Characteristics and research analysis of wireless communications with examples

火辣西米秀

Characteristics and research analysis of wireless communications with examples [Copy link]

The most elusive thing in wireless communication is the invisible and intangible wireless channel. However, it is precisely because of its unpredictable changes that wireless communication has a unique charm.

Wireless channels can be modeled into a variety of models according to their own characteristics and research needs. The most classic one is known as the "independently and identically distributed model (IID)". For example, when introducing a propagation environment, we say "... in a 4x1 MISO system, assuming that the transmission success rate of each path is 1/2..." This is describing this model. The two terms "independent" and "identically distributed" are both derived from probability theory. "Independent" means that the success or failure of the transmission of each path does not affect each other; and "identically distributed" means that the probability distribution is the same, that is, the success rate is 1/2.

We already know that one of the most effective ways to deal with this kind of channel is diversity. The more diversity gain you get, the higher the reliability of the transmission. However, the application of diversity technology did not bring peace to the world for long. The emergence of "fading correlation" has caused a stir again.

To better understand the concept of correlation, let's take a look at an example. For example, we have a truckload of goods to be transported from A to B. There are three routes to choose from, passing through cities X, Y, and Z. However, the geographical locations of City X and City Y are very close. Before departure, we heard the weather forecast saying that there would be heavy rain in City X, so we would definitely choose to take a detour to City Z instead of City Y. Why? The answer is simple. City X and City Y are so close. If there is heavy rain in City X, the weather in City Y will not be much better. This phenomenon of mutual influence between the weather shows that the weather in City X and City Y is correlated. So to summarize the correlation in one sentence, it is "If he is good, I am also good." Originally, we had three routes to choose from, but because the weather conditions in City X and City Y are similar, there are actually only two routes to choose from, and one of them mysteriously "disappeared". What kind of impact will this phenomenon have on the MIMO system?

In MIMO systems, "fading correlation" plays the same role. Let's first look at a 2x1 MISO system. In order to ensure transmission quality, we use transmit diversity technology. The 2x1 MISO system has two propagation paths, and the maximum diversity gain is 2. Now consider the following environment: Assume that the distance from the transmitter to the receiver is very far, much greater than the antenna spacing between A and B. At this time, we suddenly find that the two propagation paths reach antenna C almost in parallel, and the two propagation paths are very close. At this time, if severe fading occurs along this propagation direction, the signals on the two propagation paths will be affected at the same time. This is the power of "fading correlation". Since the two paths are so close and experience the same fading, we simply merge them into one, and the 2x1 MISO system degenerates into a 1x1 SISO system!