Radio-over-Fibre access for sustainable Digital Cities
Bibliographic record
Abstract
Pervasive broadband access will transform cities to the net social, environmental and economic benefit of the e-City dweller as did the introduction of utility and transport network infrastructures. Yet without action, the quantity of greenhouse gas emissions attributable to the increasing energy consumption of access networks will become a serious threat to the environment. This paper introduces the vision of a ‘sustainable Digital City’ and then considers strategies to overcome economic and technical hurdles faced by engineers responsible for developing the information and communications technology (ICT) network infrastructure of a Digital City. In particular, ICT energy consumption, already an issue from an operating cost perspective, is responsible for 3 % of global energy consumption and is growing unsustainably. A grand challenge is to conceive of networks, systems and devices that together can cap wireless network energy consumption whilst accommodating growth in the number of subscribers and the bandwidth of services. This paper provides some first research directions to tackle this grand challenge. A distributed antenna system with radio frequency (RF) transport over an optical fibre (or optical wireless in benign environments) distribution network is identified as best suited to wireless access in cluttered urban environments expected in a Digital City from an energy consumption perspective. This is a similar architecture to Radio-over-Fibre which, for decades, has been synonymous with RF transport over analogue intensity-modulated direct detection optical links. However, it is suggested herein that digital coherent optical transport of RF holds greater promise than the orthodox approach. The composition of the wireless and optical channels is then linear, which eases the digital signal processing tasks and permits robust wireless protocols to be used end-to-end natively which offers gains in terms of capacity and energy efficiency. The arguments are supported by simulation studies of distributed antenna systems and digital coherent Radio-over-Fibre links.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".