Throughput and Delay Optimal Scheduling in Cognitive Radio Networks under Interference Temperature Constraints

The fixed spectrum assignment policy in today's wireless networks leads to inefficient spectrum usage. Cognitive radio network is a new communication paradigm that enables the unlicensed users to opportunistically use the spatio-temporally unoccupied portions of the spectrum, and hence realizing a dynamic spectrum access (DSA) methodology. Interference temperature model proposed by Federal Communications Commission (FCC) permits the unlicensed users to utilize the licensed frequencies simultaneously with the primary users provided that they adhere to the interference temperature constraints. In this paper, we formulate two NP-hard optimal scheduling methods that meet the interference temperature constraints for cognitive radio networks. The first one maximizes the network throughput, whereas the second one minimizes the scheduling delay. Furthermore, we also propose suboptimal schedulers with linear complexity, referred to as maximum frequency selection (MFS) and probabilistic frequency selection (PFS). We simulate the throughput and delay performance of the optimal as well as the suboptimal schedulers for varying number of cognitive nodes, number of primary neighbors for each cognitive node, and interference temperature limits for the frequencies. We also evaluate the performance of our proposed schedulers under both additive white gaussian noise (AWGN) channels and Gilbert-Elliot fading channels.