This paper describes an iterative learning framework consisting of multi-layer prediction processes for underwater link adaptation. To obtain a dataset in real underwater environments, we implemented OFDM (Orthogonal Frequency Division Multiplexing)-based acoustic communications testbeds for the first time. Actual underwater data measured in Yellow Sea, South Korea, were used for training the iterative learning model. Remarkably, the iterative learning model achieves up to 25% performance improvement over the conventional benchmark model.

The signals received at the receiving antenna are consist of signals from base station (or mobile terminal) and transmitted signals from the other side of antenna in a RF repeater. And the amplification rate of the RF repeater is limited by the feedbacked signals from the same repeater. Therefore, a receiving antenna has to be isolated from a transmitting antenna in order to reduce the effect of the feedbacked signal in the RF repeater. Or, recently, ICS (interference cancellation system) techniques have been studied to estimate and to remove the feedback signals. However, it requires lots of hardware complexity and computational delay. To solve these problems, we have studied the implementation and adaptation of smart antenna system for RF repeaters. We have designed a smart antenna module with switching beam structure in order to reduce the hardware and computational complexity. After analyzing the proposed smart antenna module, we found out that the proposed repeater increases the amplification rate 15dB compare to the conventional RF repeater.

In this paper, we have applied diversity technologies to the IEEE 802.11g to use the IEEE 802.11g in moving channel environments. After analyzing the measurement data, it is shown that the diversity gain is 14 dB at the probability of 1%. The reliable communication range is 63 m when transmitting power is 10 dBm, while the range increases to 163 m when the diversity technique is used. Also the communication range increases from 122 m to 306 m by using diversity technologies when the transmitting power is 20 dBm

The performance of a spatial filtering system depends on the spatial properties of wireless channels, such as an angular spread, and an angular distribution of mobile users. We analyzed the wireless channel characteristics in an urban area using the data collected from the real environments.