Cost-Efficient Citywide Neutral Host Design: A Micro-Operator Business Model for Expedited 5G and Beyond Network Infrastructure Rollout
Bibliographic record
Abstract
Recently, skepticism has surrounded the ability of mobile network operators (MNOs) to achieve a timely mass rollout of 5G mobile network infrastructure. This is mainly due to the staggering number of antennas required and the unforeseen complexities involved, leading to a considerable disparity between total ownership cost (TCO) and return on investment (ROI), which is the primary concern from MNOs perspective. This amplifies significantly when contemplating universal 5G and beyond (5GB) coverage, pivotal for unlocking a myriad of innovative use cases and applications. Amidst these challenges, the concept of micro-operators represents a potential solution to augment the role of traditional MNOs in expediting the deployment of widespread 5GB infrastructure. Particularly, the newly emerging neutral host business model, wherein a third party assumes responsibility for providing coverage across multiple MNOs, stands as a compelling micro-operator solution, offering the sough-after cost-effectiveness and reliability. Our proposal in this paper relies on the dynamics observed in tidal traffic patterns of multi-tenant venues and citywide deployments to outline a cost-optimized design for a citywide neutral host micro-operator. Leveraging network slicing and statistical multiplexing techniques, our design approach enables real-time dynamic resource allocation. Additionally, the design integrates radio over Ethernet (RoE) and high availability seamless redundancy (HSR) protocols to meet the diverse service quality demands of 5GB applications. Simulation results demonstrate the scalability of our proposed design, meeting diverse 5GB QoS requirements across a spectrum of real-world citywide deployment scenarios, and cost-effectiveness by driving the TCO down by over 67%.
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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 | 0.000 | 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 | 0.000 | 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".