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36
Informationtheoretic analysis of an energy harvesting communication system
 in Proc. 2010 IEEE Int. Symp.Pers., Indoor Mobile Radio Commun
"... Abstract—In energy harvesting communication systems, an exogenous recharge process supplies energy for the data transmission and arriving energy can be buffered in a battery before consumption. Transmission is interrupted if there is not sufficient energy. We address communication with such random e ..."
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Cited by 34 (0 self)
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Abstract—In energy harvesting communication systems, an exogenous recharge process supplies energy for the data transmission and arriving energy can be buffered in a battery before consumption. Transmission is interrupted if there is not sufficient energy. We address communication with such random energy arrivals in an informationtheoretic setting. Based on the classical additive white Gaussian noise (AWGN) channel model, we study the coding problem with random energy arrivals at the transmitter. We show that the capacity of the AWGN channel with stochastic energy arrivals is equal to the capacity with an average power constraint equal to the average recharge rate. We provide two different capacity achieving schemes: saveandtransmit and bestefforttransmit. Next, we consider the case where energy arrivals have timevarying average in a larger time scale. We derive the optimal offline power allocation for maximum average throughput and provide an algorithm that finds the optimal power allocation. I.
AWGN channel under timevarying amplitude constraints with causal information at the transmitter
 in Asilomar Conference on Signals, Systems and Computers
, 2011
"... Abstract—We consider the classical AWGN channel where the channel input is constrained to an amplitude constraint that stochastically varies at each channel use, independent of the message. This is an abstraction of an energy harvesting transmitter where the code symbol energy at each channel use is ..."
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Cited by 24 (15 self)
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Abstract—We consider the classical AWGN channel where the channel input is constrained to an amplitude constraint that stochastically varies at each channel use, independent of the message. This is an abstraction of an energy harvesting transmitter where the code symbol energy at each channel use is determined by an exogenous energy arrival process and there is no battery for energy storage. At each channel use, an independent realization of the amplitude constraint process is observed by the transmitter causally. This scenario is a statedependent channel with perfect causal state information at the transmitter. We derive the capacity of this channel using Shannon’s coding scheme with causal state information. We prove that the code symbols must be selected from a finite set in the capacity achieving scheme, as in the case of Smith. We numerically study the binary onoff energy arrivals where the amplitude constraint is either zero or a nonzero constant. I.
Binary Energy Harvesting Channel with Finite Energy Storage
"... Abstract—We consider the capacity of an energy harvesting communication channel with a finitesized battery. As an abstraction of this problem, we consider a system where energy arrives at the encoder in multiples of a fixed quantity, and the physical layer is modeled accordingly as a finite discret ..."
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Cited by 13 (8 self)
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Abstract—We consider the capacity of an energy harvesting communication channel with a finitesized battery. As an abstraction of this problem, we consider a system where energy arrives at the encoder in multiples of a fixed quantity, and the physical layer is modeled accordingly as a finite discrete alphabet channel based on this fixed quantity. Further, for tractability, we consider the case of binary energy arrivals into a unitcapacity battery over a noiseless binary channel. Viewing the available energy as state, this is a statedependent channel with causal state information available only at the transmitter. Further, the state is correlated over time and the channel inputs modify the future states. We show that this channel is equivalent to an additive geometricnoise timing channel with causal information of the noise available at the transmitter. We provide a singleletter capacity expression involving an auxiliary random variable, and evaluate this expression with certain auxiliary random variable selection, which resembles noise concentration and latticetype coding in the timing channel. We evaluate the achievable rates by the proposed auxiliary selection and extend our results to noiseless ternary channels. I.
An energy harvesting AWGN channel with a finite battery
 In IEEE ISIT
, 2014
"... Abstract—In energy harvesting communication systems, the transmitter is adapted to harvest energy per time slot. The harvested energy is either used right away or is stored in a battery to facilitate future transmissions. We consider the problem of determining the Shannon capacity of an energy harve ..."
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Cited by 8 (0 self)
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Abstract—In energy harvesting communication systems, the transmitter is adapted to harvest energy per time slot. The harvested energy is either used right away or is stored in a battery to facilitate future transmissions. We consider the problem of determining the Shannon capacity of an energy harvesting transmitter communicating over an additive white Gaussian noise (AWGN) channel, where the amount of energy harvested per time slot is a constant ρ and the battery has capacity σ. This imposes a new kind of power constraint on the transmitted codewords, and we call the resulting constrained channel a (σ, ρ) power constrained AWGN channel. When σ is 0 or ∞, the capacity of this channel is known. For the finite battery case, we obtain an expression for the channel capacity. We obtain bounds on capacity by considering the volume of Sn(σ, ρ) ⊆ Rn, which is the set of all length n sequences satisfying the (σ, ρ) constraints.
Movers and shakers: Kinetic energy harvesting for the internet of things
 IEEE J. Sel. Areas Commun., Special Issue on Wireless Communications
, 2015
"... Abstract—Numerous energy harvesting wireless devices that will serve as building blocks for the Internet of Things (IoT) are currently under development. However, there is still only limited understanding of the properties of various energy sources and their impact on energy harvesting adaptive algo ..."
