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Cognitive radios that are employed in a network with dynamic frequency assignments must operate efficiently in the presence of uncertainties and variations in the propagation characteristics of the network's communication links. A low-complexity adaptive transmission protocol is described and evaluated for use in cognitive radio networks whose links have unknown and possibly time-varying propagation losses as a result of such phenomena as slow fading or variations in shadowing. The cognitive radios are required to derive only simple statistics in the receivers in order to provide the information that is needed by our protocol; no estimates or measurements of received power or channel gain are used. The protocol's primary mechanism for responding to changes in propagation loss is to adjust the modulation and coding. Because of disruptions that can be caused by higher levels of interference to other radios in the network, the transmitter power is increased only if the most powerful combination of coding and modulation is inadequate. We employ finite-state Markov models for slowly varying channels, and we demonstrate that for such channels our protocol performs nearly as well as an ideal protocol that is told the exact value of the propagation loss for each packet transmission. Thus, the additional complexity that is required to enable cognitive radios to obtain precise channel-gain estimates is not justified and would lead to only negligible improvement in throughput. The throughput of our adaptive transmission protocol is compared with an upper bound that is derived from information theory for a hypothetical ideal protocol that is given perfect channel-state information, and some preliminary results on learning the adaptation decision intervals are included.
Date of Publication: Jan. 2008