Subblock-Constrained Codes for Real-Time Simultaneous Energy and Information Transfer

Abstract

Consider an energy-harvesting receiver that uses the same received signal both for decoding information and for harvesting energy, which is employed to power its circuitry. In the scenario where the receiver has limited battery size, a signal with bursty energy content may cause power outage at the receiver, since the battery will drain during intervals with low signal energy. In this paper, we analyze subblock energy-constrained codes (SECCs), which ensure that sufficient energy is carried within every subblock duration. We consider discrete memoryless channels and characterize the SECC capacity and the SECC error exponent, and provide useful bounds for these values. We also study constant subblock-composition codes (CSCCs), which are a subclass of SECCs where all the subblocks in every codeword have the same fixed composition, and this subblock composition is chosen to maximize the rate of information transfer while meeting the energy requirement. Compared with constant composition codes (CCCs), we show that CSCCs incur a rate loss and that the error exponent for CSCCs is also related to the error exponent for CCCs by the same rate loss term. We exploit the symmetry in CSCCs to obtain a necessary and sufficient condition on the subblock length for avoiding power outage at the receiver. Furthermore, for CSCC sequences, we present a tight lower bound on the average energy per symbol within a sliding time window. We provide numerical examples highlighting the tradeoff between the delivery of sufficient energy to the receiver and achieving high information transfer rates. It is observed that the ability to use energy in real-time imposes less of penalty compared with the ability to use information in real-time.

Generalized Sphere-Packing Bound for Subblock-Constrained Codes

“…Usually, we consider spaces S whose size is exponential in n. Therefore, determining the exact value of (1) by solving the linear program directly is computationally prohibitive. Hence, most authors [7], [9] chose certain feasible points in the linear program (1) and evaluated the objective functions to obtain upper bounds on A(n, d; S). In particular, Cullina and Kiyavash [9] introduced the local degree iterative algorithm to pick "good" feasible points.…”

“…Our contributions are as follow: (A) We provide exact solutions to the optimization problem given by (1).…”

“…For all m, L, w, t, the exact solution to(1) for SECCs can be computed in time polynomial in L m and N (t).…”

Rate Control for Wireless-Powered Communication Network With Reliability and Delay Constraints

“…Very recently, energy harvesting (EH) has emerged as a promising technology to achieve a sustained and low-cost operation of low-power devices such as sensors or tiny actuators. Apart from the conventional EH sources [1] such as solar, wind, kinetic, or other ambient energy source, the radio-frequency (RF) signals are very attractive because they can carry energy and information simultaneously [2], which enables energy constrained nodes to harvest energy and receive information. Notice that RF EH, also known as Wireless Energy Transfer (WET), is envisioned to be of utmost importance in scenarios where replacing or recharging the batteries may be dangerous, as in a toxic environment, or even impossible, as in sensors implanted in human bodies.…”

Performance of Energy Harvesting Receivers With Power Optimization

“…Specially, the transmitter can work as an energy source, as in the dedicated RF charging [4], [5]. Thus, both energy and information can be delivered to the receiver via RF waves [6], [7]. Compared with ambient energy sources, the amount of energy harvested from dedicated RF sources can be controlled and dynamically adjusted.…”