Energy And Spectrally Efficient Solutions For Cognitive Wireless Networks
Bibliographic record
Abstract
Although different spectrum bands are allocated to specific services, it has been identified that these bands are unoccupied or partially used most of the time. Indeed, recent studies show that 70% of the allocated spectrum is not utilized. As wireless communication systems evolve, an efficient spectrum management solution is required in order to satisfy the need of current spectrum-greedy applications. In this context, cognitive radio (CR) has been proposed as a promising solution to optimize the spectrum utilization. Under the umbrella of cognitive radio, spectrum-sharing systems allow different wireless communication systems to coexist and cooperate in order to increase their spectral efficiency. In these spectrum-sharing systems, primary (licensed) users and secondary (unlicensed) users are allowed to coexist in the same frequency spectrum and transmit simultaneously as long as the interference of the secondary user to the primary user stays below a predetermined threshold. Several techniques have been proposed in order to meet the required quality of service of the secondary user while respecting the primary user's constraints. While these techniques, including multiple-input multiple-output (MIMO) solutions, are optimized from a spectrally-efficiency perspective, they are generally not well designed to address the related complexity and power consumption issues. Thus, the achievement of high data rates with these techniques comes at the expense of high-energy consumption and increased system complexity. Due to these challenges, a trade-off between spectral and energy efficiencies has to be considered in the design of future transmission technologies. In this context, we have recently introduced adaptive spatial modulation (ASM), which comprises both adaptive modulation (AM) and spatial modulation (SM), with the aim of enhancing the average spectral efficiency (ASE) of multiple antenna systems. This technique was shown to offer high energy efficiency and low system complexity thanks to the use of SM while achieving high data rates thanks to the use of AM. Motivated by this technique and the need of such performance in a CR scenario, we study in this abstract the concept of ASM in spectrum sharing systems. In this work, we propose the ASM-CR scheme as an energy-efficient, spectrally-efficient, and low-complexity scheme for spectrum sharing systems. The performance of the proposed scheme is analyzed in terms of ASE and average bit error rate and confirmed with selected numerical results using Monte-Carlo simulations. These results confirm that the use of such techniques comes with an improvement in terms of spectral efficiency, energy efficiency, and overall system complexity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".