Deep Learning-Based Resource Allocation for 5G Broadband TV Service
Bibliographic record
Abstract
The vision of next-generation TV is to support media services to achieve sharing of cross-domain experience, and the eMBB scenario of the 5G network is one of its important driving forces. Considering the bandwidth and resource requirements of different services, such as unicast and multicast services of multimedia TV broadcasting, rationally allocating resources while providing high-quality services and realizing green energy savings of base stations is one of the challenges. This paper is aimed at the resource allocation for TV multimedia service in the 5G wireless cloud network (C-RAN) scenario, which can support unicast services for cellular users and multicast services for broadcast services simultaneously, and it proposes the corresponding slice resources allocation architecture based on the concept of a self-organizing network. The management architecture first builds the functions and processes of the corresponding autonomous resource management. Based on the multidimensional data, an effective deep learning model named LSTM (long short-term memory) is used to construct the dynamic traffic model of the multicast service in space-time, which provides a basis for further network resource allocation. Based on the prediction results and the condition of satisfying the changing requirements of users, the corresponding optimization model is constructed with the goal of minimizing the energy usage of the RRHs (remote radio heads) and taking the QoS constraints of the users into account. A deep reinforcement learning (DRL) framework combined with a convex optimization method are then used to complete the users' bandwidth and power resource allocation. The experimental results show that the proposed method can not only predict the multicast service requirement accurately but also effectively improve the energy efficiency of the network under targeted QoS requirements along with time variations.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".