With technology taking advantage of more complex channel characteristics, channel modeling is becoming both more critical and complex.
With technology taking advantage of more complex channel characteristics, channel modeling is becoming both more critical and complex. It must account for all aspects of the radio environment in order to ensure that measurements in the lab accurately correlate to the quality of the user’s experience. This goal is made difficult, however, by aspects like the effects of bandwidth on a receiver’s ability to resolve multipath components. A 22-page white paper from Spirent Communications titled, "Fading Basics: Narrow Band, Wide Band, and Spatial Channels" explains that the receiver’s ability to resolve different paths rises as bandwidth increases. An increased number of paths is therefore required by an accurate channel model.
In a discussion of flat versus frequency-selecting fading, the paper notes that the RF environments seen in narrowband frequency-modulation (FM) technologies like AMPS, NAMPS, and TACS can be modeled by "flat" fading channels. Yet digital radio technologies—such as CDMA, WCDMA, LTE, and WiMAX—transmit digital signals in a bandwidth larger than the channel’s coherence bandwidth. As a result, the fading is frequency selective with different signal strengths present at different frequencies across the band. Usually, more than one strong path is received—each with a delay based on the distance traveled by the signal. This is multipath propagation.
To model today’s multiple-antenna approaches, a fading channel is required with the correct correlation between antenna branches. If the correlation is high, the signals are very similar. Both branches may then experience a strong or weak signal at the same time, which makes it difficult to withstand a given fade. In contrast, low correlation means that the signals are more random. For instance, a fade on one branch could be mitigated by a stronger signal on the other branch.
In the case of multiple-input multiple-output (MIMO) antenna systems, adding correlation diminishes the transceiver’s ability to spatially separate the channel into orthogonal components in order to support additional transmission streams. High degrees of correlation therefore limit a MIMO system’s potential capacity. As such, they must be included in a spatial channel model.
Simply put, more complex models are required because today’s air interfaces adapt between different techniques based on the channel dynamics. For example, each path’s power azimuth spectrum (PAS) is typically modeled by a Laplacian distribution. Here, the signal drops off exponentially (linearly in dB) as the angle increases in magnitude from the average direction of arrival.
When channel bandwidth increases, so does the ability to resolve multipath. For extended bandwidths (at or greater than 20 MHz), it is best to have more paths or some intrapath delay spread to enhance the modeling of frequency selectivity. Wider bandwidths allow the spaced-frequency correlation function to exhibit periodic oscillation across frequency. The paper also describes how different frequencies are correlated across the band. By providing an overview of these and other details, it offers a solid tutorial on fading.
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