Tewodros A. Zewde

Simultaneous Wireless Information and Power Transfer with Finite-Alphabet Input Signals

Simultaneous wireless information and power transfer (SWIPT) has emerged as a promising technology, enabling the transmission of both data and energy to a receiver equipped with an RF energy-harvesting circuitry. In this paper, we consider a point-to-point communication system in which a source transmits finite-alphabet signals. The receiver has information-decoding (ID) and energy-harvesting (EH) components, and power-splitting scheme is applied to carry out these operations concurrently. In order to improve the rate-energy tradeoff characteristics, we have introduced a novel approach that assigns probabilities non-uniformly to different signals in the constellation. According to the relationship between signal probabilities and energy consumption, these signal probabilities can be dynamically adjusted using two techniques, namely static slope characteristics and dynamic slope characteristics, given the minimum harvested energy constraint. Intuitively, advantage of one approach over the other depends on the improvement of the power-splitting factor when high energy input signals become more likely to be transmitted. In order to determine the optimal solution, we formulate an optimization problem and develop an algorithm taking into account the key parameters, e.g., splitting factor and signal probabilities. Numerical results are provided to justify the theoretical characterizations, considering 16-QAM.

Wireless-powered communication under statistical quality of service constraints

In this paper, we study the performance of wireless information and power transfer in the presence of statistical queuing constraints. We consider harvest-then-transmit protocol in which users first harvest energy from a dedicated source and then transmit information through an uplink multiple access channel (MAC). Each user is subject to limitations on the buffer overflow probability, specified by the quality of service (QoS) exponent θ, and the optimal time allocation for energy harvesting and information decoding operations depends on these constraints in addition to the channel characteristics. We formulate optimization problems to maximize the throughput with and without QoS constraints. In both cases, the problems are convex, and hence Karush-Kuhn-Tucker (KKT) conditions are necessary and sufficient for global optimality. However, it is difficult to obtain closed-form expressions for optimal time interval since we assume that operating intervals are independent of each fading state realization. Hence, we develop an algorithm to obtain optimal solutions numerically. Simulation results justify that QoS constraints primarily affect achievable rate distribution among the users, and override the channel conditions.

Energy-Efficient Time Allocation for Wireless Energy Harvesting Communication Networks

In this paper, we study the performance of energy harvesting communication networks focusing on the system energy efficiency. We consider multiple wireless-powered users that harvest energy from a wireless power source (WPS) and then transmit information uplink through time-division multiple access channels to the access point (AP). Besides, users can also scavenge energy from an information-bearing signal transmitted by a user scheduled for uplink data transfer. Each user is subject to limitations on the buffer overflow probability, specified by the quality of service (QoS) exponent

Energy-Efficient Full-Duplex Wireless Information and Power Transfer

\theta

Energy-efficient resource allocation for SWIPT in multiple access channels

. The optimal time allocation strategies, i.e., energy harvesting and data transmission intervals, are affected by such QoS constraints in addition to the channel characteristics. Thus, we formulate optimization problems to maximize the system energy efficiency (measured by the sum effective capacity per total consumed energy) while taking statistical queuing constraints into account. In addition, we provide details for the optimal time allocation strategies in the absence of these constraints. Since the problems, in both cases, are pseudo-concave, Karush-Kuhn-Tucker (KKT) conditions guarantee global optimality. However, it is difficult to obtain closed-form expressions for the optimal solution. Hence, we employ the Dinkelbachs method to solve the problems using standard numerical tools. Simulation results demonstrate that QoS constraints are critical, dictating time allocation, and correspondingly rate distribution, among the wireless-powered users in the presence of delay-sensitive sources.

