Distributed Generation Communications Utilizing a Real-time FPGA-based Channel Simulator in the ISM Frequency Band
Bibliographic record
Abstract
Deregulation of the power industry, increases in consumer demand of electricity and mandates to reduce greenhouse gases has significantly heightened the interest in distributed generation (DG). Typical DG sources include wind turbines, fuel cells, micro gas/diesel turbines, small hydro generators and photovoltaics. Currently most of these generation sources are optimized for local operation and thus the potential of DG that is both dispatchable and cost-effective has yet to be fully realized. One key technical challenge is the development of low-cost communications for remote telemetry and control of individual DG sources. Along with cost, other key communication system requirements are modularity, range and reliability. This paper presents a distributed generation communication system (DGCS) that is based on a wide area network (WAN) and local area network (LAN) topology and utilizes two variants of low-cost radio modems (RM) to satisfy the desired connectivity between the DG sites and a centralized control center. For the LAN, low-cost RMs operating in the industrial, scientific, and medical (ISM) frequency band offers point-to-multipoint scalability to multiple DG sites within a limited range. Integration of the ISM RMs with a cellular RM provides connectivity to a WAN or Internet thus providing long-range access to DG sites from any facility that has internet-access. Given the LAN scalability requirement, an ISM field programmable gate array (FPGA) channel simulator was developed to assess DGCS performance using channel impairments such as free-space path loss, multipath reflections, additive white Gaussian noise (AWGN), multiple access interference, and narrowband interference. This study provides the evaluation of two commercially available ISM radio modems and recommendations are made based on LAN requirements such as cost and performance given difficult channel conditions.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".