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Cited by 7 (2 self)
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Abstract—Numerous energy harvesting wireless devices that will serve as building blocks for the Internet of Things (IoT) are currently under development. However, there is still only limited understanding of the properties of various energy sources and their impact on energy harvesting adaptive algorithms. Hence, we focus on characterizing the kinetic (motion) energy that can be harvested by a wireless node with an IoT form factor and on developing energy allocation algorithms for such nodes. In this paper, we describe methods for estimating harvested energy from acceleration traces. To characterize the energy availability associated with specific human activities (e.g., relaxing, walking, cycling), we analyze a motion dataset with over 40 participants. Based on acceleration measurements that we collected for over 200 hours, we study energy generation processes associated with daylong human routines. We also briefly summarize our experiments with moving objects. We develop energy allocation algorithms that take into account practical IoT node design considerations, and evaluate the algorithms using the collected measurements. Our observations provide insights into the design of motion energy harvesters, IoT nodes, and energy harvesting adaptive algorithms. Index Terms—Energy harvesting, motion energy, measurements, lowpower networking, algorithms, Internet of Things. I.
Capacity of the Discrete Memoryless Energy Harvesting Channel with Side Information
"... Abstract—We determine the capacity of a discrete memoryless communication channel with an energy harvesting transmitter and its battery state information available at the transmitter and the receiver. This capacity is an upper bound for the problem where side information is available only at the tra ..."
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Cited by 4 (4 self)
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Abstract—We determine the capacity of a discrete memoryless communication channel with an energy harvesting transmitter and its battery state information available at the transmitter and the receiver. This capacity is an upper bound for the problem where side information is available only at the transmitter. Since channel output feedback does not increase the capacity in this case, we equivalently study the resulting finitestate Markov channel with feedback. We express the capacity in terms of directed information. Additionally, we provide sufficient conditions under which the capacity expression is further simplified to include the stationary distribution of the battery state. We also obtain a singleletter expression for the capacity with battery state information at both sides and an infinitesized battery. Lastly, we consider achievable schemes when side information is available only at the transmitter for the case of an arbitrary finitesized battery. We numerically evaluate the capacity and achievable rates with and without receiver side information. I.
Improved Capacity Bounds for the Binary Energy Harvesting Channel
"... Abstract—We consider a binary energy harvesting channel (BEHC) where the encoder has unit energy storage capacity. We first show that an encoding scheme based on block indexing is asymptotically optimal for small energy harvesting rates. We then present a novel upper bounding technique, which upper ..."
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Cited by 4 (4 self)
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Abstract—We consider a binary energy harvesting channel (BEHC) where the encoder has unit energy storage capacity. We first show that an encoding scheme based on block indexing is asymptotically optimal for small energy harvesting rates. We then present a novel upper bounding technique, which upper bounds the rate by lowerbounding the rate of information leakage to the receiver regarding the energy harvesting process. Finally, we propose a timing based hybrid encoding scheme that achieves rates within 0.03 bits/channel use of the upper bound; hence determining the capacity to within 0.03 bits/channel use. I.
Optimum Policies for an Energy Harvesting Transmitter Under Energy Storage Losses
, 2015
"... We consider an energy harvesting network where the transmitter harvests energy from nature, and the harvested energy can be saved in an imperfect battery which suffers from charging/ discharging inefficiency. In particular, when E units of energy is to be stored in the battery, only ηE units is save ..."
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Cited by 3 (3 self)
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We consider an energy harvesting network where the transmitter harvests energy from nature, and the harvested energy can be saved in an imperfect battery which suffers from charging/ discharging inefficiency. In particular, when E units of energy is to be stored in the battery, only ηE units is saved and (1 − η)E is lost due to charging/discharging inefficiency, where 0 ≤ η ≤ 1 represents the storing efficiency. We determine the optimum offline transmit power schedule for such a system for singleuser and broadcast channel models, for static and fading channels, with and without a finite battery size. We show that the optimum policy is a doublethreshold policy: specifically, we store energy in the battery only when the harvested energy is above an upper threshold, and retrieve energy from the battery only when the harvested energy is below a lower threshold; when the harvested energy is in between these two thresholds, we use it in its entirety in the current slot. We show that the two thresholds remain constant unless the battery is depleted or full. We provide an algorithm to determine the sequence of optimum thresholds. For the case with fading, we develop a directional waterfilling algorithm which has a doublethreshold structure. Finally, we formulate the online problem using dynamic programming, and numerically observe that the online policy exhibits a doublethreshold structure as well.
On the Capacity Region of the Gaussian MAC with Batteryless Energy Harvesting Transmitters
"... Abstract—We consider the twouser additive Gaussian multiple access channel (MAC) where the transmitters communicate by using energy harvested from nature. Energy arrivals of the users are i.i.d. in time, and for any given time, they are distributed according to a joint distribution. Energy arrivals ..."
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Cited by 3 (2 self)
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Abstract—We consider the twouser additive Gaussian multiple access channel (MAC) where the transmitters communicate by using energy harvested from nature. Energy arrivals of the users are i.i.d. in time, and for any given time, they are distributed according to a joint distribution. Energy arrivals cause timevariations for the amplitude constraints of the users. We first consider the static amplitude constrained Gaussian MAC and prove that the boundary of the capacity region is achieved by discrete input distributions of finite support. When both of the transmitters are equipped with no battery, Shannon strategies applied by users provide an inner bound for the capacity region. We prove that the boundary of this inner bound is achieved by input distributions with support set of zero Lebesgue measure.