01841nas a2200169   4500008004100000245005800041210005800099260004700157520127100204100002201475700001901497700001901516700001501535700002001550700001601570856008501586       2018                        eng d00aCognitive Engine Testbed for Vehicular Communications0 aCognitive Engine Testbed for Vehicular Communications  aTucson, ArizonabThe University of Arizona3 a<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>
1 aAnderson, William1 aAponik, Ashley1 aDaoud, Youssef1 aTeku, Noel1 aVanhoy, Garrett1 aBose, Tamal  uhttp://csl.arizona.edu/content/cognitive-engine-testbed-vehicular-communications01844nas a2200145   4500008004100000245005400041210005400095260006600149520131100215100002101526700001701547700001701564700002001581856009701601       2017                        eng d00aValidation of a CRV Model Using TVWS Measurements0 aValidation of a CRV Model Using TVWS Measurements  aSan Diego, CaliforniabThe Wireless Innovation Forumc11/20173 a<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’ 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>
1 aAnderson, Khalil1 aLusk, Lauren1 aHands, Marti1 aVanhoy, Garrett  uhttps://www.wirelessinnovation.org/assets/Proceedings/2017/winncomm%202017%20proceedings.pdf