In this work, the novel fingerprinting evaluation parameter, which is called the punishment cost, is proposed. This parameter can be calculated from the designed matrix, the punishment matrix, and the confusion matrix. The punishment cost can describe how well the result of positioning is in the designated grid or not, by which the conventional parameter, the accuracy, cannot describe. The experiment is done with real measured data on weekdays and weekends. The results are considered in terms of accuracy and the punishment cost. Three well-known machine learning algorithms, i.e. Decision Tree, k-Nearest Neighbors, and Artificial Neural Network, are verified in fingerprinting positioning. In experimental environment, Decision Tree can perform well on the data from weekends whereas the performance is underrated on the data from weekdays. The k-Nearest Neighbors has proper punishment costs, even though it has lower accuracy than that of Artificial Neural Network, which has moderate accuracies but lower punishment costs. Therefore, other criteria should be considered in order to select the algorithm for indoor positioning. In addition, punishment cost can facilitate the conversion spot positioning to floor positioning without data modification.

Orthogonal Frequency Division Multiple Access (OFDMA) is the technique for the next generation wireless networks, whose enhanced capacity is to serve a combination of traffic with diverse QoS requirements. To realize this, the resource allocation scheme has to be carefully designed so that the instantaneous channel condition, QoS provision, and the network utilization are integrated. In this paper, we propose the resource allocation scheme for downlink traffic of 2 classes; guaranteed and non-guaranteed, having different traffic contracts. We provide guaranteed throughput for the guaranteed class by considering the cost incurred from serving this class. Then, we formulate the assignment problem with the objective of minimizing this cost. For the non-guaranteed class, we aim to maximize network utilization and to maintain throughput fairness, by employing Proportional Fairness (PF) utility function and emphasizing on the portion of network resource that the user received and the individual user's queue length. We use a heuristic approach to schedule users' data into the downlink subframe by exploiting multi-user multi-channel diversity to utilize system's bandwidth efficiently. Intensive simulation shows that our scheme differentiates classes of traffic and provides satisfied throughput, lower packet drop rate, and lower queuing delay to the guaranteed class, comparing with those of the non-guaranteed class. Furthermore, the results also show that the scheme is fair to users in the same class in both throughput and service time.

We have developed a novel downlink packet scheduling scheme for a multiuser OFDMA system in which a subchannel can be time-multiplexed among multiple users. This scheme which is called Matrixed-based Proportional Fairness can provide a high system throughput while ensuring fairness. The scheme is based on a Proportional Fairness (PF) utility function and can be applied to any of the PF-based schedulers. Our scheduler explores multichannel multiuser diversity by using a two-dimensional matrix combining user selection, subchannel assignment, and time slot allocation. Furthermore, unlike other PF-based schemes, our scheme considers finitely backlogged queues during the time slot allocation. By doing so, it can exploit multichannel multiuser diversity to utilize bandwidth efficiently and with throughput fairness. Additionally, fairness in the time domain is enhanced by limiting the number of allocated time slots. Intensive simulations considering finitely backlogged queues and user mobility prove the scheme's effectiveness.