QoS-Driven Resource Allocation for SWIPT with Finite-Alphabet Inputs

In this paper, we study optimal resource allocation strategies focusing on the system energy efficiency with simultaneous power transfer and information decoding operation at the access point (AP). We consider a three node wireless system that consists of the AP, energy harvesting user (EHU) and non-energy harvesting user (N-EHU), and EHU opportunistically harvests energy from N-EHU and the AP. Using this model, we propose introducing an energy- bearing signal in addition to the information- bearing signal at the N-EHU, and investigate their significance to improve the overall energy efficiency. We formulate an optimization problem that maximizes the system energy efficiency by taking the harvested energy constraint at the EHU into account. Based on this, we provide analytical expressions for the optimal transmit power level when the constraint is satisfied with strict inequality by the energy-efficiency-maximizing input. In such a case, system energy efficiency improves along with the energy demand, and using only information-bearing signal is optimal. On the other hand, when the constraint needs to be satisfied with equality, including an energy- bearing signal leads to better performance. However, explicit expressions to determine power allocation strategies are not immediately available, and hence we develop an algorithm using the subgradient method to solve the problem numerically. To justify these theoretical framework, we provide simulation results. In fact, we observe that having an energy-bearing signal together with the information-bearing signal improves the system energy efficiency and leads to higher levels of harvested energy. We also demonstrate the impact of peak power on the resource allocation policies.

Energy efficiency analysis for wireless-powered cellular networks

In this paper, we study optimal resource allocation strategies for simultaneous information and power transfer (SWIPT) focusing on the system energy efficiency. We consider two-user multiple access channels in which energy harvesting (EH) and information decoding (ID) nodes are spatially separated. We formulate optimization problems that maximize system energy efficiency while taking harvested energy constraints into account. These are concave-linear fractional problems, and hence Karush-Kuhn-Tucker (KKT) conditions are necessary and sufficient to obtain globally optimal solution. Solving these optimization problems, we provide analytical expressions for optimal transmit power allocation among the source nodes, and identify the corresponding energy efficiency. We confirm the theoretical analysis via numerical results. Furthermore, we also characterize the effect of circuit power consumption on the systems efficiency as the harvested energy demand varies.

Performance Analysis of Wireless Powered Cellular Networks with Downlink SWIPT - Invited Paper

In this paper, we consider a wireless scenario in which multiple-nodes operating under delay constraints transmit finite alphabet input signals for simultaneous wireless information and power transfer (SWIPT). These nodes communicate through time-division multiple access channels, and the receiving node harvests energy from the received signal while decoding information by applying power splitting scheme. In addition, the transmitting nodes are subject to limitations on the buffer overflow probability, specified by the quality of service (QoS) exponent. Due to harvested energy constraint, we have introduced a novel approach that assigns probabilities non-uniformly to different signals in the constellation which improves the overall performance in terms of throughput and energy efficiency (EE). We formulate optimization problems to maximize the effective capacity and effective EE while taking input signal probabilities, operating intervals, and splitting factor into account. Since obtaining closed-form expressions for the optimization parameters is unlikely, we develop an algorithm to determine the solutions numerically. In the numerical results, we observe that QoS constraints primarily affect achievable rate distribution among the users, and override the channel conditions. In addition, having static slope characteristics for non-uniform probability assignment is more energy efficient than having dynamic characteristics.

Optimal Resource Allocation for SWIPT with Full-Duplex Operation and Secrecy

In this paper, we study the performance of wireless networks with randomly located access points (APs) and randomly located energy-harvesting user equipments (UE) focusing on network throughput and system energy efficiency. We consider harvest-and-transmit protocol for wireless-powered UEs that do not have embedded power sources, but harvest energy from densely deployed APs. We first characterize outage probabilities and achievable data rates as a function of the system parameters, i.e., uplink and downlink operating intervals, using tools from stochastic geometry. Then, we formulate an optimization problem that maximize the system energy efficiency (measured by throughput per total consumed energy) while taking the feasible operation interval constraint into account. Despite the difficulty in obtaining closed-form expressions for the optimal solution, the problem can be solved using standard numerical tools. Simulation results demonstrate that broadcasting energy-bearing signal at higher power level improves energy efficiency. Furthermore, circuit power consumption is shown to be a key factor affecting the performance gain as well as optimal downlink/uplink time allocation strategy.