A simple analytical model of the wireless infrared communication channel in indoor environments is presented. The infrared signal is modeled as the combination of a diffuse component and a line-of-sight (LOS) or direct component. For the diffuse component alone, the properties of the channel are found using Ulbricht's integrating sphere. When a LOS component is also present, the transfer function depends upon the Rician factor K given by the ratio of the electrical power in the LOS and diffuse signals after the detector. For small K, the transfer function shows notches down to low frequencies, but due to the nature of light never for zero frequency. We confirm that a K-factor >or=13 dB is required also in infrared wireless links in order to support distortionless data transmission beyond 100 Mbit/s. Increasing the directivity at the receiver and/or at the transmitter improves the effective value of K. Here, we show that a moderate directivity will be sufficient for high-speed infrared communication in typical indoor scenarios

The paper discusses channel inversion which is a spatial equalization technique when channel state information is available at the transmitter. Channel inversion is a straightforward concept without iterations and it might be useful when the data transmission is critical with time e.g. high data rate applications. We discuss performance degradation caused by channel estimation errors, clipping due to the limited range of the transmitted power and the effect of cochannel interference. These results give an insight into the technical constraints of this transmission technique and show how these critical issues can be limited or reduced

Wide-band radio channel measurements at 5.2 GHz with four transmit and four receive antennas at variable element spacing are reported, aiming to evaluate the potential of compact antenna arrays at mobile terminals. We show that, for an element spacing d<0.5. lambda (down to 0.2. lambda ), the link capacity is not smaller than that for much larger d. This is explained by the observation that mutual coupling changes the radiation patterns of closely spaced antenna elements, individually. Compact multi-antenna terminals may thus become practical

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Abstract-Causal processing of a signal's samples is crucial in on-line applications such as audio rate conversion, compression, tracking and more. This paper addresses the problems of predicting future samples and causally interpolating deterministic signals. We treat a rich variety of sampling mechanisms encountered in practice, namely in which each sampling function is obtained by applying a unitary operator on its predecessor. Examples include pointwise sampling at the output of an antialiasing filter and magnetic resonance imaging (MRI), which correspond respectively to the translation and modulation operators. From an abstract Hilbert-space viewpoint, such sequences of functions were studied extensively in the context of stationary random processes. We thus utilize powerful tools from this discipline, although our problems are deterministic by nature. In particular, we provide necessary and sufficient conditions on the sampling mechanism such that perfect prediction is possible. For cases where perfect prediction is impossible, we derive the predictor minimizing the prediction error. We also derive a causal interpolation method that best approximates the commonly used noncausal solution. Finally, we study when causal processing of the samples of a signal can be performed in a stable manner.