Abstract
The channel capacity of multi-channel cognitive radio systems is studied with the assumption of limited sensing capability. The randomness of sub-channel selection is utilized to convey information. Two types of sub-channels, memoryless and finite state, are considered. For both cases, the separation of sub-channel input distribution optimization and sub-channel selection policy optimization is proved. For the memoryless case, explicit expressions for optimal sensing policy are obtained. For the finite state case, the optimization of channel capacity is considered as a Markov decision problem that maximizes average award. By using Markov decision theory, it is shown that, for the finite state case, the channel capacity is determined by the static distribution of state.
Highlights
Cognitive radio [16, 20], in which secondary users sense licensed channels and use them for data transmission if there are no primary users, is becoming a flourishing technology for wireless communications due to its efficient utilization of frequency spectrum
As is in most communication systems, a fundamental question for cognitive radio system is the channel capacity, i.e., the maximal transmission rate for reliable communications, when there are multiple usable channels. This looks like a solved problem since we can optimize the input distribution for every channel and optimize the spectrum sensing probability independently
We can utilize the randomness of the channel selection to convey information, in addition to the information directly transmitted over the channels
Summary
Cognitive radio [16, 20], in which secondary (unlicensed) users sense licensed channels and use them for data transmission if there are no primary (licensed) users, is becoming a flourishing technology for wireless communications due to its efficient utilization of frequency spectrum. We study the channel capacity of multichannel cognitive radio systems having limited sensing capability for two types of channels, namely, memoryless channels and finite state Markovian channels. In both cases, we have shown that the optimization of input distribution can be separated from that of channel selection. In each time slot, the secondary transmitter senses a subset of sub-channels before the transmission stage If it finds that a sub-channel is idle, it transmits over this sub-channel for M channel uses.
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