Reinforcement Learning for Routing in Cognitive Radio Ad Hoc Networks

<table>SU throughput for varying PU mean arrival rate <svg height="12.9375" id="M489" style="vertical-align:-3.25792pt" version="1.1" viewbox="0 0 32.037498 12.9375" width="32.037498" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
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</svg> for different levels of standard deviation of PUL <svg height="12.3375" id="M490" style="vertical-align:-3.25792pt" version="1.1" viewbox="0 0 32.212502 12.3375" width="32.212502" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
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<g transform="matrix(.012,-0,0,-.012,9.663,12.112)"><path d="M46 650h247q112 0 169 -41q64 -49 64 -141q0 -91 -56.5 -143t-138.5 -62q-23 -3 -45 -1l-74 19v-157q0 -63 15.5 -77t83.5 -19v-28h-271v28q60 5 74 20t14 77v400q0 65 -12.5 79t-69.5 18v28zM212 559v-245q31 -12 73 -12q59 0 102 38t43 124q0 77 -42.5 115.5
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</svg>.</table>

The Scientific World Journal

fig19

Figure 19

Figure 19: Reinforcement Learning for Routing in Cognitive Radio Ad Hoc Networks 