Optimized beamforming and backhaul compression for uplink MIMO cloud radio access networks
Bibliographic record
Abstract
This paper studies the optimization of transmit beamforming and backhaul compression strategies for the uplink of cloud radio access networks (C-RAN), in which multi-antenna user terminals communicate with a cloud-computing based central processor (CP) through multi-antenna base-stations (BSs) serving as relay nodes. The BSs perform compress-and-forward strategy to quantize the received signals and send the quantization bits to the CP via capacity-limited backhaul links for decoding. In contrast to the previous works on the uplink C-RAN, which mostly focus on the backhaul compression strategies only, this paper proposes the joint optimization of the transmit beamformers and the quantization noise covariance matrices at the BSs for maximizing the benefit brought by the C-RAN architecture. A weighted sum-rate maximization problem is formulated under the user power and backhaul capacity constraints. A novel weighted minimum-mean-square-error successive convex approximation (WMMSE-SCA) algorithm is developed for finding a local optimum solution to the problem. This paper further proposes a low-complexity approximation scheme consisting of beamformers matching to the strongest channel vectors at the user side along with per-antenna scalar quantizers with uniform quantization noise levels across the antennas at each BS. This simple separate design strategy is derived by exploring the structure of the optimal solution to the sum-rate maximization problem under successive interference cancellation (SIC) while assuming high signal-to-quantization-noise ratio (SQNR). Simulation results show that with optimized beamforming and backhaul compression, C-RAN can significantly improve the overall performance of wireless cellular networks. With SIC, the proposed separate design performs very close to the optimized joint design in the SQNR regime of practical interest.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".