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On Optimum Power Allocation for the VBLAST
 IEEE Trans. Communications, submitted
"... Abstract—A unified analytical framework for optimum power allocation in the unordered VBLAST algorithm and its comparative performance analysis are presented. Compact closedform approximations for the optimum power allocation are derived, based on average total and block error rates. The choice of ..."
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Abstract—A unified analytical framework for optimum power allocation in the unordered VBLAST algorithm and its comparative performance analysis are presented. Compact closedform approximations for the optimum power allocation are derived, based on average total and block error rates. The choice of the criterion has little impact on the power allocation and, overall, the optimum strategy is to allocate more power to lower step transmitters and less to higher ones. HighSNR approximations for optimized average block and total error rates are given. The SNR gain of optimization is rigorously defined and studied using analytical tools, including lower and upper bounds, high and low SNR approximations. The gain is upper bounded by the number of transmit antennas, for any modulation format and type of fading channel. While the average optimization is less complex than the instantaneous one, its performance is almost as good at high SNR. A measure of robustness of the optimized algorithm is introduced and evaluated. The optimized algorithm is shown to be robust to perturbations in individual and total transmit powers. Based on the algorithm robustness, a preset power allocation is suggested as a lowcomplexity alternative to the other optimization strategies, which exhibits only a minor loss in performance over the practical SNR range. Index Terms—Multiantenna (MIMO) system, VBLAST, power allocation, performance analysis. I.
Applications of stochastic ordering to wireless communications
 IEEE Trans. Wireless Commun
, 2011
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Optimum power allocation for average power constrained jammers in the presence of nonGaussian noise,”
 IEEE Commun. Lett.,
, 2012
"... AbstractWe study the problem of determining the optimum power allocation policy for an average power constrained jammer operating over an arbitrary additive noise channel, where the aim is to minimize the detection probability of an instantaneously and fully adaptive receiver employing the Neyman ..."
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AbstractWe study the problem of determining the optimum power allocation policy for an average power constrained jammer operating over an arbitrary additive noise channel, where the aim is to minimize the detection probability of an instantaneously and fully adaptive receiver employing the NeymanPearson (NP) criterion. We show that the optimum jamming performance can be achieved via power randomization between at most two different power levels. We also provide sufficient conditions for the improvability and nonimprovability of the jamming performance via power randomization in comparison to a fixed power jamming scheme. Numerical examples are presented to illustrate theoretical results.
Error Rates of CapacityAchieving Codes Are Convex
"... Abstract — Motivated by a widespread use of convex optimization techniques, convexity properties of bit error rate of the maximum likelihood detector operating in the AWGN channel are studied for arbitrary constellations and bit mappings, which also includes coding under maximumlikelihood decoding ..."
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Abstract — Motivated by a widespread use of convex optimization techniques, convexity properties of bit error rate of the maximum likelihood detector operating in the AWGN channel are studied for arbitrary constellations and bit mappings, which also includes coding under maximumlikelihood decoding. Under this generic setting, the pairwise probability of error and bit error rate are shown to be convex functions of the SNR and noise power in the high SNR/low noise regime with explicitlydetermined boundary. Any code, including capacityachieving ones, whose decision regions include the hardened noise spheres (from the noise sphere hardening argument in the channel coding theorem) satisfies this high SNR requirement and thus has convex error rates in both SNR and noise power. We conjecture that all capacityachieving codes have convex error rates. I.
On Convexity of Error Rates in Digital Communications
 AVAILABLE AT HTTP://ARXIV.ORG/ABS/1304.8102). 2013 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY45
, 2013
"... Convexity properties of error rates of a class of decoders, including the maximumlikelihood/mindistance one as a special case, are studied for arbitrary constellations, bit mapping, and coding. Earlier results obtained for the additive white Gaussian noise channel are extended to a wide class of n ..."
