On the equality constraints tolerance of Constrained Optimization Problems

Si, Chengyong; An, Jing; Lan, Tian; Ußmüller, Thomas; Wang, Lei; Wu, Qidi

doi:10.1016/j.tcs.2014.05.012

Cited by 11 publications

(3 citation statements)

References 23 publications

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“…In the following, we develop an iterative algorithm, which involves an inner and an outer loop, to obtain a suboptimal solution of (15). In the outer loop of the algorithm, as in [30], we relax the equality constraints in (15e) to inequality constraints and tighten the relaxation in each iteration. In the inner loop, adopting alternating optimization, we obtain a stationary point of the relaxed version of problem (15).…”

Section: Algorithm For Solving (15)mentioning

confidence: 99%

“…Hence, applying alternating optimization directly to (15) may lead to a strongly suboptimal solution since, in each iteration, the degrees of freedom for the optimization of p rx and γ are very limited. Thus, to overcome this issue, similar to [30], we first relax the equality constraints in (15e) to inequality constraints as follows rx p rx (r x )…”

Section: ) Outer Loopmentioning

confidence: 99%

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Markov Decision Process Based Design of SWIPT Systems: Non-linear EH Circuits, Memory, and Impedance Mismatch

Shanin,

Cottatellucci,

Schober

2020

Preprint

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In this paper, we study simultaneous wireless information and power transfer (SWIPT) systems employing practical non-linear energy harvester (EH) circuits. Since the voltage across the reactive elements of realistic EH circuits cannot drop or rise instantaneously, EHs have memory which we model with a Markov decision process (MDP). Moreover, since an analytical model that accurately models all non-linear effects and the unavoidable impedance mismatch of EHs is not tractable, we propose a learning based model for the EH circuit. We optimize the input signal distribution for maximization of the harvested power under a constraint on the minimum mutual information between transmitter (TX) and information receiver (IR). We distinguish the cases where the MDP state is known and not known at TX and IR. When the MDP state is known, the formulated optimization problem for the harvested power is convex. In contrast, if TX and IR do not know the MDP state, the resulting optimization problem is non-convex and solved via alternating optimization, which is shown to yield a stationary point of the problem. Our simulation results reveal that the rate-power region of the considered SWIPT system depends on the symbol duration, the EH input power level, the EH impedance mismatch, and the type of EH circuit. In particular, a shorter symbol duration enables higher bit rates at the expense of a significant decrease in the average harvested power. Furthermore, whereas half-wave rectifiers outperform full-wave rectifiers in the low and medium input power regimes, full-wave rectifiers are preferable if the input power at the EH is high.

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Section: Algorithm For Solving (15)mentioning

confidence: 99%

Section: ) Outer Loopmentioning

confidence: 99%

Markov Decision Process Based Design of SWIPT Systems: Non-linear EH Circuits, Memory, and Impedance Mismatch

Shanin,

Cottatellucci,

Schober

2020

Preprint

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show abstract

“…where every equality constraint (ℎ 𝑒𝑒 ), 𝜖𝜖 𝑒𝑒 is initialized with a large value and is then reduced to 0.0001. Setting the initial value of 𝜖𝜖 𝑒𝑒 is problem dependent, as indicated in [32].…”

Section: The Proposed Algorithmmentioning

confidence: 99%