Modeling and analysis of user association and wireless backhauling in small cell networks
Bibliographic record
Abstract
Dense deployment of small cells underlaying the traditional macrocells is considered as a key enabling technique for the emerging fifth generation (5G) cellular networks. However, the diverse transmit powers of the base stations (BSs) in such a network lead to uneven distribution of the traffic loads among different BSs when received signal power (RSP)-based user association is used. Moreover, provisioning of efficient and economical backhauling for these small cells is a crucial challenge. To combat this, wireless backhauling is been considered as a viable and cost-effective approach that allows operators to obtain end-to-end control of their network rather than leasing third party wired backhaul connections. But the scarcity of radio frequency (RF) spectrum in the licensed bands is still a major constraint which necessitates efficient spectrum planning for backhaul/access links of small cells. Emerging communications techniques such as full-duplexing, which allows transmission and reception in the same spectrum band, can be used to tackle the problem of spectrum scarcity. In the above contexts, the objective of the research work presented in this thesis is to develop efficient user association and wireless backhauling schemes for small cell networks and analyze their performances. In particular, i) A channel-access aware user association scheme is proposed to tackle the problem of uneven distribution of traffic load among different BSs, ii) Performance analysis of full-duplex (FD) wireless backhauling of small cells is carried out when compared to half-duplex (HD) wireless backhauling), iii) A method for downlink spectrum allocation for in-band and out-of-band wireless backhauling of full-duplex small cells is presented to optimally allocate spectrum for access and backhaul links, iv) A method for optimal channel and power allocation is presented for downlink access and backhaul links for half-duplex small cells. The proposed methods and performance analysis models will be useful for optimizing the design and deployment of small cell networks.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".