Dynamic Resource Allocation for Virtualized Wireless Networks in Massive-MIMO-Aided and Fronthaul-Limited C-RAN
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper considers the uplink dynamic resource allocation in a cloud radio access network (C-RAN) serving users belonging to different service providers (called slices) to form virtualized wireless networks (VWN). In particular, the C-RAN supports a pool of base-station (BS) baseband units (BBUs), which are connected to BS radio remote heads (RRHs) equipped with massive massive multiple input multiple output (MIMO), via fronthaul links with limited capacity. Assuming that each user can be assigned to a single RRH-BBU pair, we formulate a resource allocation problem aiming to maximize the total system rate, constrained on the minimum rates required by the slices and the maximum number of antennas and power allocated to each user. The effects of pilot contamination error on the VWN performance are investigated and pilot duration is considered as a new optimization variable in resource allocation. This problem is inherently nonconvex, NP-hard and, thus, computationally inefficient. By applying the successive convex approximation and complementary geometric programming approach, we propose a two-step iterative algorithm: one to adjust the RRH, BBU, and fronthaul parameters, and the other for power and antenna allocation to users. Simulation results illustrate the performance of the developed algorithm for VWNs in a massive-MIMO-aided and fronthaul-limited C-RAN, and demonstrate the effects of imperfect channel state information estimation due to pilot contamination error, and the optimal pilot duration.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 it