Interference Mitigation for Cellular Networks: Fundamental Limits and Applications
Bibliographic record
Abstract
Interference is a key limiting factor in modern communication systems. In a wireless cellular network, the performance of cell-edge users is severely limited by the intercell interference. This thesis studies the use of interference-channel and relay-channel techniques to mitigate intercell interference and to improve the throughput and coverage of cellular networks. The aim of this thesis is to demonstrate the benefit of the proposed interference mitigation schemes through both information theoretical studies and applications in the cellular network.\n\nThere are three mains results in this thesis: First, it is shown that for the $K$-user cyclic Gaussian interference channel, where the $k$th user interferes with only the ($k -1$)th user (mod $K$) in the network, the Etkin-Tse Wang power splitting strategy achieves the capacity region to within 2 bits in the weak interference regime. For the special 3-user case, this gap can be sharpened to $1\\frac{1}{2}$ bits by the time-sharing technique. Second, it is shown that for a two-user Gaussian interference channel with an in-band-reception and out-of-band transmission relay, generalized hash-and-forward together with Han-Kobayashi information splitting can achieve the capacity region of this channel to within a constant number of bits in a certain weak-relay regime. A generalized-degrees-of-freedom analysis in the high signal-to-noise ratio regime reveals that in the symmetric channel setting, each common relay bit improves the sum rate up to two bits. The third part of this thesis studies an uplink multicell joint processing model in which the base stations are connected to a centralized processing server via rate-limited digital backhaul links. This thesis proposes a suboptimal achievability scheme employing the Wyner-Ziv compress-and-forward relaying technique and successive-interference-cancellation decoding. The main advantage of the proposed approach is that it results in achievable rate regions that are easily computable, in contrast to previous schemes in which the rate regions can only be characterized by exponential number of rate constraints.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".