Auction-based channel allocation approach in wireless networks

We investigate various applications of graph theory and classify them based on 7 layers of OSI. Most of its applications are related to coloring and channel assignment problem (MAC). Other applications can be summarized as routing, topology control, interference reduction, sensing function allocation, trellis and state diagrams. As a cross layer application (MAC and physical layers) of graph theory, we consider the problem of throughput maximization during spectrum allocation under Signal to Interference plus Noise Ratio (SINR) constraint in cognitive radio networks. We propose a novel auction-based channel allocation algorithm, in which graph coloring and bidding theory play an important role and which tries to maximize both total and primary users? utilities while satisfying SINR constraint on primary receivers, without controlling secondary user powers. For comparison, we discuss a greedy algorithm as well, however, which does not consider interference issue. In order to compare results of proposed and greedy algorithms, we define net throughput by taking into account outage probability of primary receiver. Simulation results show that exposing higher SINR (outage) threshold not only decreases total gain and primary users? utilities but also worsens channel distribution performance. On the other hand adding auction mechanism significantly increases total gain throughput and primary user? s utility. Especially till SINR threshold values of 20 dBs, auction provides outstanding performance and proposed algorithm has total throughput results close to those of the greedy one even though no interference constraint is applied in the greedy algorithm. Another noticeable point of simulation results is crossover of net throughputs of proposed and greedy algorithms at a SINR threshold level after which results of ABSA-UNIC and NASA-UNIC are much better. This clearly shows superiority of proposed mechanism.