4 research outputs found
Maximization of average number of correctly received symbols over multiple channels in the presence of idle periods
In this study, optimal channel switching (time sharing) strategies are investigated under average power and cost constraints for maximizing the average number of correctly received symbols between a transmitter and a receiver that are connected via multiple flat-fading channels with additive Gaussian noise. The optimal strategy is shown to correspond to channel switching either among at most three different channels with full channel utilization (i.e., no idle periods), or between at most two different channels with partial channel utilization. Also, it is stated that the optimal solution must operate at the maximum average power and the maximum average cost, which facilitates low-complexity approaches for obtaining the optimal strategy. For two-channel strategies, an upper bound is derived, in terms of the parameters of the employed channels, on the ratio between the optimal power levels. In addition, theoretical results are derived for characterizing the optimal solution for channel switching between two channels, and for comparing performance of single channel strategies. Sufficient conditions that depend solely on the systems parameters are obtained for specifying when partial channel utilization cannot be optimal. Furthermore, the proposed optimal channel switching problem is investigated for logarithmic cost functions, and various theoretical results are obtained related to the optimal strategy. Numerical examples are presented to illustrate the validity of the theoretical results. © 2016 Elsevier Inc. All rights reserved
Optimal Detector Randomization for Multiuser Communications Systems
Cataloged from PDF version of article.Optimal detector randomization is studied for the
downlink of a multiuser communications system, in which
users can perform time-sharing among multiple detectors. A
formulation is provided to obtain optimal signal amplitudes,
detectors, and detector randomization factors. It is shown that
the solution of this joint optimization problem can be calculated
in two steps, resulting in significant reduction in computational
complexity. It is proved that the optimal solution is achieved via
randomization among at most min{K, Nd} detector sets, where
K is the number of users and Nd is the number of detectors
at each receiver. Lower and upper bounds are derived on the
performance of optimal detector randomization, and it is proved
that the optimal detector randomization approach can reduce the
worst-case average probability of error of the optimal approach
that employs a single detector for each user by up to K times.
Various sufficient conditions are obtained for the improvability
and nonimprovability via detector randomization. In the special
case of equal crosscorrelations and noise powers, a simple
solution is developed for the optimal detector randomization
problem, and necessary and sufficient conditions are presented
for the uniqueness of that solution. Numerical examples are
provided to illustrate the improvements achieved via detector
randomization