This paper presents a comprehensive analysis of the information-energy capacity region for simultaneous lightwave information and power transfer (SLIPT) systems over lognormal fading channels. Unlike conventional studies that primarily focus on additive white Gaussian noise channels, we study the complex impact of lognormal fading, which is prevalent in optical wireless communication systems such as underwater and atmospheric channels. By applying the Smith’s framework to these channels, we demonstrate that the optimal input distribution is discrete, characterized by a finite number of mass points. We further investigate the properties of these mass points, especially at the transition points, to reveal critical insights into the rate-power trade-off inherent in SLIPT systems. Additionally, we introduce a novel cooperative information-energy capacity learning framework, leveraging generative adversarial networks, to effectively estimate and optimize the information-energy capacity region under practical constraints. Numerical results validate our theoretical findings, illustrating the significant influence of channel fading on system performance. The insights and methodologies presented in this work provide a solid foundation for the design and optimization of future SLIPT systems operating in challenging environments.

In this paper, we study the fundamental limits of simultaneous semantic information and power transfer in wireless networks, where we consider both the point-to-point case as well as the Gaussian multiple access channel (MAC). Specifically, for the point-to-point case, we consider a three-party communication system, where a transmitter aims to simultaneously convey semantic information to an information receiver and energy to an energy harvesting receiver (ER). An achievable and a converse region in terms of information and energy rates are presented for both the discrete memoryless (DM) and Gaussian channel. For the DM channel, the achievable region is obtained by utilizing the asymptotic equipartition property and a converse region is obtained by using outer bounds on the semantic information rates. For the Gaussian channel, we characterize an achievable region by applying a power splitting technique between the information and the semantic context parts. A converse region is obtained that provides an estimate on the information-energy capacity while taking into account semantics. On the other hand, for the Gaussian MAC case, we consider an hybrid setup where a semantic transmitter and a conventional transmitter are employed subject to an energy harvesting constraint at the ER. Specifically, we characterize the semantic-bit information energy region, by providing an achievable and a converse region. Numerical results show that in both cases a higher performance can be achieved in terms of information and energy rates when considering a low semantic ambiguity code in comparison to the classical coding scheme (without semantic). Moreover, in the context of Gaussian MAC, it is shown that it is preferable to use semantic communications in scenarios with low signal-to-noise ratio (SNR), while conventional communications is more suitable at high SNRs.

In this paper, we study the fundamental limits of simultaneous lightwave information and power transfer (SLIPT) systems over channels with path loss and lognormal fading conditions. We consider a system with a single transmitter transferring information to a photodiode-based receiver as well as transferring energy to a photovoltaic cell receiver. In particular, we study the information-energy capacity region and the optimal input distribution under (a) peak-power and average-power constraints at the transmitter, and (b) the minimum harvest energy at the energy harvesting receiver. To this end, an expression for the transition probability distribution function of the lognormal channel is derived. By extending Smith's framework and using Hermite polynomial bases, we prove that the optimal input distribution is discrete with a finite number of mass points. Information-energy capacity region for SLIPT over lognormal channel conditions is illustrated and compared with the case of additive white Gaussian noise channel.

In this paper, we study the fundamental limits of simultaneous semantic information and power transfer. In particular, a three-party communication system is considered, where an information transmitter aims to simultaneously convey semantic information to an information receiver (IR) and deliver energy to an energy harvesting receiver. An achievable and a converse region in terms of information and energy rates (in bits per channel use and energy-units per channel use, respectively) are presented for the discrete memoryless (DM) channel. The achievable region is obtained by using the asymptotic equipartition property (AEP) and a converse region is obtained by using outer bounds on the semantic information rates. In addition, we characterize an achievable region for the Gaussian case by using a power splitting technique between the information and the semantic context parts. A converse region is also obtained that provides an estimate of the information-energy capacity while taking into account semantics. Numerical results show a higher performance in terms of information and energy rates by considering a low semantic ambiguity code in comparison to the classical coding scheme (without semantic).

