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Middleton's class A interference model has properties that make it possible to represent a wide class of interference signals. By choice of model parameters, interference signals ranging from pure Gaussian distributed to pure impulsive interference can be modelled. These properties make the model very useful for a large variety of applications. However, an expression for the channel capacity of the class A interference channel has not yet been published. The channel capacity of this model is derived, and numerical examples are given for some useful sets of model parameters.Introduction: The performance of communication systems is impaired by interference, or noise, from both external and internal sources. A common approach in the analysis of interference impact is to model the interference as having a Gaussian amplitude distribution with flat power spectral density (additive white Gaussian noise, AWGN). The advantage of the AWGN model is its simplicity for analysis and that a large amount of results can be found in the literature, most notably Shannon's channel capacity results for the AWGN channel [1]. However, the AWGN model does not cover the behaviour of a large class of commonly occurring interference signals, especially impulsive interference. A more accurate interference model is the Middleton's class A interference model [2]. This model has the advantage that it can represent a number of interference signals with arbitrary impulsiveness content. By choice of model parameters, we can model a large class of interference ranging from purely Gaussian distributed noise to highly impulsive interference. A communication channel experiencing class A interference is referred to as an additive white class-A noise (AWCN) channel [3]. The drawback is that the analysis of the AWCN channel is not as simple as in the AWGN case. This is probably the reason why the model has not gained as much popularity as the simpler AWGN model. However, the interest for the class A model is increasing and it is more and more often seen in interference analyses.Areas where the AWCN channel model is applicable include dynamic spectrum access (DSA) for cognitive radios (CRs) where other communication systems act as impulsive interferers and IEEE 802.11 g, an orthogonal frequency division multiplexing (OFDM) system operating in the 2.4 GHz ISM band where microwave ovens can create impulsive interference. To maximise the spectrum utilisation in, for example, an OFDM system, waterfilling can be used to efficiently distribute signals over parallel channels. However, waterfilling requires that the channel capacity is known. Such an expression has not yet been published. In [4], an upper bound of the channel capacity is calculated for bivariate Middleton class A interference, which is an extension of the original Middleton's class A interference. However, that work is based on a different approach, which results in an upper bound of the capacity. Our present work is based on the fact that Middleton's class A model can be viewed as a Markov chai...

Multiple-input multiple-output (MIMO)systems operating at frequencies in the upper VHF and lower UHF region is attractive for peer-to-peer communication applications where robustness is of high importance, e.g., in tactile networks and emergency response systems. When designing and evaluating such systems, knowledge of realistic propagation conditions is required. This paper presents results from an uran MIMO measurement campaign at 300 MHz. Measurements are performed along 25 receiver routes and for three fixed transmitter locations, using antenna arrays mounted on two cars. Channel characteristics and ergodic capacity for the 7X7 MIMO channels are extracted from the measured data. A path-loss model is derived for the measured scenario, and the distributions of the large-scale fading, the Ricean K-factor, the delay spread, and the ergodic capacity are studied in deatail. The correlation distance for the different channel parameters is also examined. Furthermore, the analysis reveals that several of the channel parameters are correlated, and also have a strong correlation with the capacity. MILCOM 2008This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Abstract-Multiple-input multiple-output (MIMO) systems operating at frequencies in the upper VHF and lower UHF region is attractive for peer-to-peer communication applications where robustness is of high importance, e.g., in tactical networks and emergency response systems. When designing and evaluating such systems, knowledge of realistic propagation conditions is required. This paper presents results from an urban MIMO measurement campaign at 300 MHz. Measurements are performed along 25 receiver routes and for three fixed transmitter locations, using antenna arrays mounted on two cars. Channel characteristics and ergodic capacity for the 7×7 MIMO channels are extracted from the measured data. A path-loss model is derived for the measured scenario, and the distributions of the large-scale fading, the Ricean K-factor, the delay spread, and the ergodic capacity are studied in detail. The correlation distance for the different channel parameters is also examined. Furthermore, the analysis reveals that several of the channel parameters are correlated, and also have a strong correlation with the capacity.

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