In this paper, we evaluate the system throughput and the method of optimal wireless resource allocation for the access zone (AZ) and relay zone (RZ) in downlink when the cell coverage is extended using the non-transparent Relay Station (RS) in a 2-hop cellular relay system based on IEEE802.16j, which uses the OFDMA-TDD structure. For the analyses, we first introduce the MAC (Media Access Control) frame structure in the non-transparent mode, then we investigate the interfering elements in both AZ and RZ for the network devices such as the Mobile Station (MS) and RS. Through computer simulation, we analyze the cell coverage extension and system throughput in terms of the distance between Base Station (BS) and RS, then we present the amount of the optimal allocation of wireless resource for the AZ and RZ in downlink using our results.
In this paper, we proposed reliability and capacity enhancement methods for IEEE 802.15.3 HDR-WPAN (High Data Rate-Wireless Personal Area Network) system which is currently getting an interest in home network technology adopting a MIMO technique. We also analyzed performance or the proposed system through a computer simulation. The HDR-WPAN system using V-BLAST algorithm, transmitting the different signal vector to each other's sub-channel, can get the transmission speed of more than 110Mbps using two Tx/Px antenna without bandwidth expansion in TCM-64QAM mode. Also the proposed system has reliability of 104 at
iATA (Internet Advanced Technology Attachment) is a block-level protocol developed to transfer ATA commands over TCP/IP network, as an alternative network storage solution to address insufficient storage problem in mobile devices. This paper employs RAID5 distributed storage servers concept into iATA, in which the idea behind is to combine several machines with relatively inexpensive disk drives into a server array that works as a single virtual storage device, thus increasing the reliability and speed of operations. In the case of one machine failed, the server array will not destroy immediately but able to function in a degradation mode. Meanwhile, information can be easily recovered by using boolean exclusive OR (XOR) logical function with the bit information on the remaining machines. We perform I/O measurement and benchmark tool result indicates that additional fault tolerance feature does not delay read/write operations with reasonable file size ranged in 4KB-2MB, yet higher data integrity objective is achieved.
Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used