In this paper, based on the theory of finite projective plane (FPP), a novel channel assignment is proposed with Markov chain modeling of N channels and N users, especially when the channel resources are often idle due to inefficient uses. The arrangements of intermixed grouping of channels based on FPP mechanism relate Markov chain modeling with our proposed punch through ratio (PTR) formulations successfully provide fairness among users as well. The defined throughput is obviously enhanced from simulation results with the proposed FPP scheme, especially when the traffic load is light. Nevertheless, if the proposed scheme is combined with successive interference cancellation (SIC) techniques, much better throughput for heavy traffic load is also predicted.

Location prediction has attracted a significant amount of research effort. Given an object's recent movements and a future time, the goal of location prediction is to predict the location of this object at the future time specified. Prior works have elaborated on mining association relationships among regions, in which objects frequently appear, to predict locations. Association relationships among regions are represented as association rules. By exploring association relationships among regions, prior works are able to have a good accuracy for location prediction. However, with a large amount of trajectory data produced, a huge amount of association rules is expected. Furthermore, trajectory data has both the spatial and temporal information. To further enhance the accuracy of location prediction, one could utilize not only spatial information but also temporal information to estimate locations of objects. In this paper, we propose a spatial-temporal trajectory model (abbreviated as STT) to capture movement behaviors of objects. STT is represented as a probabilistic suffix tree with both spatial and temporal information of movements. Note that STT is able to discover sequential traversal relationships among regions and, for each region, STT derives the corresponding probabilities about the time when objects appear. With the nature of probabilistic suffix tree, we could use a compact structure to capture movement behavior of objects compared to association rules proposed. In light of STT, we further propose an algorithm to traverse STT for location prediction. By exploring both the spatial and temporal information of STT, the accuracy of location prediction is improved. To evaluate our proposed STT and prediction algorithm, we conduct experiments on the synthetic dataset generated from real datasets. Experimental results shows that our proposed STT is able to capture both spatial and temporal patterns of movement behaviors and, by exploring STT, our proposed prediction algorithm outperforms existing works.

The performance of frequency-hopping multiple-access systems with side information and binary frequency-shift keying signaling is evaluated. The side information is generated by Viterbi's ratio threshold test (RTT). Both synchronous and asynchronous hopping systems over fading channels are examined. The system with RTT side information performs significantly better than that with perfect side information or hard decision. Compared with hard decision, the RTT side information provides more than 20% improvement in capacity for E/sub b//N/sub 0/=10 dB over Rayleigh channels. Also, we find that the optimum threshold in the RTT scheme should be dynamically adjusted with the number of simultaneous users in the system.