A Coalition Formation Game for Energy-Efficient Cooperative Spectrum Sensing in Cognitive Radio Networks with Multiple Channels
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Spectrum sensing is one of the key technologies to realize spectrum reuse and increase the spectrum efficiency in cognitive radio networks (CRNs). In this paper, we study energy-efficient cooperative multi-channel spectrum sensing in CRNs. We first propose a cooperative spectrum sensing and accessing (CSSA) scheme for all the secondary users (SUs). The SUs cooperatively sense the licensed channels of the primary users (PUs) in the sensing slot. If a channel is determined to be idle, the SUs which have sensed that channel will have a chance to transmit packets in the data transmission slot. We then formulate this multi- channel spectrum sensing problem as a coalition formation game, where a coalition corresponds to the SUs that have chosen to sense and access a particular channel. The utility function of each coalition takes into account both the sensing accuracy and energy efficiency. We propose distributed algorithms to find the optimal partition that maximizes the aggregate utility of all the coalitions in the system. We prove analytically that the proposed algorithms terminate at a stable partition that achieves the optimal aggregate utility. Simulation results show that the proposed algorithms result in the self-organization of the SUs that achieves a higher aggregate utility after each iteration. Also, the convergence and optimality of the proposed algorithms are proved by simulation results.
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.
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 | 0.000 | 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 it