• The Proceeding of the Korean Institute of Electromagnetic Engineering and Science
• /
• v.3 no.2
• /
• pp.30-38
• /
• 1992

In this paper, in order to apply the $\pi$/4 shift QPSK to TCM, we propose the $\pi$/8 shift 8PSK modulation technique and the trellis-coded $\pi$/8 shift 8PSK performing signal set expansion and set partition by phase difference. In addition, the Viterbi decoder with branch metrics of the squared Euclidean distance of the first phase difference as well as the Lth phase difference is introduced in order to improve the bit error rate(BER) performance in differential detection of the trellis-coded $\pi$/8 shift 8 PSK. The proposed Viterbi decoder is conceptually the same as the sliding multiple de- tection by using the branch metric with first and Lth order phase difference. We investigate the performance of the uncoded .pi. /4 shift QPSK and the trellis-coded $\pi$/8 shift 8PSK with or without the Lth phase difference metric in an additive white Gaussian noise (AWGN) and Rayleigh fading channel using the Monte Carlo simulation. The study shows that the $\pi$/4 shift QPSK with the Trellis-code i. e. the trellis-coded $\pi$/8 shift 8PSK is an attractive scheme for power and bandlimited systems and especially, the Viterbi decoder with first and Lth phase difference metrics improves BER performance. Also, the next proposed algorithm can be used in the TC $\pi$/8 shift 8PSK as well as TC MDPSK.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.10
• /
• pp.79-89
• /
• 2019

While most research focuses on the k-nearest neighbors (kNN) queries in the database community, an important type of proximity queries called k-farthest neighbors (kFN) queries has not received much attention. This paper addresses the problem of finding the k-farthest neighbors in road networks. Given a positive integer k, a query object q, and a set of data points P, a kFN query returns k data objects farthest from the query object q. Little attention has been paid to processing kFN queries in road networks. The challenge of processing kFN queries in road networks is reducing the number of network distance computations, which is the most prominent difference between a road network and a Euclidean space. In this study, we propose an efficient algorithm called FANS for k-FArthest Neighbor Search in road networks. We present a shared computation strategy to avoid redundant computation of the distances between a query object and data objects. We also present effective pruning techniques based on the maximum distance from a query object to data segments. Finally, we demonstrate the efficiency and scalability of our proposed solution with extensive experiments using real-world roadmaps.