An empirical study of volatility predictions: stock market analysis using neural networks
WINE'05 Proceedings of the First international conference on Internet and Network Economics
| Hi-index | 0.43 |
Wireless networks often operate under harsh environmental conditions with many uncontrollable factors causing data corruption and network outage. Atmospheric phenomena such as rain and snow make signal reception difficult for such systems. Furthermore, factors such as multipath fading and depolarization due to rain attenuation can severely and adversely impact the performance of wireless networks. This paper presents an equalization algorithm that uses spatial and temporal diversity. By using such information of the received signal, it is possible to minimize the effects of intersymbol interference, and leads to a reduction in computational complexity. Results show that the proposed equalization algorithm offers a noticeable improvement with single-carrier modulated signals.