issue Date: | 2018 | Publisher: | Cape Peninsula University of Technology | Abstract: | The concept of forward error correction (FEC) coding introduced the capability of achieving near Shannon limit digital transmission with bit error rates (BER) approaching 10-9 for signal to noise power (Eb/No) values as low as 0.7. This brought about the ability to transmit large amounts of data at fast rates on bad/noisy communication channels. In nano-satellites, however, the constraints on power that limit the energy that can be allocated for data transmission result in significantly reduced communication system performance. One of the effects of these constraints is the limitation on the type of channel coding technique that can be implemented in these communication systems. Another limiting factor on nano-satellite communication systems is the limited space available due to the compact nature of these satellites, where numerous complex systems are tightly packed into a space as small as 10x10x10cm. With the miniaturisation of Integrated-Circuit (IC) technology and the affordability of Field-Programmable-Gate-Arrays (FPGAs) with reduced power consumption, complex circuits can now be implemented within small form factors and at low cost. This thesis describes the design, implementation and cost evaluation of a ½-rate convolutional encoder and the corresponding Viterbi decoder on an FPGA for nano-satellites applications. The code for the FPGA implementation is described in VHDL and implemented on devices from the Artix7 (Xilinx), Cyclone V (Intel-fpga), and Igloo2 (Microsemi) families. The implemented channel code has a coding gain of ~3dB at a BER of 10-3. It can be noted that the implementation of the encoder is quite straightforward and that the main challenge is in the implementation of the decoder. | Description: | Thesis (MEng (Electrical Engineering))--Cape Peninsula University of Technology, 2017. | URI: | http://hdl.handle.net/20.500.11838/2768 |
Appears in Collections: | Electrical, Electronic and Computer Engineering - Master's Degree |
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File | Description | Size | Format | |
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209130261-Tafadzwa Shumba-Angela-MEng-Electrical Engineering-Eng-2018.pdf | Thesis | 3.27 MB | Adobe PDF | View/Open |
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