Impact of Heterogeneous Fading Channels in Power Limited Cognitive Radio Networks
Bibliographic record
Abstract
The tradeoff between decreasing the interference to primary user (PU) and increasing secondary users' (SUs') achievable throughput is an important problem in cognitive radio networks. Heterogeneous fading channels from PU to multiple SUs, PU's traffic distribution, limited SU's power and multiple SUs' access contention impact both these two conflicting objectives. In this paper, we study the joint impact of these four factors on the tradeoff. More specifically, we consider that the channels from PU to SUs are exposed to non-identically independent free space path losses, PU's traffic randomly arrives and departs from the channel, every SU's average power consumption is limited, while multiple SUs contend to transmit. We first model the impact of these factors on SUs' spectrum sensing and data transmission. Then, we formulate the tradeoff aiming at maximizing SUs' aggregated throughput under two constraints: 1) interference probability to PU and 2) SUs' average power consumption. To solve the optimization problem, we design a novel cluster based particle swarm optimization (C-PSO) algorithm. By iteratively updating the particles in a cluster based on the comparison of their fitnesses, the cluster converges to the optimal solution rapidly. Simulation results validate the feasibility of the C-PSO algorithm and the outperformance of our proposal compared against related contributions which consider the homogeneous fading channel. They also show how the optimal solution varies with path losses and PU's traffic distribution.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".