@conference {475,
	title = {Cognitive Engine Testbed for Vehicular Communications},
	booktitle = {CAT Vehicle Research Experience for Undergraduates},
	year = {2018},
	publisher = {The University of Arizona},
	organization = {The University of Arizona},
	address = {Tucson, Arizona},
	abstract = {<p>Ad-hoc networks have the potential to increase the safety and reliability of autonomous vehicles. The amount of radio spectrum available for such networks is limited, however. The use of cognitive radio, especially when integrated with reinforcement learning algorithms, may help to ease the issue of limited spectrum by finding optimal transmission policies and detecting the presence of other users, especially in a scenario where a Primary User and Secondary User are contesting for spectrum. This paper presents a testbed for simulating cognitive engines in these networks using a variety of reinforcement learning algorithms, including -greedy, Softmax Strategy, and Q-Learning. The goal of these cognitive engines is to learn to choose the best modulation and coding rates given various channel conditions and a user-defined optimization goal (i.e. maximize throughput, minimize bit error rate). The cognitive engine then learns the optimal coding rates and modulation schemes for the given environment, and the testbed displays a visual of the performance of the cognitive engine at each time step. In the case of Q-learning in a Primary User vs. Secondary User scenario, the cognitive engine also learns to choose the best channel for transmission.</p>
},
	url = {http://csl.arizona.edu/content/cognitive-engine-testbed-vehicular-communications},
	author = {William Anderson and Ashley Aponik and Youssef Daoud and Noel Teku and Garrett Vanhoy and Tamal Bose}
}
@conference {474,
	title = {Validation of a CRV Model Using TVWS Measurements},
	booktitle = { Proceedings of SDR-WinnComm 2017 Wireless Innovation Conference on Wireless Communications Technologies and Software Defined Radio},
	year = {2017},
	month = {11/2017},
	publisher = {The Wireless Innovation Forum},
	organization = {The Wireless Innovation Forum},
	address = {San Diego, California},
	abstract = {<p>As autonomous vehicles advance, their commercial popularity will rise. This growth will increase the need for wireless transmission of data to these cars not only to drive more efficiently, but to also entertain the driver. As people are freed from the task of driving, the demand for in-car internet applications, such as Netflix or Skype, will grow. Currently, autonomous vehicles are allowed to transmit using the band specified by the IEEE protocol 802.11p. While vehicles can transmit data using the 5.9 GHz band (5.850-5.925 GHz), the band may not support wireless transmission of media to vehicles{\textquoteright} infotainment systems. This requires an alternative. With the switch from analog to digital television, the government has vacated the analog TV bands. These bands provide a possible solution to the limitations of 802.11p transmissions. The vacated space is called TV white space. One proposed use of this white space is to provide Wi-Fi. This idea has been called White-Fi. According to our research, researchers have measured whether the specific frequencies are occupied but do not provide the unprocessed data. With this in mind, we measure the occupancy of the TV white space and we simulate how a network using this band would perform under the multiple scenarios of everyday driving.</p>
},
	url = {https://www.wirelessinnovation.org/assets/Proceedings/2017/winncomm\%202017\%20proceedings.pdf},
	author = {Khalil Anderson and Lauren Lusk and Marti Hands and Garrett Vanhoy}
}