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Convexity properties of error rates of a class of decoders, including the maximumlikelihood/mindistance one as a special case, are studied for arbitrary constellations, bit mapping, and coding. Earlier results obtained for the additive white Gaussian noise channel are extended to a wide class of noise densities, including unimodal and spherically invariant noise. Under these broad conditions, symbol and bit error rates are shown to be convex functions of the signaltonoise ratio (SNR) in the highSNR regime with an explicitly determined threshold, which depends only on the constellation dimensionality and minimum distance, thus enabling an application of the powerful tools of convex optimization to such digital communication systems in a rigorous way. It is the decreasing nature of the noise power density around the decision region boundaries that ensures the convexity of symbol error rates in the general case. The known high/lowSNR bounds of the convexity/concavity regions are tightened and no further improvement is shown to be possible in general. The highSNR bound fits closely into the channel coding theorem: all codes, including capacityachieving ones, whose decision regions include the hardened noise spheres (from the noise sphere hardening argument in the channel coding theorem), satisfy this highSNR requirement and thus has convex error rates in both SNR and noise power. We conjecture that all capacityachieving codes have convex error rates. Convexity properties in signal amplitude and noise power are also investigated. Some applications of the results are discussed. In particular, it is shown that fading is convexitypreserving and is never good in low dimensions under spherically invariant noise, which may also include any linear diversity combining.
OPTIMAL CHANNEL SWITCHING FOR AVERAGE CAPACITY MAXIMIZATION
"... Optimal channel switching is proposed for average capacity maximization in the presence of average and peak power constraints. A necessary and sufficient condition is derived in order to determine when the proposed optimal channel switching approach can or cannot outperform the optimal single channe ..."
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Optimal channel switching is proposed for average capacity maximization in the presence of average and peak power constraints. A necessary and sufficient condition is derived in order to determine when the proposed optimal channel switching approach can or cannot outperform the optimal single channel approach, which performs no channel switching. Also, it is stated that the optimal channel switching solution can be realized by channel switching between at most two different channels. In addition, a lowcomplexity optimization problem is derived in order to obtain the optimal channel switching solution. Numerical examples are provided to exemplify the derived theoretical results. Index Terms — Channel switching, capacity, timesharing. 1.
Bit Error Rate is Convex at High SNR
"... Abstract — Motivated by a widespread use of convex optimization techniques, convexity properties of bit error rate of the maximum likelihood detector operating in the AWGN channel are studied for arbitrary constellations and bit mappings, which may also include coding under maximumlikelihood decod ..."
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Abstract — Motivated by a widespread use of convex optimization techniques, convexity properties of bit error rate of the maximum likelihood detector operating in the AWGN channel are studied for arbitrary constellations and bit mappings, which may also include coding under maximumlikelihood decoding. Under this generic setting, the pairwise probability of error and bit error rate are shown to be convex functions of the SNR in the high SNR regime with explicitlydetermined boundary. The bit error rate is also shown to be a convex function of the noise power in the low noise/high SNR regime. I.
Secure DiversityMultiplexing Tradeoff of ZeroForcing Transmit Scheme at FiniteSNR
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Convexity of Error Rates in Digital Communications Under NonGaussian Noise
"... Abstract—Convexity properties of error rates of a class of decoders, including the ML/mindistance one as a special case, are studied for arbitrary constellations. Earlier results obtained for the AWGN channel are extended to a wide class of (nonGaussian) noise densities, including unimodal and sph ..."
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Abstract—Convexity properties of error rates of a class of decoders, including the ML/mindistance one as a special case, are studied for arbitrary constellations. Earlier results obtained for the AWGN channel are extended to a wide class of (nonGaussian) noise densities, including unimodal and sphericallyinvariant noise. Under these broad conditions, symbol error rates are shown to be convex functions of the SNR in the highSNR regime with an explicitlydetermined threshold, which depends only on the constellation dimensionality and minimum distance, thus enabling an application of the powerful tools of convex optimization to such digital communication systems in a rigorous way. It is the decreasing nature of the noise power density around the decision region boundaries that insures the convexity of symbol error rates in the general case. The known high/low SNR bounds of the convexity/concavity regions are tightened and no further improvement is shown to be possible in general. I.