In this paper, we study the fundamental limits of simultaneous information and power transfer over a Rayleighfading channel, where the channel input is constrained to peak-power (PP) constraints that vary in each channel use by taking into account high-power amplifier (HPA) nonlinearities.In particular, a three-party communication system is considered, where a transmitter aims simultaneously conveying information to an information receiver and delivering energy to an energy harvesting receiver.For the special case of static PP constraints, we study the information-energy capacity region and the associated input distribution under: a) average-power and PP constraints at the transmitter, b) an HPA nonlinearity at the transmitter, and c) nonlinearity of the energy harvesting circuit at the energy receiver.By extending Smith's mathematical framework [1], we show that the optimal input distribution under those constraints is discrete with a finite number of mass points.We show that HPA significantly reduces the information energy capacity region.In addition, we derive a closed-form expression of the capacityachieving distribution for the low PP regime, where there is no trade-off between information and energy transfer.For the case with time-varying PP constraints, we characterize the optimal input distribution of this channel by using Shannon's coding scheme.Specifically, we numerically study a particular scenario for the time-varing PP constraints, where the PP constraint probabilistically is either zero or equal to a non-zero constant.

In this paper, we study the fundamental limits of a simultaneous wireless information and power transfer communication system, which is facilitated by an intelligent reflecting surface (IRS). Specifically, we propose a low-complexity optimization scheme that optimizes iteratively the input probability distribution at the transmitter side and the phase shift coefficient at the IRS to enhance the information-energy capacity region. By using alternating optimization and Lagrangian decomposition, analytical expressions for the optimal input distribution and the phase shifts are derived which decrease the computational complexity of the proposed scheme. A novel joint optimization scheme is also proposed inspired by the one in [1] for SWIPT systems. Numerical results show that the iterative optimization scheme almost achieves the same performance in comparison with the proposed joint optimization scheme while ensuring a reduced computation for high energy-harvesting thresholds.

In this paper, we study the fundamental limits of simultaneous information and power transfer over a Rayleigh fading channel in the presence of high-power amplifier (HPA) nonlinearity. In particular, a three-party communication system is considered, where a transmitter aims simultaneously conveying information to an information receiver and delivering energy to an energy harvester receiver. We study the information-energy capacity region and the associated input distribution under: i) average-power, peak-power (PP) constraints at the transmitter, b) HPA nonlinearity at the transmitter, and c) nonlinearity of the energy harvesting circuit at the energy receiver. By extending Smith’s mathematical framework [1], we show that the optimal input distribution under those constraints is discrete with a finite number of mass points. Moreover, we derive a closed-form expression of the capacity-achieving distribution for the low PP regime, where there is no trade-off between information and energy transfer. Finally, we show that HPA significantly reduces the information energy capacity region.

In this paper, the fundamental limits of simultaneous information and energy transmission in the two-user Gaussian interference channel with and without perfect channel-output feedback are approximated by two regions in each case, i.e., an achievable region and a converse region. When the energy transmission rate is normalized by the maximum energy rate, the approximation is within a constant gap. In the proof of achievability, the key idea is the use of power-splitting between two signal components: an information-carrying component and a no-information component. The construction of the former is based on random coding arguments, whereas the latter consists of a deterministic sequence known by all transmitters and receivers. The proof of the converse is obtained via cut-set bounds, genie-aided channel models, Fano's inequality, and some concentration inequalities considering that channel inputs might have a positive mean. Finally, the energy transmission enhancement due to feedback is quantified and it is shown that feedback can at most double the energy transmission rate at high signal-to-noise ratios.

In many resource allocation problems, optimal allocation strategies must be determined when only a quantized version of the relevant parameters are available, for instance, power allocation in wireless communications. The contribution of this work is threefold. First, the quantization problem is revisited and a framework which encompasses the classical problem of quantization is proposed. Instead of minimizing the distortion, the goal is to minimize the gap between the maximum of a general payoff function (which would be reached by knowing all parameters of the function) and what is effectively reached when only the quantized version of the parameters is available. Then, to determine such a quantizer, the well-known Lloyd-Max algorithm is generalized. At last, we show how this framework can be applied to the problem of power control in wireless communications; the obtained numerical results clearly show the potential of such a framework.