Can interference alignment impact network utility maximization?
Bibliographic record
Abstract
This paper examines whether interference alignment (IA) can be leveraged to improve network utility in a multiuser multi-antenna wireless cellular network. Optimality of IA from a DoF standpoint has the potential to aid conventional network optimization algorithms that typically can only find locally optimal solutions. This paper investigates the usefulness of IA for interference coordination and utility maximization by proposing a two-stage optimization framework for a G-cell multi-antenna network with K users/cell, and with full channel state information (CSI) available at all base-stations. The first stage of the proposed framework focuses exclusively on nulling interference from a set of dominant interferers using IA, while the second stage optimizes the transmit and receive beamformers iteratively to maximize a network-wide utility using the IA solution as the initial point. The number of dominant interferers to be nulled in the first stage is guided by a set of new feasibility results for partial IA. This paper focuses on maximizing the specific network utility of minimum rate over all users in the network. Through simulations on two different topologies of cluster of BSs either in isolation or in the presence of other non- cooperating BSs, the proposed framework with IA initialization is observed to outperform straightforward optimization on an isolated cluster of BSs. But, IA loses its impact when there is significant out-of-cluster interference. Thus, in a large-scale dense cellular deployment, the benefit of IA is likely to be limited, even with centralized network optimization and full CSI.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".