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We proposed mode selection techniques between transmit beamforming and antenna grouping for a given channel.
Familiar measures are probability related, e.g., the probability of a set for a given channel distribution.
The channel capacity of a MIMO system for a given channel realization H can be computed as [22] (21).
Thus, for a given channel coefficient vector the applied weight is found after solving the finite dimensional optimization problem (2).
Then, the error probabilities are used to compute for a given channel model, the configuration which maximizes the authentication performance.
For a given channel fading, the average achievable multicast rate of ECOMF,, can be optimized by selecting and.
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In other words, if the above assumption comes true, for a given channel-optimized encoder in [7], receivers absolutely know temporal ε and can judge if there exist empty cells in encoding cells and whether the receiving index belongs to empty cells or not.
However, for given channel instances of Hm 1 and Hm 2, the estimation error becomes a random variable.
Looking at (3), the signal-to-interference plus noise ratio (SINR) in the k-th sub-channel, for a given fading channel realization, can be expressed as follows SIN R k β = 1 SN R k β + 1 SI R k β − 1, (7).
Since the channel constraints for a given PR channel are well defined, it is natural to wonder whether a more efficient way to compute the probability expressed in (13) exists.
The approximation for Q was obtained empirically by calculating the average number of interfering symbols in the new channel U in (13) for a given original channel length L, after the transformation in (12) is applied.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com