Comments on “Optimal Target Channel Sequence Design for Multiple Spectrum Handoffs in Cognitive Radio Networks”

Abstract

This paper elaborates on the suboptimal greedy algorithm for target channel sequence selection as presented by Wang et al. in the paper "Optimal Target Channel Sequence Design for Multiple Spectrum Handoffs in Cognitive Radio Networks," IEEE Transactions on Communications, vol.60, no.9, pp.2444-2455. They claimed that the greedy algorithm requires comparing six target channel sequences. We prove that only five target channel sequences comparisons are sufficient. Hence, we present a modified algorithm and test it with a set of numerical results. It appears that the new algorithm reduces the processing time compared to the original one especially for a network with a large number of candidate channels.

Probabilistic Approach for Channel Selection in Centralized Cognitive Scheme Utilizing Invasive Weed Optimization

“…One channel can be modelled by the queuing model of pre-emptive resume priority (PRP) M/G/1. The former researches show concern regarding the schemes to select proactive target channel in the [4][5][6][7][8][9][10][11], but what is proposed now is the extension of those concepts utilizing a better algorithm for optimizing to serve a generalized channels which are not identical. The service rates in this situation differ among different channels.…”

Time triggered handoff schemes in cognitive radio networks: A survey

“…Based on network status, the algorithm allows SUs to select suboptimal channels by comparing six different mathematically generated sequences (for details please refer to Theorem 2 and Figure 5 of [56]). The authors of [108], on the other hand, believe that instead of six sequences discussed in [56], a set of five target channel sequences are enough for the production of suboptimal greedy behavior (one of the six sequences is "redundant"). This modification can minimize the processing time spent during optimal target channel selection especially where the candidate channels are huge in numbers as reflected in presented results.…”

“…This modification can minimize the processing time spent during optimal target channel selection especially where the candidate channels are huge in numbers as reflected in presented results. Moreover, the algorithm of [108] can sufficiently decrease the probability of selecting the worst channel which was not addressed in [56].…”

“…Total service time comprises of waiting time, sensing time, channel processing time, and transmission time. The approaches presented in [27] [108] consider waiting time to be an important factor affecting the service time of SUs. Likewise, waiting time reduction is shown in the results presented in [78] [76].…”