On the Coverage of Cognitive mmWave Networks with Directional Sensing and Communication
Authors: Shuchi Tripathi, Abhishek K. Gupta, SaiDhiraj Amuru
Word Count: 5400
Average Reading Time: ~30 minutes
Highlights:
• The authors propose an analytical framework to evaluate the performance of a cognitive mmWave network consisting of a primary link and multiple secondary links using stochastic geometry.
• They consider directional channel sensing and communication in contrast to omnidirectional sensing, which allows secondary transmitters to transmit based on their orientation instead of being outside a certain distance. This provides better spatial reuse for secondary transmitters.
• They analyze the medium access probability, activity factor, and coverage probability of the primary and secondary links considering various parameters like directionality, threshold, density, etc.
• They show that directionality can improve the trade-off between the primary and secondary link performances by increasing both link coverages for appropriate threshold values.
• However, the effect of primary and secondary directionality depends on the location and orientation of the secondary links. While primary directionality does not always aid secondary coverage, secondary directionality always improves it.
• They propose an adaptive directional sensing where secondary links can choose higher or lower directionality based on their location to achieve similar coverage performances.
In summary, this work provides useful analytical insights into the performance of cognitive mmWave networks with directional sensing and communication. The proposed mathematical framework and results could potentially aid in the design and optimization of such networks.
Regarding applications of large language models, the analytical approach and results in this work could provide useful guidelines and inputs for developing agent-based simulations of cognitive mmWave networks. The simulations could leverage language models to emulate the behaviors of cognitive transmitters based on the derived insights to validate and extend the proposed framework.