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Also, if m is large, then m is very large, which explains why the depth of field (δp) of a microscope is extremely small.

We suppose that M and p u are fixed and M is large.

Hence, we can obtain (6) and (7) when M is large enough.

In general, it is suggested to consider only the dominant paths if M is large.

The term (m^{gamma }) in Eq. (23) increases the value even more when m is large.

Assume M is large enough, K HR users can be selected from these candidate users.

When M is large enough, Ψ l obeys the Gaussian distribution approximatively according to the central limit theorem.

When M is large, it is reasonable to approximate s t as a zero-mean complex white Gaussian vector.

In addition, we also discover that the MSE does not depend much on the SNR, especially when M is large.

We can see that the lower bounds are very close to the simulation results when M is large enough.

If (C(M)) is large enough, then we assert that (I_{lambda}(tv to-infty) as (tv to-infty

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