An orthogonal wavelet division multiple-access processor architecture for LTE-advanced wireless/radio-over-fiber systems over heterogeneous networks
Bibliographic record
Abstract
The increase in internet traffic, number of users, and availability of mobile devices poses a challenge to wireless technologies. In long-term evolution (LTE) advanced system, heterogeneous networks (HetNet) using centralized coordinated multipoint (CoMP) transmitting radio over optical fibers (LTE A-ROF) have provided a feasible way of satisfying user demands. In this paper, an orthogonal wavelet division multiple-access (OWDMA) processor architecture is proposed, which is shown to be better suited to LTE advanced systems as compared to orthogonal frequency division multiple access (OFDMA) as in LTE systems 3GPP rel.8 (3GPP, http://www.3gpp.org/DynaReport/36300.htm ). ROF systems are a viable alternative to satisfy large data demands; hence, the performance in ROF systems is also evaluated. To validate the architecture, the circuit is designed and synthesized on a Xilinx vertex-6 field-programmable gate array (FPGA). The synthesis results show that the circuit performs with a clock period as short as 7.036 ns (i.e., a maximum clock frequency of 142.13 MHz) for transform size of 512. A pipelined version of the architecture reduces the power consumption by approximately 89%. We compare our architecture with similar available architectures for resource utilization and timing and provide performance comparison with OFDMA systems for various quality metrics of communication systems. The OWDMA architecture is found to perform better than OFDMA for bit error rate (BER) performance versus signal-to-noise ratio (SNR) in wireless channel as well as ROF media. It also gives higher throughput and mitigates the bad effect of peak-to-average-power ratio (PAPR).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".