Optimization criteria for joint communication and positioning networks
Bibliographic record
Abstract
The current mobile system, namely 4G, has limitations in applications where low latency and high data rates are needed. In addition, new applications require a very precise user positioning, which 4G is not able to provide with high availability. In order to face these problems, a new mobile system is being developed, namely 5G. The 5G system is likely to be designed as a joint communication and positioning system. This thesis focuses on a couple of performance criteria in the context of 5G systems, namely the accuracy of the estimation of time-delay, which is directly proportional to the positioning accuracy, and the Quality of Service (QoS) of the communications, measured here in terms of the Bit Error Rates (BER). Our studies are based on three different waveforms proposed in the context of future 5G and mmWave frequencies (3-300GHz). The analysis is performed in two different scenarios, outdoor and indoor, and with different modulation orders. For each scenario, a channel model has been developed. The outdoor channel model is based on the channel model created for the 5G system in the European METIS project. For the indoor scenarios, the indoor maps of one multi-floor building in Tampere University of Technology are used. The results show that the modulation order has no influence on the positioning accuracy, but it is very important in the communication QoS. In addition, minor differences are observed from the selected three waveforms in terms of the joint positioning and communication performance, in such a way that there is no clear advantage in terms of positioning accuracy of one waveform over the other, among the three considered cases. The performance difference is better on the communication side, where a difference of 1dB between the waveforms is obtained to achieve the same BER value, the best being CP-OFDM.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".