Throughput optimization in wireless multihop networks with Successive Interference Cancellation
Bibliographic record
Abstract
Successive Interference Cancellation (SIC) is a potentially powerful technique for improving the performance of wireless multihop networks. This work presents a method to compute the maximum achievable throughput of such networks. We consider the case of a network that uses conflict-free scheduling and has multi-rate and multi-power capabilities. We also consider the case of different levels of SIC, i.e., we will denote by SIC-n a technique in which a receiver can possibly decode up to n signals at a time. We formulate a flexible framework to quantify the throughput improvement that can be obtained in a realistic size multihop network by using SIC-n for n = 2,3. The optimization framework is formulated as a joint routing, scheduling, and SIC problem under the physical layer interference model for any multihop network and common utility functions. This joint optimization problem is then numerically solved for max-min throughput for several cases of interest and insights are provided into the gains that can be provided with SIC-n in the case of mesh networks with multi-rate and multi-power capabilities. Specifically, we find that, not surprisingly, when enabling SIC at each node, very significant throughput gains at high transmission power can be obtained. We find that at low transmission power, gains in the range of 25-40% are possible with SIC-2 at each node and, when in addition the flow pattern is symmetrical, SIC-3 does not bring significant gains over SIC-2. Moreover, compared to mesh networks without SIC, where in the high power regime single-hop transmission to the gateway is optimal, with SIC at each node, this is not necessarily the case. We also show that performing SIC only at the gateway enables non-negligible gains in a multi-hop context. We believe that this study can be useful to network operators to quantify the gains that SIC can provide in a managed mesh network.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".