Comparative Analysis of On-Chip FPGA Memory Architectures for Viterbi Decoder Implementation in DVB Systems
Bibliographic record
Abstract
High-definition Digital Video Broadcasting (DVB) systems demand high data rates, resulting in increased hardware complexity and power consumption, with the Viterbi decoder (VD) being a key contributor. The substantial memory resources required for these high data rates drive this research, which investigates the impact of diverse static random-access memory (SRAM) architectures on Zynq FPGA and their embedded memory resources, aiming to design power-efficient and less complex Viterbi decoders. Besides, the effect of different memories architectures on the transceiver (Tx-Rx) system has been studied. Viterbi decoder is implemented with different memory architectures on xczu7ev-2ffvc1156: flip flops, distributed RAM, block ram (BRAM), and UltraRAMTM (URAM). BRAM IP from AMD has significantly improved the dynamic power of Viterbi decoder by 97% compared to other available memories. Effectiveness of the employed BRAM is ensured by saving about 50% of the total power of the baseband transceiver system. That Tx-Rx operates at a frequency of 125MHz with a throughput of 62.3 Mbps, a code rate: ½, and 16APSK modulation scheme. Viterbi decoder has achieved a reduction in power compared to sleepy keeper and space time trellis code (STTC) with about 44% and 61% respectively. Functionality of the proposed VD architecture for signal to noise ratio (SNR) of 0 dB at additive white Gaussian noise (AWGN) channel and vector length of 3,264 bits is verified. Hardware validation on ZCU104 based on DVB standard is also done and reported.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".